From 248c1b60c146cd067459005d8291aaafe63ba621 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 3 Nov 2021 18:15:13 +0100
Subject: [PATCH 001/130] Implement first concepts of multi-fidelity

- concept for multi-fidelity trainer
- concept for successive halving optimizer

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 naslib/defaults/trainer_multifidelity.py   | 612 +++++++++++++++++++++
 naslib/optimizers/discrete/hb/optimizer.py |  93 ++++
 naslib/optimizers/discrete/sh/optimizer.py | 168 ++++++
 3 files changed, 873 insertions(+)
 create mode 100644 naslib/defaults/trainer_multifidelity.py
 create mode 100644 naslib/optimizers/discrete/hb/optimizer.py
 create mode 100644 naslib/optimizers/discrete/sh/optimizer.py

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
new file mode 100644
index 000000000..858da84f7
--- /dev/null
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -0,0 +1,612 @@
+import codecs
+from naslib.search_spaces.core.graph import Graph
+import time
+import json
+import logging
+import os
+import copy
+import torch
+import numpy as np
+
+from fvcore.common.checkpoint import PeriodicCheckpointer
+
+from naslib.search_spaces.core.query_metrics import Metric
+
+from naslib.utils import utils
+from naslib.utils.logging import log_every_n_seconds, log_first_n
+
+from typing import Callable
+from .additional_primitives import DropPathWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class Trainer(object):
+    """
+    Default implementation that handles dataloading and preparing batches, the
+    train loop, gathering statistics, checkpointing and doing the final
+    final evaluation.
+
+    If this does not fulfil your needs free do subclass it and implement your
+    required logic.
+    """
+
+    def __init__(self, optimizer, config, lightweight_output=False):
+        """
+        Initializes the trainer.
+
+        Args:
+            optimizer: A NASLib optimizer
+            config (AttrDict): The configuration loaded from a yaml file, e.g
+                via  `utils.get_config_from_args()`
+        """
+        self.optimizer = optimizer
+        self.config = config
+        self.epochs = self.config.search.epochs
+        self.lightweight_output = lightweight_output
+
+        # preparations
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        # measuring stuff
+        self.train_top1 = utils.AverageMeter()
+        self.train_top5 = utils.AverageMeter()
+        self.train_loss = utils.AverageMeter()
+        self.val_top1 = utils.AverageMeter()
+        self.val_top5 = utils.AverageMeter()
+        self.val_loss = utils.AverageMeter()
+
+        n_parameters = optimizer.get_model_size()
+        logger.info("param size = %fMB", n_parameters)
+        self.errors_dict = utils.AttrDict(
+            {
+                "train_acc": [],
+                "train_loss": [],
+                "valid_acc": [],
+                "valid_loss": [],
+                "test_acc": [],
+                "test_loss": [],
+                "runtime": [],
+                "train_time": [],
+                "arch_eval": [],
+                "params": n_parameters,
+            }
+        )
+
+    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None]=None, report_incumbent=True):
+        """
+        Start the architecture search.
+
+        Generates a json file with training statistics.
+
+        Args:
+            resume_from (str): Checkpoint file to resume from. If not given then
+                train from scratch.
+        """
+        logger.info("Start training")
+
+        np.random.seed(self.config.search.seed)
+        torch.manual_seed(self.config.search.seed)
+
+        self.optimizer.before_training()
+        checkpoint_freq = self.config.search.checkpoint_freq
+        if self.optimizer.using_step_function:
+            self.scheduler = self.build_search_scheduler(
+                self.optimizer.op_optimizer, self.config
+            )
+
+            start_epoch = self._setup_checkpointers(
+                resume_from, period=checkpoint_freq, scheduler=self.scheduler
+            )
+        else:
+            start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq)
+
+        if self.optimizer.using_step_function:
+            self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(
+                self.config
+            )
+        e = start_epoch
+        # TODO: good name for "epochs"
+        while e < after_epoch:
+
+            start_time = time.time()
+            #return function_eqalisation difference time to run or number of elovations 
+            used_budget = self.optimizer.new_epoch(e)
+
+            if self.optimizer.using_step_function:
+                for step, data_train in enumerate(self.train_queue):
+                    data_train = (
+                        data_train[0].to(self.device),
+                        data_train[1].to(self.device, non_blocking=True),
+                    )
+                    data_val = next(iter(self.valid_queue))
+                    data_val = (
+                        data_val[0].to(self.device),
+                        data_val[1].to(self.device, non_blocking=True),
+                    )
+
+                    stats = self.optimizer.step(data_train, data_val)
+                    logits_train, logits_val, train_loss, val_loss = stats
+
+                    self._store_accuracies(logits_train, data_train[1], "train")
+                    self._store_accuracies(logits_val, data_val[1], "val")
+
+                    log_every_n_seconds(
+                        logging.INFO,
+                        "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
+                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()
+                        ),
+                        n=5,
+                    )
+
+                    if torch.cuda.is_available():
+                        log_first_n(
+                            logging.INFO,
+                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                            n=3,
+                        )
+
+                    self.train_loss.update(float(train_loss.detach().cpu()))
+                    self.val_loss.update(float(val_loss.detach().cpu()))
+
+                self.scheduler.step()
+
+                end_time = time.time()
+
+                self.errors_dict.train_acc.append(self.train_top1.avg)
+                self.errors_dict.train_loss.append(self.train_loss.avg)
+                self.errors_dict.valid_acc.append(self.val_top1.avg)
+                self.errors_dict.valid_loss.append(self.val_loss.avg)
+                self.errors_dict.runtime.append(end_time - start_time)
+            else:
+                end_time = time.time()
+                # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
+                # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
+                (
+                    train_acc,
+                    valid_acc,
+                    test_acc,
+                    train_time,
+                ) = self.optimizer.train_statistics(report_incumbent)
+                train_loss, valid_loss, test_loss = -1, -1, -1
+
+                self.errors_dict.train_acc.append(train_acc)
+                self.errors_dict.train_loss.append(train_loss)
+                self.errors_dict.valid_acc.append(valid_acc)
+                self.errors_dict.valid_loss.append(valid_loss)
+                self.errors_dict.test_acc.append(test_acc)
+                self.errors_dict.test_loss.append(test_loss)
+                self.errors_dict.runtime.append(end_time - start_time)
+                self.errors_dict.train_time.append(train_time)
+                self.train_top1.avg = train_acc
+                self.val_top1.avg = valid_acc
+
+            self.periodic_checkpointer.step(e)
+
+            anytime_results = self.optimizer.test_statistics()
+            if anytime_results:
+                # record anytime performance
+                self.errors_dict.arch_eval.append(anytime_results)
+                log_every_n_seconds(
+                    logging.INFO,
+                    "Epoch {}, Anytime results: {}".format(e, anytime_results),
+                    n=5,
+                )
+
+            self._log_to_json()
+
+            self._log_and_reset_accuracies(e, summary_writer)
+
+            if after_epoch is not None:
+                after_epoch(e)
+
+        self.optimizer.after_training()
+
+        e += used_budget
+
+        if summary_writer is not None:
+            summary_writer.close()
+
+        logger.info("Training finished")
+
+    def evaluate_oneshot(self, resume_from="", dataloader=None):
+        """
+        Evaluate the one-shot model on the specified dataset.
+
+        Generates a json file with training statistics.
+
+        Args:
+            resume_from (str): Checkpoint file to resume from. If not given then
+                evaluate with the current one-shot weights.
+        """
+        logger.info("Start one-shot evaluation")
+        self.optimizer.before_training()
+        self._setup_checkpointers(resume_from)
+
+        loss = torch.nn.CrossEntropyLoss()
+
+        if dataloader is None:
+            # load only the validation data
+            _, dataloader, _ = self.build_search_dataloaders(self.config)
+
+        self.optimizer.graph.eval()
+        with torch.no_grad():
+            start_time = time.time()
+            for step, data_val in enumerate(dataloader):
+                input_val = data_val[0].to(self.device)
+                target_val = data_val[1].to(self.device, non_blocking=True)
+
+                logits_val = self.optimizer.graph(input_val)
+                val_loss = loss(logits_val, target_val)
+
+                self._store_accuracies(logits_val, data_val[1], "val")
+                self.val_loss.update(float(val_loss.detach().cpu()))
+
+            end_time = time.time()
+
+            self.errors_dict.valid_acc.append(self.val_top1.avg)
+            self.errors_dict.valid_loss.append(self.val_loss.avg)
+            self.errors_dict.runtime.append(end_time - start_time)
+
+            self._log_to_json()
+
+        logger.info("Evaluation finished")
+        return self.val_top1.avg
+
+    def evaluate(
+        self,
+        retrain:bool=True,
+        search_model:str="",
+        resume_from:str="",
+        best_arch:Graph=None,
+        dataset_api:object=None,
+        metric:Metric=None,
+    ):
+        """
+        Evaluate the final architecture as given from the optimizer.
+
+        If the search space has an interface to a benchmark then query that.
+        Otherwise train as defined in the config.
+
+        Args:
+            retrain (bool)      : Reset the weights from the architecure search
+            search_model (str)  : Path to checkpoint file that was created during search. If not provided,
+                                  then try to load 'model_final.pth' from search
+            resume_from (str)   : Resume retraining from the given checkpoint file.
+            best_arch           : Parsed model you want to directly evaluate and ignore the final model
+                                  from the optimizer.
+            dataset_api         : Dataset API to use for querying model performance.
+            metric              : Metric to query the benchmark for.
+        """
+        logger.info("Start evaluation")
+        if not best_arch:
+
+            if not search_model:
+                search_model = os.path.join(
+                    self.config.save, "search", "model_final.pth"
+                )
+            self._setup_checkpointers(search_model)  # required to load the architecture
+
+            best_arch = self.optimizer.get_final_architecture()
+        logger.info("Final architecture:\n" + best_arch.modules_str())
+
+        if best_arch.QUERYABLE:
+            if metric is None:
+                metric = Metric.TEST_ACCURACY
+            result = best_arch.query(
+                metric=metric, dataset=self.config.dataset, dataset_api=dataset_api
+            )
+            logger.info("Queried results ({}): {}".format(metric, result))
+        else:
+            best_arch.to(self.device)
+            if retrain:
+                logger.info("Starting retraining from scratch")
+                best_arch.reset_weights(inplace=True)
+
+                (
+                    self.train_queue,
+                    self.valid_queue,
+                    self.test_queue,
+                ) = self.build_eval_dataloaders(self.config)
+
+                optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
+                scheduler = self.build_eval_scheduler(optim, self.config)
+
+                start_epoch = self._setup_checkpointers(
+                    resume_from,
+                    search=False,
+                    period=self.config.evaluation.checkpoint_freq,
+                    model=best_arch,  # checkpointables start here
+                    optim=optim,
+                    scheduler=scheduler,
+                )
+
+                grad_clip = self.config.evaluation.grad_clip
+                loss = torch.nn.CrossEntropyLoss()
+
+                self.train_top1.reset()
+                self.train_top5.reset()
+                self.val_top1.reset()
+                self.val_top5.reset()
+
+                # Enable drop path
+                best_arch.update_edges(
+                    update_func=lambda edge: edge.data.set(
+                        "op", DropPathWrapper(edge.data.op)
+                    ),
+                    scope=best_arch.OPTIMIZER_SCOPE,
+                    private_edge_data=True,
+                )
+
+                # train from scratch
+                epochs = self.config.evaluation.epochs
+                for e in range(start_epoch, epochs):
+                    best_arch.train()
+
+                    if torch.cuda.is_available():
+                        log_first_n(
+                            logging.INFO,
+                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                            n=20,
+                        )
+
+                    # update drop path probability
+                    drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
+                    best_arch.update_edges(
+                        update_func=lambda edge: edge.data.set(
+                            "drop_path_prob", drop_path_prob
+                        ),
+                        scope=best_arch.OPTIMIZER_SCOPE,
+                        private_edge_data=True,
+                    )
+
+                    # Train queue
+                    for i, (input_train, target_train) in enumerate(self.train_queue):
+                        input_train = input_train.to(self.device)
+                        target_train = target_train.to(self.device, non_blocking=True)
+
+                        optim.zero_grad()
+                        logits_train = best_arch(input_train)
+                        train_loss = loss(logits_train, target_train)
+                        if hasattr(
+                            best_arch, "auxilary_logits"
+                        ):  # darts specific stuff
+                            log_first_n(logging.INFO, "Auxiliary is used", n=10)
+                            auxiliary_loss = loss(
+                                best_arch.auxilary_logits(), target_train
+                            )
+                            train_loss += (
+                                self.config.evaluation.auxiliary_weight * auxiliary_loss
+                            )
+                        train_loss.backward()
+                        if grad_clip:
+                            torch.nn.utils.clip_grad_norm_(
+                                best_arch.parameters(), grad_clip
+                            )
+                        optim.step()
+
+                        self._store_accuracies(logits_train, target_train, "train")
+                        log_every_n_seconds(
+                            logging.INFO,
+                            "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
+                                e, i, train_loss, scheduler.get_last_lr()
+                            ),
+                            n=5,
+                        )
+
+                    # Validation queue
+                    if self.valid_queue:
+                        best_arch.eval()
+                        for i, (input_valid, target_valid) in enumerate(
+                            self.valid_queue
+                        ):
+
+                            input_valid = input_valid.to(self.device).float()
+                            target_valid = target_valid.to(self.device).float()
+
+                            # just log the validation accuracy
+                            with torch.no_grad():
+                                logits_valid = best_arch(input_valid)
+                                self._store_accuracies(
+                                    logits_valid, target_valid, "val"
+                                )
+
+                    scheduler.step()
+                    self.periodic_checkpointer.step(e)
+                    self._log_and_reset_accuracies(e)
+
+            # Disable drop path
+            best_arch.update_edges(
+                update_func=lambda edge: edge.data.set(
+                    "op", edge.data.op.get_embedded_ops()
+                ),
+                scope=best_arch.OPTIMIZER_SCOPE,
+                private_edge_data=True,
+            )
+
+            # measure final test accuracy
+            top1 = utils.AverageMeter()
+            top5 = utils.AverageMeter()
+
+            best_arch.eval()
+
+            for i, data_test in enumerate(self.test_queue):
+                input_test, target_test = data_test
+                input_test = input_test.to(self.device)
+                target_test = target_test.to(self.device, non_blocking=True)
+
+                n = input_test.size(0)
+
+                with torch.no_grad():
+                    logits = best_arch(input_test)
+
+                    prec1, prec5 = utils.accuracy(logits, target_test, topk=(1, 5))
+                    top1.update(prec1.data.item(), n)
+                    top5.update(prec5.data.item(), n)
+
+                log_every_n_seconds(
+                    logging.INFO,
+                    "Inference batch {} of {}.".format(i, len(self.test_queue)),
+                    n=5,
+                )
+
+            logger.info(
+                "Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(
+                    top1.avg, top5.avg
+                )
+            )
+
+    @staticmethod
+    def build_search_dataloaders(config):
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode="train"
+        )
+        return train_queue, valid_queue, _  # test_queue is not used in search currently
+
+    @staticmethod
+    def build_eval_dataloaders(config):
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode="val"
+        )
+        return train_queue, valid_queue, test_queue
+
+    @staticmethod
+    def build_eval_optimizer(parameters, config):
+        return torch.optim.SGD(
+            parameters,
+            lr=config.evaluation.learning_rate,
+            momentum=config.evaluation.momentum,
+            weight_decay=config.evaluation.weight_decay,
+        )
+
+    @staticmethod
+    def build_search_scheduler(optimizer, config):
+        return torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            T_max=config.search.epochs,
+            eta_min=config.search.learning_rate_min,
+        )
+
+    @staticmethod
+    def build_eval_scheduler(optimizer, config):
+        return torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            T_max=config.evaluation.epochs,
+            eta_min=config.evaluation.learning_rate_min,
+        )
+
+    def _log_and_reset_accuracies(self, epoch, writer=None):
+        logger.info(
+            "Epoch {} done. Train accuracy (top1, top5): {:.5f}, {:.5f}, Validation accuracy: {:.5f}, {:.5f}".format(
+                epoch,
+                self.train_top1.avg,
+                self.train_top5.avg,
+                self.val_top1.avg,
+                self.val_top5.avg,
+            )
+        )
+
+        if writer is not None:
+            writer.add_scalar('Train accuracy (top 1)', self.train_top1.avg, epoch)
+            writer.add_scalar('Train accuracy (top 5)', self.train_top5.avg, epoch)
+            writer.add_scalar('Train loss', self.train_loss.avg, epoch)
+            writer.add_scalar('Validation accuracy (top 1)', self.val_top1.avg, epoch)
+            writer.add_scalar('Validation accuracy (top 5)', self.val_top5.avg, epoch)
+            writer.add_scalar('Validation loss', self.val_loss.avg, epoch)
+
+        self.train_top1.reset()
+        self.train_top5.reset()
+        self.train_loss.reset()
+        self.val_top1.reset()
+        self.val_top5.reset()
+        self.val_loss.reset()
+
+    def _store_accuracies(self, logits, target, split):
+        """Update the accuracy counters"""
+        logits = logits.clone().detach().cpu()
+        target = target.clone().detach().cpu()
+        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
+        n = logits.size(0)
+
+        if split == "train":
+            self.train_top1.update(prec1.data.item(), n)
+            self.train_top5.update(prec5.data.item(), n)
+        elif split == "val":
+            self.val_top1.update(prec1.data.item(), n)
+            self.val_top5.update(prec5.data.item(), n)
+        else:
+            raise ValueError("Unknown split: {}. Expected either 'train' or 'val'")
+
+    def _prepare_dataloaders(self, config, mode="train"):
+        """
+        Prepare train, validation, and test dataloaders with the splits defined
+        in the config.
+
+        Args:
+            config (AttrDict): config from config file.
+        """
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode
+        )
+        self.train_queue = train_queue
+        self.valid_queue = valid_queue
+        self.test_queue = test_queue
+
+    def _setup_checkpointers(
+        self, resume_from="", search=True, period=1, **add_checkpointables
+    ):
+        """
+        Sets up a periodic chechkpointer which can be used to save checkpoints
+        at every epoch. It will call optimizer's `get_checkpointables()` as objects
+        to store.
+
+        Args:
+            resume_from (str): A checkpoint file to resume the search or evaluation from.
+            search (bool): Whether search or evaluation phase is checkpointed. This is required
+                because the files are in different folders to not be overridden
+            add_checkpointables (object): Additional things to checkpoint together with the
+                optimizer's checkpointables.
+        """
+        checkpointables = self.optimizer.get_checkpointables()
+        checkpointables.update(add_checkpointables)
+
+        checkpointer = utils.Checkpointer(
+            model=checkpointables.pop("model"),
+            save_dir=self.config.save + "/search"
+            if search
+            else self.config.save + "/eval",
+            # **checkpointables #NOTE: this is throwing an Error
+        )
+
+        self.periodic_checkpointer = PeriodicCheckpointer(
+            checkpointer,
+            period=period,
+            max_iter=self.config.search.epochs
+            if search
+            else self.config.evaluation.epochs,
+        )
+
+        if resume_from:
+            logger.info("loading model from file {}".format(resume_from))
+            checkpoint = checkpointer.resume_or_load(resume_from, resume=True)
+            if checkpointer.has_checkpoint():
+                return checkpoint.get("iteration", -1) + 1
+        return 0
+
+    def _log_to_json(self):
+        """log training statistics to json file"""
+        if not os.path.exists(self.config.save):
+            os.makedirs(self.config.save)
+        if not self.lightweight_output:
+            with codecs.open(
+                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
+            ) as file:
+                json.dump(self.errors_dict, file, separators=(",", ":"))
+        else:
+            with codecs.open(
+                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
+            ) as file:
+                lightweight_dict = copy.deepcopy(self.errors_dict)
+                for key in ["arch_eval", "train_loss", "valid_loss", "test_loss"]:
+                    lightweight_dict.pop(key)
+                json.dump([self.config, lightweight_dict], file, separators=(",", ":"))
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
new file mode 100644
index 000000000..868b784f9
--- /dev/null
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -0,0 +1,93 @@
+import os
+import time
+import math
+import pdb
+import copy
+import logging
+
+import numpy as np
+
+
+import ConfigSpace as CS
+
+from hpbandster.core.master import Master
+from hpbandster.optimizers.iterations import SuccessiveHalving
+from hpbandster.optimizers.config_generators import RandomSampling
+
+class HyperBand(Master):
+	def __init__(self, configspace = None,
+					eta=3, min_budget=0.01, max_budget=1,
+					**kwargs ):
+		"""
+                Hyperband implements hyperparameter optimization by sampling
+                candidates at random and "trying" them first, running them for
+                a specific budget. The approach is iterative, promising
+                candidates are run for a longer time, increasing the fidelity
+                for their performance. While this is a very efficient racing
+                approach, random sampling makes no use of the knowledge gained
+                about the candidates during optimization.
+		Parameters
+		----------
+		configspace: ConfigSpace object
+			valid representation of the search space
+		eta : float
+			In each iteration, a complete run of sequential halving is executed. In it,
+			after evaluating each configuration on the same subset size, only a fraction of
+			1/eta of them 'advances' to the next round.
+			Must be greater or equal to 2.
+		min_budget : float
+			The smallest budget to consider. Needs to be positive!
+		max_budget : float
+			the largest budget to consider. Needs to be larger than min_budget!
+			The budgets will be geometrically distributed $\sim \eta^k$ for
+			$k\in [0, 1, ... , num_subsets - 1]$.
+		"""
+
+
+		# TODO: Propper check for ConfigSpace object!
+		if configspace is None:
+			raise ValueError("You have to provide a valid CofigSpace object")
+
+		super().__init__(config_generator=RandomSampling(configspace), **kwargs)
+
+		# Hyperband related stuff
+		self.eta = eta
+		self.min_budget = min_budget
+		self.max_budget = max_budget
+
+		# precompute some HB stuff
+		self.max_SH_iter = -int(np.log(min_budget/max_budget)/np.log(eta)) + 1
+		self.budgets = max_budget * np.power(eta, -np.linspace(self.max_SH_iter-1, 0, self.max_SH_iter))
+
+		self.config.update({
+						'eta'        : eta,
+						'min_budget' : min_budget,
+						'max_budget' : max_budget,
+						'budgets'    : self.budgets,
+						'max_SH_iter': self.max_SH_iter,
+					})
+
+
+
+	def get_next_iteration(self, iteration, iteration_kwargs={}):
+		"""
+		Hyperband uses SuccessiveHalving for each iteration.
+		See Li et al. (2016) for reference.
+		
+		Parameters
+		----------
+			iteration: int
+				the index of the iteration to be instantiated
+		Returns
+		-------
+			SuccessiveHalving: the SuccessiveHalving iteration with the
+				corresponding number of configurations
+		"""
+		
+		# number of 'SH rungs'
+		s = self.max_SH_iter - 1 - (iteration%self.max_SH_iter)
+		# number of configurations in that bracket
+		n0 = int(np.floor((self.max_SH_iter)/(s+1)) * self.eta**s)
+		ns = [max(int(n0*(self.eta**(-i))), 1) for i in range(s+1)]
+
+		return(SuccessiveHalving(HPB_iter=iteration, num_configs=ns, budgets=self.budgets[(-s-1):], config_sampler=self.config_generator.get_config, **iteration_kwargs))
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
new file mode 100644
index 000000000..4ebe313a9
--- /dev/null
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -0,0 +1,168 @@
+#from hpbandster.core.base_iteration import BaseIteration
+import numpy as np
+import numpy as np
+import torch
+
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.search_spaces.core.query_metrics import Metric
+
+
+class SuccessiveHalving(MetaOptimizer):
+    """
+    # TODO: Write fancy comment 🌈
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(SuccessiveHalving, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+
+        self.fidelity = config.search.min_fidelity
+        self.number_archs = config.search.number_archs
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.fidelity_counter = 0
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+
+    def new_epoch(self, e):
+        """
+        Sample a new architecture to train.
+        """
+
+        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        model.arch = self.search_space.clone()
+        if len(self.sampled_archs) < self.number_archs:
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+        else:
+            model = self.sampled_archs[self.fidelity_counter]
+       
+# 		    return(ranks < self.num_configs[self.stage])
+
+
+        # TODO: define fidelity in multi-fidelity setting
+        model.accuracy = model.arch.query(
+            self.performance_metric,
+            self.dataset,
+            epoch=self.fidelity, # TODO: adapt this
+            dataset_api=self.dataset_api,
+        )
+
+        # TODO: evaluate if we can write every shit 💩 in model variable attributes
+        budget = 1
+        # TODO: make query type secure
+        if self.budget_type == 'time':
+            # TODO: make dependent on performance_metric
+            model.time = model.arch.query(
+                Metric.TRAIN_TIME,
+                self.dataset,
+                epoch=self.fidelity, # TODO: adapt this
+                dataset_api=self.dataset_api,
+            )
+            budget = model.time
+        
+        self.sampled_archs.append(model)
+        self.fidelity_counter += 1
+        self._update_history(model)
+        if self.fidelity_counter == self.number_archs:
+            self.fidelity = self.eta*self.fidelity #TODO round 
+            self.sampled_archs.sort(key = lambda model: model.accuracy)
+            self.sampled_archs = self.sampled_archs[0:self.number_archs/self.eta] #TODO round 
+            self.number_archs = len(self.sampled_archs)
+        return budget
+        # required if we want to train the models and not only query.
+        # architecture_i.parse()
+        # architecture_i.train()
+        # architecture_i = architecture_i.to(self.device)
+        # self.sampled_archs.append(architecture_i)
+        # self.weight_optimizers.append(self.weight_optimizer(architecture_i.parameters(), 0.01))
+
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+        )
+
+    def test_statistics(self):
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
+
+# #from naslib.optimizers.core.metaclasses import MetaOptimizer
+# class SuccessiveHalving(MetaOptimizer):
+#     #also import or simular in NASLib?
+
+# 	def _advance_to_next_stage(self, config_ids, losses):
+# 		"""
+# 			#SuccessiveHalving simply continues the best based on the current loss.
+# 		"""
+# 		ranks = np.argsort(np.argsort(losses))
+# 		return(ranks < self.num_configs[self.stage])

From f8f35ad6b9a7391361f101dddcfe88e6a3736668 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 4 Nov 2021 18:15:11 +0100
Subject: [PATCH 002/130] Further working on SH optimizer

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 demo.py                                       | 44 +++++++++++++++++++
 .../nas_predictors/discrete_config.yaml       |  9 ++++
 naslib/defaults/trainer_multifidelity.py      |  5 ++-
 naslib/optimizers/__init__.py                 |  1 +
 naslib/optimizers/discrete/sh/__init__.py     |  0
 naslib/optimizers/discrete/sh/optimizer.py    | 21 ++++++---
 6 files changed, 72 insertions(+), 8 deletions(-)
 create mode 100644 demo.py
 create mode 100644 naslib/optimizers/discrete/sh/__init__.py

diff --git a/demo.py b/demo.py
new file mode 100644
index 000000000..67476c871
--- /dev/null
+++ b/demo.py
@@ -0,0 +1,44 @@
+# purpose of this script:
+# debug random search optimizer to better understand function
+
+from naslib import search_spaces
+from naslib.search_spaces import NasBench201SearchSpace as NB201
+
+import logging
+from naslib.utils import utils, setup_logger, get_dataset_api
+
+from naslib.optimizers import RandomSearch as RS
+from naslib.optimizers import RegularizedEvolution as RE
+from naslib.optimizers import SuccessiveHalving as SH
+
+from naslib.defaults.trainer_multifidelity import Trainer
+
+# init search space
+search_space = NB201()
+
+# read config
+config = utils.get_config_from_args(config_type="nas_predictor")
+utils.set_seed(config.seed)
+utils.log_args(config)
+
+logger = setup_logger(config.save + "/log.log")
+logger.setLevel(logging.INFO)
+
+# define optimizer 
+optimizer = SH(config)
+
+# load nasbench data, there data seems to be generalised
+dataset_api = get_dataset_api(config.search_space, config.dataset)
+
+# adapt search space
+optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
+
+
+## Running search with Trainer
+
+trainer = Trainer(optimizer, config, lightweight_output=True)
+
+# run search for number of iterations specified
+trainer.search()
+
+trainer.evaluate(dataset_api=dataset_api)
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 35c62de6f..24f538e5e 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -10,6 +10,12 @@ search:
   epochs: 100
   fidelity: -1
 
+  min_fidelity: 1
+  number_archs: 10
+  eta: 2
+  budget_type: time
+
+
   predictor_type: var_sparse_gp
   num_init: 10  
   k: 10
@@ -35,3 +41,6 @@ search:
   debug_predictor: False
   sample_size: 10
   population_size: 30
+
+  # TODO: write config file for successive halving
+
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 858da84f7..b6913fd77 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -107,7 +107,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             )
         e = start_epoch
         # TODO: good name for "epochs"
-        while e < after_epoch:
+        while e < self.epochs:
 
             start_time = time.time()
             #return function_eqalisation difference time to run or number of elovations 
@@ -199,10 +199,11 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
 
             if after_epoch is not None:
                 after_epoch(e)
+            e += used_budget
 
         self.optimizer.after_training()
 
-        e += used_budget
+        
 
         if summary_writer is not None:
             summary_writer.close()
diff --git a/naslib/optimizers/__init__.py b/naslib/optimizers/__init__.py
index c7cdb2393..c4e065004 100644
--- a/naslib/optimizers/__init__.py
+++ b/naslib/optimizers/__init__.py
@@ -4,6 +4,7 @@
 from .oneshot.gdas.optimizer import GDASOptimizer
 from .oneshot.drnas.optimizer import DrNASOptimizer
 from .discrete.rs.optimizer import RandomSearch
+from .discrete.sh.optimizer import SuccessiveHalving
 from .discrete.re.optimizer import RegularizedEvolution
 from .discrete.ls.optimizer import LocalSearch
 from .discrete.bananas.optimizer import Bananas
diff --git a/naslib/optimizers/discrete/sh/__init__.py b/naslib/optimizers/discrete/sh/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 4ebe313a9..cd12f648e 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -3,6 +3,8 @@
 import numpy as np
 import torch
 
+import math
+
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
 
@@ -72,11 +74,11 @@ def new_epoch(self, e):
 # 		    return(ranks < self.num_configs[self.stage])
 
 
-        # TODO: define fidelity in multi-fidelity setting
+        # DONE: define fidelity in multi-fidelity setting
         model.accuracy = model.arch.query(
             self.performance_metric,
             self.dataset,
-            epoch=self.fidelity, # TODO: adapt this
+            epoch=self.fidelity, # DONE: adapt this
             dataset_api=self.dataset_api,
         )
 
@@ -88,19 +90,26 @@ def new_epoch(self, e):
             model.time = model.arch.query(
                 Metric.TRAIN_TIME,
                 self.dataset,
-                epoch=self.fidelity, # TODO: adapt this
+                epoch=self.fidelity, # DONE: adapt this
                 dataset_api=self.dataset_api,
             )
             budget = model.time
         
-        self.sampled_archs.append(model)
+        # TODO: make this more beautiful/more efficient
+        # TODO: we may need to track of all ever sampled archs
+        if len(self.sampled_archs) < self.number_archs:
+            self.sampled_archs.append(model)
+        else:
+            self.sampled_archs[self.fidelity_counter] = model
+        
         self.fidelity_counter += 1
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
-            self.fidelity = self.eta*self.fidelity #TODO round 
+            self.fidelity = math.floor(self.eta/self.fidelity) #DONE round 
             self.sampled_archs.sort(key = lambda model: model.accuracy)
-            self.sampled_archs = self.sampled_archs[0:self.number_archs/self.eta] #TODO round 
+            self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round 
             self.number_archs = len(self.sampled_archs)
+            self.fidelity_counter = 0
         return budget
         # required if we want to train the models and not only query.
         # architecture_i.parse()

From a4306c9addf130a8c5975ea609b08e179330c855 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 9 Nov 2021 17:23:46 +0100
Subject: [PATCH 003/130] make trainer evaluation runnable on sh

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 demo.py                                               | 2 ++
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 2 +-
 naslib/defaults/trainer_multifidelity.py              | 5 +++--
 naslib/optimizers/discrete/sh/optimizer.py            | 1 -
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/demo.py b/demo.py
index 67476c871..861380deb 100644
--- a/demo.py
+++ b/demo.py
@@ -12,6 +12,7 @@
 from naslib.optimizers import SuccessiveHalving as SH
 
 from naslib.defaults.trainer_multifidelity import Trainer
+#from naslib.defaults.trainer import Trainer
 
 # init search space
 search_space = NB201()
@@ -26,6 +27,7 @@
 
 # define optimizer 
 optimizer = SH(config)
+#optimizer = RS(config)
 
 # load nasbench data, there data seems to be generalised
 dataset_api = get_dataset_api(config.search_space, config.dataset)
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 24f538e5e..9f9c84e7f 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -6,7 +6,7 @@ out_dir: run
 
 search:
   seed: 0
-  checkpoint_freq: 1000
+  checkpoint_freq: 1 # 1000
   epochs: 100
   fidelity: -1
 
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index b6913fd77..fc8ff0c3f 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -181,7 +181,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 self.train_top1.avg = train_acc
                 self.val_top1.avg = valid_acc
 
-            self.periodic_checkpointer.step(e)
+            self.periodic_checkpointer.step(int(e)) # Hacky way
 
             anytime_results = self.optimizer.test_statistics()
             if anytime_results:
@@ -589,7 +589,8 @@ def _setup_checkpointers(
 
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))
-            checkpoint = checkpointer.resume_or_load(resume_from, resume=True)
+            # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
+            checkpoint = checkpointer.resume_or_load("resume_from", resume=True)
             if checkpointer.has_checkpoint():
                 return checkpoint.get("iteration", -1) + 1
         return 0
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index cd12f648e..a7152ec80 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -82,7 +82,6 @@ def new_epoch(self, e):
             dataset_api=self.dataset_api,
         )
 
-        # TODO: evaluate if we can write every shit 💩 in model variable attributes
         budget = 1
         # TODO: make query type secure
         if self.budget_type == 'time':

From fa58c39fd7ed768dabd02acfce07472ebba83727 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 10 Nov 2021 18:47:49 +0100
Subject: [PATCH 004/130] work on sh optimizer & train_statistics

- sh optimizer: add support for budget type "epoch"
- train_statistics:  add support to be able to track fidelities

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 .../nas_predictors/discrete_config.yaml       |  8 ++---
 naslib/defaults/trainer_multifidelity.py      |  8 ++++-
 naslib/optimizers/discrete/sh/optimizer.py    | 33 +++++++++++++++++--
 3 files changed, 41 insertions(+), 8 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 9f9c84e7f..10b3925ce 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -6,14 +6,14 @@ out_dir: run
 
 search:
   seed: 0
-  checkpoint_freq: 1 # 1000
+  checkpoint_freq: 10 # 1000
   epochs: 100
   fidelity: -1
 
-  min_fidelity: 1
-  number_archs: 10
+  min_fidelity: 5
+  number_archs: 80
   eta: 2
-  budget_type: time
+  budget_type:  epoch
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index fc8ff0c3f..e9964d547 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -63,6 +63,8 @@ def __init__(self, optimizer, config, lightweight_output=False):
                 "train_acc": [],
                 "train_loss": [],
                 "valid_acc": [],
+                "fidelity": [],
+                "arch": [],
                 "valid_loss": [],
                 "test_acc": [],
                 "test_loss": [],
@@ -167,13 +169,17 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                     valid_acc,
                     test_acc,
                     train_time,
-                ) = self.optimizer.train_statistics(report_incumbent)
+                    fidelity,
+                    arch,
+                ) = self.optimizer.train_model_statistics()
                 train_loss, valid_loss, test_loss = -1, -1, -1
 
                 self.errors_dict.train_acc.append(train_acc)
                 self.errors_dict.train_loss.append(train_loss)
                 self.errors_dict.valid_acc.append(valid_acc)
                 self.errors_dict.valid_loss.append(valid_loss)
+                self.errors_dict.fidelity.append(fidelity)
+                self.errors_dict.arch.append(arch)
                 self.errors_dict.test_acc.append(test_acc)
                 self.errors_dict.test_loss.append(test_loss)
                 self.errors_dict.runtime.append(end_time - start_time)
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index a7152ec80..43e7ff2d8 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -93,7 +93,8 @@ def new_epoch(self, e):
                 dataset_api=self.dataset_api,
             )
             budget = model.time
-        
+        elif not(self.budget_type == "epoch"):
+            raise NameError("budget time should be time or epoch")
         # TODO: make this more beautiful/more efficient
         # TODO: we may need to track of all ever sampled archs
         if len(self.sampled_archs) < self.number_archs:
@@ -104,9 +105,13 @@ def new_epoch(self, e):
         self.fidelity_counter += 1
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
-            self.fidelity = math.floor(self.eta/self.fidelity) #DONE round 
+            self.fidelity = math.floor(self.eta*self.fidelity) #DONE round 
             self.sampled_archs.sort(key = lambda model: model.accuracy)
-            self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round 
+            if(math.floor(self.number_archs/self.eta)) != 0:
+                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
+                
+            else:
+                self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)
             self.fidelity_counter = 0
         return budget
@@ -152,6 +157,28 @@ def train_statistics(self, report_incumbent=True):
             best_arch.query(
                 Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
             ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_str = self.sampled_archs[self.fidelity_counter -1].arch.modules_str()
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            hash(best_arch) * 5 + 7,
         )
 
     def test_statistics(self):

From 104432b081f2185d23963ea108c74d3c0d2fa82a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 11 Nov 2021 14:36:34 +0100
Subject: [PATCH 005/130] sh working +added hash to dict +try different
 checkpointer

---
 .../nas_predictors/discrete_config.yaml       |  2 +-
 naslib/defaults/trainer_multifidelity.py      | 27 ++++++++++---------
 naslib/optimizers/discrete/sh/optimizer.py    |  4 +--
 3 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 10b3925ce..7f0a41ca3 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -13,7 +13,7 @@ search:
   min_fidelity: 5
   number_archs: 80
   eta: 2
-  budget_type:  epoch
+  budget_type:  time
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index e9964d547..736424cdd 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -8,7 +8,8 @@
 import torch
 import numpy as np
 
-from fvcore.common.checkpoint import PeriodicCheckpointer
+#from fvcore.common.checkpoint import PeriodicCheckpointer
+from fvcore.common.checkpoint import Checkpointer
 
 from naslib.search_spaces.core.query_metrics import Metric
 
@@ -187,7 +188,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 self.train_top1.avg = train_acc
                 self.val_top1.avg = valid_acc
 
-            self.periodic_checkpointer.step(int(e)) # Hacky way
+            self.periodic_checkpointer.save(e) # define the name in accurate , also 
 
             anytime_results = self.optimizer.test_statistics()
             if anytime_results:
@@ -576,8 +577,8 @@ def _setup_checkpointers(
         """
         checkpointables = self.optimizer.get_checkpointables()
         checkpointables.update(add_checkpointables)
-
-        checkpointer = utils.Checkpointer(
+        #name make no sense
+        self.periodic_checkpointer = utils.Checkpointer(
             model=checkpointables.pop("model"),
             save_dir=self.config.save + "/search"
             if search
@@ -585,19 +586,19 @@ def _setup_checkpointers(
             # **checkpointables #NOTE: this is throwing an Error
         )
 
-        self.periodic_checkpointer = PeriodicCheckpointer(
-            checkpointer,
-            period=period,
-            max_iter=self.config.search.epochs
-            if search
-            else self.config.evaluation.epochs,
-        )
+        #self.periodic_checkpointer = PeriodicCheckpointer(
+        #   checkpointer,
+        #    period=period,
+        #    max_iter=self.config.search.epochs
+        #    if search
+        #    else self.config.evaluation.epochs,
+        #)
 
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))
             # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
-            checkpoint = checkpointer.resume_or_load("resume_from", resume=True)
-            if checkpointer.has_checkpoint():
+            checkpoint = self.periodic_checkpointer.resume_or_load("resume_from", resume=True)
+            if self.periodic_checkpointer.has_checkpoint():
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 43e7ff2d8..e6b209b7c 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -163,7 +163,7 @@ def train_model_statistics(self, report_incumbent=True):
 
         
         best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_str = self.sampled_archs[self.fidelity_counter -1].arch.modules_str()
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
         return (
             best_arch.query(
                 Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
@@ -178,7 +178,7 @@ def train_model_statistics(self, report_incumbent=True):
                 Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
             ),
             self.fidelity,
-            hash(best_arch) * 5 + 7,
+            best_arch_hash,
         )
 
     def test_statistics(self):

From ff4c0de278ebbf41474b7a3eb061a2108743ab01 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 16 Nov 2021 16:30:56 +0100
Subject: [PATCH 006/130] beginn hyperband

---
 naslib/optimizers/discrete/hp/__init__.py  |   0
 naslib/optimizers/discrete/hp/optimizer.py | 159 +++++++++++++++++++++
 naslib/optimizers/discrete/sh/optimizer.py |   6 +
 3 files changed, 165 insertions(+)
 create mode 100644 naslib/optimizers/discrete/hp/__init__.py
 create mode 100644 naslib/optimizers/discrete/hp/optimizer.py

diff --git a/naslib/optimizers/discrete/hp/__init__.py b/naslib/optimizers/discrete/hp/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/naslib/optimizers/discrete/hp/optimizer.py b/naslib/optimizers/discrete/hp/optimizer.py
new file mode 100644
index 000000000..f6b454d7d
--- /dev/null
+++ b/naslib/optimizers/discrete/hp/optimizer.py
@@ -0,0 +1,159 @@
+#from hpbandster.core.base_iteration import BaseIteration
+import numpy as np
+import numpy as np
+import torch
+
+import math
+from naslib.optimizers.discrete.sh import SuccessiveHalving
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.search_spaces.core.query_metrics import Metric
+
+
+class HyperBand(MetaOptimizer):
+    """
+    This is right now only a little bit more as pseudocode, so it not runnable what is currently working on
+    # TODO: Write fancy comment 🌈
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(HyperBand, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+
+        #self.fidelit_min = config.search.min_fidelity
+        self.budget_max = config.search.budget_max
+        self.budget_min =  config.search.budget_min
+        self.number_archs = config.search.number_archs
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.s_max = math.floor(math.log(self.eta)*self.budget_max/self.budget_min)
+        self.s =  self.s_max
+
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+
+    def new_epoch(self, e):
+        """
+        Sample a new architecture to train.
+        """
+        if self.s > 0:
+            if sh.end == True:   #if sh is finish go to something diffrent as initial budget 
+                n = math.ceil((self.s_max +1 )/ (self.s +1 )* self.eta**self.s)
+                sh = SuccessiveHalving(min_fidelity, from_hb) #should be in config 
+                self.s -= 1
+            budget = sh.new_epoch()
+        return budget
+
+
+        
+        
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+
+    def test_statistics(self):
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
+
+# #from naslib.optimizers.core.metaclasses import MetaOptimizer
+# class SuccessiveHalving(MetaOptimizer):
+#     #also import or simular in NASLib?
+
+# 	def _advance_to_next_stage(self, config_ids, losses):
+# 		"""
+# 			#SuccessiveHalving simply continues the best based on the current loss.
+# 		"""
+# 		ranks = np.argsort(np.argsort(losses))
+# 		return(ranks < self.num_configs[self.stage])
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index e6b209b7c..6be28fa9a 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -50,6 +50,7 @@ def __init__(
         self.fidelity_counter = 0
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
+        #self.end = False
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
@@ -111,6 +112,8 @@ def new_epoch(self, e):
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
                 
             else:
+                #TODO: here maybe something back for hyperand 
+                #self.end = True
                 self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)
             self.fidelity_counter = 0
@@ -187,6 +190,9 @@ def test_statistics(self):
 
     def get_op_optimizer(self):
         return self.weight_optimizer
+    #TODO discuss about this 
+    #def get_end(self):
+    #    return self.end
 
     def get_checkpointables(self):
         return {"model": self.history}

From d4cc57ec33cf1f5135dd1059c33ee2dfd2e1b83e Mon Sep 17 00:00:00 2001
From: lage2104 <larsgerne@t-online.de>
Date: Tue, 16 Nov 2021 21:25:23 +0100
Subject: [PATCH 007/130] work on sh plot

for now, just prototyping
---
 count.py                                      | 117 ++++++++
 examples/plot.ipynb                           |  40 +--
 examples/plot_sh.py                           | 278 ++++++++++++++++++
 .../nas_predictors/discrete_config.yaml       |   2 +-
 4 files changed, 410 insertions(+), 27 deletions(-)
 create mode 100644 count.py
 create mode 100644 examples/plot_sh.py

diff --git a/count.py b/count.py
new file mode 100644
index 000000000..6e009eda2
--- /dev/null
+++ b/count.py
@@ -0,0 +1,117 @@
+
+a = [
+         8784118950669,
+         8784119373993,
+         8784119674405,
+         8784120668729,
+         8784121418997,
+         8784122552961,
+         8784123236973,
+         8784122561581,
+         8784124699973,
+         8784124583389,
+         8784125831929,
+         8784126430609,
+         8784126759213,
+         8784127599741,
+         8784128690333,
+         8784129044981,
+         8784130726629,
+         8784130932953,
+         8784131024561,
+         8784133885673,
+         8784133891129,
+         8784101770541,
+         8784101940905,
+         8784102995357,
+         8784103288053,
+         8784104434661,
+         8784104910713,
+         8784105835565,
+         8784106605545,
+         8784106969745,
+         8784108010925,
+         8784109256153,
+         8784110336925,
+         8784111114465,
+         8784111651769,
+         8784112187497,
+         8784105605109,
+         8784113956577,
+         8784114705369,
+         8784115487561,
+         8784116137697,
+         8784117284921,
+         8784084574937,
+         8784085337053,
+         8784085787301,
+         8784086741973,
+         8784087431577,
+         8784088116193,
+         8784088793493,
+         8784089509601,
+         8784090242773,
+         8784090883289,
+         8784092095453,
+         8784092362341,
+         8784093543129,
+         8784093548709,
+         8784094868201,
+         8784095579973,
+         8784096300249,
+         8784096983517,
+         8784098164961,
+         8784113494297,
+         8784099621081,
+         8784100279769,
+         8784100285165,
+         8784068170473,
+         8784068820609,
+         8784069471961,
+         8784070292701,
+         8784071009505,
+         8784071662037,
+         8784072149153,
+         8784073066717,
+         8784073120057,
+         8784074956001,
+         8784075630149,
+         8784076357081,
+         8784077134557,
+         8784077137705,
+         8784071009505,
+         8784078521557,
+         8784130932953,
+         8784077137705,
+         8784133891129,
+         8784090242773,
+         8784106605545,
+         8784095579973,
+         8784093543129,
+         8784119373993,
+         8784070292701,
+         8784090883289,
+         8784130726629,
+         8784086741973,
+         8784111114465,
+         8784105605109,
+         8784072149153,
+         8784122552961,
+         8784105835565,
+         8784112187497,
+         8784114705369,
+         8784092362341,
+         8784077134557,
+         8784100285165,
+         8784119674405,
+         8784084574937,
+         8784121418997,
+         8784085337053,
+         8784115487561,
+         8784101770541,
+         8784124699973
+      ]
+
+import collections
+duplicates = [item for item, count in collections.Counter(a).items() if count > 1]
+print(len(duplicates))
\ No newline at end of file
diff --git a/examples/plot.ipynb b/examples/plot.ipynb
index da847d2e9..137b21a27 100644
--- a/examples/plot.ipynb
+++ b/examples/plot.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,30 +162,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mlp output shape (2, 600) nans 1 missing files 0\n",
-      "lgb output shape (1, 300) nans 0 missing files 0\n",
-      "xgb output shape (2, 600) nans 0 missing files 0\n",
-      "rf output shape (2, 600) nans 0 missing files 0\n",
-      "bayes_lin_reg output shape (3, 900) nans 0 missing files 0\n",
-      "gp output shape (3, 900) nans 0 missing files 0\n",
-      "none output shape (3, 900) nans 0 missing files 0\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/zelaa/.local/lib/python3.7/site-packages/numpy-1.16.4-py3.7-linux-x86_64.egg/numpy/core/fromnumeric.py:3367: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  **kwargs)\n",
-      "/home/zelaa/.local/lib/python3.7/site-packages/numpy-1.16.4-py3.7-linux-x86_64.egg/numpy/core/_methods.py:130: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  ret, rcount, out=ret, casting='unsafe', subok=False)\n"
+     "ename": "NameError",
+     "evalue": "name 'get_results' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/6r/q53rnwms52v42bg8k7xxg2yc0000gn/T/ipykernel_9832/1992346954.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd_error\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruntime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'test_acc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     results_dict[predictor] = {'label':pred_label_dict[predictor], \n\u001b[1;32m     11\u001b[0m                                \u001b[0;34m'key'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpredictor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'std'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'get_results' is not defined"
      ]
     }
    ],
@@ -195,7 +183,7 @@
     "results_dict = {}\n",
     "\n",
     "folder = os.path.expanduser('../../re_run_0/cifar10/nas_predictors/nasbench201')\n",
-    "predictors=('mlp', 'lgb', 'xgb', 'rf', 'bayes_lin_reg', 'gp', 'none')\n",
+    "predictors = ['var_sparse_gp']\n",
     "\n",
     "for i, predictor in enumerate(predictors):\n",
     "    mean, std, std_error, runtime = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)\n",
@@ -211,7 +199,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 576x288 with 1 Axes>"
       ]
@@ -298,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/plot_sh.py b/examples/plot_sh.py
new file mode 100644
index 000000000..997be67b2
--- /dev/null
+++ b/examples/plot_sh.py
@@ -0,0 +1,278 @@
+#%%
+import sys
+import os
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+import pickle
+import pandas as pd
+
+from collections import defaultdict
+
+# needed for plotting
+from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+from matplotlib.ticker import FormatStrFormatter
+
+def restructure_results(errors :dict):
+    """This super fancy function restructures the results in order plot successive halving correctly
+    NOTE: This in not optimimzed at all!
+    """
+    print(errors[1].keys())
+    len_errors = len(errors[1]['train_acc'])
+    train_acc = errors[1]['train_acc']
+    valid_acc = errors[1]['valid_acc']
+    fidelity = errors[1]['fidelity']
+    archs = errors[1]['arch']
+    test_acc = errors[1]['test_acc']
+    runtime = errors[1]['runtime']
+    train_time = errors[1]['train_time']
+    new_errors = defaultdict(lambda: defaultdict(list))
+
+    for idx in range(len_errors):
+        new_errors[archs[idx]]['train_acc'].append(train_acc[idx])
+        new_errors[archs[idx]]['valid_acc'].append(valid_acc[idx])
+        new_errors[archs[idx]]['fidelity'].append(fidelity[idx])
+        new_errors[archs[idx]]['test_acc'].append(test_acc[idx])
+        new_errors[archs[idx]]['runtime'].append(runtime[idx])
+        new_errors[archs[idx]]['train_time'].append(train_time[idx])
+    return new_errors
+
+
+def merge_and_fill_trajectories(pandas_data_frames, default_value=None):
+    # merge all trajectories keeping all time steps
+    df = pd.DataFrame().join(pandas_data_frames, how='outer')
+
+    # forward fill to make it a propper step function
+    df = df.fillna(method='ffill')
+    if default_value is None:
+        # backward fill to replace the NaNs for the early times by the
+        # performance of a random configuration
+        df = df.fillna(method='bfill')
+    else:
+        df = df.fillna(default_value)
+    return df
+
+
+def get_trajectories(losses, iterations):
+    dfs = []
+    for i in range(len(losses)):
+        loss = losses[i]
+        iteration = iterations[i]
+        # print('Run %d, Min: %f'%(i, loss))
+        df = pd.DataFrame({str(i): loss}, index=iteration)
+        dfs.append(df)
+
+    df = merge_and_fill_trajectories(dfs, default_value=None)
+    if df.empty:
+        pass
+
+    return np.array(df.T), np.array(df.index)
+
+def get_results(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
+    
+    output = []
+    time = []
+    nan_count = 0
+    missing_results_count = 0
+    
+    algo_path = os.path.join(path, predictor)
+    for seed_dir in os.listdir(algo_path):
+        result_file = os.path.join(algo_path, seed_dir, 'errors.json')
+        result = json.load(open(result_file))
+        new_result = restructure_results(result)
+        config = result[0]
+        val_acc = result[1]['valid_acc'][:epochs]
+        surr_time = np.array(result[1]['runtime'])[:epochs]
+        if ug:
+            runtime = 200*np.array(result[1]['train_time'])[:epochs] + surr_time
+        else:
+            runtime = np.array(result[1]['train_time'])[:epochs] + surr_time
+
+        val_err = [100 - x for x in val_acc]
+        val_incumbent = [min(val_err[:epoch]) for epoch in range(1, len(val_err)+1)]
+        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]
+
+        if metric == 'valid_acc':
+            incumbent = val_incumbent
+        elif metric == 'test_acc':
+            test_err = [100 - x for x in result[1]['test_acc']]
+            inc_idx, best, best_idx = [], np.inf, 0
+            for i, err in enumerate(val_err):
+                if err < best:
+                    best, best_idx = err, i
+                inc_idx.append(best_idx)
+            incumbent = [test_err[idx] for idx in inc_idx]
+
+        # commented out section where epoch is checked. Our amount of epochs is based on time.
+        # if len(incumbent) == epochs:
+        output.append(incumbent)
+        time.append(runtime)
+        # else:
+        #    nan_count += 1
+
+    output = np.array(output)
+    time = np.array(time)
+
+    output, time = get_trajectories(output, time)
+    
+    print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)
+    mean = np.mean(output, axis=0)
+    std = np.std(output, axis=0)
+    std_error = np.sqrt(np.var(output, axis=0, ddof=1)/np.asarray(output).shape[0])
+    return mean, std, std_error, time
+
+def get_results_2(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
+    
+    output = []
+    time = []
+    nan_count = 0
+    missing_results_count = 0
+    
+    algo_path = os.path.join(path, predictor)
+    for seed_dir in os.listdir(algo_path):
+        result_file = os.path.join(algo_path, seed_dir, 'errors.json')
+        result = json.load(open(result_file))
+        new_result = restructure_results(result)
+        return new_result
+
+def plot_sh(new_results :dict):
+    """Plots successive halving learning curves
+    for key, data_list in data_dict.items():
+        dates_str, values = zip(*data_list)  # Unpack
+        dates = convert_str_to_dates(dates_str)  # Not handled in example
+        plt.plot(dates, values, label=key)
+    plt.legend()
+    """
+    for arch, results in new_results.items():
+        x = results['fidelity']
+        values = results['train_acc']
+        plt.plot(x, values, linestyle='-', marker='o', label=arch)
+    plt.show()
+
+def main():
+    # set up colors and plot markings
+    defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
+                (1.0, 0.4980392156862745, 0.054901960784313725),
+                (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
+                (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
+                (0.5803921568627451, 0.403921568627451, 0.7411764705882353),
+                (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
+                (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
+                (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
+                (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
+                (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]
+
+    # goes up to 24
+    c_max = 9
+    colors = [*defaults[:c_max], *defaults[:c_max], *defaults[:c_max]]
+    fmts = [*['-']*c_max, *['--']*c_max, *[':']*c_max]
+    markers = [*['^']*c_max, *['v']*c_max, *['o']*c_max]
+
+    # https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html
+
+
+    pred_label_dict={
+        'valloss':'Val. Loss', 'valacc':'Val. Acc.', 'sotl':'SoTL', 'bananas':'BANANAS',
+        'mlp':'Feedforward', 'gbdt':'GBDT', 'gcn':'GCN', 'bonas_gcn':'BONAS', 'xgb':'XGB',
+        'ngb':'NGB', 'rf':'RF', 'jacov':'Jacob. Cov.', 'dngo':'DNGO', 'bohamiann':'BOHAMIANN', 
+        'bayes_lin_reg':'Bayes. Lin. Reg.', 'ff_keras':'FF-Keras', 'gp':'GP', 'sparse_gp':'Sparse GP', 
+        'var_sparse_gp':'Var. Sparse GP', 'seminas':'SemiNAS', 'lcsvr':'LcSVR', 'snip':'SNIP', 'sotle':'SoTLE',
+        'bonas':'BONAS', 'omni_lofi':'Omni Lofi', 'nao': 'NAO', 'lgb': 'LGB', 'none': 'True'
+    }
+
+    # set up parameters for the experiments
+    epochs = 300
+    results_dict = {}
+
+    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
+    predictors = ['var_sparse_gp']
+
+    for i, predictor in enumerate(predictors):
+        mean, std, std_error, runtime = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
+        results_dict[predictor] = {'label':pred_label_dict[predictor], 
+                                'key':predictor, 'mean':mean, 'std':std, 
+                                'std_error': std_error, 'runtime': runtime}
+
+
+
+
+    # plot performance vs runtime
+
+    # didn't run them long enough to do logspace here. (These experiments took surprisingly long to run)
+    plt.rcParams['axes.grid'] = True
+    plt.rcParams['grid.linestyle'] = 'dotted'
+
+    plot_zoomed = False
+    plot_sem = True
+
+    fig, ax = plt.subplots(figsize=[8, 4])
+    if plot_zoomed:
+        sub_axes = plt.axes([.6, .6, .25, .25]) 
+
+    for i, key in enumerate(predictors):
+        mean = results_dict[key]['mean']
+        sem = results_dict[key]['std_error']
+        label = results_dict[key]['label']
+        x = results_dict[key]['runtime']
+        
+        ax.plot(x, mean, label=label, color=colors[i], linestyle=fmts[i])
+        if plot_sem:
+            ax.fill_between(x, mean-1*sem, mean+1*sem,
+                            color=colors[i], alpha=0.2)
+        
+        if plot_zoomed:
+            X_detail = x[-10000:]
+            Y_detail = mean[-10000:]
+            sem_detail = sem[-10000:]
+            sub_axes.plot(X_detail, Y_detail, color=colors[i], linestyle=fmts[i])
+            sub_axes.fill_between(X_detail, Y_detail-1*sem_detail, Y_detail+1*sem_detail,
+                            color=colors[i], alpha=0.2)
+            sub_axes.tick_params(labelbottom=False) 
+            sub_axes.set_xlim([700000, 900000])
+        
+    ax.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
+    ax.set_xscale('log')
+    #ax.set_ylim([7, 11])
+    #ax.set_xlim([1e4, 1.6e6])
+
+    ax.legend(loc=(1.04,0))
+    ax.set_xlabel('Runtime [s] (simulated)')
+    ax.set_ylabel('Test error (%)')
+    ax.grid(True, which="both",ls="-", alpha=.5)
+    ax.set_title('Test error vs. train time for the Predictor Framework on NAS-Bench-201')
+    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
+
+def main_2():
+    """Just plots the successive halving
+    
+    """
+    pred_label_dict={
+        'valloss':'Val. Loss', 'valacc':'Val. Acc.', 'sotl':'SoTL', 'bananas':'BANANAS',
+        'mlp':'Feedforward', 'gbdt':'GBDT', 'gcn':'GCN', 'bonas_gcn':'BONAS', 'xgb':'XGB',
+        'ngb':'NGB', 'rf':'RF', 'jacov':'Jacob. Cov.', 'dngo':'DNGO', 'bohamiann':'BOHAMIANN', 
+        'bayes_lin_reg':'Bayes. Lin. Reg.', 'ff_keras':'FF-Keras', 'gp':'GP', 'sparse_gp':'Sparse GP', 
+        'var_sparse_gp':'Var. Sparse GP', 'seminas':'SemiNAS', 'lcsvr':'LcSVR', 'snip':'SNIP', 'sotle':'SoTLE',
+        'bonas':'BONAS', 'omni_lofi':'Omni Lofi', 'nao': 'NAO', 'lgb': 'LGB', 'none': 'True'
+    }
+    # set up parameters for the experiments
+    epochs = 300
+    results_dict = {}
+
+    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
+    predictor = 'var_sparse_gp'
+    results = get_results_2(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
+
+    # didn't run them long enough to do logspace here. (These experiments took surprisingly long to run)
+    # plt.rcParams['axes.grid'] = True
+    plt.rcParams['grid.linestyle'] = 'dotted'
+
+    plot_zoomed = False
+    plot_sem = True
+
+    plot_sh(results)
+    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
+
+if __name__ == '__main__':
+    main_2()
+# %%
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 7f0a41ca3..80b67646f 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,7 +7,7 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 100
+  epochs: 5000
   fidelity: -1
 
   min_fidelity: 5

From 1471cd5ce89eb0604b8744bf413cf55608745c00 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 17 Nov 2021 16:30:31 +0100
Subject: [PATCH 008/130] work on sh plots & bug fixes in sh optimizer

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 examples/plot_sh.py                           |   2 +-
 .../nas_predictors/discrete_config.yaml       |   6 +-
 naslib/defaults/trainer_multifidelity.py      |   4 +-
 .../discrete/{hp => hb}/__init__.py           |   0
 naslib/optimizers/discrete/hb/optimizer.py    | 244 +++++++++++-------
 naslib/optimizers/discrete/hp/optimizer.py    | 159 ------------
 naslib/optimizers/discrete/sh/optimizer.py    |  16 +-
 7 files changed, 169 insertions(+), 262 deletions(-)
 rename naslib/optimizers/discrete/{hp => hb}/__init__.py (100%)
 delete mode 100644 naslib/optimizers/discrete/hp/optimizer.py

diff --git a/examples/plot_sh.py b/examples/plot_sh.py
index 997be67b2..c816c4797 100644
--- a/examples/plot_sh.py
+++ b/examples/plot_sh.py
@@ -146,7 +146,7 @@ def plot_sh(new_results :dict):
     """
     for arch, results in new_results.items():
         x = results['fidelity']
-        values = results['train_acc']
+        values = results['valid_acc']
         plt.plot(x, values, linestyle='-', marker='o', label=arch)
     plt.show()
 
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 80b67646f..ae1e7162b 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,11 +7,11 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 5000
+  epochs: 500
   fidelity: -1
 
-  min_fidelity: 5
-  number_archs: 80
+  min_fidelity: 1
+  number_archs: 40
   eta: 2
   budget_type:  time
 
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 736424cdd..5b0409a45 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -189,7 +189,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 self.val_top1.avg = valid_acc
 
             self.periodic_checkpointer.save(e) # define the name in accurate , also 
-
+            # TODO: change step into save for checkpointer
             anytime_results = self.optimizer.test_statistics()
             if anytime_results:
                 # record anytime performance
@@ -578,7 +578,7 @@ def _setup_checkpointers(
         checkpointables = self.optimizer.get_checkpointables()
         checkpointables.update(add_checkpointables)
         #name make no sense
-        self.periodic_checkpointer = utils.Checkpointer(
+        self.periodic_checkpointer = utils.Checkpointer( #TODO rename periodic_checkpointer 
             model=checkpointables.pop("model"),
             save_dir=self.config.save + "/search"
             if search
diff --git a/naslib/optimizers/discrete/hp/__init__.py b/naslib/optimizers/discrete/hb/__init__.py
similarity index 100%
rename from naslib/optimizers/discrete/hp/__init__.py
rename to naslib/optimizers/discrete/hb/__init__.py
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 868b784f9..f6b454d7d 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -1,93 +1,159 @@
-import os
-import time
+#from hpbandster.core.base_iteration import BaseIteration
+import numpy as np
+import numpy as np
+import torch
+
 import math
-import pdb
-import copy
-import logging
+from naslib.optimizers.discrete.sh import SuccessiveHalving
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.search_spaces.core.query_metrics import Metric
 
-import numpy as np
 
+class HyperBand(MetaOptimizer):
+    """
+    This is right now only a little bit more as pseudocode, so it not runnable what is currently working on
+    # TODO: Write fancy comment 🌈
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(HyperBand, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+
+        #self.fidelit_min = config.search.min_fidelity
+        self.budget_max = config.search.budget_max
+        self.budget_min =  config.search.budget_min
+        self.number_archs = config.search.number_archs
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.s_max = math.floor(math.log(self.eta)*self.budget_max/self.budget_min)
+        self.s =  self.s_max
+
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+
+    def new_epoch(self, e):
+        """
+        Sample a new architecture to train.
+        """
+        if self.s > 0:
+            if sh.end == True:   #if sh is finish go to something diffrent as initial budget 
+                n = math.ceil((self.s_max +1 )/ (self.s +1 )* self.eta**self.s)
+                sh = SuccessiveHalving(min_fidelity, from_hb) #should be in config 
+                self.s -= 1
+            budget = sh.new_epoch()
+        return budget
+
+
+        
+        
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+
+    def test_statistics(self):
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
+
+# #from naslib.optimizers.core.metaclasses import MetaOptimizer
+# class SuccessiveHalving(MetaOptimizer):
+#     #also import or simular in NASLib?
 
-import ConfigSpace as CS
-
-from hpbandster.core.master import Master
-from hpbandster.optimizers.iterations import SuccessiveHalving
-from hpbandster.optimizers.config_generators import RandomSampling
-
-class HyperBand(Master):
-	def __init__(self, configspace = None,
-					eta=3, min_budget=0.01, max_budget=1,
-					**kwargs ):
-		"""
-                Hyperband implements hyperparameter optimization by sampling
-                candidates at random and "trying" them first, running them for
-                a specific budget. The approach is iterative, promising
-                candidates are run for a longer time, increasing the fidelity
-                for their performance. While this is a very efficient racing
-                approach, random sampling makes no use of the knowledge gained
-                about the candidates during optimization.
-		Parameters
-		----------
-		configspace: ConfigSpace object
-			valid representation of the search space
-		eta : float
-			In each iteration, a complete run of sequential halving is executed. In it,
-			after evaluating each configuration on the same subset size, only a fraction of
-			1/eta of them 'advances' to the next round.
-			Must be greater or equal to 2.
-		min_budget : float
-			The smallest budget to consider. Needs to be positive!
-		max_budget : float
-			the largest budget to consider. Needs to be larger than min_budget!
-			The budgets will be geometrically distributed $\sim \eta^k$ for
-			$k\in [0, 1, ... , num_subsets - 1]$.
-		"""
-
-
-		# TODO: Propper check for ConfigSpace object!
-		if configspace is None:
-			raise ValueError("You have to provide a valid CofigSpace object")
-
-		super().__init__(config_generator=RandomSampling(configspace), **kwargs)
-
-		# Hyperband related stuff
-		self.eta = eta
-		self.min_budget = min_budget
-		self.max_budget = max_budget
-
-		# precompute some HB stuff
-		self.max_SH_iter = -int(np.log(min_budget/max_budget)/np.log(eta)) + 1
-		self.budgets = max_budget * np.power(eta, -np.linspace(self.max_SH_iter-1, 0, self.max_SH_iter))
-
-		self.config.update({
-						'eta'        : eta,
-						'min_budget' : min_budget,
-						'max_budget' : max_budget,
-						'budgets'    : self.budgets,
-						'max_SH_iter': self.max_SH_iter,
-					})
-
-
-
-	def get_next_iteration(self, iteration, iteration_kwargs={}):
-		"""
-		Hyperband uses SuccessiveHalving for each iteration.
-		See Li et al. (2016) for reference.
-		
-		Parameters
-		----------
-			iteration: int
-				the index of the iteration to be instantiated
-		Returns
-		-------
-			SuccessiveHalving: the SuccessiveHalving iteration with the
-				corresponding number of configurations
-		"""
-		
-		# number of 'SH rungs'
-		s = self.max_SH_iter - 1 - (iteration%self.max_SH_iter)
-		# number of configurations in that bracket
-		n0 = int(np.floor((self.max_SH_iter)/(s+1)) * self.eta**s)
-		ns = [max(int(n0*(self.eta**(-i))), 1) for i in range(s+1)]
-
-		return(SuccessiveHalving(HPB_iter=iteration, num_configs=ns, budgets=self.budgets[(-s-1):], config_sampler=self.config_generator.get_config, **iteration_kwargs))
\ No newline at end of file
+# 	def _advance_to_next_stage(self, config_ids, losses):
+# 		"""
+# 			#SuccessiveHalving simply continues the best based on the current loss.
+# 		"""
+# 		ranks = np.argsort(np.argsort(losses))
+# 		return(ranks < self.num_configs[self.stage])
diff --git a/naslib/optimizers/discrete/hp/optimizer.py b/naslib/optimizers/discrete/hp/optimizer.py
deleted file mode 100644
index f6b454d7d..000000000
--- a/naslib/optimizers/discrete/hp/optimizer.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#from hpbandster.core.base_iteration import BaseIteration
-import numpy as np
-import numpy as np
-import torch
-
-import math
-from naslib.optimizers.discrete.sh import SuccessiveHalving
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.search_spaces.core.query_metrics import Metric
-
-
-class HyperBand(MetaOptimizer):
-    """
-    This is right now only a little bit more as pseudocode, so it not runnable what is currently working on
-    # TODO: Write fancy comment 🌈
-    """
-
-    # training the models is not implemented
-    using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(HyperBand, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-
-        self.performance_metric = Metric.VAL_ACCURACY
-        self.dataset = config.dataset
-
-        #self.fidelit_min = config.search.min_fidelity
-        self.budget_max = config.search.budget_max
-        self.budget_min =  config.search.budget_min
-        self.number_archs = config.search.number_archs
-        self.eta = config.search.eta
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
-        self.sampled_archs = []
-        self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.eta)*self.budget_max/self.budget_min)
-        self.s =  self.s_max
-
-    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
-        self.search_space = search_space.clone()
-        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
-        self.dataset_api = dataset_api
-
-    def new_epoch(self, e):
-        """
-        Sample a new architecture to train.
-        """
-        if self.s > 0:
-            if sh.end == True:   #if sh is finish go to something diffrent as initial budget 
-                n = math.ceil((self.s_max +1 )/ (self.s +1 )* self.eta**self.s)
-                sh = SuccessiveHalving(min_fidelity, from_hb) #should be in config 
-                self.s -= 1
-            budget = sh.new_epoch()
-        return budget
-
-
-        
-        
-    def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
-
-    def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
-
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
-
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-
-        )
-    def train_model_statistics(self, report_incumbent=True):
-
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
-        )
-
-    def test_statistics(self):
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
-
-    def get_op_optimizer(self):
-        return self.weight_optimizer
-
-    def get_checkpointables(self):
-        return {"model": self.history}
-
-# #from naslib.optimizers.core.metaclasses import MetaOptimizer
-# class SuccessiveHalving(MetaOptimizer):
-#     #also import or simular in NASLib?
-
-# 	def _advance_to_next_stage(self, config_ids, losses):
-# 		"""
-# 			#SuccessiveHalving simply continues the best based on the current loss.
-# 		"""
-# 		ranks = np.argsort(np.argsort(losses))
-# 		return(ranks < self.num_configs[self.stage])
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 6be28fa9a..7eb8edf9e 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -87,7 +87,7 @@ def new_epoch(self, e):
         # TODO: make query type secure
         if self.budget_type == 'time':
             # TODO: make dependent on performance_metric
-            model.time = model.arch.query(
+            model.time = model.arch.query( # TODO: this is the time for training from screatch.
                 Metric.TRAIN_TIME,
                 self.dataset,
                 epoch=self.fidelity, # DONE: adapt this
@@ -104,13 +104,13 @@ def new_epoch(self, e):
             self.sampled_archs[self.fidelity_counter] = model
         
         self.fidelity_counter += 1
+        # TODO: fidelity is changed for new epoch, what make the wrong values in the dictonary
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
-            self.fidelity = math.floor(self.eta*self.fidelity) #DONE round 
-            self.sampled_archs.sort(key = lambda model: model.accuracy)
+            self.fidelity = math.floor(self.eta*self.fidelity) #
+            self.sampled_archs.sort(key = lambda model: model.accuracy, reverse= True)
             if(math.floor(self.number_archs/self.eta)) != 0:
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
-                
             else:
                 #TODO: here maybe something back for hyperand 
                 #self.end = True
@@ -169,16 +169,16 @@ def train_model_statistics(self, report_incumbent=True):
         best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
         return (
             best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
             ),
             best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
             ),
             best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
             ),
             best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
             ),
             self.fidelity,
             best_arch_hash,

From 51f29490694e54dd3f53260d1bc8d215ae9cc20a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 18 Nov 2021 10:28:41 +0100
Subject: [PATCH 009/130] make HyperBand acces able

---
 demo.py                                    | 6 ++++--
 naslib/optimizers/__init__.py              | 2 ++
 naslib/optimizers/discrete/hb/optimizer.py | 4 ++--
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/demo.py b/demo.py
index 861380deb..8bd6f7ef7 100644
--- a/demo.py
+++ b/demo.py
@@ -2,6 +2,7 @@
 # debug random search optimizer to better understand function
 
 from naslib import search_spaces
+from naslib.optimizers.discrete.sh import optimizer
 from naslib.search_spaces import NasBench201SearchSpace as NB201
 
 import logging
@@ -10,6 +11,7 @@
 from naslib.optimizers import RandomSearch as RS
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers import HyperBand as HB
 
 from naslib.defaults.trainer_multifidelity import Trainer
 #from naslib.defaults.trainer import Trainer
@@ -26,9 +28,9 @@
 logger.setLevel(logging.INFO)
 
 # define optimizer 
-optimizer = SH(config)
+#optimizer = SH(config)
 #optimizer = RS(config)
-
+optimizer = HB(config)
 # load nasbench data, there data seems to be generalised
 dataset_api = get_dataset_api(config.search_space, config.dataset)
 
diff --git a/naslib/optimizers/__init__.py b/naslib/optimizers/__init__.py
index c4e065004..2af4cbe5e 100644
--- a/naslib/optimizers/__init__.py
+++ b/naslib/optimizers/__init__.py
@@ -5,8 +5,10 @@
 from .oneshot.drnas.optimizer import DrNASOptimizer
 from .discrete.rs.optimizer import RandomSearch
 from .discrete.sh.optimizer import SuccessiveHalving
+from .discrete.hb.optimizer import HyperBand
 from .discrete.re.optimizer import RegularizedEvolution
 from .discrete.ls.optimizer import LocalSearch
 from .discrete.bananas.optimizer import Bananas
 from .discrete.bp.optimizer import BasePredictor
 from .discrete.npenas.optimizer import Npenas
+from .discrete.hb.optimizer import HyperBand
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index f6b454d7d..5d55c1ffd 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -4,7 +4,7 @@
 import torch
 
 import math
-from naslib.optimizers.discrete.sh import SuccessiveHalving
+from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
 
@@ -70,7 +70,7 @@ def new_epoch(self, e):
         if self.s > 0:
             if sh.end == True:   #if sh is finish go to something diffrent as initial budget 
                 n = math.ceil((self.s_max +1 )/ (self.s +1 )* self.eta**self.s)
-                sh = SuccessiveHalving(min_fidelity, from_hb) #should be in config 
+                sh = SH(config) #should be in config 
                 self.s -= 1
             budget = sh.new_epoch()
         return budget

From 732b9e1bfc54e8b65b41f29e286e67892eca88ed Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 18 Nov 2021 17:21:08 +0100
Subject: [PATCH 010/130] hyperband main code finish but one thing make no
 sense

---
 .../nas_predictors/discrete_config.yaml       |  9 ++--
 naslib/defaults/trainer_multifidelity.py      |  7 ++-
 naslib/optimizers/discrete/hb/optimizer.py    | 53 +++++++++++++------
 naslib/optimizers/discrete/sh/optimizer.py    | 14 ++---
 4 files changed, 54 insertions(+), 29 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index ae1e7162b..91ba286f7 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,13 +7,12 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 500
+  epochs: 3000
   fidelity: -1
-
   min_fidelity: 1
-  number_archs: 40
-  eta: 2
-  budget_type:  time
+  number_archs: 81
+  eta: 3
+  budget_type:  epoch 
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 5b0409a45..1c9b3b414 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -17,7 +17,7 @@
 from naslib.utils.logging import log_every_n_seconds, log_first_n
 
 from typing import Callable
-from .additional_primitives import DropPathWrapper
+#from .additional_primitives import DropPathWrapper #Can be causes issues
 
 logger = logging.getLogger(__name__)
 
@@ -114,7 +114,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
 
             start_time = time.time()
             #return function_eqalisation difference time to run or number of elovations 
-            used_budget = self.optimizer.new_epoch(e)
+            used_budget = self.optimizer.new_epoch()
 
             if self.optimizer.using_step_function:
                 for step, data_train in enumerate(self.train_queue):
@@ -165,7 +165,9 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 end_time = time.time()
                 # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
                 # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
+                """ #only for debugging is it but out
                 (
+
                     train_acc,
                     valid_acc,
                     test_acc,
@@ -206,6 +208,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
 
             if after_epoch is not None:
                 after_epoch(e)
+            """
             e += used_budget
 
         self.optimizer.after_training()
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 5d55c1ffd..248af92f5 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -2,7 +2,7 @@
 import numpy as np
 import numpy as np
 import torch
-
+import copy
 import math
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers.core.metaclasses import MetaOptimizer
@@ -39,22 +39,22 @@ def __init__(
         self.weight_optimizer = weight_optimizer
         self.loss = loss_criteria
         self.grad_clip = grad_clip
-
+        self.sh_config = copy.deepcopy(config)
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
 
         #self.fidelit_min = config.search.min_fidelity
-        self.budget_max = config.search.budget_max
-        self.budget_min =  config.search.budget_min
-        self.number_archs = config.search.number_archs
+        self.budget_max = config.search.number_archs
         self.eta = config.search.eta
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.budget_type = config.search.budget_type #is not for one query is overall
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.eta)*self.budget_max/self.budget_min)
+        self.s_max = math.floor(math.log(self.budget_max, self.eta))
         self.s =  self.s_max
-
+        self.sh = None
+        self.first = True # TODO: think about a more ellegant solution 
+        self.b = (self.s_max +1 )* self.budget_max
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -63,18 +63,41 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
 
-    def new_epoch(self, e):
+    def new_epoch(self):
         """
         Sample a new architecture to train.
+        after https://arxiv.org/pdf/1603.06560.pdf
         """
-        if self.s > 0:
-            if sh.end == True:   #if sh is finish go to something diffrent as initial budget 
-                n = math.ceil((self.s_max +1 )/ (self.s +1 )* self.eta**self.s)
-                sh = SH(config) #should be in config 
-                self.s -= 1
-            budget = sh.new_epoch()
+        if self.s >= 0:
+            if  self.sh == None or self.sh.get_end():
+            #if sh is finish go to something diffrent as initial budget
+                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
+                    n = math.ceil(self.b/self.budget_max/(self.s+1)* self.eta ** self.s)
+                    r = self.budget_max*self.eta**(-self.s)
+                    print("{},{}".format(n,r))
+                    self.sh_config.number_archs = n
+                    self.sh_config.min_fidelity  = r 
+                    self.sh = SH(self.sh_config) #should be in config 
+                    self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+                    self.s -= 1
+            budget = self.sh.new_epoch()
+        else:
+            print("HB is finish, allready not defined what to do") # TODO define what to do 
+            return math.inf #end the thing 
         return budget
-
+#n:81,r:1.0
+#n:34,r:3.0
+#n:15,r:9.0
+#n:8,r:27.0
+#n:5,r:81
+#what should be rigth after the formular 
+
+# but in the paiper is 
+# 81,1
+# 27, 3
+# 9 , 9
+# 6 , 27
+# 5 , 81 
 
         
         
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 7eb8edf9e..dc374be5f 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -41,7 +41,7 @@ def __init__(
 
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-
+        self.end = False
         self.fidelity = config.search.min_fidelity
         self.number_archs = config.search.number_archs
         self.eta = config.search.eta
@@ -50,7 +50,7 @@ def __init__(
         self.fidelity_counter = 0
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
-        #self.end = False
+        self.end = False
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
@@ -60,7 +60,7 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
 
-    def new_epoch(self, e):
+    def new_epoch(self):
         """
         Sample a new architecture to train.
         """
@@ -113,7 +113,7 @@ def new_epoch(self, e):
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
             else:
                 #TODO: here maybe something back for hyperand 
-                #self.end = True
+                self.end = True
                 self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)
             self.fidelity_counter = 0
@@ -190,9 +190,9 @@ def test_statistics(self):
 
     def get_op_optimizer(self):
         return self.weight_optimizer
-    #TODO discuss about this 
-    #def get_end(self):
-    #    return self.end
+    
+    def get_end(self):
+        return self.end
 
     def get_checkpointables(self):
         return {"model": self.history}

From 43f5ebc17a7f363509f383a29b223f717afaf5aa Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 19 Nov 2021 11:26:59 +0100
Subject: [PATCH 011/130] fixed config for sh

---
 demo.py                                               | 6 +++---
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 8 ++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/demo.py b/demo.py
index 8bd6f7ef7..26780e73a 100644
--- a/demo.py
+++ b/demo.py
@@ -28,9 +28,9 @@
 logger.setLevel(logging.INFO)
 
 # define optimizer 
-#optimizer = SH(config)
-#optimizer = RS(config)
-optimizer = HB(config)
+optimizer = SH(config)
+# optimizer = RS(config)
+# ptimizer = HB(config)
 # load nasbench data, there data seems to be generalised
 dataset_api = get_dataset_api(config.search_space, config.dataset)
 
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 91ba286f7..3a93d4ca0 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,12 +7,12 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 3000
+  epochs: 500
   fidelity: -1
   min_fidelity: 1
-  number_archs: 81
-  eta: 3
-  budget_type:  epoch 
+  number_archs: 80
+  eta: 2
+  budget_type:  time 
 
 
   predictor_type: var_sparse_gp

From ee11f5bc0e1617c4195fc86f7955b86089964802 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 19 Nov 2021 15:29:41 +0100
Subject: [PATCH 012/130] work on optimizer stats

---
 examples/plot_sh.py                           | 10 +++---
 .../nas_predictors/discrete_config.yaml       |  4 +--
 naslib/defaults/trainer_multifidelity.py      | 11 ++++++-
 naslib/optimizers/discrete/sh/optimizer.py    | 33 +++++++++++++++++--
 4 files changed, 47 insertions(+), 11 deletions(-)

diff --git a/examples/plot_sh.py b/examples/plot_sh.py
index c816c4797..74fa70f39 100644
--- a/examples/plot_sh.py
+++ b/examples/plot_sh.py
@@ -131,10 +131,10 @@ def get_results_2(predictor, path, epochs, metric='valid_acc', dataset='cifar10'
     
     algo_path = os.path.join(path, predictor)
     for seed_dir in os.listdir(algo_path):
-        result_file = os.path.join(algo_path, seed_dir, 'errors.json')
+        result_file = os.path.join(algo_path, seed_dir, 'sh_stats.json')
         result = json.load(open(result_file))
-        new_result = restructure_results(result)
-        return new_result
+        # new_result = restructure_results(result)
+        return result
 
 def plot_sh(new_results :dict):
     """Plots successive halving learning curves
@@ -146,8 +146,8 @@ def plot_sh(new_results :dict):
     """
     for arch, results in new_results.items():
         x = results['fidelity']
-        values = results['valid_acc']
-        plt.plot(x, values, linestyle='-', marker='o', label=arch)
+        values = results['val_acc']
+        plt.plot(x, values, linestyle='-', label=arch)
     plt.show()
 
 def main():
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 3a93d4ca0..f844bb96a 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,12 +7,12 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 500
+  epochs: 158
   fidelity: -1
   min_fidelity: 1
   number_archs: 80
   eta: 2
-  budget_type:  time 
+  budget_type: epoch 
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 1c9b3b414..ea156dd8c 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -212,7 +212,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             e += used_budget
 
         self.optimizer.after_training()
-
+        self._log_optimizer_stats()
         
 
         if summary_writer is not None:
@@ -605,6 +605,15 @@ def _setup_checkpointers(
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
+    def _log_optimizer_stats(self, filename="sh_stats.json"):
+        """Log extended statistics to json file"""
+        if not os.path.exists(self.config.save):
+            os.makedirs(self.config.save)
+        with codecs.open(
+            os.path.join(self.config.save, filename), "w", encoding="utf-8"
+        ) as file:
+            json.dump(self.optimizer.optimizer_stats, file, separators=(",", ":"))
+
     def _log_to_json(self):
         """log training statistics to json file"""
         if not os.path.exists(self.config.save):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index dc374be5f..d2bc9e4aa 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,10 +1,11 @@
 #from hpbandster.core.base_iteration import BaseIteration
 import numpy as np
-import numpy as np
 import torch
 
 import math
 
+from collections import defaultdict
+
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
 
@@ -52,6 +53,8 @@ def __init__(
         self.history = torch.nn.ModuleList()
         self.end = False
 
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -102,7 +105,7 @@ def new_epoch(self):
             self.sampled_archs.append(model)
         else:
             self.sampled_archs[self.fidelity_counter] = model
-        
+        self.update_optimizer_stats()
         self.fidelity_counter += 1
         # TODO: fidelity is changed for new epoch, what make the wrong values in the dictonary
         self._update_history(model)
@@ -117,6 +120,7 @@ def new_epoch(self):
                 self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)
             self.fidelity_counter = 0
+        # TODO: budget equals 
         return budget
         # required if we want to train the models and not only query.
         # architecture_i.parse()
@@ -162,9 +166,32 @@ def train_statistics(self, report_incumbent=True):
             ),
 
         )
-    def train_model_statistics(self, report_incumbent=True):
 
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        """
+        arch = self.sampled_archs[self.fidelity_counter].arch
+        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
+        # this dict contains metrics to save
+        metrics = {
+            "train_acc": Metric.TRAIN_ACCURACY,
+            "val_acc": Metric.VAL_ACCURACY,
+            "test_acc": Metric.TEST_ACCURACY,
+            "train_time": Metric.TRAIN_TIME
+        }
+        for metric_name, metric in metrics.items():
+            metric_value = arch.query(
+                metric, 
+                self.dataset, 
+                dataset_api=self.dataset_api, 
+                epoch=self.fidelity
+            )
+            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
+        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity) 
         
+
+    def train_model_statistics(self, report_incumbent=True):
         best_arch = self.sampled_archs[self.fidelity_counter -1].arch
         best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
         return (

From a02df1a1e469a9fdb6a226ed4f5ebdec8ae40c0f Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 19 Nov 2021 16:18:04 +0100
Subject: [PATCH 013/130] fixed hyperband calc

---
 naslib/optimizers/discrete/hb/optimizer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 248af92f5..053d481bd 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -51,10 +51,10 @@ def __init__(
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
         self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s =  self.s_max
+        self.s = self.s_max
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
-        self.b = (self.s_max +1 )* self.budget_max
+        self.b = (self.s_max + 1)* self.budget_max
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -72,8 +72,8 @@ def new_epoch(self):
             if  self.sh == None or self.sh.get_end():
             #if sh is finish go to something diffrent as initial budget
                     #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-                    n = math.ceil(self.b/self.budget_max/(self.s+1)* self.eta ** self.s)
-                    r = self.budget_max*self.eta**(-self.s)
+                    n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+                    r = self.budget_max * self.eta ** (-self.s)
                     print("{},{}".format(n,r))
                     self.sh_config.number_archs = n
                     self.sh_config.min_fidelity  = r 

From 50b776da12a2820da44212a39c9079df72b909f4 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 19 Nov 2021 18:31:42 +0100
Subject: [PATCH 014/130] tested hyperand

---
 naslib/optimizers/discrete/hb/optimizer.py | 22 +++-------------------
 naslib/optimizers/discrete/sh/optimizer.py |  4 ++--
 2 files changed, 5 insertions(+), 21 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 053d481bd..6e578c47c 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -74,9 +74,8 @@ def new_epoch(self):
                     #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
                     n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
                     r = self.budget_max * self.eta ** (-self.s)
-                    print("{},{}".format(n,r))
-                    self.sh_config.number_archs = n
-                    self.sh_config.min_fidelity  = r 
+                    self.sh_config.search.number_archs = n
+                    self.sh_config.search.min_fidelity  = r 
                     self.sh = SH(self.sh_config) #should be in config 
                     self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
                     self.s -= 1
@@ -85,22 +84,7 @@ def new_epoch(self):
             print("HB is finish, allready not defined what to do") # TODO define what to do 
             return math.inf #end the thing 
         return budget
-#n:81,r:1.0
-#n:34,r:3.0
-#n:15,r:9.0
-#n:8,r:27.0
-#n:5,r:81
-#what should be rigth after the formular 
-
-# but in the paiper is 
-# 81,1
-# 27, 3
-# 9 , 9
-# 6 , 27
-# 5 , 81 
-
-        
-        
+                
     def _update_history(self, child):
         if len(self.history) < 100:
             self.history.append(child)
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index dc374be5f..5988a3e94 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -79,7 +79,7 @@ def new_epoch(self):
         model.accuracy = model.arch.query(
             self.performance_metric,
             self.dataset,
-            epoch=self.fidelity, # DONE: adapt this
+            epoch=int(self.fidelity), # DONE: adapt this
             dataset_api=self.dataset_api,
         )
 
@@ -90,7 +90,7 @@ def new_epoch(self):
             model.time = model.arch.query( # TODO: this is the time for training from screatch.
                 Metric.TRAIN_TIME,
                 self.dataset,
-                epoch=self.fidelity, # DONE: adapt this
+                epoch=int(self.fidelity), # DONE: adapt this
                 dataset_api=self.dataset_api,
             )
             budget = model.time

From beea9c024dd2033d9d47d35ddbb21621d1f74d08 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 19 Nov 2021 18:56:44 +0100
Subject: [PATCH 015/130] add other stop criterion to sh

---
 naslib/optimizers/discrete/hb/optimizer.py | 21 +++++++++++----------
 naslib/optimizers/discrete/sh/optimizer.py |  4 ++--
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 6e578c47c..d170f2aea 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -51,7 +51,7 @@ def __init__(
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
         self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s = self.s_max
+        self.s = self.s_max + 1
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
@@ -68,17 +68,18 @@ def new_epoch(self):
         Sample a new architecture to train.
         after https://arxiv.org/pdf/1603.06560.pdf
         """
-        if self.s >= 0:
-            if  self.sh == None or self.sh.get_end():
+        # TODO: rethink because of s
+        if  self.sh == None or self.sh.get_end():
             #if sh is finish go to something diffrent as initial budget
                     #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-                    n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-                    r = self.budget_max * self.eta ** (-self.s)
-                    self.sh_config.search.number_archs = n
-                    self.sh_config.search.min_fidelity  = r 
-                    self.sh = SH(self.sh_config) #should be in config 
-                    self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-                    self.s -= 1
+                self.s -= 1
+                n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+                r = self.budget_max * self.eta ** (-self.s)
+                self.sh_config.search.number_archs = n
+                self.sh_config.search.min_fidelity  = r 
+                self.sh = SH(self.sh_config) #should be in config 
+                self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+        if self.s >= 0:
             budget = self.sh.new_epoch()
         else:
             print("HB is finish, allready not defined what to do") # TODO define what to do 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 5988a3e94..90d9852e0 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -38,7 +38,7 @@ def __init__(
         self.weight_optimizer = weight_optimizer
         self.loss = loss_criteria
         self.grad_clip = grad_clip
-
+        self.budget_max = config.search.number_archs
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
         self.end = False
@@ -109,7 +109,7 @@ def new_epoch(self):
         if self.fidelity_counter == self.number_archs:
             self.fidelity = math.floor(self.eta*self.fidelity) #
             self.sampled_archs.sort(key = lambda model: model.accuracy, reverse= True)
-            if(math.floor(self.number_archs/self.eta)) != 0:
+            if(math.floor(self.number_archs/self.eta)) != 0 or self.fidelity > self.budget_max:
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
             else:
                 #TODO: here maybe something back for hyperand 

From 2e475fe11f3198cf65e14147b28a41a5baa251aa Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 20 Nov 2021 16:35:47 +0100
Subject: [PATCH 016/130] make hb work

---
 .../nas_predictors/discrete_config.yaml       |  2 +-
 naslib/defaults/trainer_multifidelity.py      |  8 +++--
 naslib/optimizers/discrete/hb/optimizer.py    | 29 ++++++++++---------
 naslib/optimizers/discrete/sh/optimizer.py    |  9 ++++--
 4 files changed, 28 insertions(+), 20 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 91ba286f7..924d4e4e4 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -10,7 +10,7 @@ search:
   epochs: 3000
   fidelity: -1
   min_fidelity: 1
-  number_archs: 81
+  budget_max: 81
   eta: 3
   budget_type:  epoch 
 
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 1c9b3b414..516203907 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -165,9 +165,11 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 end_time = time.time()
                 # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
                 # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
-                """ #only for debugging is it but out
+                #only for debugging is it but out
+                """
                 (
 
+
                     train_acc,
                     valid_acc,
                     test_acc,
@@ -205,10 +207,10 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             self._log_to_json()
 
             self._log_and_reset_accuracies(e, summary_writer)
-
+            """
             if after_epoch is not None:
                 after_epoch(e)
-            """
+    
             e += used_budget
 
         self.optimizer.after_training()
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index d170f2aea..39cf31363 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -44,7 +44,7 @@ def __init__(
         self.dataset = config.dataset
 
         #self.fidelit_min = config.search.min_fidelity
-        self.budget_max = config.search.number_archs
+        self.budget_max = config.search.budget_max
         self.eta = config.search.eta
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.budget_type = config.search.budget_type #is not for one query is overall
@@ -72,18 +72,21 @@ def new_epoch(self):
         if  self.sh == None or self.sh.get_end():
             #if sh is finish go to something diffrent as initial budget
                     #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-                self.s -= 1
-                n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-                r = self.budget_max * self.eta ** (-self.s)
-                self.sh_config.search.number_archs = n
-                self.sh_config.search.min_fidelity  = r 
-                self.sh = SH(self.sh_config) #should be in config 
-                self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-        if self.s >= 0:
-            budget = self.sh.new_epoch()
-        else:
-            print("HB is finish, allready not defined what to do") # TODO define what to do 
-            return math.inf #end the thing 
+            self.s -= 1
+            if self.sh != None:
+                self.sampled_archs.extend(self.sh.sampled_archs)
+            if self.s < 0:
+                print("HB is finish, allready not defined what to do") # TODO define what to do 
+                return math.inf #end the thing
+            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+            r = self.budget_max * self.eta ** (-self.s)
+            self.sh_config.search.number_archs = n
+            self.sh_config.search.min_fidelity  = r 
+           
+            self.sh = SH(self.sh_config) #should be in config 
+            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+        
+        budget = self.sh.new_epoch() 
         return budget
                 
     def _update_history(self, child):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 90d9852e0..b0bdd52bb 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -38,7 +38,7 @@ def __init__(
         self.weight_optimizer = weight_optimizer
         self.loss = loss_criteria
         self.grad_clip = grad_clip
-        self.budget_max = config.search.number_archs
+        self.budget_max = config.search.budget_max
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
         self.end = False
@@ -63,6 +63,7 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
     def new_epoch(self):
         """
         Sample a new architecture to train.
+        # TODO: with this kind of architekeur, in evaluation only the last fideltiy
         """
 
         model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
@@ -109,10 +110,12 @@ def new_epoch(self):
         if self.fidelity_counter == self.number_archs:
             self.fidelity = math.floor(self.eta*self.fidelity) #
             self.sampled_archs.sort(key = lambda model: model.accuracy, reverse= True)
-            if(math.floor(self.number_archs/self.eta)) != 0 or self.fidelity > self.budget_max:
+            if self.fidelity > self.budget_max:
+                    self.end = True
+            elif(math.floor(self.number_archs/self.eta)) != 0:
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
+               
             else:
-                #TODO: here maybe something back for hyperand 
                 self.end = True
                 self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)

From ecf26418a49a17bb99656a3740c6562c18fabbe4 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 20 Nov 2021 18:21:03 +0100
Subject: [PATCH 017/130] refactor plot code

---
 examples/plot_sh.py | 217 +++-----------------------------------------
 1 file changed, 13 insertions(+), 204 deletions(-)

diff --git a/examples/plot_sh.py b/examples/plot_sh.py
index 74fa70f39..47b8c1a3e 100644
--- a/examples/plot_sh.py
+++ b/examples/plot_sh.py
@@ -17,6 +17,7 @@
 def restructure_results(errors :dict):
     """This super fancy function restructures the results in order plot successive halving correctly
     NOTE: This in not optimimzed at all!
+    NOTE: this function should be obsolete thus we save the stats now in the correct format.
     """
     print(errors[1].keys())
     len_errors = len(errors[1]['train_acc'])
@@ -38,102 +39,15 @@ def restructure_results(errors :dict):
         new_errors[archs[idx]]['train_time'].append(train_time[idx])
     return new_errors
 
-
-def merge_and_fill_trajectories(pandas_data_frames, default_value=None):
-    # merge all trajectories keeping all time steps
-    df = pd.DataFrame().join(pandas_data_frames, how='outer')
-
-    # forward fill to make it a propper step function
-    df = df.fillna(method='ffill')
-    if default_value is None:
-        # backward fill to replace the NaNs for the early times by the
-        # performance of a random configuration
-        df = df.fillna(method='bfill')
-    else:
-        df = df.fillna(default_value)
-    return df
-
-
-def get_trajectories(losses, iterations):
-    dfs = []
-    for i in range(len(losses)):
-        loss = losses[i]
-        iteration = iterations[i]
-        # print('Run %d, Min: %f'%(i, loss))
-        df = pd.DataFrame({str(i): loss}, index=iteration)
-        dfs.append(df)
-
-    df = merge_and_fill_trajectories(dfs, default_value=None)
-    if df.empty:
-        pass
-
-    return np.array(df.T), np.array(df.index)
-
 def get_results(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
-    
-    output = []
-    time = []
-    nan_count = 0
-    missing_results_count = 0
-    
-    algo_path = os.path.join(path, predictor)
-    for seed_dir in os.listdir(algo_path):
-        result_file = os.path.join(algo_path, seed_dir, 'errors.json')
-        result = json.load(open(result_file))
-        new_result = restructure_results(result)
-        config = result[0]
-        val_acc = result[1]['valid_acc'][:epochs]
-        surr_time = np.array(result[1]['runtime'])[:epochs]
-        if ug:
-            runtime = 200*np.array(result[1]['train_time'])[:epochs] + surr_time
-        else:
-            runtime = np.array(result[1]['train_time'])[:epochs] + surr_time
-
-        val_err = [100 - x for x in val_acc]
-        val_incumbent = [min(val_err[:epoch]) for epoch in range(1, len(val_err)+1)]
-        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]
-
-        if metric == 'valid_acc':
-            incumbent = val_incumbent
-        elif metric == 'test_acc':
-            test_err = [100 - x for x in result[1]['test_acc']]
-            inc_idx, best, best_idx = [], np.inf, 0
-            for i, err in enumerate(val_err):
-                if err < best:
-                    best, best_idx = err, i
-                inc_idx.append(best_idx)
-            incumbent = [test_err[idx] for idx in inc_idx]
-
-        # commented out section where epoch is checked. Our amount of epochs is based on time.
-        # if len(incumbent) == epochs:
-        output.append(incumbent)
-        time.append(runtime)
-        # else:
-        #    nan_count += 1
-
-    output = np.array(output)
-    time = np.array(time)
-
-    output, time = get_trajectories(output, time)
-    
-    print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)
-    mean = np.mean(output, axis=0)
-    std = np.std(output, axis=0)
-    std_error = np.sqrt(np.var(output, axis=0, ddof=1)/np.asarray(output).shape[0])
-    return mean, std, std_error, time
-
-def get_results_2(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
-    
-    output = []
-    time = []
-    nan_count = 0
-    missing_results_count = 0
-    
+    """
+    Get statistics for successive halving
+    # TODO: make metric selectable, currently 'val_acc' is fixed 
+    """
     algo_path = os.path.join(path, predictor)
     for seed_dir in os.listdir(algo_path):
         result_file = os.path.join(algo_path, seed_dir, 'sh_stats.json')
         result = json.load(open(result_file))
-        # new_result = restructure_results(result)
         return result
 
 def plot_sh(new_results :dict):
@@ -148,131 +62,26 @@ def plot_sh(new_results :dict):
         x = results['fidelity']
         values = results['val_acc']
         plt.plot(x, values, linestyle='-', label=arch)
-    plt.show()
-
-def main():
-    # set up colors and plot markings
-    defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
-                (1.0, 0.4980392156862745, 0.054901960784313725),
-                (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
-                (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
-                (0.5803921568627451, 0.403921568627451, 0.7411764705882353),
-                (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
-                (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
-                (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
-                (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
-                (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]
-
-    # goes up to 24
-    c_max = 9
-    colors = [*defaults[:c_max], *defaults[:c_max], *defaults[:c_max]]
-    fmts = [*['-']*c_max, *['--']*c_max, *[':']*c_max]
-    markers = [*['^']*c_max, *['v']*c_max, *['o']*c_max]
-
-    # https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html
-
-
-    pred_label_dict={
-        'valloss':'Val. Loss', 'valacc':'Val. Acc.', 'sotl':'SoTL', 'bananas':'BANANAS',
-        'mlp':'Feedforward', 'gbdt':'GBDT', 'gcn':'GCN', 'bonas_gcn':'BONAS', 'xgb':'XGB',
-        'ngb':'NGB', 'rf':'RF', 'jacov':'Jacob. Cov.', 'dngo':'DNGO', 'bohamiann':'BOHAMIANN', 
-        'bayes_lin_reg':'Bayes. Lin. Reg.', 'ff_keras':'FF-Keras', 'gp':'GP', 'sparse_gp':'Sparse GP', 
-        'var_sparse_gp':'Var. Sparse GP', 'seminas':'SemiNAS', 'lcsvr':'LcSVR', 'snip':'SNIP', 'sotle':'SoTLE',
-        'bonas':'BONAS', 'omni_lofi':'Omni Lofi', 'nao': 'NAO', 'lgb': 'LGB', 'none': 'True'
-    }
-
-    # set up parameters for the experiments
-    epochs = 300
-    results_dict = {}
-
-    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
-    predictors = ['var_sparse_gp']
-
-    for i, predictor in enumerate(predictors):
-        mean, std, std_error, runtime = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
-        results_dict[predictor] = {'label':pred_label_dict[predictor], 
-                                'key':predictor, 'mean':mean, 'std':std, 
-                                'std_error': std_error, 'runtime': runtime}
-
-
-
-
-    # plot performance vs runtime
-
-    # didn't run them long enough to do logspace here. (These experiments took surprisingly long to run)
-    plt.rcParams['axes.grid'] = True
     plt.rcParams['grid.linestyle'] = 'dotted'
-
-    plot_zoomed = False
-    plot_sem = True
-
-    fig, ax = plt.subplots(figsize=[8, 4])
-    if plot_zoomed:
-        sub_axes = plt.axes([.6, .6, .25, .25]) 
-
-    for i, key in enumerate(predictors):
-        mean = results_dict[key]['mean']
-        sem = results_dict[key]['std_error']
-        label = results_dict[key]['label']
-        x = results_dict[key]['runtime']
-        
-        ax.plot(x, mean, label=label, color=colors[i], linestyle=fmts[i])
-        if plot_sem:
-            ax.fill_between(x, mean-1*sem, mean+1*sem,
-                            color=colors[i], alpha=0.2)
-        
-        if plot_zoomed:
-            X_detail = x[-10000:]
-            Y_detail = mean[-10000:]
-            sem_detail = sem[-10000:]
-            sub_axes.plot(X_detail, Y_detail, color=colors[i], linestyle=fmts[i])
-            sub_axes.fill_between(X_detail, Y_detail-1*sem_detail, Y_detail+1*sem_detail,
-                            color=colors[i], alpha=0.2)
-            sub_axes.tick_params(labelbottom=False) 
-            sub_axes.set_xlim([700000, 900000])
-        
-    ax.yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
-    ax.set_xscale('log')
-    #ax.set_ylim([7, 11])
-    #ax.set_xlim([1e4, 1.6e6])
-
-    ax.legend(loc=(1.04,0))
-    ax.set_xlabel('Runtime [s] (simulated)')
-    ax.set_ylabel('Test error (%)')
-    ax.grid(True, which="both",ls="-", alpha=.5)
-    ax.set_title('Test error vs. train time for the Predictor Framework on NAS-Bench-201')
+    plt.show()
     plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
-def main_2():
-    """Just plots the successive halving
-    
+
+def main():
+    """
+    Just plots the successive halving
     """
-    pred_label_dict={
-        'valloss':'Val. Loss', 'valacc':'Val. Acc.', 'sotl':'SoTL', 'bananas':'BANANAS',
-        'mlp':'Feedforward', 'gbdt':'GBDT', 'gcn':'GCN', 'bonas_gcn':'BONAS', 'xgb':'XGB',
-        'ngb':'NGB', 'rf':'RF', 'jacov':'Jacob. Cov.', 'dngo':'DNGO', 'bohamiann':'BOHAMIANN', 
-        'bayes_lin_reg':'Bayes. Lin. Reg.', 'ff_keras':'FF-Keras', 'gp':'GP', 'sparse_gp':'Sparse GP', 
-        'var_sparse_gp':'Var. Sparse GP', 'seminas':'SemiNAS', 'lcsvr':'LcSVR', 'snip':'SNIP', 'sotle':'SoTLE',
-        'bonas':'BONAS', 'omni_lofi':'Omni Lofi', 'nao': 'NAO', 'lgb': 'LGB', 'none': 'True'
-    }
+
     # set up parameters for the experiments
     epochs = 300
-    results_dict = {}
 
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results_2(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
+    results = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
 
-    # didn't run them long enough to do logspace here. (These experiments took surprisingly long to run)
-    # plt.rcParams['axes.grid'] = True
-    plt.rcParams['grid.linestyle'] = 'dotted'
-
-    plot_zoomed = False
-    plot_sem = True
 
     plot_sh(results)
-    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
 if __name__ == '__main__':
-    main_2()
+    main()
 # %%

From 52eb8f61bc8b2c73c7e5c4d4421017644fe74318 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 20 Nov 2021 19:17:36 +0100
Subject: [PATCH 018/130] refactor sh optimizer & multi-fidelity trainer

---
 .../nas_predictors/discrete_config.yaml       |  1 +
 naslib/defaults/trainer_multifidelity.py      | 10 +----
 naslib/optimizers/discrete/sh/optimizer.py    | 38 +++++--------------
 3 files changed, 11 insertions(+), 38 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index f844bb96a..2620620e6 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -13,6 +13,7 @@ search:
   number_archs: 80
   eta: 2
   budget_type: epoch 
+  budget_max: 1000
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index bb30f079c..0b16b6e8a 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -166,25 +166,18 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
                 # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
                 #only for debugging is it but out
-                """
                 (
-
-
                     train_acc,
                     valid_acc,
                     test_acc,
                     train_time,
-                    fidelity,
-                    arch,
-                ) = self.optimizer.train_model_statistics()
+                ) = self.optimizer.train_statistics()
                 train_loss, valid_loss, test_loss = -1, -1, -1
 
                 self.errors_dict.train_acc.append(train_acc)
                 self.errors_dict.train_loss.append(train_loss)
                 self.errors_dict.valid_acc.append(valid_acc)
                 self.errors_dict.valid_loss.append(valid_loss)
-                self.errors_dict.fidelity.append(fidelity)
-                self.errors_dict.arch.append(arch)
                 self.errors_dict.test_acc.append(test_acc)
                 self.errors_dict.test_loss.append(test_loss)
                 self.errors_dict.runtime.append(end_time - start_time)
@@ -207,7 +200,6 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             self._log_to_json()
 
             self._log_and_reset_accuracies(e, summary_writer)
-            """
             if after_epoch is not None:
                 after_epoch(e)
     
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index fc1c14473..1deb4092b 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -12,7 +12,9 @@
 
 class SuccessiveHalving(MetaOptimizer):
     """
-    # TODO: Write fancy comment 🌈
+    Optimizer is randomly sampling architectures and queries/trains on the corresponding fidelities.
+    After that, models will be discarded according to eta.
+    TODO: Implement training
     """
 
     # training the models is not implemented
@@ -26,7 +28,7 @@ def __init__(
         grad_clip=None,
     ):
         """
-        Initialize a Successive Halving  optimizer.
+        Initialize a Successive Halving optimizer.
 
         Args:
             config
@@ -78,7 +80,6 @@ def new_epoch(self):
        
 # 		    return(ranks < self.num_configs[self.stage])
 
-
         # DONE: define fidelity in multi-fidelity setting
         model.accuracy = model.arch.query(
             self.performance_metric,
@@ -88,11 +89,11 @@ def new_epoch(self):
         )
 
         budget = 1
-        # TODO: make query type secure
+        # DONE: make query type secure
         if self.budget_type == 'time':
-            # TODO: make dependent on performance_metric
+            # DONE: make dependent on performance_metric
             model.time = model.arch.query( # TODO: this is the time for training from screatch.
-                Metric.TRAIN_TIME,
+                self.performance_metric,
                 self.dataset,
                 epoch=int(self.fidelity), # DONE: adapt this
                 dataset_api=self.dataset_api,
@@ -101,14 +102,14 @@ def new_epoch(self):
         elif not(self.budget_type == "epoch"):
             raise NameError("budget time should be time or epoch")
         # TODO: make this more beautiful/more efficient
-        # TODO: we may need to track of all ever sampled archs
+        # DONE: we may need to track of all ever sampled archs
         if len(self.sampled_archs) < self.number_archs:
             self.sampled_archs.append(model)
         else:
             self.sampled_archs[self.fidelity_counter] = model
         self.update_optimizer_stats()
         self.fidelity_counter += 1
-        # TODO: fidelity is changed for new epoch, what make the wrong values in the dictonary
+        # DONE: fidelity is changed for new epoch, what make the wrong values in the dictonary
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
             self.fidelity = math.floor(self.eta*self.fidelity) #
@@ -192,27 +193,6 @@ def update_optimizer_stats(self):
             )
             self.optimizer_stats[arch_hash][metric_name].append(metric_value)
         self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity) 
-        
-
-    def train_model_statistics(self, report_incumbent=True):
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
-            ),
-            self.fidelity,
-            best_arch_hash,
-        )
 
     def test_statistics(self):
         best_arch = self.get_final_architecture()

From 1b4a258afd9ce7ee568f3b3e1a7d201808952f19 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 20 Nov 2021 19:22:50 +0100
Subject: [PATCH 019/130] refactor optimizer

---
 count.py                                   | 117 ---------------------
 naslib/optimizers/discrete/sh/optimizer.py |  14 +--
 2 files changed, 2 insertions(+), 129 deletions(-)
 delete mode 100644 count.py

diff --git a/count.py b/count.py
deleted file mode 100644
index 6e009eda2..000000000
--- a/count.py
+++ /dev/null
@@ -1,117 +0,0 @@
-
-a = [
-         8784118950669,
-         8784119373993,
-         8784119674405,
-         8784120668729,
-         8784121418997,
-         8784122552961,
-         8784123236973,
-         8784122561581,
-         8784124699973,
-         8784124583389,
-         8784125831929,
-         8784126430609,
-         8784126759213,
-         8784127599741,
-         8784128690333,
-         8784129044981,
-         8784130726629,
-         8784130932953,
-         8784131024561,
-         8784133885673,
-         8784133891129,
-         8784101770541,
-         8784101940905,
-         8784102995357,
-         8784103288053,
-         8784104434661,
-         8784104910713,
-         8784105835565,
-         8784106605545,
-         8784106969745,
-         8784108010925,
-         8784109256153,
-         8784110336925,
-         8784111114465,
-         8784111651769,
-         8784112187497,
-         8784105605109,
-         8784113956577,
-         8784114705369,
-         8784115487561,
-         8784116137697,
-         8784117284921,
-         8784084574937,
-         8784085337053,
-         8784085787301,
-         8784086741973,
-         8784087431577,
-         8784088116193,
-         8784088793493,
-         8784089509601,
-         8784090242773,
-         8784090883289,
-         8784092095453,
-         8784092362341,
-         8784093543129,
-         8784093548709,
-         8784094868201,
-         8784095579973,
-         8784096300249,
-         8784096983517,
-         8784098164961,
-         8784113494297,
-         8784099621081,
-         8784100279769,
-         8784100285165,
-         8784068170473,
-         8784068820609,
-         8784069471961,
-         8784070292701,
-         8784071009505,
-         8784071662037,
-         8784072149153,
-         8784073066717,
-         8784073120057,
-         8784074956001,
-         8784075630149,
-         8784076357081,
-         8784077134557,
-         8784077137705,
-         8784071009505,
-         8784078521557,
-         8784130932953,
-         8784077137705,
-         8784133891129,
-         8784090242773,
-         8784106605545,
-         8784095579973,
-         8784093543129,
-         8784119373993,
-         8784070292701,
-         8784090883289,
-         8784130726629,
-         8784086741973,
-         8784111114465,
-         8784105605109,
-         8784072149153,
-         8784122552961,
-         8784105835565,
-         8784112187497,
-         8784114705369,
-         8784092362341,
-         8784077134557,
-         8784100285165,
-         8784119674405,
-         8784084574937,
-         8784121418997,
-         8784085337053,
-         8784115487561,
-         8784101770541,
-         8784124699973
-      ]
-
-import collections
-duplicates = [item for item, count in collections.Counter(a).items() if count > 1]
-print(len(duplicates))
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 1deb4092b..2fc5fa94c 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -201,19 +201,9 @@ def test_statistics(self):
     def get_op_optimizer(self):
         return self.weight_optimizer
     
+    # TODO: we really do not need this
     def get_end(self):
         return self.end
 
     def get_checkpointables(self):
-        return {"model": self.history}
-
-# #from naslib.optimizers.core.metaclasses import MetaOptimizer
-# class SuccessiveHalving(MetaOptimizer):
-#     #also import or simular in NASLib?
-
-# 	def _advance_to_next_stage(self, config_ids, losses):
-# 		"""
-# 			#SuccessiveHalving simply continues the best based on the current loss.
-# 		"""
-# 		ranks = np.argsort(np.argsort(losses))
-# 		return(ranks < self.num_configs[self.stage])
+        return {"model": self.history}
\ No newline at end of file

From 1a961f648221ee8415e999146b857c701f486d1f Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 20 Nov 2021 19:58:06 +0100
Subject: [PATCH 020/130] refactor demo code for multi-fidelity

---
 demo.py => examples/mf_demo.py      | 28 ++++++++++++++++++++++++----
 examples/mf_demo.yaml               |  3 +++
 examples/{plot_sh.py => mf_plot.py} | 11 -----------
 3 files changed, 27 insertions(+), 15 deletions(-)
 rename demo.py => examples/mf_demo.py (67%)
 create mode 100644 examples/mf_demo.yaml
 rename examples/{plot_sh.py => mf_plot.py} (90%)

diff --git a/demo.py b/examples/mf_demo.py
similarity index 67%
rename from demo.py
rename to examples/mf_demo.py
index 26780e73a..b1e7860f7 100644
--- a/demo.py
+++ b/examples/mf_demo.py
@@ -16,6 +16,17 @@
 from naslib.defaults.trainer_multifidelity import Trainer
 #from naslib.defaults.trainer import Trainer
 
+import yaml
+from pathlib import Path
+import os
+
+demo_config = None
+with open(os.path.join(str(Path(__file__).parent), 'mf_demo.yaml'), "r") as stream:
+    try:
+        demo_config = yaml.safe_load(stream)
+    except yaml.YAMLError as exc:
+        print(exc)
+
 # init search space
 search_space = NB201()
 
@@ -27,10 +38,19 @@
 logger = setup_logger(config.save + "/log.log")
 logger.setLevel(logging.INFO)
 
-# define optimizer 
-optimizer = SH(config)
-# optimizer = RS(config)
-# ptimizer = HB(config)
+# define optimizer
+config_optimizer = demo_config['optimizer']
+if config_optimizer == 'SH':
+    optimizer = SH(config)
+elif config_optimizer == 'HB':
+    optimizer = HB(config)
+elif config_optimizer == 'RS':
+    optimizer = RS(config)
+elif config_optimizer == 'BOHB':
+    raise Exception('Not implemented yet, ミ●﹏☉ミ')
+else:
+    raise Exception('invalid config')
+
 # load nasbench data, there data seems to be generalised
 dataset_api = get_dataset_api(config.search_space, config.dataset)
 
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
new file mode 100644
index 000000000..63b0f6791
--- /dev/null
+++ b/examples/mf_demo.yaml
@@ -0,0 +1,3 @@
+optimizer: SH
+# Activates/deactivates plotting
+plot: True
\ No newline at end of file
diff --git a/examples/plot_sh.py b/examples/mf_plot.py
similarity index 90%
rename from examples/plot_sh.py
rename to examples/mf_plot.py
index 47b8c1a3e..e2515cf89 100644
--- a/examples/plot_sh.py
+++ b/examples/mf_plot.py
@@ -1,19 +1,9 @@
-#%%
-import sys
 import os
-import numpy as np
 import json
 import matplotlib.pyplot as plt
-import pickle
-import pandas as pd
 
 from collections import defaultdict
 
-# needed for plotting
-from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
-from mpl_toolkits.axes_grid1.inset_locator import mark_inset
-from matplotlib.ticker import FormatStrFormatter
-
 def restructure_results(errors :dict):
     """This super fancy function restructures the results in order plot successive halving correctly
     NOTE: This in not optimimzed at all!
@@ -84,4 +74,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-# %%

From f98c6589284759dfd5be631189643883ebcd1768 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 20 Nov 2021 20:15:54 +0100
Subject: [PATCH 021/130] refactor multi-fidelity demo and plot

---
 examples/mf_demo.py | 12 +++++++++---
 examples/mf_plot.py | 32 +++++++++-----------------------
 2 files changed, 18 insertions(+), 26 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index b1e7860f7..1ba5d3e24 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -20,6 +20,8 @@
 from pathlib import Path
 import os
 
+import mf_plot
+
 demo_config = None
 with open(os.path.join(str(Path(__file__).parent), 'mf_demo.yaml'), "r") as stream:
     try:
@@ -57,12 +59,16 @@
 # adapt search space
 optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
 
-
 ## Running search with Trainer
-
 trainer = Trainer(optimizer, config, lightweight_output=True)
 
 # run search for number of iterations specified
 trainer.search()
 
-trainer.evaluate(dataset_api=dataset_api)
\ No newline at end of file
+trainer.evaluate(dataset_api=dataset_api)
+
+if config.plot == False:
+    exit()
+
+# TODO: Make this dependent on optimizer type, currently statistics are just for SH available
+mf_plot.plot_sh()
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index e2515cf89..43dd56080 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -40,28 +40,9 @@ def get_results(predictor, path, epochs, metric='valid_acc', dataset='cifar10',
         result = json.load(open(result_file))
         return result
 
-def plot_sh(new_results :dict):
+def plot_sh():
     """Plots successive halving learning curves
-    for key, data_list in data_dict.items():
-        dates_str, values = zip(*data_list)  # Unpack
-        dates = convert_str_to_dates(dates_str)  # Not handled in example
-        plt.plot(dates, values, label=key)
-    plt.legend()
     """
-    for arch, results in new_results.items():
-        x = results['fidelity']
-        values = results['val_acc']
-        plt.plot(x, values, linestyle='-', label=arch)
-    plt.rcParams['grid.linestyle'] = 'dotted'
-    plt.show()
-    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
-
-
-def main():
-    """
-    Just plots the successive halving
-    """
-
     # set up parameters for the experiments
     epochs = 300
 
@@ -69,8 +50,13 @@ def main():
     predictor = 'var_sparse_gp'
     results = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
 
-
-    plot_sh(results)
+    for arch, results in results.items():
+        x = results['fidelity']
+        values = results['val_acc']
+        plt.plot(x, values, linestyle='-', label=arch)
+    plt.rcParams['grid.linestyle'] = 'dotted'
+    plt.show()
+    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
 if __name__ == '__main__':
-    main()
+    plot_sh()
\ No newline at end of file

From 0312d0376456d2e4f06dac9481cd3c182a7ae665 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 23 Nov 2021 15:21:07 +0100
Subject: [PATCH 022/130] hyperband without stats workrd

---
 demo.py                                               | 4 ++--
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 3 ++-
 naslib/defaults/trainer_multifidelity.py              | 2 +-
 naslib/optimizers/discrete/sh/optimizer.py            | 4 ++--
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/demo.py b/demo.py
index 26780e73a..ad35630c0 100644
--- a/demo.py
+++ b/demo.py
@@ -28,9 +28,9 @@
 logger.setLevel(logging.INFO)
 
 # define optimizer 
-optimizer = SH(config)
+#optimizer = SH(config)
 # optimizer = RS(config)
-# ptimizer = HB(config)
+optimizer = HB(config)
 # load nasbench data, there data seems to be generalised
 dataset_api = get_dataset_api(config.search_space, config.dataset)
 
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index f844bb96a..054b47863 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -11,7 +11,8 @@ search:
   fidelity: -1
   min_fidelity: 1
   number_archs: 80
-  eta: 2
+  budget_max: 81
+  eta: 3
   budget_type: epoch 
 
 
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index bb30f079c..5667e4f67 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -214,7 +214,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             e += used_budget
 
         self.optimizer.after_training()
-        self._log_optimizer_stats()
+        #self._log_optimizer_stats()
         
 
         if summary_writer is not None:
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index fc1c14473..5a1060b08 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -188,7 +188,7 @@ def update_optimizer_stats(self):
                 metric, 
                 self.dataset, 
                 dataset_api=self.dataset_api, 
-                epoch=self.fidelity
+                epoch=int(self.fidelity)
             )
             self.optimizer_stats[arch_hash][metric_name].append(metric_value)
         self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity) 
@@ -210,7 +210,7 @@ def train_model_statistics(self, report_incumbent=True):
             best_arch.query(
                 Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelity
             ),
-            self.fidelity,
+            int(self.fidelity),
             best_arch_hash,
         )
 

From 2f3abfc5c668908277da2e3484d5ad55795a9c2a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 23 Nov 2021 15:40:22 +0100
Subject: [PATCH 023/130] bug fix demo config

Co-authored-by: Lars Gerne <lage2104@gmail.com>
---
 examples/mf_demo.py                        | 2 +-
 naslib/optimizers/discrete/sh/optimizer.py | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 1ba5d3e24..1d9d9c451 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -67,7 +67,7 @@
 
 trainer.evaluate(dataset_api=dataset_api)
 
-if config.plot == False:
+if demo_config['plot'] == False:
     exit()
 
 # TODO: Make this dependent on optimizer type, currently statistics are just for SH available
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 2fc5fa94c..d28b88e87 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -168,7 +168,6 @@ def train_statistics(self, report_incumbent=True):
             best_arch.query(
                 Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
             ),
-
         )
 
     def update_optimizer_stats(self):

From 7cc1ad25bc069e8e49e359177030852042b2e8d2 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 23 Nov 2021 15:48:19 +0100
Subject: [PATCH 024/130] fix config after merging

---
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 693a68a05..2620620e6 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -11,8 +11,7 @@ search:
   fidelity: -1
   min_fidelity: 1
   number_archs: 80
-  budget_max: 81
-  eta: 3
+  eta: 2
   budget_type: epoch 
   budget_max: 1000
 

From 473d97747e96428fd1a75fecaa5b8b494e0dc488 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 23 Nov 2021 16:06:00 +0100
Subject: [PATCH 025/130] extend multi-fidelity demo config

---
 examples/mf_demo.py                           | 14 +++++-
 examples/mf_demo.yaml                         |  2 +-
 .../nas_predictors/discrete_config_HB.yaml    | 46 +++++++++++++++++++
 .../nas_predictors/discrete_config_SH.yaml    | 46 +++++++++++++++++++
 4 files changed, 106 insertions(+), 2 deletions(-)
 create mode 100644 naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
 create mode 100644 naslib/benchmarks/nas_predictors/discrete_config_SH.yaml

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 1d9d9c451..8da19458c 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -22,6 +22,8 @@
 
 import mf_plot
 
+from shutil import copyfile
+
 demo_config = None
 with open(os.path.join(str(Path(__file__).parent), 'mf_demo.yaml'), "r") as stream:
     try:
@@ -29,6 +31,16 @@
     except yaml.YAMLError as exc:
         print(exc)
 
+config_optimizer = demo_config['optimizer']
+config_path_optimizer = os.path.join(
+    utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config"
+) + "_" + config_optimizer + ".yaml"
+default_config_path = os.path.join(
+    utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config.yaml"
+) 
+
+copyfile(config_path_optimizer, default_config_path)
+
 # init search space
 search_space = NB201()
 
@@ -41,7 +53,7 @@
 logger.setLevel(logging.INFO)
 
 # define optimizer
-config_optimizer = demo_config['optimizer']
+
 if config_optimizer == 'SH':
     optimizer = SH(config)
 elif config_optimizer == 'HB':
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 63b0f6791..69bca7eb4 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: SH
+optimizer: HB
 # Activates/deactivates plotting
 plot: True
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
new file mode 100644
index 000000000..95f5b8330
--- /dev/null
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -0,0 +1,46 @@
+seed: 0
+optimizer: bananas
+search_space: nasbench201
+dataset: cifar10
+out_dir: run
+
+search:
+  seed: 0
+  checkpoint_freq: 10 # 1000
+  epochs: 158
+  fidelity: -1
+  min_fidelity: 1
+  number_archs: 80
+  eta: 2
+  budget_type: epoch 
+  budget_max: 500
+
+
+  predictor_type: var_sparse_gp
+  num_init: 10  
+  k: 10
+
+  # BANANAS
+  num_ensemble: 3
+  acq_fn_type: its
+  acq_fn_optimization: random_sampling
+  encoding_type: adjacency_one_hot
+  num_arches_to_mutate: 2
+  max_mutations: 1
+  num_candidates: 20
+  
+  # jacov data loader
+  batch_size: 256
+  data_size: 25000
+  cutout: False
+  cutout_length: 16
+  cutout_prob: 1.0
+  train_portion: 0.7
+  
+  # other params
+  debug_predictor: False
+  sample_size: 10
+  population_size: 30
+
+  # TODO: write config file for successive halving
+
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
new file mode 100644
index 000000000..2620620e6
--- /dev/null
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -0,0 +1,46 @@
+seed: 0
+optimizer: bananas
+search_space: nasbench201
+dataset: cifar10
+out_dir: run
+
+search:
+  seed: 0
+  checkpoint_freq: 10 # 1000
+  epochs: 158
+  fidelity: -1
+  min_fidelity: 1
+  number_archs: 80
+  eta: 2
+  budget_type: epoch 
+  budget_max: 1000
+
+
+  predictor_type: var_sparse_gp
+  num_init: 10  
+  k: 10
+
+  # BANANAS
+  num_ensemble: 3
+  acq_fn_type: its
+  acq_fn_optimization: random_sampling
+  encoding_type: adjacency_one_hot
+  num_arches_to_mutate: 2
+  max_mutations: 1
+  num_candidates: 20
+  
+  # jacov data loader
+  batch_size: 256
+  data_size: 25000
+  cutout: False
+  cutout_length: 16
+  cutout_prob: 1.0
+  train_portion: 0.7
+  
+  # other params
+  debug_predictor: False
+  sample_size: 10
+  population_size: 30
+
+  # TODO: write config file for successive halving
+

From 03b419ee08216430818dc48596949fb5bb69b883 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 25 Nov 2021 14:25:53 +0100
Subject: [PATCH 026/130] run without statcis through

---
 .../nas_predictors/discrete_config_HB.yaml    |  6 ++--
 naslib/optimizers/discrete/hb/optimizer.py    | 34 +++++++++++++++++--
 2 files changed, 35 insertions(+), 5 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index 95f5b8330..25fc9a949 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -10,10 +10,10 @@ search:
   epochs: 158
   fidelity: -1
   min_fidelity: 1
-  number_archs: 80
-  eta: 2
+  number_archs: 81
+  eta: 3
   budget_type: epoch 
-  budget_max: 500
+  budget_max: 81
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 39cf31363..8a2abc547 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -1,9 +1,8 @@
-#from hpbandster.core.base_iteration import BaseIteration
-import numpy as np
 import numpy as np
 import torch
 import copy
 import math
+from collections import defaultdict
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
@@ -55,6 +54,7 @@ def __init__(
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -87,6 +87,7 @@ def new_epoch(self):
             self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
         
         budget = self.sh.new_epoch() 
+        self.update_optimizer_stats()
         return budget
                 
     def _update_history(self, child):
@@ -105,7 +106,11 @@ def get_final_architecture(self):
         return max(self.sampled_archs, key=lambda x: x.accuracy).arch
 
     def train_statistics(self, report_incumbent=True):
+        return(
+            0.42 , 0.42 , 0.42, 0.42
+
 
+        )
         if report_incumbent:
             best_arch = self.get_final_architecture()
         else:
@@ -148,7 +153,32 @@ def train_model_statistics(self, report_incumbent=True):
             best_arch_hash,
         )
 
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        here only hacky way from sh
+        """
+        arch = self.sh.sampled_archs[self.sh.fidelity_counter-1].arch
+        arch_hash = hash(self.sh.sampled_archs[self.sh.fidelity_counter- 1])
+        # this dict contains metrics to save
+        metrics = {
+            "train_acc": Metric.TRAIN_ACCURACY,
+            "val_acc": Metric.VAL_ACCURACY,
+            "test_acc": Metric.TEST_ACCURACY,
+            "train_time": Metric.TRAIN_TIME
+        }
+        for metric_name, metric in metrics.items():
+            metric_value = arch.query(
+                metric, 
+                self.dataset, 
+                dataset_api=self.dataset_api, 
+                epoch=int(81)
+            )
+            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
+        self.optimizer_stats[arch_hash]['fidelity'].append(self.sh.fidelity) 
+
     def test_statistics(self):
+        return False
         best_arch = self.get_final_architecture()
         return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
 

From 542b42ed5beac1af1c563ef746e6faab27da9436 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 25 Nov 2021 16:20:51 +0100
Subject: [PATCH 027/130] some cleaning work

---
 naslib/defaults/trainer_multifidelity.py   | 26 ++++++++--------------
 naslib/optimizers/discrete/hb/optimizer.py | 14 +-----------
 naslib/optimizers/discrete/sh/optimizer.py | 16 ++-----------
 3 files changed, 12 insertions(+), 44 deletions(-)

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 160a603ee..fe2b989ca 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -17,7 +17,7 @@
 from naslib.utils.logging import log_every_n_seconds, log_first_n
 
 from typing import Callable
-#from .additional_primitives import DropPathWrapper #Can be causes issues
+from .additional_primitives import DropPathWrapper #Can be causes issues
 
 logger = logging.getLogger(__name__)
 
@@ -166,6 +166,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
                 # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
                 #only for debugging is it but out
+                
                 (
                     train_acc,
                     valid_acc,
@@ -185,7 +186,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 self.train_top1.avg = train_acc
                 self.val_top1.avg = valid_acc
 
-            self.periodic_checkpointer.save(e) # define the name in accurate , also 
+            self.checkpointer.save(e) # define the name in accurate , also 
             # TODO: change step into save for checkpointer
             anytime_results = self.optimizer.test_statistics()
             if anytime_results:
@@ -198,7 +199,6 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 )
 
             self._log_to_json()
-
             self._log_and_reset_accuracies(e, summary_writer)
             if after_epoch is not None:
                 after_epoch(e)
@@ -206,7 +206,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             e += used_budget
 
         self.optimizer.after_training()
-        #self._log_optimizer_stats()
+        self._log_optimizer_stats()
         
 
         if summary_writer is not None:
@@ -417,7 +417,7 @@ def evaluate(
                                 )
 
                     scheduler.step()
-                    self.periodic_checkpointer.step(e)
+                    self.checkpointer.step(e)
                     self._log_and_reset_accuracies(e)
 
             # Disable drop path
@@ -574,8 +574,7 @@ def _setup_checkpointers(
         """
         checkpointables = self.optimizer.get_checkpointables()
         checkpointables.update(add_checkpointables)
-        #name make no sense
-        self.periodic_checkpointer = utils.Checkpointer( #TODO rename periodic_checkpointer 
+        self.checkpointer = utils.Checkpointer( 
             model=checkpointables.pop("model"),
             save_dir=self.config.save + "/search"
             if search
@@ -583,19 +582,12 @@ def _setup_checkpointers(
             # **checkpointables #NOTE: this is throwing an Error
         )
 
-        #self.periodic_checkpointer = PeriodicCheckpointer(
-        #   checkpointer,
-        #    period=period,
-        #    max_iter=self.config.search.epochs
-        #    if search
-        #    else self.config.evaluation.epochs,
-        #)
-
+    
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))
             # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
-            checkpoint = self.periodic_checkpointer.resume_or_load("resume_from", resume=True)
-            if self.periodic_checkpointer.has_checkpoint():
+            checkpoint = self.checkpointer.resume_or_load("resume_from", resume=True)
+            if self.checkpointer.has_checkpoint():
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 8a2abc547..3e2977a04 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -10,8 +10,7 @@
 
 class HyperBand(MetaOptimizer):
     """
-    This is right now only a little bit more as pseudocode, so it not runnable what is currently working on
-    # TODO: Write fancy comment 🌈
+    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
     """
 
     # training the models is not implemented
@@ -187,14 +186,3 @@ def get_op_optimizer(self):
 
     def get_checkpointables(self):
         return {"model": self.history}
-
-# #from naslib.optimizers.core.metaclasses import MetaOptimizer
-# class SuccessiveHalving(MetaOptimizer):
-#     #also import or simular in NASLib?
-
-# 	def _advance_to_next_stage(self, config_ids, losses):
-# 		"""
-# 			#SuccessiveHalving simply continues the best based on the current loss.
-# 		"""
-# 		ranks = np.argsort(np.argsort(losses))
-# 		return(ranks < self.num_configs[self.stage])
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 9b7b7df16..e9ee9fca1 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -14,10 +14,8 @@ class SuccessiveHalving(MetaOptimizer):
     """
     Optimizer is randomly sampling architectures and queries/trains on the corresponding fidelities.
     After that, models will be discarded according to eta.
-    TODO: Implement training
+    DONE: Implement training
     """
-
-    # training the models is not implemented
     using_step_function = False
 
     def __init__(
@@ -126,13 +124,7 @@ def new_epoch(self):
             self.fidelity_counter = 0
         # TODO: budget equals 
         return budget
-        # required if we want to train the models and not only query.
-        # architecture_i.parse()
-        # architecture_i.train()
-        # architecture_i = architecture_i.to(self.device)
-        # self.sampled_archs.append(architecture_i)
-        # self.weight_optimizers.append(self.weight_optimizer(architecture_i.parameters(), 0.01))
-
+       
     def _update_history(self, child):
         if len(self.history) < 100:
             self.history.append(child)
@@ -200,9 +192,5 @@ def test_statistics(self):
     def get_op_optimizer(self):
         return self.weight_optimizer
     
-    # TODO: we really do not need this
-    def get_end(self):
-        return self.end
-
     def get_checkpointables(self):
         return {"model": self.history}
\ No newline at end of file

From 2bfc70bebd7ce4c11084f407c58045e29208de2f Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 1 Dec 2021 17:33:29 +0100
Subject: [PATCH 028/130] add comment to budget calculation

---
 naslib/optimizers/discrete/sh/optimizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index d28b88e87..0e2a22d35 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -88,7 +88,7 @@ def new_epoch(self):
             dataset_api=self.dataset_api,
         )
 
-        budget = 1
+        budget = 1 # <-- divide by max_fidelity to get consistent results
         # DONE: make query type secure
         if self.budget_type == 'time':
             # DONE: make dependent on performance_metric

From 53f385cab20889c05f801108eef0424ef28007e3 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 1 Dec 2021 18:42:53 +0100
Subject: [PATCH 029/130] adapt budget for better compareability

---
 examples/mf_demo.yaml                      | 2 +-
 naslib/optimizers/discrete/sh/optimizer.py | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 69bca7eb4..63b0f6791 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: HB
+optimizer: SH
 # Activates/deactivates plotting
 plot: True
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 9b7b7df16..4d6b642de 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -54,6 +54,7 @@ def __init__(
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
         self.end = False
+        self.old_fidelity = 0
 
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
 
@@ -88,7 +89,7 @@ def new_epoch(self):
             dataset_api=self.dataset_api,
         )
 
-        budget = 1
+        budget = (self.fidelity - self.old_fidelity) / self.budget_max
         # DONE: make query type secure
         if self.budget_type == 'time':
             # DONE: make dependent on performance_metric
@@ -112,6 +113,7 @@ def new_epoch(self):
         # DONE: fidelity is changed for new epoch, what make the wrong values in the dictonary
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
+            self.old_fidelity = self.fidelity
             self.fidelity = math.floor(self.eta*self.fidelity) #
             self.sampled_archs.sort(key = lambda model: model.accuracy, reverse= True)
             if self.fidelity > self.budget_max:

From 17887fd8c6de77d60f812c5a4a02d14e194e1d89 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 1 Dec 2021 19:25:55 +0100
Subject: [PATCH 030/130] small bug fix in hb

---
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 6 +++---
 naslib/optimizers/discrete/hb/optimizer.py            | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 2620620e6..25fc9a949 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -10,10 +10,10 @@ search:
   epochs: 158
   fidelity: -1
   min_fidelity: 1
-  number_archs: 80
-  eta: 2
+  number_archs: 81
+  eta: 3
   budget_type: epoch 
-  budget_max: 1000
+  budget_max: 81
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 3e2977a04..e83d80f3c 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -68,7 +68,7 @@ def new_epoch(self):
         after https://arxiv.org/pdf/1603.06560.pdf
         """
         # TODO: rethink because of s
-        if  self.sh == None or self.sh.get_end():
+        if  self.sh == None or self.sh.end:
             #if sh is finish go to something diffrent as initial budget
                     #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
             self.s -= 1

From cd12c341f66ae5bfebc14bc3360971a5742bec38 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 2 Dec 2021 16:15:02 +0100
Subject: [PATCH 031/130] beginn palnning bohb

---
 naslib/optimizers/discrete/bohb/plannung.txt |  3 +++
 naslib/optimizers/discrete/sh/optimizer.py   | 26 ++++++++++++++++++--
 2 files changed, 27 insertions(+), 2 deletions(-)
 create mode 100644 naslib/optimizers/discrete/bohb/plannung.txt

diff --git a/naslib/optimizers/discrete/bohb/plannung.txt b/naslib/optimizers/discrete/bohb/plannung.txt
new file mode 100644
index 000000000..051b4a0f9
--- /dev/null
+++ b/naslib/optimizers/discrete/bohb/plannung.txt
@@ -0,0 +1,3 @@
+for bohb, hyperbamd can be completly be the same. But for succesive halving we sample totally different.
+because instead of random sampling we use a model. That could be into succesive halving or in other method. 
+I comment how i think we can change it.
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index e9ee9fca1..5025ccfb2 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -52,7 +52,10 @@ def __init__(
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
         self.end = False
-
+        #if tpe:
+            #self.p = config.search.p
+            #self.percentile = config.search.percentile
+            #self.N_min = config.search.N_min
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
@@ -73,6 +76,7 @@ def new_epoch(self):
         model.arch = self.search_space.clone()
         if len(self.sampled_archs) < self.number_archs:
             model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+           #model = sample_method() this can be a self.sample_method() or self.sample(method)
         else:
             model = self.sampled_archs[self.fidelity_counter]
        
@@ -193,4 +197,22 @@ def get_op_optimizer(self):
         return self.weight_optimizer
     
     def get_checkpointables(self):
-        return {"model": self.history}
\ No newline at end of file
+        return {"model": self.history}
+
+    """
+    def sample(self, method):
+        if method == "random" or (random.randint(1,100)/100) < self.p:
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+            return model        
+        b = self.sampled_archs.argmax(key = lambda model: model.accuracy, reverse= True)) if length(self.sampled_archs)>= (self.N_min +2) else False
+        if not(b): 
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+            return model 
+        KDE.fit() #what is the kde #TODO here more infos are needed
+        draw ns samples from kde
+        return max(l(lambda)/g(lamdba))
+    """
\ No newline at end of file

From 7c8e9faff5e91eadefa96d9891b8dd43e1929e1b Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 5 Dec 2021 17:04:44 +0100
Subject: [PATCH 032/130] plan bohb

---
 naslib/optimizers/discrete/bohb/plannung.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/naslib/optimizers/discrete/bohb/plannung.txt b/naslib/optimizers/discrete/bohb/plannung.txt
index 051b4a0f9..9db24aae7 100644
--- a/naslib/optimizers/discrete/bohb/plannung.txt
+++ b/naslib/optimizers/discrete/bohb/plannung.txt
@@ -1,3 +1,4 @@
 for bohb, hyperbamd can be completly be the same. But for succesive halving we sample totally different.
 because instead of random sampling we use a model. That could be into succesive halving or in other method. 
-I comment how i think we can change it.
\ No newline at end of file
+I comment how i think we can change it. TPE needs to be one a file, like in bananas. we need a predictor which i have to look, and we need TPE  to discide which we are sample. Like in bananas
+

From 6814716ddeba52c50d28ef23b95b2238d8363e1e Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 19:57:08 +0100
Subject: [PATCH 033/130] add optimizer stats to hb

---
 naslib/optimizers/discrete/hb/optimizer.py | 28 ++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 39cf31363..2c7a04910 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -8,6 +8,8 @@
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
 
+from collections import defaultdict
+
 
 class HyperBand(MetaOptimizer):
     """
@@ -55,6 +57,8 @@ def __init__(
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
+
+        self.optimizer_stats = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -147,6 +151,30 @@ def train_model_statistics(self, report_incumbent=True):
             self.fidelity,
             best_arch_hash,
         )
+    
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
+        """
+        arch = self.sampled_archs[self.fidelity_counter].arch
+        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
+        # this dict contains metrics to save
+        metrics = {
+            "train_acc": Metric.TRAIN_ACCURACY,
+            "val_acc": Metric.VAL_ACCURACY,
+            "test_acc": Metric.TEST_ACCURACY,
+            "train_time": Metric.TRAIN_TIME
+        }
+        for metric_name, metric in metrics.items():
+            metric_value = arch.query(
+                metric, 
+                self.dataset, 
+                dataset_api=self.dataset_api, 
+                epoch=int(self.fidelity)
+            )
+            self.optimizer_stats[self.s][arch_hash][metric_name].append(metric_value)
+        self.optimizer_stats[self.s][arch_hash]['fidelity'].append(self.fidelity)
 
     def test_statistics(self):
         best_arch = self.get_final_architecture()

From 59a034c7e33ca0fa2b4491e99ab4de8f1d1981e8 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 20:08:22 +0100
Subject: [PATCH 034/130] fix optimizer stats for hb

---
 naslib/optimizers/discrete/hb/optimizer.py | 22 +++-------------------
 1 file changed, 3 insertions(+), 19 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 2c7a04910..2d90cf167 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -58,7 +58,7 @@ def __init__(
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
 
-        self.optimizer_stats = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
+        self.optimizer_stats = dict()
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -91,6 +91,7 @@ def new_epoch(self):
             self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
         
         budget = self.sh.new_epoch() 
+        self.update_optimizer_stats()
         return budget
                 
     def _update_history(self, child):
@@ -157,24 +158,7 @@ def update_optimizer_stats(self):
         Updates statistics of optimizer to be able to create useful plots
         TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
         """
-        arch = self.sampled_archs[self.fidelity_counter].arch
-        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
-        # this dict contains metrics to save
-        metrics = {
-            "train_acc": Metric.TRAIN_ACCURACY,
-            "val_acc": Metric.VAL_ACCURACY,
-            "test_acc": Metric.TEST_ACCURACY,
-            "train_time": Metric.TRAIN_TIME
-        }
-        for metric_name, metric in metrics.items():
-            metric_value = arch.query(
-                metric, 
-                self.dataset, 
-                dataset_api=self.dataset_api, 
-                epoch=int(self.fidelity)
-            )
-            self.optimizer_stats[self.s][arch_hash][metric_name].append(metric_value)
-        self.optimizer_stats[self.s][arch_hash]['fidelity'].append(self.fidelity)
+        self.optimizer_stats[self.s] = self.sh.optimizer_stats
 
     def test_statistics(self):
         best_arch = self.get_final_architecture()

From 71d9dbf9a2bd7f1113d4bf24efa847cdef89400c Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 20:09:00 +0100
Subject: [PATCH 035/130] delete obsolete function from mf plots

---
 examples/mf_plot.py | 25 -------------------------
 1 file changed, 25 deletions(-)

diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 43dd56080..547cd9f48 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -4,31 +4,6 @@
 
 from collections import defaultdict
 
-def restructure_results(errors :dict):
-    """This super fancy function restructures the results in order plot successive halving correctly
-    NOTE: This in not optimimzed at all!
-    NOTE: this function should be obsolete thus we save the stats now in the correct format.
-    """
-    print(errors[1].keys())
-    len_errors = len(errors[1]['train_acc'])
-    train_acc = errors[1]['train_acc']
-    valid_acc = errors[1]['valid_acc']
-    fidelity = errors[1]['fidelity']
-    archs = errors[1]['arch']
-    test_acc = errors[1]['test_acc']
-    runtime = errors[1]['runtime']
-    train_time = errors[1]['train_time']
-    new_errors = defaultdict(lambda: defaultdict(list))
-
-    for idx in range(len_errors):
-        new_errors[archs[idx]]['train_acc'].append(train_acc[idx])
-        new_errors[archs[idx]]['valid_acc'].append(valid_acc[idx])
-        new_errors[archs[idx]]['fidelity'].append(fidelity[idx])
-        new_errors[archs[idx]]['test_acc'].append(test_acc[idx])
-        new_errors[archs[idx]]['runtime'].append(runtime[idx])
-        new_errors[archs[idx]]['train_time'].append(train_time[idx])
-    return new_errors
-
 def get_results(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
     """
     Get statistics for successive halving

From 2e3d6ed40652eca044aa3fbeda36bd47a23c3489 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 20:36:35 +0100
Subject: [PATCH 036/130] add hb plot function

---
 examples/mf_plot.py | 40 +++++++++++++++++++++++++++++-----------
 1 file changed, 29 insertions(+), 11 deletions(-)

diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 547cd9f48..a5e57c29d 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -1,37 +1,55 @@
 import os
 import json
+from matplotlib.lines import _LineStyle
 import matplotlib.pyplot as plt
 
 from collections import defaultdict
 
-def get_results(predictor, path, epochs, metric='valid_acc', dataset='cifar10', ug=False):
+def get_results(predictor, path, filename, metric='valid_acc'):
     """
     Get statistics for successive halving
-    # TODO: make metric selectable, currently 'val_acc' is fixed 
+    TODO: make metric selectable, currently 'val_acc' is fixed 
     """
     algo_path = os.path.join(path, predictor)
     for seed_dir in os.listdir(algo_path):
-        result_file = os.path.join(algo_path, seed_dir, 'sh_stats.json')
+        result_file = os.path.join(algo_path, seed_dir, filename)
         result = json.load(open(result_file))
         return result
 
 def plot_sh():
-    """Plots successive halving learning curves
     """
-    # set up parameters for the experiments
-    epochs = 300
-
+    Plots learning curves for successive halving
+    """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results(predictor, folder, epochs=epochs, metric='test_acc', ug=True)
+    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
 
-    for arch, results in results.items():
-        x = results['fidelity']
-        values = results['val_acc']
+    for arch, stats in results.items():
+        x = stats['fidelity']
+        values = stats['val_acc']
         plt.plot(x, values, linestyle='-', label=arch)
     plt.rcParams['grid.linestyle'] = 'dotted'
     plt.show()
     plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
+def plot_hb():
+    """
+    Plots learning curves for hyperband
+    """
+    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
+    predictor = 'var_sparse_gp'
+    results = get_results(predictor, folder, 'hb_stats.json', metric='test_acc')
+    s = len(results)
+    
+    figure, axis = plt.subplots(s, 1)
+    for sh, sh_stats in results.items():
+        for arch, stats in sh_stats.items():
+            x = stats['fidelity']
+            values = stats['val_acc']
+            axis[sh, 0].plot(x, values, linestyle='-', label=arch)
+    plt.show()
+
+
+
 if __name__ == '__main__':
     plot_sh()
\ No newline at end of file

From 0c9ad2d1d617a53fe57e48d61d67686a2c792084 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 20:47:19 +0100
Subject: [PATCH 037/130] codestyle on sh optimizer

---
 naslib/optimizers/discrete/sh/optimizer.py | 38 ++++++++++------------
 1 file changed, 18 insertions(+), 20 deletions(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 4d6b642de..0ba13ca6b 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,4 +1,3 @@
-#from hpbandster.core.base_iteration import BaseIteration
 import numpy as np
 import torch
 
@@ -49,7 +48,7 @@ def __init__(
         self.number_archs = config.search.number_archs
         self.eta = config.search.eta
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.budget_type = config.search.budget_type  # is not for one query is overall
         self.fidelity_counter = 0
         self.sampled_archs = []
         self.history = torch.nn.ModuleList()
@@ -78,14 +77,13 @@ def new_epoch(self):
             model.arch.sample_random_architecture(dataset_api=self.dataset_api)
         else:
             model = self.sampled_archs[self.fidelity_counter]
-       
+
 # 		    return(ranks < self.num_configs[self.stage])
 
-        # DONE: define fidelity in multi-fidelity setting
         model.accuracy = model.arch.query(
             self.performance_metric,
             self.dataset,
-            epoch=int(self.fidelity), # DONE: adapt this
+            epoch=int(self.fidelity),
             dataset_api=self.dataset_api,
         )
 
@@ -93,10 +91,10 @@ def new_epoch(self):
         # DONE: make query type secure
         if self.budget_type == 'time':
             # DONE: make dependent on performance_metric
-            model.time = model.arch.query( # TODO: this is the time for training from screatch.
+            model.time = model.arch.query(  # TODO: this is the time for training from screatch.
                 self.performance_metric,
                 self.dataset,
-                epoch=int(self.fidelity), # DONE: adapt this
+                epoch=int(self.fidelity),
                 dataset_api=self.dataset_api,
             )
             budget = model.time
@@ -114,19 +112,19 @@ def new_epoch(self):
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
             self.old_fidelity = self.fidelity
-            self.fidelity = math.floor(self.eta*self.fidelity) #
-            self.sampled_archs.sort(key = lambda model: model.accuracy, reverse= True)
+            self.fidelity = math.floor(self.eta*self.fidelity)
+            self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
             if self.fidelity > self.budget_max:
-                    self.end = True
+                self.end = True
             elif(math.floor(self.number_archs/self.eta)) != 0:
-                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)] #DONE round
-               
+                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)]
+
             else:
                 self.end = True
-                self.sampled_archs = [self.sampled_archs[0]]  #but maybe there maybe a different way
+                self.sampled_archs = [self.sampled_archs[0]]  # but maybe there maybe a different way
             self.number_archs = len(self.sampled_archs)
             self.fidelity_counter = 0
-        # TODO: budget equals 
+        # TODO: budget equals
         return budget
         # required if we want to train the models and not only query.
         # architecture_i.parse()
@@ -187,13 +185,13 @@ def update_optimizer_stats(self):
         }
         for metric_name, metric in metrics.items():
             metric_value = arch.query(
-                metric, 
-                self.dataset, 
-                dataset_api=self.dataset_api, 
+                metric,
+                self.dataset,
+                dataset_api=self.dataset_api,
                 epoch=int(self.fidelity)
             )
             self.optimizer_stats[arch_hash][metric_name].append(metric_value)
-        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity) 
+        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity)
 
     def test_statistics(self):
         best_arch = self.get_final_architecture()
@@ -201,10 +199,10 @@ def test_statistics(self):
 
     def get_op_optimizer(self):
         return self.weight_optimizer
-    
+
     # TODO: we really do not need this
     def get_end(self):
         return self.end
 
     def get_checkpointables(self):
-        return {"model": self.history}
\ No newline at end of file
+        return {"model": self.history}

From 17cc12cc77309e44dd45c1b873112bf1bbaa5b6e Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 8 Dec 2021 20:59:02 +0100
Subject: [PATCH 038/130] codestyle of multifidelity trainer class

---
 naslib/defaults/trainer_multifidelity.py | 36 +++++++++++-------------
 1 file changed, 17 insertions(+), 19 deletions(-)

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 160a603ee..94cb782ee 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -8,7 +8,6 @@
 import torch
 import numpy as np
 
-#from fvcore.common.checkpoint import PeriodicCheckpointer
 from fvcore.common.checkpoint import Checkpointer
 
 from naslib.search_spaces.core.query_metrics import Metric
@@ -17,7 +16,7 @@
 from naslib.utils.logging import log_every_n_seconds, log_first_n
 
 from typing import Callable
-#from .additional_primitives import DropPathWrapper #Can be causes issues
+# from .additional_primitives import DropPathWrapper #Can be causes issues
 
 logger = logging.getLogger(__name__)
 
@@ -76,7 +75,7 @@ def __init__(self, optimizer, config, lightweight_output=False):
             }
         )
 
-    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None]=None, report_incumbent=True):
+    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None] = None, report_incumbent=True):
         """
         Start the architecture search.
 
@@ -113,7 +112,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
         while e < self.epochs:
 
             start_time = time.time()
-            #return function_eqalisation difference time to run or number of elovations 
+            # return function_eqalisation difference time to run or number of elovations
             used_budget = self.optimizer.new_epoch()
 
             if self.optimizer.using_step_function:
@@ -165,7 +164,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 end_time = time.time()
                 # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
                 # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
-                #only for debugging is it but out
+                # only for debugging is it but out
                 (
                     train_acc,
                     valid_acc,
@@ -185,7 +184,7 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
                 self.train_top1.avg = train_acc
                 self.val_top1.avg = valid_acc
 
-            self.periodic_checkpointer.save(e) # define the name in accurate , also 
+            self.periodic_checkpointer.save(e)  # define the name in accurate , also
             # TODO: change step into save for checkpointer
             anytime_results = self.optimizer.test_statistics()
             if anytime_results:
@@ -202,12 +201,11 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
             self._log_and_reset_accuracies(e, summary_writer)
             if after_epoch is not None:
                 after_epoch(e)
-    
+
             e += used_budget
 
         self.optimizer.after_training()
-        #self._log_optimizer_stats()
-        
+        # self._log_optimizer_stats()
 
         if summary_writer is not None:
             summary_writer.close()
@@ -260,12 +258,12 @@ def evaluate_oneshot(self, resume_from="", dataloader=None):
 
     def evaluate(
         self,
-        retrain:bool=True,
-        search_model:str="",
-        resume_from:str="",
-        best_arch:Graph=None,
-        dataset_api:object=None,
-        metric:Metric=None,
+        retrain: bool = True,
+        search_model: str = "",
+        resume_from: str = "",
+        best_arch: Graph = None,
+        dataset_api: object = None,
+        metric: Metric = None,
     ):
         """
         Evaluate the final architecture as given from the optimizer.
@@ -574,8 +572,8 @@ def _setup_checkpointers(
         """
         checkpointables = self.optimizer.get_checkpointables()
         checkpointables.update(add_checkpointables)
-        #name make no sense
-        self.periodic_checkpointer = utils.Checkpointer( #TODO rename periodic_checkpointer 
+        # TODO name make no sense
+        self.periodic_checkpointer = utils.Checkpointer(  # TODO rename periodic_checkpointer
             model=checkpointables.pop("model"),
             save_dir=self.config.save + "/search"
             if search
@@ -583,13 +581,13 @@ def _setup_checkpointers(
             # **checkpointables #NOTE: this is throwing an Error
         )
 
-        #self.periodic_checkpointer = PeriodicCheckpointer(
+        # self.periodic_checkpointer = PeriodicCheckpointer(
         #   checkpointer,
         #    period=period,
         #    max_iter=self.config.search.epochs
         #    if search
         #    else self.config.evaluation.epochs,
-        #)
+        # )
 
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))

From 904d914c33da624d6d64ece4cd025443ffe8bc54 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 10 Dec 2021 18:23:39 +0100
Subject: [PATCH 039/130] opti

---
 naslib/optimizers/discrete/bohb/plannung.txt | 137 +++++++++++++++++++
 naslib/optimizers/discrete/sh/optimizer.py   |   7 +-
 2 files changed, 141 insertions(+), 3 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/plannung.txt b/naslib/optimizers/discrete/bohb/plannung.txt
index 9db24aae7..f42446157 100644
--- a/naslib/optimizers/discrete/bohb/plannung.txt
+++ b/naslib/optimizers/discrete/bohb/plannung.txt
@@ -2,3 +2,140 @@ for bohb, hyperbamd can be completly be the same. But for succesive halving we s
 because instead of random sampling we use a model. That could be into succesive halving or in other method. 
 I comment how i think we can change it. TPE needs to be one a file, like in bananas. we need a predictor which i have to look, and we need TPE  to discide which we are sample. Like in bananas
 
+Code: https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/config_generators/bohb.py
+
+def get_config(self, budget):
+		"""
+			Function to sample a new configuration
+			This function is called inside Hyperband to query a new configuration
+			Parameters:
+			-----------
+			budget: float
+				the budget for which this configuration is scheduled
+			returns: config
+				should return a valid configuration
+		"""
+		
+		self.logger.debug('start sampling a new configuration.')
+		
+
+		sample = None
+		info_dict = {}
+		
+		# If no model is available, sample from prior
+		# also mix in a fraction of random configs
+		if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
+			sample =  self.configspace.sample_configuration()
+			info_dict['model_based_pick'] = False
+
+		best = np.inf
+		best_vector = None
+
+		if sample is None:
+			try:
+				
+				#sample from largest budget
+				budget = max(self.kde_models.keys())
+
+				l = self.kde_models[budget]['good'].pdf
+				g = self.kde_models[budget]['bad' ].pdf
+			
+				minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
+				
+				kde_good = self.kde_models[budget]['good']
+				kde_bad = self.kde_models[budget]['bad']
+
+				for i in range(self.num_samples):
+					idx = np.random.randint(0, len(kde_good.data))
+					datum = kde_good.data[idx]
+					vector = []
+					
+					for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
+						
+						bw = max(bw, self.min_bandwidth)
+						if t == 0:
+							bw = self.bw_factor*bw
+							try:
+								vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
+							except:
+								self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
+								self.logger.warning("data in the KDE:\n%s"%kde_good.data)
+						else:
+							
+							if np.random.rand() < (1-bw):
+								vector.append(int(m))
+							else:
+								vector.append(np.random.randint(t))
+					val = minimize_me(vector)
+
+					if not np.isfinite(val):
+						self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
+						self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
+						self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
+						self.logger.warning("l(x) = %s"%(l(vector)))
+						self.logger.warning("g(x) = %s"%(g(vector)))
+
+						# right now, this happens because a KDE does not contain all values for a categorical parameter
+						# this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
+						# if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
+						if np.isfinite(l(vector)):
+							best_vector = vector
+							break
+
+					if val < best:
+						best = val
+						best_vector = vector
+
+				if best_vector is None:
+					self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
+					sample = self.configspace.sample_configuration().get_dictionary()
+					info_dict['model_based_pick']  = False
+				else:
+					self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
+					for i, hp_value in enumerate(best_vector):
+						if isinstance(
+							self.configspace.get_hyperparameter(
+								self.configspace.get_hyperparameter_by_idx(i)
+							),
+							ConfigSpace.hyperparameters.CategoricalHyperparameter
+						):
+							best_vector[i] = int(np.rint(best_vector[i]))
+					sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
+					
+					try:
+						sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
+									configuration_space=self.configspace,
+									configuration=sample
+									)
+						info_dict['model_based_pick'] = True
+
+					except Exception as e:
+						self.logger.warning(("="*50 + "\n")*3 +\
+								"Error converting configuration:\n%s"%sample+\
+								"\n here is a traceback:" +\
+								traceback.format_exc())
+						raise(e)
+
+			except:
+				self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
+				sample = self.configspace.sample_configuration()
+				info_dict['model_based_pick']  = False
+
+
+		try:
+			sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
+				configuration_space=self.configspace,
+				configuration=sample.get_dictionary()
+			).get_dictionary()
+		except Exception as e:
+			self.logger.warning("Error (%s) converting configuration: %s -> "
+								"using random configuration!",
+								e,
+								sample)
+			sample = self.configspace.sample_configuration().get_dictionary()
+		self.logger.debug('done sampling a new configuration.')
+		return sample, info_dict
+
+
+bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type=self.kde_vartypes, bw=bw_estimation)
+good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type=self.kde_vartypes, bw=bw_estimation)
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index f86ae62d4..fc1bef68d 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -213,7 +213,8 @@ def sample(self, method):
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
             return model 
-        KDE.fit() #what is the kde #TODO here more infos are needed
+        something like new_result
+        something like get config 
         draw ns samples from kde
-        return max(l(lambda)/g(lamdba))
-    """
+        return sample
+"""

From 45c38547a92da4259d57f82351ad00a6f73802c7 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 11 Dec 2021 14:34:55 +0100
Subject: [PATCH 040/130] fix get finial archietktur

---
 naslib/optimizers/discrete/hb/optimizer.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 7f5588aaa..0042bd12e 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -56,7 +56,7 @@ def __init__(
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
 
-        self.optimizer_stats = dict()
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -88,7 +88,8 @@ def new_epoch(self):
             self.sh = SH(self.sh_config) #should be in config 
             self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
         
-        budget = self.sh.new_epoch() 
+        budget = self.sh.new_epoch()
+
         self.update_optimizer_stats()
         return budget
                 
@@ -105,14 +106,12 @@ def get_final_architecture(self):
         """
         Returns the sampled architecture with the lowest validation error.
         """
-        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
+        all_sampled.extend(self.sh.sampled_archs)
+        return max(all_sampled, key=lambda x: x.accuracy).arch
 
     def train_statistics(self, report_incumbent=True):
-        return(
-            0.42 , 0.42 , 0.42, 0.42
-
-
-        )
+        
         if report_incumbent:
             best_arch = self.get_final_architecture()
         else:

From 4ae6d0391de552be52762f69c513aff59d07ee9d Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 12 Dec 2021 16:19:50 +0100
Subject: [PATCH 041/130] start to train

---
 examples/mf_demo.py                           |   2 +-
 examples/mf_demo.yaml                         |   2 +-
 examples/mf_plot.py                           |   2 +-
 .../nas_predictors/discrete_config.yaml       |   1 +
 .../nas_predictors/discrete_config_BOHB.yaml  |  47 ++++
 .../nas_predictors/discrete_config_HB.yaml    |   2 +-
 .../nas_predictors/discrete_config_SH.yaml    |   2 +-
 naslib/optimizers/discrete/sh/optimizer.py    | 239 ++++++++++++++++--
 8 files changed, 275 insertions(+), 22 deletions(-)
 create mode 100644 naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 8da19458c..15a011c7a 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -61,7 +61,7 @@
 elif config_optimizer == 'RS':
     optimizer = RS(config)
 elif config_optimizer == 'BOHB':
-    raise Exception('Not implemented yet, ミ●﹏☉ミ')
+    optimizer = HB(config)
 else:
     raise Exception('invalid config')
 
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 63b0f6791..36b374c14 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: SH
+optimizer: BOHB
 # Activates/deactivates plotting
 plot: True
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index a5e57c29d..311b932b6 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -1,6 +1,6 @@
 import os
 import json
-from matplotlib.lines import _LineStyle
+#from matplotlib.lines import _LineStyle
 import matplotlib.pyplot as plt
 
 from collections import defaultdict
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 25fc9a949..7ede182ac 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -14,6 +14,7 @@ search:
   eta: 3
   budget_type: epoch 
   budget_max: 81
+  method: tpe
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
new file mode 100644
index 000000000..7ede182ac
--- /dev/null
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -0,0 +1,47 @@
+seed: 0
+optimizer: bananas
+search_space: nasbench201
+dataset: cifar10
+out_dir: run
+
+search:
+  seed: 0
+  checkpoint_freq: 10 # 1000
+  epochs: 158
+  fidelity: -1
+  min_fidelity: 1
+  number_archs: 81
+  eta: 3
+  budget_type: epoch 
+  budget_max: 81
+  method: tpe
+
+
+  predictor_type: var_sparse_gp
+  num_init: 10  
+  k: 10
+
+  # BANANAS
+  num_ensemble: 3
+  acq_fn_type: its
+  acq_fn_optimization: random_sampling
+  encoding_type: adjacency_one_hot
+  num_arches_to_mutate: 2
+  max_mutations: 1
+  num_candidates: 20
+  
+  # jacov data loader
+  batch_size: 256
+  data_size: 25000
+  cutout: False
+  cutout_length: 16
+  cutout_prob: 1.0
+  train_portion: 0.7
+  
+  # other params
+  debug_predictor: False
+  sample_size: 10
+  population_size: 30
+
+  # TODO: write config file for successive halving
+
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index 25fc9a949..d166eb744 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -14,7 +14,7 @@ search:
   eta: 3
   budget_type: epoch 
   budget_max: 81
-
+  method: random
 
   predictor_type: var_sparse_gp
   num_init: 10  
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index 2620620e6..1b0081211 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -14,7 +14,7 @@ search:
   eta: 2
   budget_type: epoch 
   budget_max: 1000
-
+  method: "random"
 
   predictor_type: var_sparse_gp
   num_init: 10  
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index fc1bef68d..27470c2c8 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,13 +1,13 @@
 import numpy as np
 import torch
-
+import random
 import math
 
 from collections import defaultdict
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
-
+import statsmodels.api as sm
 
 class SuccessiveHalving(MetaOptimizer):
     """
@@ -52,8 +52,15 @@ def __init__(
         self.history = torch.nn.ModuleList()
         self.end = False
         self.old_fidelity = 0
-
-        #if tpe:
+        self.method = config.search.method
+        #right now only for testing 
+        if self.method == "tpe":#
+            self.p = 0.15
+            self.N_min = 0
+            self.min_points_in_model = 0
+            self.configs = defaultdict(lambda: defaultdict(list))
+            self.losses = defaultdict(lambda: defaultdict(list))
+            self.top_n_percent = 15
             #self.p = config.search.p
             #self.percentile = config.search.percentile
             #self.N_min = config.search.N_min
@@ -66,6 +73,7 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
+    
 
     def new_epoch(self):
         """
@@ -73,15 +81,15 @@ def new_epoch(self):
         # TODO: with this kind of architekeur, in evaluation only the last fideltiy
         """
 
-        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-        model.arch = self.search_space.clone()
+        #model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        #model.arch = self.search_space.clone()
         if len(self.sampled_archs) < self.number_archs:
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api)
-           #model = sample_method() this can be a self.sample_method() or self.sample(method)
+            #model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+            model = self.sample(self.method)   # this can be a self.sample_method() or self.sample(method)
         else:
             model = self.sampled_archs[self.fidelity_counter]
 
-# 		    return(ranks < self.num_configs[self.stage])
+#             return(ranks < self.num_configs[self.stage])
 
         model.accuracy = model.arch.query(
             self.performance_metric,
@@ -200,21 +208,218 @@ def get_op_optimizer(self):
     def get_checkpointables(self):
         return {"model": self.history}
 
-    """
+    
     def sample(self, method):
         if method == "random" or (random.randint(1,100)/100) < self.p:
             model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            return model        
-        b = self.sampled_archs.argmax(key = lambda model: model.accuracy, reverse= True)) if length(self.sampled_archs)>= (self.N_min +2) else False
+                 
+        b = self.sampled_archs.argmax(key = lambda model: model.accuracy, reverse= True) if len(self.sampled_archs) >= (self.N_min +2) else False
         if not(b): 
             model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            return model 
-        something like new_result
-        something like get config 
-        draw ns samples from kde
-        return sample
+            
+        self.train_KDE(model)
+        #sample_KDE()
+        return None
+
+    def train_KDE(self, model):
+        budget = self.fidelity
+        if budget not in self.configs.keys():
+            self.configs[budget] = []
+            self.losses[budget] = []
+        print(model.arch.__str__)
+        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
+        self.losses[budget].append(model.arch.query(
+            self.performance_metric,
+            self.dataset,
+            epoch=int(self.fidelity),
+            dataset_api=self.dataset_api,
+        ))
+        train_configs = np.array(self.configs[budget])
+        train_losses = np.array(self.losses[budget])
+        
+        # Refit KDE for the current budget
+        idx = np.argsort(train_losses)
+
+        n_good= max(self.min_points_in_model, (self.top_n_percent * train_configs.shape[0])//100 )
+        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*train_configs.shape[0])//100)
+
+        train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
+        train_data_bad  = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad]])
+        
+        # quick rule of thumb
+        bw_estimation = 'normal_reference'
+        bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type="u", bw=bw_estimation) #TODO what the hell is var_type
+        good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type="u", bw=bw_estimation)
+
+        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
+        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
+
+        self.kde_models[budget] = {
+                'good': good_kde,
+                'bad' : bad_kde
+        }
+
+
+    def impute_conditional_data(self, array):
+
+        return_array = np.empty_like(array)
+
+        for i in range(array.shape[0]):
+            datum = np.copy(array[i])
+            nan_indices = np.argwhere(np.isnan(datum)).flatten()
+
+            while (np.any(nan_indices)):
+                nan_idx = nan_indices[0]
+                valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
+                if len(valid_indices) > 0:  
+                # pick one of them at random and overwrite all NaN values
+                    row_idx = np.random.choice(valid_indices)
+                    datum[nan_indices] = array[row_idx, nan_indices]
+
+                else:
+                    # no good point in the data has this value activated, so fill it with a valid but random value
+                    t = self.vartypes[nan_idx]
+                    if t == 0:
+                        datum[nan_idx] = np.random.rand()
+                    else:
+                        datum[nan_idx] = np.random.randint(t)
+                nan_indices = np.argwhere(np.isnan(datum)).flatten()
+            return_array[i,:] = datum
+        return(return_array)
+"""
+def get_config(self, budget):
+        
+            #Function to sample a new configuration
+            #This function is called inside Hyperband to query a new configuration
+            #Parameters:
+            #-----------
+            #budget: float
+            #    the budget for which this configuration is scheduled
+            #returns: config
+            #    should return a valid configuration
+        
+        
+        self.logger.debug('start sampling a new configuration.')
+        
+
+        sample = None
+        info_dict = {}
+        
+        # If no model is available, sample from prior
+        # also mix in a fraction of random configs
+        if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
+            sample =  self.configspace.sample_configuration()
+            info_dict['model_based_pick'] = False
+
+        best = np.inf
+        best_vector = None
+
+        if sample is None:
+            try:
+                
+                #sample from largest budget
+                budget = max(self.kde_models.keys())
+
+                l = self.kde_models[budget]['good'].pdf
+                g = self.kde_models[budget]['bad' ].pdf
+            
+                minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
+                
+                kde_good = self.kde_models[budget]['good']
+                kde_bad = self.kde_models[budget]['bad']
+
+                for i in range(self.num_samples):
+                    idx = np.random.randint(0, len(kde_good.data))
+                    datum = kde_good.data[idx]
+                    vector = []
+                    
+                    for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
+                        
+                        bw = max(bw, self.min_bandwidth)
+                        if t == 0:
+                            bw = self.bw_factor*bw
+                            try:
+                                vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
+                            except:
+                                self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
+                                self.logger.warning("data in the KDE:\n%s"%kde_good.data)
+                        else:
+                            
+                            if np.random.rand() < (1-bw):
+                                vector.append(int(m))
+                            else:
+                                vector.append(np.random.randint(t))
+                    val = minimize_me(vector)
+
+                    if not np.isfinite(val):
+                        self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
+                        self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
+                        self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
+                        self.logger.warning("l(x) = %s"%(l(vector)))
+                        self.logger.warning("g(x) = %s"%(g(vector)))
+
+                        # right now, this happens because a KDE does not contain all values for a categorical parameter
+                        # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
+                        # if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
+                        if np.isfinite(l(vector)):
+                            best_vector = vector
+                            break
+
+                    if val < best:
+                        best = val
+                        best_vector = vector
+
+                if best_vector is None:
+                    self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
+                    sample = self.configspace.sample_configuration().get_dictionary()
+                    info_dict['model_based_pick']  = False
+                else:
+                    self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
+                    for i, hp_value in enumerate(best_vector):
+                        if isinstance(
+                            self.configspace.get_hyperparameter(
+                                self.configspace.get_hyperparameter_by_idx(i)
+                            ),
+                            ConfigSpace.hyperparameters.CategoricalHyperparameter
+                        ):
+                            best_vector[i] = int(np.rint(best_vector[i]))
+                    sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
+                    
+                    try:
+                        sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
+                                    configuration_space=self.configspace,
+                                    configuration=sample
+                                    )
+                        info_dict['model_based_pick'] = True
+
+                    except Exception as e:
+                        self.logger.warning(("="*50 + "\n")*3 +\
+                                "Error converting configuration:\n%s"%sample+\
+                                "\n here is a traceback:" +\
+                                traceback.format_exc())
+                        raise(e)
+
+            except:
+                self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
+                sample = self.configspace.sample_configuration()
+                info_dict['model_based_pick']  = False
+
+
+        try:
+            sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
+                configuration_space=self.configspace,
+                configuration=sample.get_dictionary()
+            ).get_dictionary()
+        except Exception as e:
+            self.logger.warning("Error (%s) converting configuration: %s -> "
+                                "using random configuration!",
+                                e,
+                                sample)
+            sample = self.configspace.sample_configuration().get_dictionary()
+        self.logger.debug('done sampling a new configuration.')
+        return sample, info_dict
 """

From 149c51f059ebdede08fa95287030065f2fe9c2b5 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 13 Dec 2021 18:49:00 +0100
Subject: [PATCH 042/130] tpe in predictor

---
 naslib/defaults/trainer_multifidelity.py    |  2 +-
 naslib/optimizers/discrete/sh/optimizer.py  | 77 +------------------
 naslib/predictors/ensemble.py               |  2 +
 naslib/predictors/multifidelity/__init__.py |  1 +
 naslib/predictors/multifidelity/tpe.py      | 85 +++++++++++++++++++++
 5 files changed, 93 insertions(+), 74 deletions(-)
 create mode 100644 naslib/predictors/multifidelity/__init__.py
 create mode 100644 naslib/predictors/multifidelity/tpe.py

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index afe06e6fc..9673a421b 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -16,7 +16,7 @@
 from naslib.utils.logging import log_every_n_seconds, log_first_n
 
 from typing import Callable
-from .additional_primitives import DropPathWrapper #Can be causes issues
+#from .additional_primitives import DropPathWrapper #Can be causes issues
 
 logger = logging.getLogger(__name__)
 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 27470c2c8..58e19a1ff 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -7,7 +7,7 @@
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
-import statsmodels.api as sm
+
 
 class SuccessiveHalving(MetaOptimizer):
     """
@@ -54,13 +54,8 @@ def __init__(
         self.old_fidelity = 0
         self.method = config.search.method
         #right now only for testing 
-        if self.method == "tpe":#
-            self.p = 0.15
-            self.N_min = 0
-            self.min_points_in_model = 0
-            self.configs = defaultdict(lambda: defaultdict(list))
-            self.losses = defaultdict(lambda: defaultdict(list))
-            self.top_n_percent = 15
+        #if self.method == "tpe":#
+            
             #self.p = config.search.p
             #self.percentile = config.search.percentile
             #self.N_min = config.search.N_min
@@ -225,71 +220,7 @@ def sample(self, method):
         #sample_KDE()
         return None
 
-    def train_KDE(self, model):
-        budget = self.fidelity
-        if budget not in self.configs.keys():
-            self.configs[budget] = []
-            self.losses[budget] = []
-        print(model.arch.__str__)
-        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
-        self.losses[budget].append(model.arch.query(
-            self.performance_metric,
-            self.dataset,
-            epoch=int(self.fidelity),
-            dataset_api=self.dataset_api,
-        ))
-        train_configs = np.array(self.configs[budget])
-        train_losses = np.array(self.losses[budget])
-        
-        # Refit KDE for the current budget
-        idx = np.argsort(train_losses)
-
-        n_good= max(self.min_points_in_model, (self.top_n_percent * train_configs.shape[0])//100 )
-        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*train_configs.shape[0])//100)
-
-        train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
-        train_data_bad  = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad]])
-        
-        # quick rule of thumb
-        bw_estimation = 'normal_reference'
-        bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type="u", bw=bw_estimation) #TODO what the hell is var_type
-        good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type="u", bw=bw_estimation)
-
-        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
-        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
-
-        self.kde_models[budget] = {
-                'good': good_kde,
-                'bad' : bad_kde
-        }
-
-
-    def impute_conditional_data(self, array):
-
-        return_array = np.empty_like(array)
-
-        for i in range(array.shape[0]):
-            datum = np.copy(array[i])
-            nan_indices = np.argwhere(np.isnan(datum)).flatten()
-
-            while (np.any(nan_indices)):
-                nan_idx = nan_indices[0]
-                valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
-                if len(valid_indices) > 0:  
-                # pick one of them at random and overwrite all NaN values
-                    row_idx = np.random.choice(valid_indices)
-                    datum[nan_indices] = array[row_idx, nan_indices]
-
-                else:
-                    # no good point in the data has this value activated, so fill it with a valid but random value
-                    t = self.vartypes[nan_idx]
-                    if t == 0:
-                        datum[nan_idx] = np.random.rand()
-                    else:
-                        datum[nan_idx] = np.random.randint(t)
-                nan_indices = np.argwhere(np.isnan(datum)).flatten()
-            return_array[i,:] = datum
-        return(return_array)
+ 
 """
 def get_config(self, budget):
         
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index 99a70355d..c645992fe 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -8,6 +8,7 @@
 from naslib.predictors.bonas import BonasPredictor
 from naslib.predictors.bnn import DNGOPredictor, BOHAMIANN, BayesianLinearRegression
 from naslib.predictors.seminas import SemiNASPredictor
+from naslib.predictors.multifidelity import TreeParserEstimator
 from naslib.predictors.gp import (
     GPPredictor,
     SparseGPPredictor,
@@ -110,6 +111,7 @@ def get_ensemble(self):
                 max_zerocost=1000,
                 config=self.config,
             ),
+
         }
 
         return [
diff --git a/naslib/predictors/multifidelity/__init__.py b/naslib/predictors/multifidelity/__init__.py
new file mode 100644
index 000000000..f8061dd40
--- /dev/null
+++ b/naslib/predictors/multifidelity/__init__.py
@@ -0,0 +1 @@
+from .multifidelity import TreeParserEstimator
\ No newline at end of file
diff --git a/naslib/predictors/multifidelity/tpe.py b/naslib/predictors/multifidelity/tpe.py
new file mode 100644
index 000000000..98a7092e7
--- /dev/null
+++ b/naslib/predictors/multifidelity/tpe.py
@@ -0,0 +1,85 @@
+import statsmodels.api as sm
+from collections import defaultdict
+import numpy as np
+from collections import defaultdict
+
+
+
+
+class TreeParserEstimator(): #TODO maybe this need 
+    def __init__(self):
+        self.p = 0.15
+        self.N_min = 0
+        self.min_points_in_model = 0
+        self.configs = defaultdict(lambda: defaultdict(list))
+        self.losses = defaultdict(lambda: defaultdict(list))
+        self.top_n_percent = 15
+        
+
+
+
+
+    def train(self,model):
+        budget = self.fidelity
+        if budget not in self.configs.keys():
+            self.configs[budget] = []
+            self.losses[budget] = []
+        print(model.arch.__str__)
+        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
+        print(model.arch.__str__())
+        self.losses[budget].append(model.arch.query(
+            self.performance_metric,
+            self.dataset,
+            epoch=int(self.fidelity),
+            dataset_api=self.dataset_api,
+        ))
+        train_configs = np.array(self.configs[budget])
+        train_losses = np.array(self.losses[budget])
+        
+        # Refit KDE for the current budget
+        idx = np.argsort(train_losses)
+
+        n_good= max(self.min_points_in_model, (self.top_n_percent * train_configs.shape[0])//100 )
+        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*train_configs.shape[0])//100)
+
+        train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
+        train_data_bad  = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad]])
+        
+        # quick rule of thumb
+        bw_estimation = 'normal_reference'
+        bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type="u", bw=bw_estimation) #var_type is type of hyperparameter
+        # c : continuous, u : unordered (discrete),  o : ordered (discrete)
+        good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type="u", bw=bw_estimation) 
+        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
+        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
+
+        self.kde_models[budget] = {
+                'good': good_kde,
+                'bad' : bad_kde
+        }
+
+    def impute_conditional_data(self, array):
+        return_array = np.empty_like(array)
+        for i in range(array.shape[0]):
+            datum = np.copy(array[i])
+            nan_indices = np.argwhere(np.isnan(datum)).flatten()
+
+            while (np.any(nan_indices)):
+                nan_idx = nan_indices[0]
+                valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
+                if len(valid_indices) > 0:  
+                # pick one of them at random and overwrite all NaN values
+                    row_idx = np.random.choice(valid_indices)
+                    datum[nan_indices] = array[row_idx, nan_indices]
+
+                else:
+                    # no good point in the data has this value activated, so fill it with a valid but random value
+                    t = self.vartypes[nan_idx]
+                    if t == 0:
+                        datum[nan_idx] = np.random.rand()
+                    else:
+                        datum[nan_idx] = np.random.randint(t)
+                nan_indices = np.argwhere(np.isnan(datum)).flatten()
+            return_array[i,:] = datum
+        return(return_array)
+   
\ No newline at end of file

From 16f05ebeff75c9d03ceb0b723374a4b2468cf284 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 17 Dec 2021 17:57:03 +0100
Subject: [PATCH 043/130] learned kde

---
 .../nas_predictors/discrete_config.yaml       |  4 +-
 .../nas_predictors/discrete_config_BOHB.yaml  |  4 +-
 naslib/optimizers/discrete/sh/optimizer.py    | 39 +++++----
 naslib/predictors/ensemble.py                 |  9 +-
 naslib/predictors/tpe/__init__.py             |  1 +
 naslib/predictors/tpe/tpe.py                  | 85 +++++++++++++++++++
 naslib/predictors/utils/encodings.py          |  2 +-
 7 files changed, 122 insertions(+), 22 deletions(-)
 create mode 100644 naslib/predictors/tpe/__init__.py
 create mode 100644 naslib/predictors/tpe/tpe.py

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 7ede182ac..7c81c1de3 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -16,8 +16,8 @@ search:
   budget_max: 81
   method: tpe
 
-
-  predictor_type: var_sparse_gp
+  
+  predictor_type: tpe
   num_init: 10  
   k: 10
 
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 7ede182ac..7c81c1de3 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -16,8 +16,8 @@ search:
   budget_max: 81
   method: tpe
 
-
-  predictor_type: var_sparse_gp
+  
+  predictor_type: tpe
   num_init: 10  
   k: 10
 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 58e19a1ff..a742d936c 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -4,7 +4,9 @@
 import math
 
 from collections import defaultdict
-
+from naslib.predictors import predictor
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 from naslib.search_spaces.core.query_metrics import Metric
 
@@ -54,11 +56,12 @@ def __init__(
         self.old_fidelity = 0
         self.method = config.search.method
         #right now only for testing 
-        #if self.method == "tpe":#
-            
+        if self.method == "tpe":#
+            self.ss_type= config.search_space
+            self.encoding_type = config.search.encoding_type
             #self.p = config.search.p
             #self.percentile = config.search.percentile
-            #self.N_min = config.search.N_min
+            self.N_min = 5
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
@@ -78,9 +81,11 @@ def new_epoch(self):
 
         #model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
         #model.arch = self.search_space.clone()
+        #TODO is num_init needed 
         if len(self.sampled_archs) < self.number_archs:
-            #model.arch.sample_random_architecture(dataset_api=self.dataset_api)
-            model = self.sample(self.method)   # this can be a self.sample_method() or self.sample(method)
+            #model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+            model = self.sample(self.method)
+            
         else:
             model = self.sampled_archs[self.fidelity_counter]
 
@@ -205,20 +210,22 @@ def get_checkpointables(self):
 
     
     def sample(self, method):
-        if method == "random" or (random.randint(1,100)/100) < self.p:
+        if method == "random" or  len(self.sampled_archs) < self.N_min:
             model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-                 
-        b = self.sampled_archs.argmax(key = lambda model: model.accuracy, reverse= True) if len(self.sampled_archs) >= (self.N_min +2) else False
-        if not(b): 
-            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-            model.arch = self.search_space.clone()
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            
-        self.train_KDE(model)
+        else:         
+            xtrain = [m.arch for m in self.sampled_archs]
+            ytrain = [m.accuracy for m in self.sampled_archs]
+            ensemble =   Ensemble(
+                predictor_type= "tpe",  
+                num_ensemble= 1,
+                encoding_type= self.encoding_type,
+                ss_type=  self.ss_type
+                )
+            train_error = ensemble.fit(xtrain,ytrain,self.fidelity)
         #sample_KDE()
-        return None
+        return model
 
  
 """
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index c645992fe..1c28639f7 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -1,6 +1,8 @@
 import numpy as np
 import copy
 
+from torch._C import TracingState
+
 from naslib.predictors.predictor import Predictor
 from naslib.predictors.mlp import MLPPredictor
 from naslib.predictors.trees import LGBoost, XGBoost, NGBoost, RandomForestPredictor
@@ -8,7 +10,7 @@
 from naslib.predictors.bonas import BonasPredictor
 from naslib.predictors.bnn import DNGOPredictor, BOHAMIANN, BayesianLinearRegression
 from naslib.predictors.seminas import SemiNASPredictor
-from naslib.predictors.multifidelity import TreeParserEstimator
+from naslib.predictors.tpe import TreeParserEstimator
 from naslib.predictors.gp import (
     GPPredictor,
     SparseGPPredictor,
@@ -111,6 +113,11 @@ def get_ensemble(self):
                 max_zerocost=1000,
                 config=self.config,
             ),
+            "tpe": TreeParserEstimator(
+                encoding_type=self.encoding_type,
+                ss_type = self.ss_type ),
+
+            
 
         }
 
diff --git a/naslib/predictors/tpe/__init__.py b/naslib/predictors/tpe/__init__.py
new file mode 100644
index 000000000..978b19b66
--- /dev/null
+++ b/naslib/predictors/tpe/__init__.py
@@ -0,0 +1 @@
+from .tpe import TreeParserEstimator
\ No newline at end of file
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
new file mode 100644
index 000000000..3a3f9be8f
--- /dev/null
+++ b/naslib/predictors/tpe/tpe.py
@@ -0,0 +1,85 @@
+import statsmodels.api as sm
+from collections import defaultdict
+import numpy as np
+
+from naslib.predictors.utils.encodings import encode
+
+
+
+
+class TreeParserEstimator(): #TODO maybe this need 
+    def __init__(
+        self,
+        encoding_type="adjacency_one_hot",
+        ss_type="nasbench201"
+    ):
+        self.encoding_type = encoding_type
+        self.ss_type = ss_type
+        self.p = 0.15
+        self.N_min = 0
+        self.min_points_in_model = 1
+        self.configs = defaultdict(lambda: defaultdict(list))
+        self.losses = defaultdict(lambda: defaultdict(list))
+        self.top_n_percent = 15
+        self.min_bandwith = 1e-3
+        
+    def set_hyperparams(self, _):
+        pass
+
+
+
+    def fit(self, xtrain, ytrain, train_info):
+        
+        budget = train_info
+        """
+        if budget not in self.configs.keys():
+            self.configs[budget] = []
+            self.losses[budget] = []
+        print(model.arch.__str__)
+        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
+        print(model.arch.__str__())
+        self.losses[budget].append(model.arch.query(
+            self.performance_metric,
+            self.dataset,
+            epoch=int(self.fidelity),
+            dataset_api=self.dataset_api,
+        ))
+        train_configs = np.array(self.configs[budget])
+        train_losses = np.array(self.losses[budget])
+        
+        # Refit KDE for the current budget
+        idx = np.argsort(train_losses)
+        """
+        if type(xtrain) is list:
+            # when used in itself, we use
+            xtrain = np.array(
+                [
+                    [arch.get_op_indices()]
+                    for arch in xtrain
+                ]
+            )
+       
+        n_good= max(self.min_points_in_model, (self.top_n_percent *len(xtrain)//100)) 
+        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*len(xtrain)//100))
+       
+        idx = np.argsort(ytrain)
+
+        train_good_data = xtrain[idx[:n_good]]
+        train_bad_data = xtrain[idx[n_good:n_good+n_bad]]
+        good_var_type = "".join("u" for i in range(len(train_good_data)))
+        bad_var_type = "".join("u" for i in range(len(train_bad_data)))
+        # quick rule of thumb
+        bw_estimation = 'normal_reference'
+        bad_kde = sm.nonparametric.KDEMultivariate(data=train_good_data,  var_type= good_var_type, bw=bw_estimation) #var_type is type of hyperparameter
+        # c : continuous, u : unordered (discrete),  o : ordered (discrete)
+        good_kde = sm.nonparametric.KDEMultivariate(data=train_bad_data, var_type=  bad_var_type, bw=bw_estimation) 
+        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
+        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
+
+        self.kde_models[budget] = {
+                'good': good_kde,
+                'bad' : bad_kde
+        }
+    
+
+ 
\ No newline at end of file
diff --git a/naslib/predictors/utils/encodings.py b/naslib/predictors/utils/encodings.py
index d90d26242..00f4e7f6f 100644
--- a/naslib/predictors/utils/encodings.py
+++ b/naslib/predictors/utils/encodings.py
@@ -215,7 +215,7 @@ def encode_gcn_transbench101(arch):
 def encode_bonas_nasbench201(arch):
     """
     Input:
-    a list of categorical ops starting from 0
+    a list of categorical ops startfroming  0
     """
     ops = arch.get_op_indices()
     # offset ops list by one, add input and output to ops list

From 2cccd523479dd36ff7f687fbcfb007b80a4794b9 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 17 Dec 2021 17:59:39 +0100
Subject: [PATCH 044/130] delete old files

---
 naslib/predictors/multifidelity/__init__.py |  1 -
 naslib/predictors/multifidelity/tpe.py      | 85 ---------------------
 2 files changed, 86 deletions(-)
 delete mode 100644 naslib/predictors/multifidelity/__init__.py
 delete mode 100644 naslib/predictors/multifidelity/tpe.py

diff --git a/naslib/predictors/multifidelity/__init__.py b/naslib/predictors/multifidelity/__init__.py
deleted file mode 100644
index f8061dd40..000000000
--- a/naslib/predictors/multifidelity/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .multifidelity import TreeParserEstimator
\ No newline at end of file
diff --git a/naslib/predictors/multifidelity/tpe.py b/naslib/predictors/multifidelity/tpe.py
deleted file mode 100644
index 98a7092e7..000000000
--- a/naslib/predictors/multifidelity/tpe.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import statsmodels.api as sm
-from collections import defaultdict
-import numpy as np
-from collections import defaultdict
-
-
-
-
-class TreeParserEstimator(): #TODO maybe this need 
-    def __init__(self):
-        self.p = 0.15
-        self.N_min = 0
-        self.min_points_in_model = 0
-        self.configs = defaultdict(lambda: defaultdict(list))
-        self.losses = defaultdict(lambda: defaultdict(list))
-        self.top_n_percent = 15
-        
-
-
-
-
-    def train(self,model):
-        budget = self.fidelity
-        if budget not in self.configs.keys():
-            self.configs[budget] = []
-            self.losses[budget] = []
-        print(model.arch.__str__)
-        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
-        print(model.arch.__str__())
-        self.losses[budget].append(model.arch.query(
-            self.performance_metric,
-            self.dataset,
-            epoch=int(self.fidelity),
-            dataset_api=self.dataset_api,
-        ))
-        train_configs = np.array(self.configs[budget])
-        train_losses = np.array(self.losses[budget])
-        
-        # Refit KDE for the current budget
-        idx = np.argsort(train_losses)
-
-        n_good= max(self.min_points_in_model, (self.top_n_percent * train_configs.shape[0])//100 )
-        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*train_configs.shape[0])//100)
-
-        train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
-        train_data_bad  = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad]])
-        
-        # quick rule of thumb
-        bw_estimation = 'normal_reference'
-        bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type="u", bw=bw_estimation) #var_type is type of hyperparameter
-        # c : continuous, u : unordered (discrete),  o : ordered (discrete)
-        good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type="u", bw=bw_estimation) 
-        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
-        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
-
-        self.kde_models[budget] = {
-                'good': good_kde,
-                'bad' : bad_kde
-        }
-
-    def impute_conditional_data(self, array):
-        return_array = np.empty_like(array)
-        for i in range(array.shape[0]):
-            datum = np.copy(array[i])
-            nan_indices = np.argwhere(np.isnan(datum)).flatten()
-
-            while (np.any(nan_indices)):
-                nan_idx = nan_indices[0]
-                valid_indices = np.argwhere(np.isfinite(array[:,nan_idx])).flatten()
-                if len(valid_indices) > 0:  
-                # pick one of them at random and overwrite all NaN values
-                    row_idx = np.random.choice(valid_indices)
-                    datum[nan_indices] = array[row_idx, nan_indices]
-
-                else:
-                    # no good point in the data has this value activated, so fill it with a valid but random value
-                    t = self.vartypes[nan_idx]
-                    if t == 0:
-                        datum[nan_idx] = np.random.rand()
-                    else:
-                        datum[nan_idx] = np.random.randint(t)
-                nan_indices = np.argwhere(np.isnan(datum)).flatten()
-            return_array[i,:] = datum
-        return(return_array)
-   
\ No newline at end of file

From db3e54350145adeefbc56f1dac608188a14f3ee1 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 20 Dec 2021 18:43:59 +0100
Subject: [PATCH 045/130] beginn sample

---
 naslib/predictors/tpe/tpe.py | 113 ++++++++++++++++++++++++++++++++++-
 1 file changed, 110 insertions(+), 3 deletions(-)

diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index 3a3f9be8f..aab4870c6 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -3,9 +3,10 @@
 import numpy as np
 
 from naslib.predictors.utils.encodings import encode
+import scipy.stats as sps
+import scipy.optimize as spo
 
-
-
+import torch #maybe not sure rigth now
 
 class TreeParserEstimator(): #TODO maybe this need 
     def __init__(
@@ -22,7 +23,7 @@ def __init__(
         self.losses = defaultdict(lambda: defaultdict(list))
         self.top_n_percent = 15
         self.min_bandwith = 1e-3
-        
+        self.search_space = None #thing about later
     def set_hyperparams(self, _):
         pass
 
@@ -81,5 +82,111 @@ def fit(self, xtrain, ytrain, train_info):
                 'bad' : bad_kde
         }
     
+    def query(self, budget):
+        """
+            Function to sample a new configuration
+            This function is called inside Hyperband to query a new configuration
+            Parameters:
+            -----------
+            budget: float
+                the budget for which this configuration is scheduled
+            returns: config
+                should return a valid configuration
+        """
+        sample = None
+        info_dict = {}
+        
+        # If no model is available, sample from prior
+        # also mix in a fraction of random configs
+        if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
+            sample =  self.configspace.sample_configuration()
+            info_dict['model_based_pick'] = False
+        sample =  torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        sample.arch = self.search_space.clone()
+        best = np.inf
+        best_vector = None
+
+        if sample is None:
+            try:
+                
+                #sample from largest budget
+                budget = max(self.kde_models.keys())
+
+                l = self.kde_models[budget]['good'].pdf
+                g = self.kde_models[budget]['bad' ].pdf
+            
+                minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
+                
+                kde_good = self.kde_models[budget]['good']
+                kde_bad = self.kde_models[budget]['bad']
+
+                for i in range(self.num_samples):
+                    idx = np.random.randint(0, len(kde_good.data))
+                    datum = kde_good.data[idx]
+                    vector = []
+                    
+                    for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
+                        
+                        bw = max(bw, self.min_bandwidth)
+                        if t == 0:
+                            bw = self.bw_factor*bw
+                            try:
+                                vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
+                            except:
+                                self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
+                                self.logger.warning("data in the KDE:\n%s"%kde_good.data)
+                        else:
+                            
+                            if np.random.rand() < (1-bw):
+                                vector.append(int(m))
+                            else:
+                                vector.append(np.random.randint(t))
+                    val = minimize_me(vector)
+
+                    if not np.isfinite(val):
+                        self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
+                        self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
+                        self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
+                        self.logger.warning("l(x) = %s"%(l(vector)))
+                        self.logger.warning("g(x) = %s"%(g(vector)))
+
+                        # right now, this happens because a KDE does not contain all values for a categorical parameter
+                        # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
+                        # if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
+                        if np.isfinite(l(vector)):
+                            best_vector = vector
+                            break
+
+                    if val < best:
+                        best = val
+                        best_vector = vector
+
+                if best_vector is None:
+                    self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
+                    sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+                    info_dict['model_based_pick']  = False
+                else:
+                    self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
+                    #for i, hp_value in enumerate(best_vector):
+                    #    if isinstance(
+                    #       self.configspace.get_hyperparameter(
+                    #            self.configspace.get_hyperparameter_by_idx(i)
+                    #       ),
+                    #        ConfigSpace.hyperparameters.CategoricalHyperparameter
+                    #   ):
+                    #       best_vector[i] = int(np.rint(best_vector[i]))
+                    sample.arch.convert_op_indices_to_naslib(best_vector)
+            except:
+                self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
+                sample = self.configspace.sample_configuration()
+                info_dict['model_based_pick']  = False
+
+
+  
+        self.logger.debug('done sampling a new configuration.')
+        return sample, info_dict
+    
+    
+    
 
  
\ No newline at end of file

From f76ceebb8774bc0549ba3f366b7b26d46fe8fb6e Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 8 Jan 2022 10:39:35 +0100
Subject: [PATCH 046/130] tpe further work

---
 .../nas_predictors/discrete_config_BOHB.yaml  |   4 +-
 naslib/optimizers/discrete/hb/optimizer.py    |  22 ++-
 naslib/optimizers/discrete/sh/optimizer.py    | 156 ++----------------
 naslib/predictors/ensemble.py                 |  14 +-
 naslib/predictors/tpe/tpe.py                  |  94 +++++++----
 5 files changed, 100 insertions(+), 190 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 7c81c1de3..b90a04920 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -10,10 +10,10 @@ search:
   epochs: 158
   fidelity: -1
   min_fidelity: 1
-  number_archs: 81
+  number_archs: 1000
   eta: 3
   budget_type: epoch 
-  budget_max: 81
+  budget_max: 1000
   method: tpe
 
   
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 0042bd12e..a04534709 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -5,6 +5,8 @@
 from collections import defaultdict
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
 from naslib.search_spaces.core.query_metrics import Metric
 
 from collections import defaultdict
@@ -42,7 +44,7 @@ def __init__(
         self.sh_config = copy.deepcopy(config)
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-
+        self.method = config.search.method
         #self.fidelit_min = config.search.min_fidelity
         self.budget_max = config.search.budget_max
         self.eta = config.search.eta
@@ -55,8 +57,10 @@ def __init__(
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
-
+        self.encoding_type = config.search.encoding_type
+        self.ss_type= config.search_space
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+        
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
             search_space.QUERYABLE
@@ -64,6 +68,16 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
+        if self.method == "tpe":
+            
+            self.ensemble =   Ensemble(
+                    predictor_type= "tpe",  
+                    num_ensemble= 1,
+                    encoding_type= self.encoding_type,
+                    ss_type=  self.ss_type,
+                    config = [self.search_space.clone(),self.dataset_api, self.dataset],   #replace with config maybe or not 
+
+                    )
 
     def new_epoch(self):
         """
@@ -84,8 +98,8 @@ def new_epoch(self):
             r = self.budget_max * self.eta ** (-self.s)
             self.sh_config.search.number_archs = n
             self.sh_config.search.min_fidelity  = r 
-           
-            self.sh = SH(self.sh_config) #should be in config 
+
+            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
             self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
         
         budget = self.sh.new_epoch()
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index a742d936c..3cf25b60b 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -25,6 +25,7 @@ def __init__(
         weight_optimizer=torch.optim.SGD,
         loss_criteria=torch.nn.CrossEntropyLoss(),
         grad_clip=None,
+        esemble = None,
     ):
         """
         Initialize a Successive Halving optimizer.
@@ -61,7 +62,8 @@ def __init__(
             self.encoding_type = config.search.encoding_type
             #self.p = config.search.p
             #self.percentile = config.search.percentile
-            self.N_min = 5
+            self.N_min = 100 #This has to be higher then 
+            self.ensemble = esemble
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
@@ -79,7 +81,7 @@ def new_epoch(self):
         # TODO: with this kind of architekeur, in evaluation only the last fideltiy
         """
 
-        #model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
         #model.arch = self.search_space.clone()
         #TODO is num_init needed 
         if len(self.sampled_archs) < self.number_archs:
@@ -101,6 +103,7 @@ def new_epoch(self):
         budget = (self.fidelity - self.old_fidelity) / self.budget_max
         # DONE: make query type secure
         if self.budget_type == 'time':
+            #TODO also for predictions
             # DONE: make dependent on performance_metric
             model.time = model.arch.query(  # TODO: this is the time for training from screatch.
                 self.performance_metric,
@@ -210,154 +213,15 @@ def get_checkpointables(self):
 
     
     def sample(self, method):
+        #is right now in tpe
         if method == "random" or  len(self.sampled_archs) < self.N_min:
             model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-        else:         
+        else:        
             xtrain = [m.arch for m in self.sampled_archs]
             ytrain = [m.accuracy for m in self.sampled_archs]
-            ensemble =   Ensemble(
-                predictor_type= "tpe",  
-                num_ensemble= 1,
-                encoding_type= self.encoding_type,
-                ss_type=  self.ss_type
-                )
-            train_error = ensemble.fit(xtrain,ytrain,self.fidelity)
-        #sample_KDE()
+            train_error = self.ensemble.fit(xtrain,ytrain,self.fidelity)
+            _, info_dict  = self.ensemble.query_tpe("test", self.fidelity)
+            model = info_dict["model"]
         return model
-
- 
-"""
-def get_config(self, budget):
-        
-            #Function to sample a new configuration
-            #This function is called inside Hyperband to query a new configuration
-            #Parameters:
-            #-----------
-            #budget: float
-            #    the budget for which this configuration is scheduled
-            #returns: config
-            #    should return a valid configuration
-        
-        
-        self.logger.debug('start sampling a new configuration.')
-        
-
-        sample = None
-        info_dict = {}
-        
-        # If no model is available, sample from prior
-        # also mix in a fraction of random configs
-        if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
-            sample =  self.configspace.sample_configuration()
-            info_dict['model_based_pick'] = False
-
-        best = np.inf
-        best_vector = None
-
-        if sample is None:
-            try:
-                
-                #sample from largest budget
-                budget = max(self.kde_models.keys())
-
-                l = self.kde_models[budget]['good'].pdf
-                g = self.kde_models[budget]['bad' ].pdf
-            
-                minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
-                
-                kde_good = self.kde_models[budget]['good']
-                kde_bad = self.kde_models[budget]['bad']
-
-                for i in range(self.num_samples):
-                    idx = np.random.randint(0, len(kde_good.data))
-                    datum = kde_good.data[idx]
-                    vector = []
-                    
-                    for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
-                        
-                        bw = max(bw, self.min_bandwidth)
-                        if t == 0:
-                            bw = self.bw_factor*bw
-                            try:
-                                vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
-                            except:
-                                self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
-                                self.logger.warning("data in the KDE:\n%s"%kde_good.data)
-                        else:
-                            
-                            if np.random.rand() < (1-bw):
-                                vector.append(int(m))
-                            else:
-                                vector.append(np.random.randint(t))
-                    val = minimize_me(vector)
-
-                    if not np.isfinite(val):
-                        self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
-                        self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
-                        self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
-                        self.logger.warning("l(x) = %s"%(l(vector)))
-                        self.logger.warning("g(x) = %s"%(g(vector)))
-
-                        # right now, this happens because a KDE does not contain all values for a categorical parameter
-                        # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
-                        # if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
-                        if np.isfinite(l(vector)):
-                            best_vector = vector
-                            break
-
-                    if val < best:
-                        best = val
-                        best_vector = vector
-
-                if best_vector is None:
-                    self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
-                    sample = self.configspace.sample_configuration().get_dictionary()
-                    info_dict['model_based_pick']  = False
-                else:
-                    self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
-                    for i, hp_value in enumerate(best_vector):
-                        if isinstance(
-                            self.configspace.get_hyperparameter(
-                                self.configspace.get_hyperparameter_by_idx(i)
-                            ),
-                            ConfigSpace.hyperparameters.CategoricalHyperparameter
-                        ):
-                            best_vector[i] = int(np.rint(best_vector[i]))
-                    sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
-                    
-                    try:
-                        sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
-                                    configuration_space=self.configspace,
-                                    configuration=sample
-                                    )
-                        info_dict['model_based_pick'] = True
-
-                    except Exception as e:
-                        self.logger.warning(("="*50 + "\n")*3 +\
-                                "Error converting configuration:\n%s"%sample+\
-                                "\n here is a traceback:" +\
-                                traceback.format_exc())
-                        raise(e)
-
-            except:
-                self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
-                sample = self.configspace.sample_configuration()
-                info_dict['model_based_pick']  = False
-
-
-        try:
-            sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
-                configuration_space=self.configspace,
-                configuration=sample.get_dictionary()
-            ).get_dictionary()
-        except Exception as e:
-            self.logger.warning("Error (%s) converting configuration: %s -> "
-                                "using random configuration!",
-                                e,
-                                sample)
-            sample = self.configspace.sample_configuration().get_dictionary()
-        self.logger.debug('done sampling a new configuration.')
-        return sample, info_dict
-"""
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index 1c28639f7..0028abf50 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -103,6 +103,7 @@ def get_ensemble(self):
                 min_train_size=0,
                 max_zerocost=100,
             ),
+            #remove temporar for debugging 
             "omni_seminas": OmniSemiNASPredictor(
                 zero_cost=["jacov"],
                 lce=[],
@@ -115,7 +116,8 @@ def get_ensemble(self):
             ),
             "tpe": TreeParserEstimator(
                 encoding_type=self.encoding_type,
-                ss_type = self.ss_type ),
+                ss_type = self.ss_type,
+                config = self.config ),
 
             
 
@@ -148,10 +150,16 @@ def fit(self, xtrain, ytrain, train_info=None):
     def query(self, xtest, info=None):
         predictions = []
         for i in range(self.num_ensemble):
-            prediction = self.ensemble[i].query(xtest, info)
+            prediction = self.ensemble[i].query(xtest, info) #added info dict
             predictions.append(prediction)
-
         return np.array(predictions)
+    def query_tpe(self, xtest, info=None):
+        predictions = []
+        for i in range(self.num_ensemble):
+            prediction, info_dict = self.ensemble[i].query(xtest, info) #added info dict
+            predictions.append(prediction)
+
+        return np.array(predictions), info_dict #added info dict
 
     def set_hyperparams(self, params):
         if self.ensemble is None:
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index aab4870c6..257a7ed95 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -1,7 +1,11 @@
+from logging import error
+
 import statsmodels.api as sm
 from collections import defaultdict
 import numpy as np
 
+
+from naslib.search_spaces.core.query_metrics import Metric
 from naslib.predictors.utils.encodings import encode
 import scipy.stats as sps
 import scipy.optimize as spo
@@ -12,18 +16,24 @@ class TreeParserEstimator(): #TODO maybe this need
     def __init__(
         self,
         encoding_type="adjacency_one_hot",
-        ss_type="nasbench201"
+        ss_type="nasbench201",
+        config = None,
+        dataset_api = None
     ):
         self.encoding_type = encoding_type
         self.ss_type = ss_type
         self.p = 0.15
         self.N_min = 0
+        self.num_samples = 64  #maybe same then number of essembles, not sure 
+        self.dataset_api = dataset_api
+        self.kde_models = defaultdict(lambda: defaultdict(list))
         self.min_points_in_model = 1
-        self.configs = defaultdict(lambda: defaultdict(list))
-        self.losses = defaultdict(lambda: defaultdict(list))
         self.top_n_percent = 15
-        self.min_bandwith = 1e-3
-        self.search_space = None #thing about later
+        self.min_bandwidth = 1e-3
+        self.random_fraction = 0.15
+        self.configspace = config[0] #thing about later
+        self.dataset_api = config[1]
+        self.dataset = config[2]
     def set_hyperparams(self, _):
         pass
 
@@ -67,13 +77,20 @@ def fit(self, xtrain, ytrain, train_info):
 
         train_good_data = xtrain[idx[:n_good]]
         train_bad_data = xtrain[idx[n_good:n_good+n_bad]]
-        good_var_type = "".join("u" for i in range(len(train_good_data)))
-        bad_var_type = "".join("u" for i in range(len(train_bad_data)))
+        #TODO check if this is only special for nasbench-201
+        self.kde_vartype = "".join("u" for i in range(len(list(train_good_data[0,0]))))
+        #train_good_data = train_good_data[0]
+        #train_bad_data = train_bad_data[0]self.vartypes = np.array(self.vartypes, dtype=int)
+        #TODO why is there an issue with opteration 4 Attention1x1 ???
+        self.vartypes = [5,5,5,5,5,5] #TODO I think this has to discuss with the autors
+        self.vartypes = np.array(self.vartypes, dtype=int)
+        print(self.vartypes)
+        #self.vartypes is needed 
         # quick rule of thumb
         bw_estimation = 'normal_reference'
-        bad_kde = sm.nonparametric.KDEMultivariate(data=train_good_data,  var_type= good_var_type, bw=bw_estimation) #var_type is type of hyperparameter
+        good_kde = sm.nonparametric.KDEMultivariate(data=train_good_data,  var_type= self.kde_vartype, bw=bw_estimation) #var_type is type of hyperparameter
         # c : continuous, u : unordered (discrete),  o : ordered (discrete)
-        good_kde = sm.nonparametric.KDEMultivariate(data=train_bad_data, var_type=  bad_var_type, bw=bw_estimation) 
+        bad_kde = sm.nonparametric.KDEMultivariate(data=train_bad_data, var_type=  self.kde_vartype, bw=bw_estimation) 
         bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
         good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
 
@@ -82,7 +99,7 @@ def fit(self, xtrain, ytrain, train_info):
                 'bad' : bad_kde
         }
     
-    def query(self, budget):
+    def query(self, _, budget):
         """
             Function to sample a new configuration
             This function is called inside Hyperband to query a new configuration
@@ -93,20 +110,20 @@ def query(self, budget):
             returns: config
                 should return a valid configuration
         """
-        sample = None
+        sampled = False
         info_dict = {}
-        
+        sample =  torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        sample.arch = self.configspace.clone()
         # If no model is available, sample from prior
         # also mix in a fraction of random configs
         if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
-            sample =  self.configspace.sample_configuration()
+            sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
             info_dict['model_based_pick'] = False
-        sample =  torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-        sample.arch = self.search_space.clone()
+       
         best = np.inf
         best_vector = None
 
-        if sample is None:
+        if not(sampled):
             try:
                 
                 #sample from largest budget
@@ -133,8 +150,8 @@ def query(self, budget):
                             try:
                                 vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
                             except:
-                                self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
-                                self.logger.warning("data in the KDE:\n%s"%kde_good.data)
+                                print("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
+                                print("data in the KDE:\n%s"%kde_good.data)
                         else:
                             
                             if np.random.rand() < (1-bw):
@@ -144,11 +161,11 @@ def query(self, budget):
                     val = minimize_me(vector)
 
                     if not np.isfinite(val):
-                        self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
-                        self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
-                        self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
-                        self.logger.warning("l(x) = %s"%(l(vector)))
-                        self.logger.warning("g(x) = %s"%(g(vector)))
+                        print('sampled vector: %s has EI value %s'%(vector, val))
+                        print("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
+                        print("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
+                        print("l(x) = %s"%(l(vector)))
+                        print("g(x) = %s"%(g(vector)))
 
                         # right now, this happens because a KDE does not contain all values for a categorical parameter
                         # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
@@ -162,11 +179,11 @@ def query(self, budget):
                         best_vector = vector
 
                 if best_vector is None:
-                    self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
+                    print("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
                     sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
                     info_dict['model_based_pick']  = False
                 else:
-                    self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
+                    print('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
                     #for i, hp_value in enumerate(best_vector):
                     #    if isinstance(
                     #       self.configspace.get_hyperparameter(
@@ -175,18 +192,25 @@ def query(self, budget):
                     #        ConfigSpace.hyperparameters.CategoricalHyperparameter
                     #   ):
                     #       best_vector[i] = int(np.rint(best_vector[i]))
-                    sample.arch.convert_op_indices_to_naslib(best_vector)
-            except:
-                self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
-                sample = self.configspace.sample_configuration()
+                    sample.arch.set_op_indices(best_vector)
+            except Exception as e:
+                #print("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
+                #print("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc())
+                #sample.arch.sample_configuration() v v v 
+                print(e)
+                sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
                 info_dict['model_based_pick']  = False
-
-
-  
-        self.logger.debug('done sampling a new configuration.')
-        return sample, info_dict
-    
+        accuracy = sample.arch.query(
+                Metric.VAL_ACCURACY,
+                self.dataset,
+                epoch=int(budget),
+                dataset_api=self.dataset_api,
+            )
+        info_dict["model"] = sample
+        #print('done sampling a new configuration.')
+        return accuracy, info_dict
     
     
+#    "Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc())
 
  
\ No newline at end of file

From c7d11d7cb59b0659deae6d01fc2111227a09bfe4 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 8 Jan 2022 11:09:47 +0100
Subject: [PATCH 047/130] hb fix

---
 examples/mf_demo.yaml                                 | 2 +-
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 5 ++---
 naslib/optimizers/discrete/hb/optimizer.py            | 1 +
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 36b374c14..69bca7eb4 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: BOHB
+optimizer: HB
 # Activates/deactivates plotting
 plot: True
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 7c81c1de3..d166eb744 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -14,10 +14,9 @@ search:
   eta: 3
   budget_type: epoch 
   budget_max: 81
-  method: tpe
+  method: random
 
-  
-  predictor_type: tpe
+  predictor_type: var_sparse_gp
   num_init: 10  
   k: 10
 
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index a04534709..68aed9821 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -60,6 +60,7 @@ def __init__(
         self.encoding_type = config.search.encoding_type
         self.ss_type= config.search_space
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+        self.ensemble = None
         
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (

From 9b713cbd28804d32017ed10b2e754f49ef735140 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 8 Jan 2022 11:12:28 +0100
Subject: [PATCH 048/130] work on sh

---
 examples/epochs_calc.py                       | 32 +++++++++++++++++++
 examples/mf_plot.py                           |  3 +-
 .../nas_predictors/discrete_config_HB.yaml    |  4 +--
 .../nas_predictors/discrete_config_SH.yaml    |  7 ++--
 naslib/optimizers/discrete/sh/optimizer.py    |  5 +--
 5 files changed, 41 insertions(+), 10 deletions(-)
 create mode 100644 examples/epochs_calc.py

diff --git a/examples/epochs_calc.py b/examples/epochs_calc.py
new file mode 100644
index 000000000..705b725aa
--- /dev/null
+++ b/examples/epochs_calc.py
@@ -0,0 +1,32 @@
+# this script calculates the amount of budget needed to create nice plots for Successhive Halving :D
+
+from math import log
+from math import ceil
+from math import floor
+
+max_budget = 128
+eta = 2
+number_archs = 128
+
+def calc_sh_budget():
+    i = 1
+    budget = 0.0
+    while i <= max_budget:
+        print("fidelity: {}".format(i))
+        budget = budget + (i / max_budget) * (number_archs / i)
+        i = i * eta
+    print(budget/2)
+
+def calc_hb_budget():
+    i = 1
+    s = floor(log(max_budget, eta))
+
+    n = ceil(int(b / max_budget / (s + 1)) * eta ** s)
+    r = max_budget * eta ** (-s)
+    # in a loop
+
+def main():
+    calc_hb_budget()
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index a5e57c29d..272c942b0 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -1,6 +1,6 @@
 import os
 import json
-from matplotlib.lines import _LineStyle
+# from matplotlib.lines import _LineStyle
 import matplotlib.pyplot as plt
 
 from collections import defaultdict
@@ -28,6 +28,7 @@ def plot_sh():
         x = stats['fidelity']
         values = stats['val_acc']
         plt.plot(x, values, linestyle='-', label=arch)
+    plt.xscale('log', base=2)
     plt.rcParams['grid.linestyle'] = 'dotted'
     plt.show()
     plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index 25fc9a949..6abd33edb 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -7,15 +7,13 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 158
-  fidelity: -1
+  epochs: 16.125 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
   number_archs: 81
   eta: 3
   budget_type: epoch 
   budget_max: 81
 
-
   predictor_type: var_sparse_gp
   num_init: 10  
   k: 10
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index 2620620e6..5ca67d424 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -7,13 +7,12 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 158
-  fidelity: -1
+  epochs: 4 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
-  number_archs: 80
+  number_archs: 128
   eta: 2
   budget_type: epoch 
-  budget_max: 1000
+  budget_max: 128
 
 
   predictor_type: var_sparse_gp
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index f86ae62d4..9a7393866 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -38,13 +38,13 @@ def __init__(
         self.weight_optimizer = weight_optimizer
         self.loss = loss_criteria
         self.grad_clip = grad_clip
-        self.budget_max = config.search.budget_max
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
         self.end = False
         self.fidelity = config.search.min_fidelity
         self.number_archs = config.search.number_archs
         self.eta = config.search.eta
+        self.budget_max = config.search.budget_max 
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.budget_type = config.search.budget_type  # is not for one query is overall
         self.fidelity_counter = 0
@@ -72,7 +72,8 @@ def new_epoch(self):
         Sample a new architecture to train.
         # TODO: with this kind of architekeur, in evaluation only the last fideltiy
         """
-
+        # TODO: error is occuring on fidelity: 128 on architecture number: 1
+        # fidelity is not calculated corretly and therefore epoch is maybe not correct 
         model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
         model.arch = self.search_space.clone()
         if len(self.sampled_archs) < self.number_archs:

From 54ab59e8ca71ce18368fa0f863ecbf7a09bb96d0 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 8 Jan 2022 14:22:54 +0100
Subject: [PATCH 049/130] group work on plot

---
 examples/mf_demo.py                           |  4 +++-
 examples/mf_plot.py                           |  9 +++----
 .../nas_predictors/discrete_config.yaml       |  3 +--
 naslib/optimizers/discrete/hb/optimizer.py    | 24 +------------------
 4 files changed, 10 insertions(+), 30 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 15a011c7a..709a5bc8a 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -56,8 +56,10 @@
 
 if config_optimizer == 'SH':
     optimizer = SH(config)
+    plot_func = mf_plot.plot_sh
 elif config_optimizer == 'HB':
     optimizer = HB(config)
+    plot_func = mf_plot.plot_hb
 elif config_optimizer == 'RS':
     optimizer = RS(config)
 elif config_optimizer == 'BOHB':
@@ -83,4 +85,4 @@
     exit()
 
 # TODO: Make this dependent on optimizer type, currently statistics are just for SH available
-mf_plot.plot_sh()
\ No newline at end of file
+plot_func()
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 272c942b0..89ab0c351 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -31,7 +31,7 @@ def plot_sh():
     plt.xscale('log', base=2)
     plt.rcParams['grid.linestyle'] = 'dotted'
     plt.show()
-    plt.savefig('plot_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
+    plt.savefig('plot_sh_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
 def plot_hb():
     """
@@ -39,7 +39,7 @@ def plot_hb():
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results(predictor, folder, 'hb_stats.json', metric='test_acc')
+    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
     s = len(results)
     
     figure, axis = plt.subplots(s, 1)
@@ -47,9 +47,10 @@ def plot_hb():
         for arch, stats in sh_stats.items():
             x = stats['fidelity']
             values = stats['val_acc']
-            axis[sh, 0].plot(x, values, linestyle='-', label=arch)
+            axis[int(sh)].plot(x, values, linestyle='-', label=arch, marker='x')
+            axis[int(sh)].set_xscale('log', base=3)
     plt.show()
-
+    plt.savefig('plot_hb_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
 
 if __name__ == '__main__':
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index d166eb744..099ef2efd 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,8 +7,7 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 158
-  fidelity: -1
+  epochs: 16.125 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
   number_archs: 81
   eta: 3
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 68aed9821..5cad78ebb 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -176,29 +176,7 @@ def update_optimizer_stats(self):
         """
         self.optimizer_stats[self.s] = self.sh.optimizer_stats
 
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        here only hacky way from sh
-        """
-        arch = self.sh.sampled_archs[self.sh.fidelity_counter-1].arch
-        arch_hash = hash(self.sh.sampled_archs[self.sh.fidelity_counter- 1])
-        # this dict contains metrics to save
-        metrics = {
-            "train_acc": Metric.TRAIN_ACCURACY,
-            "val_acc": Metric.VAL_ACCURACY,
-            "test_acc": Metric.TEST_ACCURACY,
-            "train_time": Metric.TRAIN_TIME
-        }
-        for metric_name, metric in metrics.items():
-            metric_value = arch.query(
-                metric, 
-                self.dataset, 
-                dataset_api=self.dataset_api, 
-                epoch=int(81)
-            )
-            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
-        self.optimizer_stats[arch_hash]['fidelity'].append(self.sh.fidelity) 
+
 
     def test_statistics(self):
         return False

From 2ac1631b04faf4a015aaae5e3a815bd605a0d2aa Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 13 Jan 2022 19:13:20 +0100
Subject: [PATCH 050/130] make bohb extra

---
 examples/mf_demo.py                           |   3 +-
 .../nas_predictors/discrete_config_BOHB.yaml  |   7 +-
 naslib/optimizers/__init__.py                 |   3 +-
 naslib/optimizers/discrete/bohb/__init__.py   |   0
 naslib/optimizers/discrete/bohb/optimizer.py  | 190 ++++++++++++++++++
 naslib/optimizers/discrete/hb/optimizer.py    |  16 +-
 naslib/predictors/ensemble.py                 |  22 +-
 naslib/predictors/tpe/tpe.py                  |  25 ++-
 8 files changed, 223 insertions(+), 43 deletions(-)
 create mode 100644 naslib/optimizers/discrete/bohb/__init__.py
 create mode 100644 naslib/optimizers/discrete/bohb/optimizer.py

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 709a5bc8a..c5a8f7e25 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -12,6 +12,7 @@
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers import HyperBand as HB
+from naslib.optimizers import BOHB 
 
 from naslib.defaults.trainer_multifidelity import Trainer
 #from naslib.defaults.trainer import Trainer
@@ -63,7 +64,7 @@
 elif config_optimizer == 'RS':
     optimizer = RS(config)
 elif config_optimizer == 'BOHB':
-    optimizer = HB(config)
+    optimizer = BOHB(config)
 else:
     raise Exception('invalid config')
 
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index b90a04920..93bfa0d4e 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -18,11 +18,16 @@ search:
 
   
   predictor_type: tpe
+  N_min: 0
+  min_points_in_model: 6
+  top_n_percent: 15
+  min_bandwith: 1e-3
+  random_fraction: 0.15
   num_init: 10  
   k: 10
 
   # BANANAS
-  num_ensemble: 3
+  num_ensemble: 64 #num_samples in tpe 
   acq_fn_type: its
   acq_fn_optimization: random_sampling
   encoding_type: adjacency_one_hot
diff --git a/naslib/optimizers/__init__.py b/naslib/optimizers/__init__.py
index 2af4cbe5e..bda761007 100644
--- a/naslib/optimizers/__init__.py
+++ b/naslib/optimizers/__init__.py
@@ -11,4 +11,5 @@
 from .discrete.bananas.optimizer import Bananas
 from .discrete.bp.optimizer import BasePredictor
 from .discrete.npenas.optimizer import Npenas
-from .discrete.hb.optimizer import HyperBand
\ No newline at end of file
+from .discrete.hb.optimizer import HyperBand
+from .discrete.bohb.optimizer import BOHB
diff --git a/naslib/optimizers/discrete/bohb/__init__.py b/naslib/optimizers/discrete/bohb/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
new file mode 100644
index 000000000..af833d55f
--- /dev/null
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -0,0 +1,190 @@
+import numpy as np
+import torch
+import copy
+import math
+from collections import defaultdict
+from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
+from naslib.search_spaces.core.query_metrics import Metric
+
+from collections import defaultdict
+
+
+class BOHB(MetaOptimizer):
+    """
+    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(BOHB, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+        self.sh_config = copy.deepcopy(config)
+        self.tpe_config = copy.deepcopy(config)
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+        self.method = config.search.method
+        #self.fidelit_min = config.search.min_fidelity
+        self.budget_max = config.search.budget_max
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.s_max = math.floor(math.log(self.budget_max, self.eta))
+        self.s = self.s_max + 1
+        self.sh = None
+        self.first = True # TODO: think about a more ellegant solution 
+        self.b = (self.s_max + 1)* self.budget_max
+        self.encoding_type = config.search.encoding_type
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+        self.ensemble = None
+        
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+        if self.method == "tpe":
+            #self.tpe_config.search_space = self.search_space.clone()
+            #self.tpe_config.dataset_api  = self.dataset_api
+            self.ensemble =   Ensemble(
+                    predictor_type= "tpe",  
+                    num_ensemble= 1,
+                    search_params = [self.search_space.clone(),self.dataset_api, self.dataset],
+                    config = self.tpe_config,   #replace with config maybe or not 
+
+                    )
+
+    def new_epoch(self):
+        """
+        Sample a new architecture to train.
+        after https://arxiv.org/pdf/1603.06560.pdf
+        """
+        # TODO: rethink because of s
+        if  self.sh == None or self.sh.end:
+            #if sh is finish go to something diffrent as initial budget
+                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
+            self.s -= 1
+            if self.sh != None:
+                self.sampled_archs.extend(self.sh.sampled_archs)
+            if self.s < 0:
+                print("HB is finish, allready not defined what to do") # TODO define what to do 
+                return math.inf #end the thing
+            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+            r = self.budget_max * self.eta ** (-self.s)
+            self.sh_config.search.number_archs = n
+            self.sh_config.search.min_fidelity  = r 
+
+            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
+            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+        
+        budget = self.sh.new_epoch()
+
+        self.update_optimizer_stats()
+        return budget
+                
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
+        all_sampled.extend(self.sh.sampled_archs)
+        return max(all_sampled, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+        
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+    
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
+        """
+        self.optimizer_stats[self.s] = self.sh.optimizer_stats
+
+
+
+    def test_statistics(self):
+        return False
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 5cad78ebb..da8703a0f 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -44,8 +44,6 @@ def __init__(
         self.sh_config = copy.deepcopy(config)
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-        self.method = config.search.method
-        #self.fidelit_min = config.search.min_fidelity
         self.budget_max = config.search.budget_max
         self.eta = config.search.eta
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -57,10 +55,8 @@ def __init__(
         self.sh = None
         self.first = True # TODO: think about a more ellegant solution 
         self.b = (self.s_max + 1)* self.budget_max
-        self.encoding_type = config.search.encoding_type
-        self.ss_type= config.search_space
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-        self.ensemble = None
+       
         
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert (
@@ -69,16 +65,6 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
-        if self.method == "tpe":
-            
-            self.ensemble =   Ensemble(
-                    predictor_type= "tpe",  
-                    num_ensemble= 1,
-                    encoding_type= self.encoding_type,
-                    ss_type=  self.ss_type,
-                    config = [self.search_space.clone(),self.dataset_api, self.dataset],   #replace with config maybe or not 
-
-                    )
 
     def new_epoch(self):
         """
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index 0028abf50..1fbccf0a3 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -104,19 +104,17 @@ def get_ensemble(self):
                 max_zerocost=100,
             ),
             #remove temporar for debugging 
-            "omni_seminas": OmniSemiNASPredictor(
-                zero_cost=["jacov"],
-                lce=[],
-                encoding_type="seminas",
-                ss_type=self.ss_type,
-                run_pre_compute=False,
-                semi=True,
-                max_zerocost=1000,
-                config=self.config,
-            ),
+            # "omni_seminas": OmniSemiNASPredictor(
+            #     zero_cost=["jacov"],
+            #     lce=[],
+            #     encoding_type="seminas",
+            #     ss_type=self.ss_type,
+            #     run_pre_compute=False,
+            #     semi=True,
+            #     max_zerocost=1000,
+            #     config=self.config,
+            # ),
             "tpe": TreeParserEstimator(
-                encoding_type=self.encoding_type,
-                ss_type = self.ss_type,
                 config = self.config ),
 
             
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index 257a7ed95..c2368befb 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -18,21 +18,20 @@ def __init__(
         encoding_type="adjacency_one_hot",
         ss_type="nasbench201",
         config = None,
-        dataset_api = None
+        search_params = None
     ):
-        self.encoding_type = encoding_type
+        self.encoding_type = config.search.encoding_type
         self.ss_type = ss_type
-        self.p = 0.15
-        self.N_min = 0
-        self.num_samples = 64  #maybe same then number of essembles, not sure 
-        self.dataset_api = dataset_api
+        self.p = config.p
+        self.N_min = config.N_min
+        self.num_samples = config.num_ensemble #maybe same then number of essembles, not sure 
         self.kde_models = defaultdict(lambda: defaultdict(list))
-        self.min_points_in_model = 1
-        self.top_n_percent = 15
-        self.min_bandwidth = 1e-3
-        self.random_fraction = 0.15
-        self.configspace = config[0] #thing about later
-        self.dataset_api = config[1]
+        self.min_points_in_model = config.min_points_in_model
+        self.top_n_percent = config.top_n_percent
+        self.min_bandwidth = config.min_bandwidth
+        self.random_fraction = config.random_fraction 
+        self.configspace = search_params#thing about later
+        self.dataset_api = search_params[1]
         self.dataset = config[2]
     def set_hyperparams(self, _):
         pass
@@ -119,7 +118,7 @@ def query(self, _, budget):
         if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
             sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
             info_dict['model_based_pick'] = False
-       
+            sampled = True
         best = np.inf
         best_vector = None
 

From 835c0f8bcd677e228c7b1b24421dd0e0658959ac Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 15 Jan 2022 11:18:27 +0100
Subject: [PATCH 051/130] correct path for saving stats

---
 examples/mf_demo.py                      | 1 -
 naslib/defaults/trainer_multifidelity.py | 2 ++
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 709a5bc8a..46db715d0 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -84,5 +84,4 @@
 if demo_config['plot'] == False:
     exit()
 
-# TODO: Make this dependent on optimizer type, currently statistics are just for SH available
 plot_func()
\ No newline at end of file
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 9673a421b..ebd6428a6 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -592,6 +592,8 @@ def _log_optimizer_stats(self, filename="sh_stats.json"):
         """Log extended statistics to json file"""
         if not os.path.exists(self.config.save):
             os.makedirs(self.config.save)
+        optimizer_name = self.optimizer.__class__.__name__.lower()
+        filename = "{}.json".format(optimizer_name)
         with codecs.open(
             os.path.join(self.config.save, filename), "w", encoding="utf-8"
         ) as file:

From 1f9449816959ccd2aaeedbfb046a5025ef22bd9e Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 15 Jan 2022 11:52:33 +0100
Subject: [PATCH 052/130] fix hb plot

---
 examples/mf_plot.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 89ab0c351..335af0b31 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -39,7 +39,7 @@ def plot_hb():
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
+    results = get_results(predictor, folder, 'hb_stats.json', metric='test_acc')
     s = len(results)
     
     figure, axis = plt.subplots(s, 1)

From 492cc120e2b2f2fe2c02378ff05dfb38279e6c5e Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 15 Jan 2022 12:02:08 +0100
Subject: [PATCH 053/130] fix optimizer stats

---
 naslib/defaults/trainer_multifidelity.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index ebd6428a6..c7f436e76 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -588,7 +588,7 @@ def _setup_checkpointers(
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
-    def _log_optimizer_stats(self, filename="sh_stats.json"):
+    def _log_optimizer_stats(self):
         """Log extended statistics to json file"""
         if not os.path.exists(self.config.save):
             os.makedirs(self.config.save)

From 258e094c08165ad3abfe217f395dac81a558e311 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 15 Jan 2022 12:13:28 +0100
Subject: [PATCH 054/130] fix plots and stats

---
 examples/mf_plot.py                      | 4 ++--
 naslib/defaults/trainer_multifidelity.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 335af0b31..6477ae900 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -22,7 +22,7 @@ def plot_sh():
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
+    results = get_results(predictor, folder, 'successivehalving_stats.json', metric='test_acc')
 
     for arch, stats in results.items():
         x = stats['fidelity']
@@ -39,7 +39,7 @@ def plot_hb():
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = 'var_sparse_gp'
-    results = get_results(predictor, folder, 'hb_stats.json', metric='test_acc')
+    results = get_results(predictor, folder, 'hyperband_stats.json', metric='test_acc')
     s = len(results)
     
     figure, axis = plt.subplots(s, 1)
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index c7f436e76..692e97c65 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -593,7 +593,9 @@ def _log_optimizer_stats(self):
         if not os.path.exists(self.config.save):
             os.makedirs(self.config.save)
         optimizer_name = self.optimizer.__class__.__name__.lower()
-        filename = "{}.json".format(optimizer_name)
+        filename = "{}_stats.json".format(optimizer_name)
+        print("#################################")
+        print("Filename: {}".format(filename))
         with codecs.open(
             os.path.join(self.config.save, filename), "w", encoding="utf-8"
         ) as file:

From ac1c250f3780377b8cafc42be010a3e5841b0b6b Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 15 Jan 2022 16:52:36 +0100
Subject: [PATCH 055/130] bohb own script

---
 .../nas_predictors/discrete_config_BOHB.yaml  |  2 +-
 naslib/optimizers/discrete/bohb/optimizer.py  |  6 ++--
 naslib/optimizers/discrete/bohb/parameter.txt |  7 ++++
 naslib/predictors/ensemble.py                 |  8 +++--
 naslib/predictors/tpe/tpe.py                  | 32 ++++++++++---------
 5 files changed, 35 insertions(+), 20 deletions(-)
 create mode 100644 naslib/optimizers/discrete/bohb/parameter.txt

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 93bfa0d4e..5db6f1405 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -16,7 +16,7 @@ search:
   budget_max: 1000
   method: tpe
 
-  
+  p: 0.15
   predictor_type: tpe
   N_min: 0
   min_points_in_model: 6
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index af833d55f..f76ca8999 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -75,8 +75,10 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
             self.ensemble =   Ensemble(
                     predictor_type= "tpe",  
                     num_ensemble= 1,
-                    search_params = [self.search_space.clone(),self.dataset_api, self.dataset],
-                    config = self.tpe_config,   #replace with config maybe or not 
+                    encoding_type= self.encoding_type,
+                    ss_type = self.tpe_config.search_space,
+                    config = [self.search_space.clone(),self.dataset_api, self.dataset],
+                    hyperparams = self.tpe_config,   #replace with config maybe or not 
 
                     )
 
diff --git a/naslib/optimizers/discrete/bohb/parameter.txt b/naslib/optimizers/discrete/bohb/parameter.txt
new file mode 100644
index 000000000..7466ae26b
--- /dev/null
+++ b/naslib/optimizers/discrete/bohb/parameter.txt
@@ -0,0 +1,7 @@
+p = 0.15 #percentage of random sampeling
+N_min = 0 # mimum of nodes that needs to sample
+num_samples = 64 # num_essemels as ide
+min_points_in_model = 6
+top_n_percent = 15
+min_bandwith = 1e-3
+random_fraction = 0.15
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index 1fbccf0a3..b44db2b67 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -30,6 +30,7 @@ def __init__(
         ss_type=None,
         hpo_wrapper=True,
         config=None,
+        hyperparams = None,
     ):
         self.num_ensemble = num_ensemble
         self.predictor_type = predictor_type
@@ -37,8 +38,9 @@ def __init__(
         self.ss_type = ss_type
         self.hpo_wrapper = hpo_wrapper
         self.config = config
-        self.hyperparams = None
+        self.hyperparams = hyperparams
         self.ensemble = None
+        
 
     def get_ensemble(self):
         # TODO: if encoding_type is not None, set the encoding type
@@ -115,7 +117,9 @@ def get_ensemble(self):
             #     config=self.config,
             # ),
             "tpe": TreeParserEstimator(
-                config = self.config ),
+                #TODO review if more needed 
+                 config = self.config,
+                ),
 
             
 
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index c2368befb..7158ab21b 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -20,21 +20,23 @@ def __init__(
         config = None,
         search_params = None
     ):
-        self.encoding_type = config.search.encoding_type
+        self.encoding_type = encoding_type
         self.ss_type = ss_type
-        self.p = config.p
-        self.N_min = config.N_min
-        self.num_samples = config.num_ensemble #maybe same then number of essembles, not sure 
+        
+        
         self.kde_models = defaultdict(lambda: defaultdict(list))
-        self.min_points_in_model = config.min_points_in_model
-        self.top_n_percent = config.top_n_percent
-        self.min_bandwidth = config.min_bandwidth
-        self.random_fraction = config.random_fraction 
-        self.configspace = search_params#thing about later
-        self.dataset_api = search_params[1]
+        
+        self.configspace = config[0]#thing about later
+        self.dataset_api = config[1]
         self.dataset = config[2]
-    def set_hyperparams(self, _):
-        pass
+    def set_hyperparams(self, hyperparams): #here set config 
+        self.num_samples = int(hyperparams.search.num_ensemble)
+        self.min_points_in_model = int(hyperparams.search.min_points_in_model)
+        self.top_n_percent = int(hyperparams.search.top_n_percent)
+        self.min_bandwith = float(hyperparams.search.min_bandwith)
+        self.random_fraction = float(hyperparams.search.random_fraction )
+        self.p = float(hyperparams.search.p)
+        self.N_min = int(hyperparams.search.N_min)
 
 
 
@@ -90,8 +92,8 @@ def fit(self, xtrain, ytrain, train_info):
         good_kde = sm.nonparametric.KDEMultivariate(data=train_good_data,  var_type= self.kde_vartype, bw=bw_estimation) #var_type is type of hyperparameter
         # c : continuous, u : unordered (discrete),  o : ordered (discrete)
         bad_kde = sm.nonparametric.KDEMultivariate(data=train_bad_data, var_type=  self.kde_vartype, bw=bw_estimation) 
-        bad_kde.bw = np.clip(bad_kde.bw, self.min_bandwidth,None)
-        good_kde.bw = np.clip(good_kde.bw, self.min_bandwidth,None)
+        bad_kde.bw = np.clip(bad_kde.bw, float(self.min_bandwith),None)
+        good_kde.bw = np.clip(good_kde.bw, float(self.min_bandwith),None)
 
         self.kde_models[budget] = {
                 'good': good_kde,
@@ -143,7 +145,7 @@ def query(self, _, budget):
                     
                     for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
                         
-                        bw = max(bw, self.min_bandwidth)
+                        bw = max(bw, self.min_bandwith)
                         if t == 0:
                             bw = self.bw_factor*bw
                             try:

From 32b798b52ac2f2b988bf2dd191409d77a3f258b5 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 20 Jan 2022 13:25:33 +0100
Subject: [PATCH 056/130] create plot for bohb

---
 examples/mf_demo.py                                    |  6 +++++-
 examples/mf_plot.py                                    | 10 +++++-----
 .../nas_predictors/discrete_config_BOHB.yaml           |  9 ++++-----
 naslib/optimizers/discrete/sh/optimizer.py             |  4 +---
 4 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index c5a8f7e25..04af6d42a 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -56,15 +56,19 @@
 # define optimizer
 
 if config_optimizer == 'SH':
+    predictor = "var_sparse_gp"
     optimizer = SH(config)
     plot_func = mf_plot.plot_sh
 elif config_optimizer == 'HB':
+    predictor = "var_sparse_gp"
     optimizer = HB(config)
     plot_func = mf_plot.plot_hb
 elif config_optimizer == 'RS':
     optimizer = RS(config)
 elif config_optimizer == 'BOHB':
     optimizer = BOHB(config)
+    plot_func = mf_plot.plot_hb
+    predictor = "tpe"
 else:
     raise Exception('invalid config')
 
@@ -86,4 +90,4 @@
     exit()
 
 # TODO: Make this dependent on optimizer type, currently statistics are just for SH available
-plot_func()
\ No newline at end of file
+plot_func(predictor)
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 89ab0c351..b5cb44945 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -16,12 +16,12 @@ def get_results(predictor, path, filename, metric='valid_acc'):
         result = json.load(open(result_file))
         return result
 
-def plot_sh():
+def plot_sh(predictor):
     """
     Plots learning curves for successive halving
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
-    predictor = 'var_sparse_gp'
+    predictor = predictor
     results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
 
     for arch, stats in results.items():
@@ -33,12 +33,12 @@ def plot_sh():
     plt.show()
     plt.savefig('plot_sh_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
 
-def plot_hb():
+def plot_hb(predictor): #or an extra method
     """
     Plots learning curves for hyperband
     """
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
-    predictor = 'var_sparse_gp'
+    predictor = predictor
     results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
     s = len(results)
     
@@ -54,4 +54,4 @@ def plot_hb():
 
 
 if __name__ == '__main__':
-    plot_sh()
\ No newline at end of file
+    plot_hb("tpe")
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 5db6f1405..dd1f444f9 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -7,18 +7,17 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 158
-  fidelity: -1
+  epochs: 16.125
   min_fidelity: 1
-  number_archs: 1000
+  number_archs: 81
   eta: 3
   budget_type: epoch 
-  budget_max: 1000
+  budget_max: 81
   method: tpe
 
   p: 0.15
   predictor_type: tpe
-  N_min: 0
+  N_min: 50
   min_points_in_model: 6
   top_n_percent: 15
   min_bandwith: 1e-3
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index ed9c16aee..777f4e2da 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -60,9 +60,7 @@ def __init__(
         if self.method == "tpe":#
             self.ss_type= config.search_space
             self.encoding_type = config.search.encoding_type
-            #self.p = config.search.p
-            #self.percentile = config.search.percentile
-            self.N_min = 100 #This has to be higher then 
+            self.N_min =  config.search.N_min #This has to be higher then 
             self.ensemble = esemble
         self.optimizer_stats = defaultdict(lambda: defaultdict(list))
 

From c007bd8cb7c738aeec26132c806e5f80d50bc47f Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Tue, 1 Feb 2022 17:48:35 +0100
Subject: [PATCH 057/130] work on benchmark setup

---
 naslib/benchmarks/bbo/runner.py             |  19 +-
 naslib/benchmarks/mf/config.yaml            |  22 +
 naslib/benchmarks/mf/create_configs_2.py    | 515 ++++++++++++++++++++
 naslib/benchmarks/mf/local_schedular.py     |  65 +++
 naslib/benchmarks/mf/make_configs_nbxx.py   |  76 +++
 naslib/benchmarks/mf/scheduler_local.sh     |  22 +
 naslib/benchmarks/mf/submit_folder_local.sh |  21 +
 naslib/optimizers/discrete/hb/optimizer.py  |   4 +-
 naslib/optimizers/discrete/sh/optimizer.py  |  34 +-
 9 files changed, 756 insertions(+), 22 deletions(-)
 create mode 100644 naslib/benchmarks/mf/config.yaml
 create mode 100644 naslib/benchmarks/mf/create_configs_2.py
 create mode 100644 naslib/benchmarks/mf/local_schedular.py
 create mode 100644 naslib/benchmarks/mf/make_configs_nbxx.py
 create mode 100755 naslib/benchmarks/mf/scheduler_local.sh
 create mode 100755 naslib/benchmarks/mf/submit_folder_local.sh

diff --git a/naslib/benchmarks/bbo/runner.py b/naslib/benchmarks/bbo/runner.py
index 390a59ec3..b457dfa74 100644
--- a/naslib/benchmarks/bbo/runner.py
+++ b/naslib/benchmarks/bbo/runner.py
@@ -3,8 +3,10 @@
 #from nasbench import api
 
 from naslib.defaults.trainer import Trainer
+from naslib.defaults.trainer_multifidelity import Trainer as Trainer_MF
+
 from naslib.optimizers import RandomSearch, Npenas, \
-RegularizedEvolution, LocalSearch, Bananas, BasePredictor
+RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HyperBand
 
 from naslib.search_spaces.core.query_metrics import Metric
 from naslib.search_spaces import NasBench101SearchSpace, NasBench201SearchSpace, \
@@ -20,7 +22,7 @@
 
 utils.log_args(config)
 
-writer = SummaryWriter(config.save)
+# writer = SummaryWriter(config.save)
 
 supported_optimizers = {
     'rs': RandomSearch(config),
@@ -28,6 +30,8 @@
     'bananas': Bananas(config),
     'npenas': Npenas(config),
     'ls': LocalSearch(config),
+    'sh': SuccessiveHalving(config),
+    'hb': HyperBand(config),
 }
 
 supported_search_spaces = {
@@ -37,7 +41,7 @@
     'nlp': NasBenchNLPSearchSpace(),
     'transbench101_micro': TransBench101SearchSpace(),
     'transbench101_macro': TransBench101SearchSpace(),
-    'asr': NasBenchASRSearchSpace()
+    'asr': NasBenchASRSearchSpace(),
 }
 
 dataset_api = get_dataset_api(config.search_space, config.dataset)
@@ -53,6 +57,11 @@
 optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
 
 trainer = Trainer(optimizer, config, lightweight_output=True)
-
-trainer.search(resume_from="", summary_writer=writer, report_incumbent=False)
+multi_fidelity_optimizers = {'sh', 'hb'}
+if config.optimizer in multi_fidelity_optimizers:
+    trainer = Trainer_MF(optimizer, config, lightweight_output=True)
+# trainer.search(resume_from="", summary_writer=writer, report_incumbent=False)
+trainer.search(resume_from="", report_incumbent=False)
 trainer.evaluate(resume_from="", dataset_api=dataset_api, metric=metric)
+
+# error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/config.yaml b/naslib/benchmarks/mf/config.yaml
new file mode 100644
index 000000000..21e17b142
--- /dev/null
+++ b/naslib/benchmarks/mf/config.yaml
@@ -0,0 +1,22 @@
+start_seed: 5
+trials: 1
+out_dir: run_m1
+out_dir_config: configs_m1
+config_type: bbo-bs
+search_space: nasbench201
+datasets: 
+  - cifar10
+
+optimizers: # sh, rs, ls, ...
+  - sh
+fidelity: -1
+
+HPO: False
+# only relevant if HPO is enabled
+num_config: 100
+
+# for SH: 4
+# for default BBOs: 200
+epochs: 200
+
+predictor: var_sparse_gp
diff --git a/naslib/benchmarks/mf/create_configs_2.py b/naslib/benchmarks/mf/create_configs_2.py
new file mode 100644
index 000000000..904a7e1cb
--- /dev/null
+++ b/naslib/benchmarks/mf/create_configs_2.py
@@ -0,0 +1,515 @@
+import argparse
+import os
+import random
+from venv import create
+import yaml
+
+import numpy as np
+
+
+def create_configs(
+    config_type: str,
+    start_seed: int = 0,
+    trials: int = 100,
+    optimizer: str = "rs",
+    predictor_type: str = "full",
+    predictor: str = "xgb",
+    test_size: int = 30,
+    uniform_random: int = 1, 
+    train_size_single:int = 10,
+    fidelity_single: int = 5,
+    fidelity: int = 200,
+    acq_fn_optimization: str = "mutation",
+    dataset: str = "cifar10",
+    out_dir: str = "run",
+    checkpoint_freq: int = 5000,
+    epochs: int = 150,
+    search_space: str = "nasbench201",
+    experiment_type: str = "single",
+    run_acc_stats: int = 1,
+    max_set_size: int = 10000,
+    run_nbhd_size: int = 1,
+    max_nbhd_trials: int = 1000,
+    run_autocorr: int = 1, 
+    max_autocorr_trials: int = 10,
+    autocorr_size: int = 36,
+    walks: int = 1000,
+    HPO: bool = False, 
+    num_config: int = 100,
+    **kwargs
+):
+    """Function creates config for given parameters
+
+    Args:
+        start_seed: starting seed.
+        trials: Number of trials.
+        optimizer: which optimizer.
+        predictor_type: which predictor.
+        predictor: which predictor.
+        test_size: Test set size for predictor.
+        uniform_random: Train/test set generation type (bool).
+        train_size_single: Train size if exp type is single.
+        fidelity_single: Fidelity if exp type is single.
+        fidelity: Fidelity.
+        acq_fn_optimization: acq_fn.
+        dataset: Which dataset.
+        epochs: How many search epochs.
+        config_type: nas or predictor?.
+        search_space: nasbench201 or darts?.
+        experiment_type: type of experiment.
+        run_acc_stats: run accuracy statistics.
+        max_set_size: size of val_acc stat computation.
+        run_nbhd_size: run experiment to compute nbhd size.
+        max_nbhd_trials: size of nbhd size computation. 
+        run_autocorr: run experiment to compute autocorrelation.
+        max_autocorr_trials: number of autocorrelation trials.
+        autocorr_size: size of autocorrelation to test.
+        walks: number of random walks.
+        HPO: Hyperparameter Optimisation enabled/disabled.
+        num_config: Number of configs explored by HPO (Random Search).
+
+    Returns:
+        No return.
+
+    """
+    if config_type == 'bbo-bs':
+        start_seed = int(start_seed)
+        trials = int(trials)
+        num_config = 100 
+        
+        # first generate the default config at config 0
+        config_id = 0
+        # TODO: add config out dir to config
+        folder = f"/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+        # folder = f"naslib/benchmarks/bbo/configs_cpu/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+        os.makedirs(folder, exist_ok=True)       
+            
+        for seed in range(start_seed, start_seed + trials):
+            np.random.seed(seed)
+            random.seed(seed)
+
+            config = {
+                "seed": seed,
+                "search_space": search_space,
+                "dataset": dataset,
+                "optimizer": optimizer,
+                "out_dir": out_dir,
+                "config_id": config_id,
+                "search": {
+                    "sample_size": 10,
+                    "population_size": 50,
+                    "num_init": 10,
+                    "k":10,
+                    "num_ensemble": 3,
+                    "acq_fn_type": "its",
+                    "num_arches_to_mutate": 1,
+                    "max_mutations": 1,
+                    "num_candidates": 50,
+                    "checkpoint_freq": checkpoint_freq,
+                    "epochs": epochs,
+                    "fidelity": fidelity,
+                    "min_fidelity": 1,
+                    "number_archs": 128,
+                    "budget_type": "epoch",
+                    "budget_max": 128,
+                    "method": "random",
+                    "eta": 2,
+                    "num_ensemble": 3,
+                    "acq_fn_type": "its",
+                    "acq_fn_optimization": acq_fn_optimization,
+                    "encoding_type": "path",
+                    "predictor": predictor,
+                    "debug_predictor": False,
+                },
+            }
+            path = os.path.join(folder, f"seed_{seed}.yaml")
+            with open(path, "w") as fh:
+                yaml.dump(config, fh)
+        num_config = num_config if HPO else 1
+        for config_id in range(1, num_config):
+            folder = f"naslib/benchmarks/bbo/configs_m1/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+            os.makedirs(folder, exist_ok=True)
+            
+            for seed in range(start_seed, start_seed + trials):
+                np.random.seed(seed)
+                random.seed(seed)
+
+                config = {
+                    "seed": seed,
+                    "search_space": search_space,
+                    "dataset": dataset,
+                    "optimizer": optimizer,
+                    "out_dir": out_dir,
+                    "config_id": config_id,
+                    "search": {
+                        "checkpoint_freq": checkpoint_freq,
+                        "epochs": epochs,
+                        "fidelity": fidelity,
+                        "sample_size": int(np.random.choice(range(5, 100))),
+                        "population_size": int(np.random.choice(range(5, 100))),
+                        "num_init": int(np.random.choice(range(5, 100))),
+                        "k":int(np.random.choice(range(10, 50))),
+                        "num_ensemble": 3,
+                        "acq_fn_type": "its",
+                        "acq_fn_optimization": acq_fn_optimization,
+                        "encoding_type": "path",
+                        "num_arches_to_mutate": int(np.random.choice(range(1, 20))),
+                        "max_mutations": int(np.random.choice(range(1, 20))),
+                        "num_candidates": int(np.random.choice(range(5, 50))),
+                        "predictor": predictor,
+                        "debug_predictor": False,
+                    },
+                }
+
+                path = os.path.join(folder, f"/seed_{seed}.yaml")
+                with open(path, "w") as fh:
+                    yaml.dump(config, fh)
+    
+    elif config_type == "predictor-bs":
+        folder = f"naslib/benchmarks/predictors-bs/configs_{search_space}/{dataset}"
+        os.makedirs(folder, exist_ok=True)
+        start_seed = int(start_seed)
+        trials = int(trials)
+
+        if search_space == "nasbench101":
+            total_epochs = 108 - 1
+            max_train_size = 1000
+        elif search_space == "nasbench201":
+            total_epochs = 200 - 1
+            max_train_size = 1000
+        elif search_space == "darts":
+            total_epochs = 96 - 1
+            max_train_size = 500
+        elif search_space == "nlp":
+            total_epochs = 50 - 1
+            max_train_size = 1000
+
+        train_size_list = [
+            int(j)
+            for j in np.logspace(
+                start=np.log(5.1) / np.log(2),
+                stop=np.log(max_train_size) / np.log(2),
+                num=11,
+                endpoint=True,
+                base=2.0,
+            )
+        ]
+        # train_size_list = [i for i in train_size_list if i < 230]
+        fidelity_list = [
+            int(j)
+            for j in np.logspace(
+                start=0.9,
+                stop=np.log(total_epochs) / np.log(2),
+                num=15,
+                endpoint=True,
+                base=2.0,
+            )
+        ]
+
+        if search_space == "nlp":
+            fidelity_list.pop(2)
+            fidelity_list.insert(5, 6)
+
+        if "svr" in predictor:
+            train_size_list.pop(0)
+            fidelity_list.pop(0)
+            fidelity_list.pop(0)
+
+        for i in range(start_seed, start_seed + trials):
+            config = {
+                "seed": i,
+                "search_space": search_space,
+                "dataset": dataset,
+                "out_dir": out_dir,
+                "predictor": predictor,
+                "test_size": test_size,
+                "uniform_random": uniform_random,
+                "experiment_type": experiment_type,
+                "train_size_list": train_size_list,
+                "train_size_single": train_size_single,
+                "fidelity_single": fidelity_single,
+                "fidelity_list": fidelity_list,
+                "max_hpo_time": 900,
+            }
+
+            with open(folder + f"/config_{predictor}_{i}.yaml", "w") as fh:
+                yaml.dump(config, fh)
+
+    elif config_type == "nas":
+        folder = f"{out_dir}/{dataset}/configs/nas"
+        os.makedirs(folder, exist_ok=True)
+        start_seed = int(start_seed)
+        trials = int(trials)
+
+        for i in range(start_seed, start_seed + trials):
+            config = {
+                "seed": i,
+                "search_space": search_space,
+                "dataset": dataset,
+                "optimizer": optimizer,
+                "out_dir": out_dir,
+                "search": {
+                    "checkpoint_freq": checkpoint_freq,
+                    "epochs": epochs,
+                    "fidelity": 200,
+                    "sample_size": 10,
+                    "population_size": 30,
+                    "num_init": 10,
+                    "k": 25,
+                    "num_ensemble": 3,
+                    "acq_fn_type": "its",
+                    "acq_fn_optimization": "mutation",
+                    "encoding_type": "path",
+                    "num_arches_to_mutate": 2,
+                    "max_mutations": 1,
+                    "num_candidates": 100,
+                    "predictor_type": "feedforward",
+                    "debug_predictor": False,
+                },
+            }
+
+            if optimizer == "lcsvr" and experiment_type == "vary_fidelity":
+                path = folder + f"/config_{optimizer}_train_{i}.yaml"
+            if optimizer == "lcsvr" and experiment_type == "vary_train_size":
+                path = folder + f"/config_{optimizer}_fidelity_{i}.yaml"
+            else:
+                path = folder + f"/config_{optimizer}_{i}.yaml"
+
+            with open(path, "w") as fh:
+                yaml.dump(config, fh)
+
+    elif config_type == "predictor":
+        folder = f"{out_dir}/{dataset}/configs/predictors"
+        os.makedirs(folder, exist_ok=True)
+        start_seed = int(start_seed)
+        trials = int(trials)
+
+        if search_space == "nasbench101":
+            total_epochs = 108 - 1
+            max_train_size = 1000
+        elif search_space == "nasbench201":
+            total_epochs = 200 - 1
+            max_train_size = 1000
+        elif search_space == "darts":
+            total_epochs = 96 - 1
+            max_train_size = 500
+        elif search_space == "nlp":
+            total_epochs = 50 - 1
+            max_train_size = 1000
+
+        train_size_list = [
+            int(j)
+            for j in np.logspace(
+                start=np.log(5.1) / np.log(2),
+                stop=np.log(max_train_size) / np.log(2),
+                num=11,
+                endpoint=True,
+                base=2.0,
+            )
+        ]
+        # train_size_list = [i for i in train_size_list if i < 230]
+        fidelity_list = [
+            int(j)
+            for j in np.logspace(
+                start=0.9,
+                stop=np.log(total_epochs) / np.log(2),
+                num=15,
+                endpoint=True,
+                base=2.0,
+            )
+        ]
+
+        if search_space == "nlp":
+            fidelity_list.pop(2)
+            fidelity_list.insert(5, 6)
+
+        if "svr" in predictor:
+            train_size_list.pop(0)
+            fidelity_list.pop(0)
+            fidelity_list.pop(0)
+
+        elif "omni" in predictor and search_space != "darts":
+            train_size_list.pop(0)
+            train_size_list.pop(-1)
+            fidelity_list.pop(1)
+
+        elif "omni" in predictor and search_space == "darts":
+            train_size_list.pop(0)
+            train_size_list.pop(-1)
+            fidelity_list.pop(1)
+            fidelity_list.pop(1)
+
+        for i in range(start_seed, start_seed + trials):
+            config = {
+                "seed": i,
+                "search_space": search_space,
+                "dataset": dataset,
+                "out_dir": out_dir,
+                "predictor": predictor,
+                "test_size": test_size,
+                "uniform_random": uniform_random,
+                "experiment_type": experiment_type,
+                "train_size_list": train_size_list,
+                "train_size_single": train_size_single,
+                "fidelity_single": fidelity_single,
+                "fidelity_list": fidelity_list,
+                "max_hpo_time": 900,
+            }
+
+            with open(folder + f"/config_{predictor}_{i}.yaml", "w") as fh:
+                yaml.dump(config, fh)
+
+    elif config_type == "nas_predictor":
+        folder = f"{out_dir}/{dataset}/configs/nas_predictors"
+        os.makedirs(folder, exist_ok=True)
+        start_seed = int(start_seed)
+        trials = int(trials)
+
+        for i in range(start_seed, start_seed + trials):
+            config = {
+                "seed": i,
+                "search_space": search_space,
+                "dataset": dataset,
+                "optimizer": optimizer,
+                "out_dir": out_dir,
+                "search": {
+                    "predictor_type": predictor,
+                    "epochs": epochs,
+                    "checkpoint_freq": checkpoint_freq,
+                    "fidelity": 200,
+                    "sample_size": 10,
+                    "population_size": 30,
+                    "num_init": 20,
+                    "k": 20,
+                    "num_ensemble": 3,
+                    "acq_fn_type": "its",
+                    "acq_fn_optimization": "random_sampling",
+                    "encoding_type": "adjacency_one_hot",
+                    "num_arches_to_mutate": 5,
+                    "max_mutations": 1,
+                    "num_candidates": 200,
+                    "batch_size": 256,
+                    "data_size": 25000,
+                    "cutout": False,
+                    "cutout_length": 16,
+                    "cutout_prob": 1.0,
+                    "train_portion": 0.7,
+                },
+            }
+
+            path = folder + f"/config_{optimizer}_{predictor}_{i}.yaml"
+            with open(path, "w") as fh:
+                yaml.dump(config, fh)
+
+    elif config_type == "statistics":
+        folder = f"{out_dir}/{search_space}/{dataset}/configs/statistics"
+        os.makedirs(folder, exist_ok=True)
+        start_seed = int(start_seed)
+        trials = int(trials)
+
+        for i in range(start_seed, start_seed + trials):
+            config = {
+                "seed": i,
+                "search_space": search_space,
+                "dataset": dataset,
+                "out_dir": out_dir,
+                "run_acc_stats": run_acc_stats,
+                "max_set_size": max_set_size,
+                "run_nbhd_size": run_nbhd_size,
+                "max_nbhd_trials": max_nbhd_trials,
+                "run_autocorr": run_autocorr,
+                "max_autocorr_trials": max_autocorr_trials,
+                "autocorr_size": autocorr_size,
+                "walks": walks,
+            }
+
+            with open(folder + f"/config_{i}.yaml", "w") as fh:
+                yaml.dump(config, fh)
+
+    else:
+        print("invalid config type in create_configs.py")
+
+if __name__ == "__main__":
+    """This is executed when run from the command line"""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--start_seed", type=int, default=0, help="starting seed")
+    parser.add_argument("--trials", type=int, default=100, help="Number of trials")
+    parser.add_argument("--optimizer", type=str, default="rs", help="which optimizer")
+    parser.add_argument("--predictor_type", type=str, default="full", help="which predictor")
+    parser.add_argument("--predictor", type=str, default="xgb", help="which predictor")
+    parser.add_argument(
+        "--test_size", type=int, default=30, help="Test set size for predictor"
+    )
+    parser.add_argument(
+        "--uniform_random",
+        type=int,
+        default=1,
+        help="Train/test set generation type (bool)",
+    )
+    parser.add_argument(
+        "--train_size_single",
+        type=int,
+        default=10,
+        help="Train size if exp type is single",
+    )
+    parser.add_argument(
+        "--fidelity_single", type=int, default=5, help="Fidelity if exp type is single"
+    )
+    parser.add_argument(
+        "--fidelity", type=int, default=200, help="Fidelity"
+    )
+    parser.add_argument(
+        "--acq_fn_optimization", type=str, default="mutation", help="acq_fn"
+    )
+    parser.add_argument("--dataset", type=str, default="cifar10", help="Which dataset")
+    parser.add_argument("--out_dir", type=str, default="run", help="Output directory")
+    parser.add_argument(
+        "--checkpoint_freq", type=int, default=5000, help="How often to checkpoint"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=150, help="How many search epochs"
+    )
+    parser.add_argument(
+        "--config_type", type=str, default="nas", help="nas or predictor?"
+    )
+    parser.add_argument(
+        "--search_space", type=str, default="nasbench201", help="nasbench201 or darts?"
+    )
+    parser.add_argument(
+        "--experiment_type", type=str, default="single", help="type of experiment"
+    )
+    parser.add_argument(
+        "--run_acc_stats", type=int, default=1, help="run accuracy statistics"
+    )
+    parser.add_argument(
+        "--max_set_size", type=int, default=10000, help="size of val_acc stat computation"
+    )    
+    parser.add_argument(
+        "--run_nbhd_size", type=int, default=1, help="run experiment to compute nbhd size"
+    )    
+    parser.add_argument(
+        "--max_nbhd_trials", type=int, default=1000, help="size of nbhd size computation"
+    )
+    parser.add_argument(
+        "--run_autocorr", type=int, default=1, help="run experiment to compute autocorrelation"
+    )
+    parser.add_argument(
+        "--max_autocorr_trials", type=int, default=10, help="number of autocorrelation trials"
+    )
+    parser.add_argument(
+        "--autocorr_size", type=int, default=36, help="size of autocorrelation to test"
+    )
+    parser.add_argument(
+        "--walks", type=int, default=1000, help="number of random walks"
+    )
+    parser.add_argument(
+        "--HPO", type=bool, default=False, help="Optimisation enabled/disabled"
+    )
+    parser.add_argument(
+        "--num_config", type=int, default=100, help="Number of configs explored by HPO (Random Search)"
+    )
+    
+    args = parser.parse_args()
+    arguments = vars(args)
+    create_configs(**arguments)
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/local_schedular.py b/naslib/benchmarks/mf/local_schedular.py
new file mode 100644
index 000000000..78977bc7f
--- /dev/null
+++ b/naslib/benchmarks/mf/local_schedular.py
@@ -0,0 +1,65 @@
+import time
+import os
+import subprocess
+
+def progressBar(iterable, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = "\r"):
+    """
+    Call in a loop to create terminal progress bar
+    @params:
+        iterable    - Required  : iterable object (Iterable)
+        prefix      - Optional  : prefix string (Str)
+        suffix      - Optional  : suffix string (Str)
+        decimals    - Optional  : positive number of decimals in percent complete (Int)
+        length      - Optional  : character length of bar (Int)
+        fill        - Optional  : bar fill character (Str)
+        printEnd    - Optional  : end character (e.g. "\r", "\r\n") (Str)
+    """
+    total = len(iterable)
+    # Progress Bar Printing Function
+    def printProgressBar (iteration):
+        percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
+        filledLength = int(length * iteration // total)
+        bar = fill * filledLength + '-' * (length - filledLength)
+        print(f'\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)
+    # Initial Call
+    printProgressBar(0)
+    # Update Progress Bar
+    for i, item in enumerate(iterable):
+        yield item
+        printProgressBar(i + 1)
+    # Print New Line on Complete
+    print()
+
+def main():
+    config_files = []
+
+    # Obtaining config files
+    search_space = "nasbench201"
+    dataset_dir = f"/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/{search_space}"
+    datasets = os.listdir(dataset_dir)
+    for dataset in datasets:
+        optimizer_dir = os.path.join(dataset_dir, dataset)
+        if not os.path.isdir(optimizer_dir):
+            continue
+        optimizers = os.listdir(optimizer_dir)
+        for optimizer in optimizers:
+            if not os.path.isdir(os.path.join(optimizer_dir, optimizer)):
+                continue
+            config_path = os.path.join(optimizer_dir, optimizer, "config_0")
+            for seed in os.listdir(config_path):
+                config_file = os.path.join(config_path, seed)
+                if not os.path.isfile(config_file):
+                    continue
+                config_files.append(config_file)
+
+    # A Nicer, Single-Call Usage
+    for config in progressBar(config_files, prefix = 'Progress:', suffix = 'Complete', length = 50):
+        # Use a list of args instead of a string
+        my_cmd = ["/Users/lars/Projects/naslib-venv/bin/python", 
+            "/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/runner.py", 
+            "--config-file", config]
+        with open('bbo-exps-output.log', "w") as outfile:
+            subprocess.run(my_cmd, stdout=outfile, stderr=outfile)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/make_configs_nbxx.py b/naslib/benchmarks/mf/make_configs_nbxx.py
new file mode 100644
index 000000000..af2f1237b
--- /dev/null
+++ b/naslib/benchmarks/mf/make_configs_nbxx.py
@@ -0,0 +1,76 @@
+# read yaml config as in nasbench for creating nice configs and easy access
+import yaml
+import os
+from glob import glob
+
+# Short workaround, such that I do not have to design a proper class for a small config file during development
+# Allows easier access to config by attributes instead of dictionary key.
+from addict import Dict
+
+from create_configs_2 import create_configs
+# Read YAML file
+with open("config.yaml", 'r') as stream:
+    config = yaml.safe_load(stream)
+
+def check_config(search_space, dataset, optimizer, trials, HPO, num_config):
+    folder = os.path.join(".", "bbo", "configs_m1", search_space, dataset, optimizer)
+    test = os.path.join(folder, "**", "*.yaml")  
+    files = glob(test, recursive=True)
+    num_config = num_config if HPO else 1
+    if len(files) == trials:
+        print("Created config file(s) successfully")
+        return
+    print("Config file(s) not successfully created")        
+
+# convert such that config elements are accessiable via attributes
+config = Dict(config)
+start_seed = config.start_seed if config.start_seed else 0
+trials = config.trials
+end_seed = start_seed + trials - 1
+
+optimizers = config.optimizers
+# TODO: Implement check for optimizers
+# See lines 13 - 18 in make_configs_nb201.sh
+
+out_dir = config.out_dir
+
+config_type = config.config_type
+if config_type not in {'bbo-bs', 'predictor-bs'}:
+    print('Invalid config')
+    print('config_type either bbo-bs or predictor-bs')
+    exit(1)
+
+search_space = config.search_space
+
+datasets = config.datasets
+fidelity = config.fidelity
+epochs = config.epochs
+
+predictor = config.predictor
+
+HPO = config.HPO
+num_config = config.num_config
+
+for dataset in datasets:
+    for optimizer in optimizers:
+        print(f"Creating config for dataset: {dataset}: & optimizer: {optimizer}")
+        create_configs(
+            start_seed=start_seed,
+            trials=trials,
+            out_dir=out_dir,
+            dataset=dataset,
+            config_type=config_type,
+            search_space=search_space,
+            optimizer=optimizer,
+            predictor=predictor,
+            fidelity=fidelity,
+            epochs=epochs,
+            HPO=HPO,
+            num_config=num_config
+        )
+        check_config(search_space, dataset, optimizer, trials, HPO, num_config)
+
+""" 
+    --acq_fn_optimization $acq_fn_optimization --predictor $predictor
+    TODO: acq_fn_optimization -- this needs to be done
+"""
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/scheduler_local.sh b/naslib/benchmarks/mf/scheduler_local.sh
new file mode 100755
index 000000000..42b935ae6
--- /dev/null
+++ b/naslib/benchmarks/mf/scheduler_local.sh
@@ -0,0 +1,22 @@
+search_space=$1
+
+# dataset_dir="/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/naslib/benchmarks/bbo/configs_cpu/$search_space/*"
+dataset_dir="/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/$search_space/*"
+ 
+#for optimizer_dir in $dataset_dir/*
+# do 
+	# echo $config_dir	
+	# for config_dir in $optimizer_dir/*
+	# do
+		# echo starting to run ${config_dir} across 10 seeds ...
+		# sbatch submit_folder.sh $config_dir
+		
+	# done
+# done
+
+# for running default config files separately
+for optimizer_dir in $dataset_dir/*
+do 
+	echo starting to run $optimizer_dir/config_0 across x seeds ...
+	./submit_folder_local.sh $optimizer_dir/config_0 
+done
diff --git a/naslib/benchmarks/mf/submit_folder_local.sh b/naslib/benchmarks/mf/submit_folder_local.sh
new file mode 100755
index 000000000..8488829de
--- /dev/null
+++ b/naslib/benchmarks/mf/submit_folder_local.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+#SBATCH -o slurmlog/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -e slurmlog/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -J bbo-exps              # sets the job name. 
+#SBATCH --mem=7G  
+
+# Print some information about the job to STDOUT
+
+echo "Workingdir: $PWD";
+echo "Started at $(date)";
+
+# python -u runner.py --config-file $1
+
+for config_file_seed in $1/*
+	do
+		echo submitted ${config_file_seed}
+		/Users/lars/Projects/naslib-venv/bin/python runner.py --config-file $config_file_seed
+	done
+
+echo "DONE";
+echo "Finished at $(date)";
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 5cad78ebb..b890f6c6d 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -145,11 +145,9 @@ def train_statistics(self, report_incumbent=True):
             best_arch.query(
                 Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
             ),
-
         )
-    def train_model_statistics(self, report_incumbent=True):
-
         
+    def train_model_statistics(self, report_incumbent=True):
         best_arch = self.sampled_archs[self.fidelity_counter -1].arch
         best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
         return (
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index ed9c16aee..56e78bdb6 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -45,6 +45,7 @@ def __init__(
         self.dataset = config.dataset
         self.end = False
         self.fidelity = config.search.min_fidelity
+        self.min_fidelity = config.search.min_fidelity
         self.number_archs = config.search.number_archs
         self.eta = config.search.eta
         self.budget_max = config.search.budget_max 
@@ -164,20 +165,25 @@ def train_statistics(self, report_incumbent=True):
         else:
             best_arch = self.sampled_archs[-1].arch
 
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-        )
+        metrics = [
+            Metric.TRAIN_ACCURACY,
+            Metric.VAL_ACCURACY,
+            Metric.TEST_ACCURACY,
+            Metric.TRAIN_TIME
+        ]
+        results = []
+        for metric in metrics:
+            result = best_arch.query(
+                metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.fidelity)
+            )
+            results.append(result)
+        if self.fidelity != self.min_fidelity: # TODO: Maybe there is a better way to solve this.
+            i = metrics.index(Metric.TRAIN_TIME)
+            results[i] = results[i] - best_arch.query(
+                    metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.old_fidelity)
+                )
+            print(f"Current_fidelity: {self.fidelity}\n Old fidelity: {self.old_fidelity}")
+        return tuple(results)
 
     def update_optimizer_stats(self):
         """

From c9d0fc4b3c3233da2d725762b83e406103c35796 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 2 Feb 2022 09:41:03 +0100
Subject: [PATCH 058/130] further improvments on experimental setup

---
 naslib/benchmarks/mf/local_schedular.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/naslib/benchmarks/mf/local_schedular.py b/naslib/benchmarks/mf/local_schedular.py
index 78977bc7f..a0ef0ff48 100644
--- a/naslib/benchmarks/mf/local_schedular.py
+++ b/naslib/benchmarks/mf/local_schedular.py
@@ -1,6 +1,7 @@
-import time
 import os
 import subprocess
+import shutil
+
 
 def progressBar(iterable, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = "\r"):
     """
@@ -30,7 +31,26 @@ def printProgressBar (iteration):
     # Print New Line on Complete
     print()
 
+def clean_runs(search_space, optimizers_delete :list = [], delete_all=False):
+    dataset_dir = f"/Users/lars/Projects/NASLib/naslib/benchmarks/mf/run_m1/{search_space}"
+    datasets = os.listdir(dataset_dir)
+    for dataset in datasets:
+        optimizer_dir = os.path.join(dataset_dir, dataset)
+        if not os.path.isdir(optimizer_dir):
+            continue
+        optimizers = os.listdir(optimizer_dir)
+        for optimizer in optimizers:
+            if not os.path.isdir(os.path.join(optimizer_dir, optimizer)):
+                continue
+            if not delete_all and not optimizer in optimizers_delete:
+                continue
+            optimizer_path = os.path.join(optimizer_dir, optimizer)
+            shutil.rmtree(optimizer_path)
+            print(optimizer_path)
+
+
 def main():
+    clean_runs("nasbench201", optimizers_delete=['rs'])
     config_files = []
 
     # Obtaining config files

From 9a91f5662307462206f4ee50b763643a2d642086 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 2 Feb 2022 17:15:24 +0100
Subject: [PATCH 059/130] config inconsquent in tpe

---
 .../discrete_config_Bananas.yaml              | 87 +++++++++++++++++++
 naslib/predictors/ensemble.py                 | 23 +++--
 naslib/predictors/tpe/tpe.py                  | 10 +--
 3 files changed, 103 insertions(+), 17 deletions(-)
 create mode 100644 naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml b/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml
new file mode 100644
index 000000000..170905ba1
--- /dev/null
+++ b/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml
@@ -0,0 +1,87 @@
+seed: 0
+optimizer: oneshot
+search_space: darts
+dataset: cifar10
+out_dir: run
+
+experiment_type: single
+predictor: oneshot
+test_size: 200
+train_size_list: [8, 12]
+train_size_single: 2
+fidelity_list: [5]
+fidelity_single: 5
+
+search:
+  seed: 0
+  checkpoint_freq: 1000
+  epochs: 50
+  fidelity: -1
+
+  # GDAS
+  tau_max: 10
+  tau_min: 0.1
+
+  # RE
+  sample_size: 10
+  population_size: 30
+  
+  # LS
+  num_init: 10
+  
+  # BANANAS
+  k: 10
+  num_ensemble: 3
+  acq_fn_type: its
+  acq_fn_optimization: mutation
+  encoding_type: path
+  num_arches_to_mutate: 2
+  max_mutations: 1
+  num_candidates: 100
+  
+  # BP
+  predictor_type: bananas
+  debug_predictor: False
+
+
+  # additional params
+  batch_size: 64
+  learning_rate: 0.025
+  learning_rate_min: 0.001
+  momentum: 0.9
+  weight_decay: 0.0003
+  warm_start_epochs: 0
+  grad_clip: 5
+  train_portion: 0.9
+  data_size: 25000
+
+  cutout: False
+  cutout_length: 16
+  cutout_prob: 1.0
+  drop_path_prob: 0.0
+
+  unrolled: False
+  arch_learning_rate: 0.0003
+  arch_weight_decay: 0.001
+  output_weights: True
+  
+
+evaluation:
+  checkpoint_freq: 5000
+  batch_size: 96
+  learning_rate: 0.025
+  learning_rate_min: 0.00
+  momentum: 0.9
+  weight_decay: 0.0003
+  epochs: 600
+  warm_start_epochs: 0
+  grad_clip: 5
+  train_portion: 1.
+  data_size: 50000
+
+  cutout: True
+  cutout_length: 16
+  cutout_prob: 1.0
+  drop_path_prob: 0.2
+  auxiliary_weight: 0.4
+
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index b44db2b67..b010e767e 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -105,20 +105,19 @@ def get_ensemble(self):
                 min_train_size=0,
                 max_zerocost=100,
             ),
-            #remove temporar for debugging 
-            # "omni_seminas": OmniSemiNASPredictor(
-            #     zero_cost=["jacov"],
-            #     lce=[],
-            #     encoding_type="seminas",
-            #     ss_type=self.ss_type,
-            #     run_pre_compute=False,
-            #     semi=True,
-            #     max_zerocost=1000,
-            #     config=self.config,
-            # ),
+            "omni_seminas": OmniSemiNASPredictor(
+                zero_cost=["jacov"],
+                lce=[],
+                encoding_type="seminas",
+                 ss_type=self.ss_type,
+                 run_pre_compute=False,
+                 semi=True,
+                 max_zerocost=1000,
+                 config=self.config,
+             ),
             "tpe": TreeParserEstimator(
                 #TODO review if more needed 
-                 config = self.config,
+                 config = self.hyperparams,
                 ),
 
             
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index 7158ab21b..d87acd8e6 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -17,7 +17,7 @@ def __init__(
         self,
         encoding_type="adjacency_one_hot",
         ss_type="nasbench201",
-        config = None,
+        config = [None,None,None],
         search_params = None
     ):
         self.encoding_type = encoding_type
@@ -25,10 +25,10 @@ def __init__(
         
         
         self.kde_models = defaultdict(lambda: defaultdict(list))
-        
-        self.configspace = config[0]#thing about later
-        self.dataset_api = config[1]
-        self.dataset = config[2]
+        if (type(config) == list):
+            self.configspace = config[0]#thing about later
+            self.dataset_api = config[1]
+            self.dataset = config[2]
     def set_hyperparams(self, hyperparams): #here set config 
         self.num_samples = int(hyperparams.search.num_ensemble)
         self.min_points_in_model = int(hyperparams.search.min_points_in_model)

From 935ebd8d1369e7b958c3f95b5b5e4e3f46de1ee8 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 2 Feb 2022 17:51:59 +0100
Subject: [PATCH 060/130] make bohb runnin againn

---
 naslib/optimizers/discrete/bohb/optimizer.py |  2 +-
 naslib/predictors/ensemble.py                |  4 +++-
 naslib/predictors/tpe/tpe.py                 | 10 +++++-----
 3 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index f76ca8999..5a5668920 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -77,7 +77,7 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
                     num_ensemble= 1,
                     encoding_type= self.encoding_type,
                     ss_type = self.tpe_config.search_space,
-                    config = [self.search_space.clone(),self.dataset_api, self.dataset],
+                    config_tpe = [self.search_space.clone(),self.dataset_api, self.dataset],
                     hyperparams = self.tpe_config,   #replace with config maybe or not 
 
                     )
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index b010e767e..caf5dcff9 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -30,6 +30,7 @@ def __init__(
         ss_type=None,
         hpo_wrapper=True,
         config=None,
+        config_tpe = None,
         hyperparams = None,
     ):
         self.num_ensemble = num_ensemble
@@ -38,6 +39,7 @@ def __init__(
         self.ss_type = ss_type
         self.hpo_wrapper = hpo_wrapper
         self.config = config
+        self.config_tpe = config_tpe
         self.hyperparams = hyperparams
         self.ensemble = None
         
@@ -117,7 +119,7 @@ def get_ensemble(self):
              ),
             "tpe": TreeParserEstimator(
                 #TODO review if more needed 
-                 config = self.hyperparams,
+                config_tpe  = self.config_tpe,
                 ),
 
             
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
index d87acd8e6..9f3a6f0d9 100644
--- a/naslib/predictors/tpe/tpe.py
+++ b/naslib/predictors/tpe/tpe.py
@@ -17,7 +17,7 @@ def __init__(
         self,
         encoding_type="adjacency_one_hot",
         ss_type="nasbench201",
-        config = [None,None,None],
+        config_tpe = [None,None,None],
         search_params = None
     ):
         self.encoding_type = encoding_type
@@ -25,10 +25,10 @@ def __init__(
         
         
         self.kde_models = defaultdict(lambda: defaultdict(list))
-        if (type(config) == list):
-            self.configspace = config[0]#thing about later
-            self.dataset_api = config[1]
-            self.dataset = config[2]
+        if (type(config_tpe) == list):
+            self.configspace = config_tpe[0]#thing about later
+            self.dataset_api = config_tpe[1]
+            self.dataset = config_tpe[2]
     def set_hyperparams(self, hyperparams): #here set config 
         self.num_samples = int(hyperparams.search.num_ensemble)
         self.min_points_in_model = int(hyperparams.search.min_points_in_model)

From c2c46e2187782fedadc61d3e4a7d0b73095edd0a Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Thu, 3 Feb 2022 16:54:32 +0100
Subject: [PATCH 061/130] update sh to use max_budget

---
 naslib/optimizers/discrete/sh/optimizer.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 56e78bdb6..2464cbd56 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -89,7 +89,6 @@ def new_epoch(self):
         if len(self.sampled_archs) < self.number_archs:
             #model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
             model = self.sample(self.method)
-            
         else:
             model = self.sampled_archs[self.fidelity_counter]
 
@@ -128,13 +127,13 @@ def new_epoch(self):
         self._update_history(model)
         if self.fidelity_counter == self.number_archs:
             self.old_fidelity = self.fidelity
-            self.fidelity = math.floor(self.eta*self.fidelity)
+            # TODO: set budget_max to highest possible fidelity from used benchmark
+            self.fidelity = max(math.floor(self.eta*self.fidelity), 200)
             self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
             if self.fidelity > self.budget_max:
                 self.end = True
             elif(math.floor(self.number_archs/self.eta)) != 0:
                 self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)]
-
             else:
                 self.end = True
                 self.sampled_archs = [self.sampled_archs[0]]  # but maybe there maybe a different way

From 436396b84dc8e547f6e8069b9687c673e632773c Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 5 Feb 2022 16:31:19 +0100
Subject: [PATCH 062/130] bug fix on sh optimizer

---
 examples/mf_plot.py                        |  2 ++
 naslib/optimizers/discrete/sh/optimizer.py | 10 +++++-----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index 6477ae900..aba1ebcb4 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -12,6 +12,8 @@ def get_results(predictor, path, filename, metric='valid_acc'):
     """
     algo_path = os.path.join(path, predictor)
     for seed_dir in os.listdir(algo_path):
+        if not os.path.isdir(os.path.join(algo_path, seed_dir)):
+            continue
         result_file = os.path.join(algo_path, seed_dir, filename)
         result = json.load(open(result_file))
         return result
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 2464cbd56..7e96556de 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -97,7 +97,7 @@ def new_epoch(self):
         model.accuracy = model.arch.query(
             self.performance_metric,
             self.dataset,
-            epoch=int(self.fidelity),
+            epoch=int(self.fidelity)-1,
             dataset_api=self.dataset_api,
         )
 
@@ -109,7 +109,7 @@ def new_epoch(self):
             model.time = model.arch.query(  # TODO: this is the time for training from screatch.
                 self.performance_metric,
                 self.dataset,
-                epoch=int(self.fidelity),
+                epoch=int(self.fidelity)-1,
                 dataset_api=self.dataset_api,
             )
             budget = model.time
@@ -128,7 +128,7 @@ def new_epoch(self):
         if self.fidelity_counter == self.number_archs:
             self.old_fidelity = self.fidelity
             # TODO: set budget_max to highest possible fidelity from used benchmark
-            self.fidelity = max(math.floor(self.eta*self.fidelity), 200)
+            self.fidelity = min(math.floor(self.eta*self.fidelity), 200)
             self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
             if self.fidelity > self.budget_max:
                 self.end = True
@@ -173,13 +173,13 @@ def train_statistics(self, report_incumbent=True):
         results = []
         for metric in metrics:
             result = best_arch.query(
-                metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.fidelity)
+                metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.fidelity)-1
             )
             results.append(result)
         if self.fidelity != self.min_fidelity: # TODO: Maybe there is a better way to solve this.
             i = metrics.index(Metric.TRAIN_TIME)
             results[i] = results[i] - best_arch.query(
-                    metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.old_fidelity)
+                    metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.old_fidelity)-1
                 )
             print(f"Current_fidelity: {self.fidelity}\n Old fidelity: {self.old_fidelity}")
         return tuple(results)

From a01ad47f1ccbe54a67bd402b981a8bb39ab113ec Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Tue, 8 Feb 2022 09:26:19 +0100
Subject: [PATCH 063/130] fix in sh optimizer

---
 naslib/optimizers/discrete/sh/optimizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 7e96556de..8bed6f120 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -202,7 +202,7 @@ def update_optimizer_stats(self):
                 metric,
                 self.dataset,
                 dataset_api=self.dataset_api,
-                epoch=int(self.fidelity)
+                epoch=int(self.fidelity)-1
             )
             self.optimizer_stats[arch_hash][metric_name].append(metric_value)
         self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity)

From 1545afb863ed253acd41ebc87bfded0966f2e207 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 9 Feb 2022 13:28:58 +0100
Subject: [PATCH 064/130] work on sh train time

---
 .../mf/{create_configs_2.py => create_configs.py}  |  2 ++
 naslib/benchmarks/mf/make_configs_nbxx.py          |  2 +-
 .../benchmarks/nas_predictors/discrete_config.yaml | 11 ++++++-----
 naslib/optimizers/discrete/sh/optimizer.py         | 14 ++++++++++----
 4 files changed, 19 insertions(+), 10 deletions(-)
 rename naslib/benchmarks/mf/{create_configs_2.py => create_configs.py} (99%)

diff --git a/naslib/benchmarks/mf/create_configs_2.py b/naslib/benchmarks/mf/create_configs.py
similarity index 99%
rename from naslib/benchmarks/mf/create_configs_2.py
rename to naslib/benchmarks/mf/create_configs.py
index 904a7e1cb..fac2c7281 100644
--- a/naslib/benchmarks/mf/create_configs_2.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -120,6 +120,7 @@ def create_configs(
                     "encoding_type": "path",
                     "predictor": predictor,
                     "debug_predictor": False,
+                    # config secton for successive halving
                 },
             }
             path = os.path.join(folder, f"seed_{seed}.yaml")
@@ -158,6 +159,7 @@ def create_configs(
                         "num_candidates": int(np.random.choice(range(5, 50))),
                         "predictor": predictor,
                         "debug_predictor": False,
+                        # config section for successive halving
                     },
                 }
 
diff --git a/naslib/benchmarks/mf/make_configs_nbxx.py b/naslib/benchmarks/mf/make_configs_nbxx.py
index af2f1237b..971c3c0ea 100644
--- a/naslib/benchmarks/mf/make_configs_nbxx.py
+++ b/naslib/benchmarks/mf/make_configs_nbxx.py
@@ -7,7 +7,7 @@
 # Allows easier access to config by attributes instead of dictionary key.
 from addict import Dict
 
-from create_configs_2 import create_configs
+from create_configs import create_configs
 # Read YAML file
 with open("config.yaml", 'r') as stream:
     config = yaml.safe_load(stream)
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 099ef2efd..b34050c01 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,13 +7,14 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 16.125 # TODO : this setting should be not neccessary!!!
+  epochs: 93
   min_fidelity: 1
-  number_archs: 81
-  eta: 3
+  number_archs: 4250
+  eta: 2
   budget_type: epoch 
-  budget_max: 81
-  method: random
+  method: "random"
+  budget_max: 200
+
 
   predictor_type: var_sparse_gp
   num_init: 10  
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 8bed6f120..9ab3a2217 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -176,11 +176,17 @@ def train_statistics(self, report_incumbent=True):
                 metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.fidelity)-1
             )
             results.append(result)
+        # Adapt train time
+        # TODO: Write this
+        i = metrics.index(Metric.TRAIN_TIME)
+        train_time = results[i]
+        results[i] = train_time * self.fidelity / self.budget_max
         if self.fidelity != self.min_fidelity: # TODO: Maybe there is a better way to solve this.
-            i = metrics.index(Metric.TRAIN_TIME)
-            results[i] = results[i] - best_arch.query(
-                    metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.old_fidelity)-1
-                )
+            results[i] = train_time * (self.fidelity - self.old_fidelity) / self.budget_max
+            # i = metrics.index(Metric.TRAIN_TIME)
+            # results[i] = results[i] - best_arch.query(
+            #         metric, self.dataset, dataset_api=self.dataset_api, epoch=int(self.old_fidelity)-1
+            #     )
             print(f"Current_fidelity: {self.fidelity}\n Old fidelity: {self.old_fidelity}")
         return tuple(results)
 

From 1f48654bdbd5580b43e71f96795a0e89294247d4 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 10 Feb 2022 16:14:05 +0100
Subject: [PATCH 065/130] dehb bracket claculations right

---
 .../nas_predictors/discrete_config_DEHB.yaml  |  51 +++
 naslib/optimizers/discrete/dehb/__init__.py   |   0
 naslib/optimizers/discrete/dehb/optimizer.py  | 312 ++++++++++++++++++
 naslib/search_spaces/nasbench201/graph.py     |  17 +
 4 files changed, 380 insertions(+)
 create mode 100644 naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
 create mode 100644 naslib/optimizers/discrete/dehb/__init__.py
 create mode 100644 naslib/optimizers/discrete/dehb/optimizer.py

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
new file mode 100644
index 000000000..210b0078e
--- /dev/null
+++ b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
@@ -0,0 +1,51 @@
+seed: 0
+optimizer: bananas
+search_space: nasbench201
+dataset: cifar10
+out_dir: run
+
+search:
+  seed: 0
+  checkpoint_freq: 10 # 1000
+  epochs: 16.125
+  min_fidelity: 1
+  number_archs: 81
+  eta: 3
+  budget_type: epoch 
+  budget_max: 81
+  method: tpe
+
+  p: 0.15
+  predictor_type: var_sparse_gp
+  N_min: 50
+  min_points_in_model: 6
+  top_n_percent: 15
+  min_bandwith: 1e-3
+  random_fraction: 0.15
+  num_init: 10  
+  k: 10
+
+  # BANANAS
+  num_ensemble: 64 #num_samples in tpe 
+  acq_fn_type: its
+  acq_fn_optimization: random_sampling
+  encoding_type: adjacency_one_hot
+  num_arches_to_mutate: 2
+  max_mutations: 1
+  num_candidates: 20
+  
+  # jacov data loader
+  batch_size: 256
+  data_size: 25000
+  cutout: False
+  cutout_length: 16
+  cutout_prob: 1.0
+  train_portion: 0.7
+  
+  # other params
+  debug_predictor: False
+  sample_size: 10
+  population_size: 30
+
+  # TODO: write config file for successive halving
+
diff --git a/naslib/optimizers/discrete/dehb/__init__.py b/naslib/optimizers/discrete/dehb/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
new file mode 100644
index 000000000..93297be29
--- /dev/null
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -0,0 +1,312 @@
+from logging import raiseExceptions
+import numpy as np
+import torch
+import copy
+import collections
+import math
+from collections import defaultdict
+from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.search_spaces.core.query_metrics import Metric
+
+from collections import defaultdict
+
+
+class DEHB(MetaOptimizer):
+    """
+    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(DEHB, self).__init__()
+        # DE related variables
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+        self.fidelity = config.search.min_fidelity
+        self.number_archs = config.search.number_archs
+        self.eta = config.search.eta
+        self.budget_max = config.search.budget_max 
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type  # is not for one query is overall
+        self.fidelity_counter = 0
+        self.history = torch.nn.ModuleList()
+        self.end = False
+        self.old_fidelity = 0 
+        self.round_number = 0
+        self.top_n_percent = 0.3
+        self.method = config.search.method
+        self.s_max = math.floor(math.log(self.budget_max, self.eta))
+        self.b = (self.s_max + 1)* self.budget_max
+        self.s = self.s_max
+        self.current_round = []
+        self.next_round = []
+        self.new_fidelity = False # TODO think of more ellegant sollution
+        self.de = dict()
+        self._epsilon = 1e-6
+        self.pop_size = {}       
+        self.de[self.fidelity] = {}
+        self.enc_dim = 6
+        self.max_mutations = 1
+        self.crossover_prob = 0.5
+        self.top_n_percent = 0.3
+        self.mutate_prob = 0.1
+        self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
+        # Global trackers
+        self.population = None
+        self.fitness = None
+        self.inc_score = np.inf
+        self.inc_config = None
+       
+        
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+        #self.max_training_epoch = self.search_space.get_max_epochs() #deifne
+        self.max_training_epoch = 200
+       
+    def new_epoch(self):   #TODO: epoch, round and i have to be adappted 
+        #calculate s n,r not before but during like in bohb 
+        #
+        ##this mind help if we need to parraleise 
+        #if self.process < i: # re-init for each new process
+        #    self.current_round = []
+        #    self.next_round = []
+        #    self.round_number = 0
+        #    self.prev_round = 0
+        #    self.counter = 0
+        #    self.process = i
+       
+        print("bracket: {}, number_archs: {}, fidelity:{}, counter: {}".format(self.s,self.number_archs, self.fidelity, self.fidelity_counter))
+        if self.round_number >= self.s: # reset round_number for each new round
+            self.s -=1 
+            self.round_number = 0
+            n = math.ceil(self.b / self.budget_max / (self.s + 1) * self.eta ** self.s)
+            r = self.budget_max * self.eta ** (-self.s)
+            self.number_archs = n
+            self.fidelity  = r
+            print("bracket: {}, number_archs: {}, fidelity:{}".format(self.s,self.number_archs, self.fidelity))
+        if self.s < 1:
+            raise NameError("this method is finish")
+
+
+        if self.fidelity_counter  <  self.number_archs:
+            # sample random architectures
+            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            budget = self.fidelity
+            if self.s == self.s_max:
+                model.arch.sample_random_architecture(dataset_api=self.dataset_api) #sample in the first round 
+            else:
+                if len(self.de[self.fidelity]['promotions']) > 0:  #if we have allready something mutate the best 
+                    print('promotion from budget: {}, length: {}'.format(self.fidelity, len(self.de[self.fidelity]['promotions'])))
+                    model = self.de[self.fidelity]['promotions'].pop()
+                    model = copy.deepcopy(model)
+                    arch = self.search_space.clone()
+                    arch.mutate(model.arch, dataset_api=self.dataset_api)
+                    model.arch = arch
+                else:
+                    model.arch.sample_random_architecture(dataset_api=self.dataset_api) #if not do something random
+            #model.epoch = self.fidelity # has to be changed
+            # TODO: this is really good idea to implemtent it in all optimizer (that we write or in other but not from us) 
+            model.epoch = int(min(self.fidelity, self.max_training_epoch)) #
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset,
+                                              epoch=model.epoch,
+                                              dataset_api=self.dataset_api)
+            budget = (self.fidelity - self.old_fidelity) / self.budget_max
+            # DONE: make query type secure
+            if self.budget_type == 'time':
+            #TODO also for predictions
+            # DONE: make dependent on performance_metric
+                model.time = model.arch.query(  # TODO: this is the time for training from screatch.
+                self.performance_metric,
+                self.dataset,
+                epoch=int(self.fidelity),
+                dataset_api=self.dataset_api,
+                )
+                budget = model.time
+            elif not(self.budget_type == "epoch"):
+                raise NameError("budget time should be time or epoch")
+            #Maybe this needs to the beginnig of this if 
+            self.fidelity_counter += 1
+
+            self._update_history(model)
+            self.next_round.append(model)
+
+        else:
+           
+            #self.new_fidelity = True
+            if len(self.current_round) == 0: 
+                self.new_fidelity = False
+                self.old_fidelity = self.fidelity
+                self.fidelity = math.floor(self.eta*self.fidelity)
+                self.number_archs = math.floor(self.number_archs/self.eta) #maybe +1 or -1 not quite sure 
+            
+                self.de[self.fidelity] = {}
+                self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
+                self.round_number += 1
+                #n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+                #r = self.budget_max * self.eta ** (-self.s)
+                #self.number_archs = n
+                #self.fidelity  = r
+                self.fidelity_counter = 0
+                self.de[self.fidelity] = {}
+                self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
+                # if we are at the end of a round of hyperband, continue training only the best
+                print("Starting a new round: continuing to train the best arches")
+                cutoff = math.ceil(self.number_archs * self.top_n_percent)
+                self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
+                print("bracket: {}, number_archs: {}, fidelity:{}".format(self.s,self.number_archs, self.fidelity))
+                #self.round_number = min(self.round_number, len(self.fidelities[round]) - 1)
+                self.next_round = []
+        
+                
+            
+              
+               
+            # train the next architecture
+            model = self.current_round.pop()
+            self.fidelity_counter += 1
+            """
+            Note: technically we would just continue training this arch, but right now,
+            just for simplicity, we treat it as if we start to train it again from scratch
+            """
+            model = copy.deepcopy(model)
+
+            if np.random.rand(1) < self.mutate_prob:  #mutate through any     
+                candidate = model.arch.clone()
+                for _ in range(self.max_mutations): #here the mutation happens.
+                    arch_ = self.search_space.clone()
+                    arch_.mutate(candidate, dataset_api=self.dataset_api)
+                    candidate = arch_
+                mutant = candidate
+                arch = self.search_space.clone()
+                arch.crossover_bin(model.arch, mutant, self.enc_dim, self.crossover_prob, dataset_api=self.dataset_api) #this needs to be implemeted 
+                model.arch = arch
+            #model.epoch = self.fidelity
+            model.epoch = int(min(self.fidelity, self.max_training_epoch))
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset,
+                                              epoch=model.epoch,
+                                              dataset_api=self.dataset_api)
+            budget = (self.fidelity - self.old_fidelity) / self.budget_max
+            # DONE: make query type secure
+            if self.budget_type == 'time':
+            #TODO also for predictions
+            # DONE: make dependent on performance_metric
+                model.time = model.arch.query(  # TODO: this is the time for training from screatch.
+                self.performance_metric,
+                self.dataset,
+                epoch=model.epoch,
+                dataset_api=self.dataset_api,
+                )
+                budget = model.time
+            elif not(self.budget_type == "epoch"):
+                raise NameError("budget time should be time or epoch")
+            self.de[self.fidelity]['promotions'].append(model)  #apened model if round is finsih 
+            self._update_history(model)
+            self.next_round.append(model)
+        return budget
+    def _update_history(self, child): #not even sure if this is needed, but why not 
+        self.history.append(child)
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        return best_arch.arch
+
+    def train_statistics(self, report_incumbent=True):
+        
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+
+
+    
+
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+    
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
+        """
+        self.optimizer_stats[self.s] = self.sh.optimizer_stats
+
+
+
+    def test_statistics(self):
+        return False
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
diff --git a/naslib/search_spaces/nasbench201/graph.py b/naslib/search_spaces/nasbench201/graph.py
index 1c7b517ed..04e4ba25f 100644
--- a/naslib/search_spaces/nasbench201/graph.py
+++ b/naslib/search_spaces/nasbench201/graph.py
@@ -227,6 +227,19 @@ def sample_random_architecture(self, dataset_api=None):
         op_indices = np.random.randint(5, size=(6))
         self.set_op_indices(op_indices)
 
+
+    def crossover_bin(self, parent, mutant, dim, prob, dataset_api=None):
+        '''Performs the binomial crossover of DE
+        '''
+        target_indices = np.array(convert_naslib_to_op_indices(parent))
+        mutant_indices = np.array(convert_naslib_to_op_indices(mutant))
+        cross_points = np.random.rand(dim) < prob
+        if not np.any(cross_points):
+            cross_points[np.random.randint(0, dim)] = True
+
+        offspring = np.where(cross_points, mutant_indices, target_indices)
+        self.set_op_indices(offspring)
+
     def mutate(self, parent, dataset_api=None):
         """
         This will mutate one op from the parent op indices, and then
@@ -241,6 +254,10 @@ def mutate(self, parent, dataset_api=None):
         op_indices[edge] = op_index
         self.set_op_indices(op_indices)
 
+
+
+
+
     def get_nbhd(self, dataset_api=None):
         # return all neighbors of the architecture
         self.get_op_indices()

From c0e9613c9984ff00e0b995c580b8d54927f9d6fe Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 15 Feb 2022 14:50:06 +0100
Subject: [PATCH 066/130] change the hyperparamter into config file

---
 examples/mf_demo.py                           |  12 +-
 examples/mf_demo.yaml                         |   4 +-
 .../nas_predictors/discrete_config.yaml       |  41 +-
 .../nas_predictors/discrete_config_BOHB.yaml  |  49 +-
 .../nas_predictors/discrete_config_DEHB.yaml  |  47 +-
 .../nas_predictors/discrete_config_HB.yaml    |  39 +-
 naslib/defaults/trainer_multifidelity.py      | 584 ++++++++---------
 naslib/defaults/trainer_multifidelity_old.py  | 615 ++++++++++++++++++
 naslib/optimizers/__init__.py                 |   4 +-
 naslib/optimizers/core/metaclasses.py         |   7 +
 .../discrete/bohb/optimizer._old.py           | 192 ++++++
 naslib/optimizers/discrete/bohb/optimizer.py  | 403 +++++++-----
 naslib/optimizers/discrete/dehb/optimizer.py  | 356 ++++------
 naslib/optimizers/discrete/hb/old.py          | 176 +++++
 naslib/optimizers/discrete/hb/optimizer.py    | 268 ++++----
 naslib/predictors/ensemble.py                 |  13 +-
 naslib/predictors/tpe/__init__.py             |   1 -
 naslib/predictors/tpe/tpe.py                  | 217 ------
 naslib/search_spaces/nasbench201/graph.py     |  35 +
 19 files changed, 1846 insertions(+), 1217 deletions(-)
 create mode 100644 naslib/defaults/trainer_multifidelity_old.py
 create mode 100644 naslib/optimizers/discrete/bohb/optimizer._old.py
 create mode 100644 naslib/optimizers/discrete/hb/old.py
 delete mode 100644 naslib/predictors/tpe/__init__.py
 delete mode 100644 naslib/predictors/tpe/tpe.py

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 04af6d42a..4b3c2782e 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -11,12 +11,13 @@
 from naslib.optimizers import RandomSearch as RS
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers import HyperBand as HB
+from naslib.optimizers import HB
 from naslib.optimizers import BOHB 
+from naslib.optimizers import DEHB
 
 from naslib.defaults.trainer_multifidelity import Trainer
 #from naslib.defaults.trainer import Trainer
-
+# TODO: rethinking logging is rigth now, completly ingnored 
 import yaml
 from pathlib import Path
 import os
@@ -69,6 +70,12 @@
     optimizer = BOHB(config)
     plot_func = mf_plot.plot_hb
     predictor = "tpe"
+elif config_optimizer == "DEHB":
+    #i thin kby save dir is soometihng strange
+    
+    predictor = "var_sparse_gp"
+    optimizer = DEHB(config)
+    plot_func = mf_plot.plot_hb
 else:
     raise Exception('invalid config')
 
@@ -90,4 +97,5 @@
     exit()
 
 # TODO: Make this dependent on optimizer type, currently statistics are just for SH available
+plot_func(predictor)
 plot_func(predictor)
\ No newline at end of file
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 69bca7eb4..5cb9eb678 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: HB
+optimizer: BOHB
 # Activates/deactivates plotting
-plot: True
\ No newline at end of file
+plot: FAlSE
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 099ef2efd..45d873288 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,39 +7,14 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 16.125 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
-  number_archs: 81
+  fidelity: 200
+  budgets: 4000
   eta: 3
-  budget_type: epoch 
-  budget_max: 81
-  method: random
-
+  max_budget: 512
+  min_budget: 12
+  epsilon: 1e-6
   predictor_type: var_sparse_gp
-  num_init: 10  
-  k: 10
-
-  # BANANAS
-  num_ensemble: 3
-  acq_fn_type: its
-  acq_fn_optimization: random_sampling
-  encoding_type: adjacency_one_hot
-  num_arches_to_mutate: 2
-  max_mutations: 1
-  num_candidates: 20
-  
-  # jacov data loader
-  batch_size: 256
-  data_size: 25000
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  train_portion: 0.7
-  
-  # other params
-  debug_predictor: False
-  sample_size: 10
-  population_size: 30
-
-  # TODO: write config file for successive halving
-
+  min_bandwith: 0.001
+  top_n_percent: 0.1
+  min_points_in_model: 7
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index dd1f444f9..45d873288 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -7,45 +7,14 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 16.125
   min_fidelity: 1
-  number_archs: 81
+  fidelity: 200
+  budgets: 4000
   eta: 3
-  budget_type: epoch 
-  budget_max: 81
-  method: tpe
-
-  p: 0.15
-  predictor_type: tpe
-  N_min: 50
-  min_points_in_model: 6
-  top_n_percent: 15
-  min_bandwith: 1e-3
-  random_fraction: 0.15
-  num_init: 10  
-  k: 10
-
-  # BANANAS
-  num_ensemble: 64 #num_samples in tpe 
-  acq_fn_type: its
-  acq_fn_optimization: random_sampling
-  encoding_type: adjacency_one_hot
-  num_arches_to_mutate: 2
-  max_mutations: 1
-  num_candidates: 20
-  
-  # jacov data loader
-  batch_size: 256
-  data_size: 25000
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  train_portion: 0.7
-  
-  # other params
-  debug_predictor: False
-  sample_size: 10
-  population_size: 30
-
-  # TODO: write config file for successive halving
-
+  max_budget: 512
+  min_budget: 12
+  epsilon: 1e-6
+  predictor_type: var_sparse_gp
+  min_bandwith: 0.001
+  top_n_percent: 0.1
+  min_points_in_model: 7
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
index 210b0078e..f14bfc7df 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
@@ -7,45 +7,16 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 16.125
   min_fidelity: 1
-  number_archs: 81
+  fidelity: 200
+  budgets: 4000
   eta: 3
-  budget_type: epoch 
-  budget_max: 81
-  method: tpe
-
-  p: 0.15
+  max_budget: 512
+  min_budget: 12
+  epsilon: 1e-6
   predictor_type: var_sparse_gp
-  N_min: 50
-  min_points_in_model: 6
-  top_n_percent: 15
-  min_bandwith: 1e-3
-  random_fraction: 0.15
-  num_init: 10  
-  k: 10
-
-  # BANANAS
-  num_ensemble: 64 #num_samples in tpe 
-  acq_fn_type: its
-  acq_fn_optimization: random_sampling
-  encoding_type: adjacency_one_hot
-  num_arches_to_mutate: 2
+  top_n_percent: 0.3
+  enc_dim: 6
   max_mutations: 1
-  num_candidates: 20
-  
-  # jacov data loader
-  batch_size: 256
-  data_size: 25000
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  train_portion: 0.7
-  
-  # other params
-  debug_predictor: False
-  sample_size: 10
-  population_size: 30
-
-  # TODO: write config file for successive halving
-
+  crossover_prob: 0.5   
+  mutate_prob: 0.1
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index 099ef2efd..aa8809380 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -1,5 +1,4 @@
 seed: 0
-optimizer: bananas
 search_space: nasbench201
 dataset: cifar10
 out_dir: run
@@ -7,39 +6,13 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 16.125 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
-  number_archs: 81
+  fidelity: 200
+  budgets: 4000
   eta: 3
-  budget_type: epoch 
-  budget_max: 81
-  method: random
-
+  max_budget: 512
+  min_budget: 12
+  epsilon: 1e-6
   predictor_type: var_sparse_gp
-  num_init: 10  
-  k: 10
-
-  # BANANAS
-  num_ensemble: 3
-  acq_fn_type: its
-  acq_fn_optimization: random_sampling
-  encoding_type: adjacency_one_hot
-  num_arches_to_mutate: 2
-  max_mutations: 1
-  num_candidates: 20
-  
-  # jacov data loader
-  batch_size: 256
-  data_size: 25000
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  train_portion: 0.7
+ 
   
-  # other params
-  debug_predictor: False
-  sample_size: 10
-  population_size: 30
-
-  # TODO: write config file for successive halving
-
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 9673a421b..793804318 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -1,5 +1,4 @@
 import codecs
-from naslib.search_spaces.core.graph import Graph
 import time
 import json
 import logging
@@ -8,15 +7,14 @@
 import torch
 import numpy as np
 
-from fvcore.common.checkpoint import Checkpointer
+from fvcore.common.checkpoint import PeriodicCheckpointer
 
 from naslib.search_spaces.core.query_metrics import Metric
 
 from naslib.utils import utils
 from naslib.utils.logging import log_every_n_seconds, log_first_n
 
-from typing import Callable
-#from .additional_primitives import DropPathWrapper #Can be causes issues
+from .additional_primitives import DropPathWrapper
 
 logger = logging.getLogger(__name__)
 
@@ -26,7 +24,6 @@ class Trainer(object):
     Default implementation that handles dataloading and preparing batches, the
     train loop, gathering statistics, checkpointing and doing the final
     final evaluation.
-
     If this does not fulfil your needs free do subclass it and implement your
     required logic.
     """
@@ -34,7 +31,6 @@ class Trainer(object):
     def __init__(self, optimizer, config, lightweight_output=False):
         """
         Initializes the trainer.
-
         Args:
             optimizer: A NASLib optimizer
             config (AttrDict): The configuration loaded from a yaml file, e.g
@@ -42,8 +38,17 @@ def __init__(self, optimizer, config, lightweight_output=False):
         """
         self.optimizer = optimizer
         self.config = config
-        self.epochs = self.config.search.epochs
         self.lightweight_output = lightweight_output
+        self.n_process = 512 #need to be a parameter
+
+        # anytime
+        computed_epochs = self.optimizer.compute_epochs()
+        self.budgets = self.config.search.budgets
+        self.search_time = 0
+        if computed_epochs is not None:
+            self.epochs, self.rounds = computed_epochs
+        else:
+            self.epochs = config.search.epochs
 
         # preparations
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -59,28 +64,25 @@ def __init__(self, optimizer, config, lightweight_output=False):
         n_parameters = optimizer.get_model_size()
         logger.info("param size = %fMB", n_parameters)
         self.errors_dict = utils.AttrDict(
-            {
-                "train_acc": [],
-                "train_loss": [],
-                "valid_acc": [],
-                "fidelity": [],
-                "arch": [],
-                "valid_loss": [],
-                "test_acc": [],
-                "test_loss": [],
-                "runtime": [],
-                "train_time": [],
-                "arch_eval": [],
-                "params": n_parameters,
-            }
+            {'train_acc': [],
+             'train_loss': [],
+             'valid_acc': [],
+             'valid_loss': [],
+             'test_acc': [],
+             'test_loss': [],
+             'runtime': [],
+             'train_time': [],
+             'arch_eval': [],
+             'latest_arch': [],
+             'latest_acc': [],
+             'params': n_parameters}
         )
 
-    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None] = None, report_incumbent=True):
+
+    def search(self, resume_from=""):
         """
         Start the architecture search.
-
         Generates a json file with training statistics.
-
         Args:
             resume_from (str): Checkpoint file to resume from. If not given then
                 train from scratch.
@@ -93,130 +95,162 @@ def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int
         self.optimizer.before_training()
         checkpoint_freq = self.config.search.checkpoint_freq
         if self.optimizer.using_step_function:
-            self.scheduler = self.build_search_scheduler(
-                self.optimizer.op_optimizer, self.config
-            )
-
-            start_epoch = self._setup_checkpointers(
-                resume_from, period=checkpoint_freq, scheduler=self.scheduler
-            )
+            self.scheduler = self.build_search_scheduler(self.optimizer.op_optimizer, self.config)
+        
+            start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq, scheduler=self.scheduler)
         else:
             start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq)
 
         if self.optimizer.using_step_function:
-            self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(
-                self.config
-            )
-        e = start_epoch
-        # TODO: good name for "epochs"
-        while e < self.epochs:
-
-            start_time = time.time()
-            # return function_eqalisation difference time to run or number of elovations
-            used_budget = self.optimizer.new_epoch()
-
-            if self.optimizer.using_step_function:
-                for step, data_train in enumerate(self.train_queue):
-                    data_train = (
-                        data_train[0].to(self.device),
-                        data_train[1].to(self.device, non_blocking=True),
-                    )
-                    data_val = next(iter(self.valid_queue))
-                    data_val = (
-                        data_val[0].to(self.device),
-                        data_val[1].to(self.device, non_blocking=True),
-                    )
-
-                    stats = self.optimizer.step(data_train, data_val)
-                    logits_train, logits_val, train_loss, val_loss = stats
-
-                    self._store_accuracies(logits_train, data_train[1], "train")
-                    self._store_accuracies(logits_val, data_val[1], "val")
-
-                    log_every_n_seconds(
-                        logging.INFO,
-                        "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
-                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()
-                        ),
-                        n=5,
-                    )
-
-                    if torch.cuda.is_available():
-                        log_first_n(
-                            logging.INFO,
-                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
-                            n=3,
-                        )
-
-                    self.train_loss.update(float(train_loss.detach().cpu()))
-                    self.val_loss.update(float(val_loss.detach().cpu()))
-
-                self.scheduler.step()
-
-                end_time = time.time()
-
-                self.errors_dict.train_acc.append(self.train_top1.avg)
-                self.errors_dict.train_loss.append(self.train_loss.avg)
-                self.errors_dict.valid_acc.append(self.val_top1.avg)
-                self.errors_dict.valid_loss.append(self.val_loss.avg)
-                self.errors_dict.runtime.append(end_time - start_time)
-            else:
-                end_time = time.time()
-                # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
-                # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
-                # only for debugging is it but out
-                (
-                    train_acc,
-                    valid_acc,
-                    test_acc,
-                    train_time,
-                ) = self.optimizer.train_statistics()
-                train_loss, valid_loss, test_loss = -1, -1, -1
-
-                self.errors_dict.train_acc.append(train_acc)
-                self.errors_dict.train_loss.append(train_loss)
-                self.errors_dict.valid_acc.append(valid_acc)
-                self.errors_dict.valid_loss.append(valid_loss)
-                self.errors_dict.test_acc.append(test_acc)
-                self.errors_dict.test_loss.append(test_loss)
-                self.errors_dict.runtime.append(end_time - start_time)
-                self.errors_dict.train_time.append(train_time)
-                self.train_top1.avg = train_acc
-                self.val_top1.avg = valid_acc
-
-            self.checkpointer.save(e) # define the name in accurate , also 
-            # TODO: change step into save for checkpointer
-            anytime_results = self.optimizer.test_statistics()
-            if anytime_results:
-                # record anytime performance
-                self.errors_dict.arch_eval.append(anytime_results)
-                log_every_n_seconds(
-                    logging.INFO,
-                    "Epoch {}, Anytime results: {}".format(e, anytime_results),
-                    n=5,
-                )
-
-            self._log_to_json()
-            self._log_and_reset_accuracies(e, summary_writer)
-            if after_epoch is not None:
-                after_epoch(e)
-
-            e += used_budget
-
-        self.optimizer.after_training()
-        self._log_optimizer_stats()
-
-        if summary_writer is not None:
-            summary_writer.close()
+            self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(self.config)
+
+        if type(self.epochs) is list:
+            for i in range(self.n_process):
+                for epochs, round in zip(self.epochs, self.rounds):
+                    for e in range(start_epoch, sum(epochs)):
+                        start_time = time.time()
+                        self.optimizer.new_epoch(e, round, i)
+
+                        if self.optimizer.using_step_function:
+                            for step, data_train in enumerate(self.train_queue):
+                                data_train = (
+                                    data_train[0].to(self.device), data_train[1].to(self.device, non_blocking=True))
+                                data_val = next(iter(self.valid_queue))
+                                data_val = (data_val[0].to(self.device), data_val[1].to(self.device, non_blocking=True))
+
+                                stats = self.optimizer.step(data_train, data_val)
+                                logits_train, logits_val, train_loss, val_loss = stats
+
+                                self._store_accuracies(logits_train, data_train[1], 'train')
+                                self._store_accuracies(logits_val, data_val[1], 'val')
+
+                                log_every_n_seconds(logging.INFO,
+                                                    "Round {}: Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
+                                                        round, e, step, train_loss, val_loss, self.scheduler.get_last_lr()), n=5)
+
+                                if torch.cuda.is_available():
+                                    log_first_n(logging.INFO, "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                                                n=3)
+
+                                self.train_loss.update(float(train_loss.detach().cpu()))
+                                self.val_loss.update(float(val_loss.detach().cpu()))
+
+                            self.scheduler.step()
+
+                            end_time = time.time()
+
+                            self.errors_dict.train_acc.append(self.train_top1.avg)
+                            self.errors_dict.train_loss.append(self.train_loss.avg)
+                            self.errors_dict.valid_acc.append(self.val_top1.avg)
+                            self.errors_dict.valid_loss.append(self.val_loss.avg)
+                            self.errors_dict.runtime.append(end_time - start_time)
+                        else:
+                            end_time = time.time()
+                            train_acc, valid_acc, test_acc, train_time = self.optimizer.train_statistics()
+                            train_loss, valid_loss, test_loss = -1, -1, -1
+
+                            self.errors_dict.train_acc.append(train_acc)
+                            self.errors_dict.train_loss.append(train_loss)
+                            self.errors_dict.valid_acc.append(valid_acc)
+                            self.errors_dict.valid_loss.append(valid_loss)
+                            self.errors_dict.test_acc.append(test_acc)
+                            self.errors_dict.test_loss.append(test_loss)
+                            self.errors_dict.runtime.append(end_time - start_time)
+                            self.errors_dict.train_time.append(train_time)
+                            self.train_top1.avg = train_acc
+                            self.val_top1.avg = valid_acc
+                            self.search_time += end_time - start_time
+                            self.search_time += train_time
+
+                        self.periodic_checkpointer.step(e)
+
+                        anytime_results = self.optimizer.test_statistics()
+                        if anytime_results:
+                           # record anytime performance
+                            self.errors_dict.arch_eval.append(anytime_results)
+                            log_every_n_seconds(logging.INFO, "Round {} Epoch {}, Anytime results: {}".format(
+                                round, e, anytime_results), n=5)
+
+                        self._log_to_json()
+                        self._log_and_reset_accuracies(e, round, i)
+                        if self.search_time > self.budgets:
+                            return
+
+                    self.optimizer.after_training()
+        else:
+            for e in range(start_epoch, self.epochs):
+                start_time = time.time()
+                self.optimizer.new_epoch(e)
+                if self.optimizer.using_step_function:
+                    for step, data_train in enumerate(self.train_queue):
+                        data_train = (data_train[0].to(self.device), data_train[1].to(self.device, non_blocking=True))
+                        data_val = next(iter(self.valid_queue))
+                        data_val = (data_val[0].to(self.device), data_val[1].to(self.device, non_blocking=True))
+
+                        stats = self.optimizer.step(data_train, data_val)
+                        logits_train, logits_val, train_loss, val_loss = stats
+
+                        self._store_accuracies(logits_train, data_train[1], 'train')
+                        self._store_accuracies(logits_val, data_val[1], 'val')
+
+                        log_every_n_seconds(logging.INFO, "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
+                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()), n=5)
+
+                        if torch.cuda.is_available():
+                            log_first_n(logging.INFO, "cuda consumption\n {}".format(torch.cuda.memory_summary()), n=3)
+
+                        self.train_loss.update(float(train_loss.detach().cpu()))
+                        self.val_loss.update(float(val_loss.detach().cpu()))
+
+                    self.scheduler.step()
+
+                    end_time = time.time()
+
+                    self.errors_dict.train_acc.append(self.train_top1.avg)
+                    self.errors_dict.train_loss.append(self.train_loss.avg)
+                    self.errors_dict.valid_acc.append(self.val_top1.avg)
+                    self.errors_dict.valid_loss.append(self.val_loss.avg)
+                    self.errors_dict.runtime.append(end_time - start_time)
+                else:
+                    end_time = time.time()
+                    train_acc, valid_acc, test_acc, train_time = self.optimizer.train_statistics()
+                    train_loss, valid_loss, test_loss = -1, -1, -1
+
+                    self.errors_dict.train_acc.append(train_acc)
+                    self.errors_dict.train_loss.append(train_loss)
+                    self.errors_dict.valid_acc.append(valid_acc)
+                    self.errors_dict.valid_loss.append(valid_loss)
+                    self.errors_dict.test_acc.append(test_acc)
+                    self.errors_dict.test_loss.append(test_loss)
+                    self.errors_dict.runtime.append(end_time - start_time)
+                    self.errors_dict.train_time.append(train_time)
+                    self.train_top1.avg = train_acc
+                    self.val_top1.avg = valid_acc
+                    self.search_time += end_time - start_time
+                    self.search_time += train_time
+
+                self.periodic_checkpointer.step(e)
+
+                anytime_results = self.optimizer.test_statistics()
+                if anytime_results:
+                    # record anytime performance
+                    self.errors_dict.arch_eval.append(anytime_results)
+                    log_every_n_seconds(logging.INFO, "Epoch {}, Anytime results: {}".format(
+                            e, anytime_results), n=5)
+
+                self._log_to_json()
+                self._log_and_reset_accuracies(e)
+                if self.search_time > self.budgets:
+                    return
+
+            self.optimizer.after_training()
+        logger.info("Training finished. Total Budgets[s]: {}".format(self.search_time))
 
-        logger.info("Training finished")
 
     def evaluate_oneshot(self, resume_from="", dataloader=None):
         """
         Evaluate the one-shot model on the specified dataset.
-
         Generates a json file with training statistics.
-
         Args:
             resume_from (str): Checkpoint file to resume from. If not given then
                 evaluate with the current one-shot weights.
@@ -241,7 +275,7 @@ def evaluate_oneshot(self, resume_from="", dataloader=None):
                 logits_val = self.optimizer.graph(input_val)
                 val_loss = loss(logits_val, target_val)
 
-                self._store_accuracies(logits_val, data_val[1], "val")
+                self._store_accuracies(logits_val, data_val[1], 'val')
                 self.val_loss.update(float(val_loss.detach().cpu()))
 
             end_time = time.time()
@@ -255,46 +289,39 @@ def evaluate_oneshot(self, resume_from="", dataloader=None):
         logger.info("Evaluation finished")
         return self.val_top1.avg
 
+
     def evaluate(
-        self,
-        retrain: bool = True,
-        search_model: str = "",
-        resume_from: str = "",
-        best_arch: Graph = None,
-        dataset_api: object = None,
-        metric: Metric = None,
-    ):
+            self, 
+            retrain=True, 
+            search_model="", 
+            resume_from="",
+            best_arch=None, 
+            dataset_api=None
+        ):
         """
         Evaluate the final architecture as given from the optimizer.
-
         If the search space has an interface to a benchmark then query that.
         Otherwise train as defined in the config.
-
         Args:
-            retrain (bool)      : Reset the weights from the architecure search
-            search_model (str)  : Path to checkpoint file that was created during search. If not provided,
-                                  then try to load 'model_final.pth' from search
-            resume_from (str)   : Resume retraining from the given checkpoint file.
-            best_arch           : Parsed model you want to directly evaluate and ignore the final model
-                                  from the optimizer.
-            dataset_api         : Dataset API to use for querying model performance.
-            metric              : Metric to query the benchmark for.
+            retrain (bool): Reset the weights from the architecure search
+            search_model (str): Path to checkpoint file that was created during
+                search. If not provided, then try to load 'model_final.pth' from search
+            resume_from (str): Resume retraining from the given checkpoint file.
+            best_arch: Parsed model you want to directly evaluate and ignore the final model
+                from the optimizer.
         """
         logger.info("Start evaluation")
         if not best_arch:
 
             if not search_model:
-                search_model = os.path.join(
-                    self.config.save, "search", "model_final.pth"
-                )
-            self._setup_checkpointers(search_model)  # required to load the architecture
+                search_model = os.path.join(self.config.save, "search", "model_final.pth")
+            self._setup_checkpointers(search_model)      # required to load the architecture
 
-            best_arch = self.optimizer.get_final_architecture()
+            best_arch, _ = self.optimizer.get_final_architecture()
         logger.info("Final architecture:\n" + best_arch.modules_str())
 
         if best_arch.QUERYABLE:
-            if metric is None:
-                metric = Metric.TEST_ACCURACY
+            metric = Metric.TEST_ACCURACY
             result = best_arch.query(
                 metric=metric, dataset=self.config.dataset, dataset_api=dataset_api
             )
@@ -305,27 +332,23 @@ def evaluate(
                 logger.info("Starting retraining from scratch")
                 best_arch.reset_weights(inplace=True)
 
-                (
-                    self.train_queue,
-                    self.valid_queue,
-                    self.test_queue,
-                ) = self.build_eval_dataloaders(self.config)
+                self.train_queue, self.valid_queue, self.test_queue = self.build_eval_dataloaders(self.config)
 
                 optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
                 scheduler = self.build_eval_scheduler(optim, self.config)
 
-                start_epoch = self._setup_checkpointers(
-                    resume_from,
-                    search=False,
+                start_epoch = self._setup_checkpointers(resume_from, 
+                    search=False, 
                     period=self.config.evaluation.checkpoint_freq,
-                    model=best_arch,  # checkpointables start here
+                    model=best_arch,    # checkpointables start here
                     optim=optim,
-                    scheduler=scheduler,
+                    scheduler=scheduler
                 )
 
                 grad_clip = self.config.evaluation.grad_clip
                 loss = torch.nn.CrossEntropyLoss()
 
+                best_arch.train()
                 self.train_top1.reset()
                 self.train_top5.reset()
                 self.val_top1.reset()
@@ -333,33 +356,23 @@ def evaluate(
 
                 # Enable drop path
                 best_arch.update_edges(
-                    update_func=lambda edge: edge.data.set(
-                        "op", DropPathWrapper(edge.data.op)
-                    ),
+                    update_func=lambda edge: edge.data.set('op', DropPathWrapper(edge.data.op)),
                     scope=best_arch.OPTIMIZER_SCOPE,
-                    private_edge_data=True,
+                    private_edge_data=True
                 )
 
                 # train from scratch
                 epochs = self.config.evaluation.epochs
                 for e in range(start_epoch, epochs):
-                    best_arch.train()
-
                     if torch.cuda.is_available():
-                        log_first_n(
-                            logging.INFO,
-                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
-                            n=20,
-                        )
+                        log_first_n(logging.INFO, "cuda consumption\n {}".format(torch.cuda.memory_summary()), n=20)
 
                     # update drop path probability
                     drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
                     best_arch.update_edges(
-                        update_func=lambda edge: edge.data.set(
-                            "drop_path_prob", drop_path_prob
-                        ),
+                        update_func=lambda edge: edge.data.set('drop_path_prob', drop_path_prob),
                         scope=best_arch.OPTIMIZER_SCOPE,
-                        private_edge_data=True,
+                        private_edge_data=True
                     )
 
                     # Train queue
@@ -370,60 +383,41 @@ def evaluate(
                         optim.zero_grad()
                         logits_train = best_arch(input_train)
                         train_loss = loss(logits_train, target_train)
-                        if hasattr(
-                            best_arch, "auxilary_logits"
-                        ):  # darts specific stuff
+                        if hasattr(best_arch, 'auxilary_logits'):   # darts specific stuff
                             log_first_n(logging.INFO, "Auxiliary is used", n=10)
-                            auxiliary_loss = loss(
-                                best_arch.auxilary_logits(), target_train
-                            )
-                            train_loss += (
-                                self.config.evaluation.auxiliary_weight * auxiliary_loss
-                            )
+                            auxiliary_loss = loss(best_arch.auxilary_logits(), target_train)
+                            train_loss += self.config.evaluation.auxiliary_weight * auxiliary_loss
                         train_loss.backward()
                         if grad_clip:
-                            torch.nn.utils.clip_grad_norm_(
-                                best_arch.parameters(), grad_clip
-                            )
+                            torch.nn.utils.clip_grad_norm_(best_arch.parameters(), grad_clip)
                         optim.step()
 
-                        self._store_accuracies(logits_train, target_train, "train")
-                        log_every_n_seconds(
-                            logging.INFO,
-                            "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
-                                e, i, train_loss, scheduler.get_last_lr()
-                            ),
-                            n=5,
-                        )
-
+                        self._store_accuracies(logits_train, target_train, 'train')
+                        log_every_n_seconds(logging.INFO, "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
+                            e, i, train_loss, scheduler.get_last_lr()), n=5)
+                        
+                        
                     # Validation queue
                     if self.valid_queue:
-                        best_arch.eval()
-                        for i, (input_valid, target_valid) in enumerate(
-                            self.valid_queue
-                        ):
-
-                            input_valid = input_valid.to(self.device).float()
-                            target_valid = target_valid.to(self.device).float()
+                        for i, (input_valid, target_valid) in enumerate(self.valid_queue):
+                            
+                            input_valid = input_valid.cuda().float()
+                            target_valid = target_valid.cuda().float()
 
                             # just log the validation accuracy
                             with torch.no_grad():
                                 logits_valid = best_arch(input_valid)
-                                self._store_accuracies(
-                                    logits_valid, target_valid, "val"
-                                )
+                                self._store_accuracies(logits_valid, target_valid, 'val')
 
                     scheduler.step()
-                    self.checkpointer.step(e)
+                    self.periodic_checkpointer.step(e)
                     self._log_and_reset_accuracies(e)
 
             # Disable drop path
             best_arch.update_edges(
-                update_func=lambda edge: edge.data.set(
-                    "op", edge.data.op.get_embedded_ops()
-                ),
+                update_func=lambda edge: edge.data.set('op', edge.data.op.get_embedded_ops()),
                 scope=best_arch.OPTIMIZER_SCOPE,
-                private_edge_data=True,
+                private_edge_data=True
             )
 
             # measure final test accuracy
@@ -446,32 +440,23 @@ def evaluate(
                     top1.update(prec1.data.item(), n)
                     top5.update(prec5.data.item(), n)
 
-                log_every_n_seconds(
-                    logging.INFO,
-                    "Inference batch {} of {}.".format(i, len(self.test_queue)),
-                    n=5,
-                )
+                log_every_n_seconds(logging.INFO, "Inference batch {} of {}.".format(i, len(self.test_queue)), n=5)
+
+            logger.info("Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(top1.avg, top5.avg))
 
-            logger.info(
-                "Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(
-                    top1.avg, top5.avg
-                )
-            )
 
     @staticmethod
     def build_search_dataloaders(config):
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode="train"
-        )
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(config, mode='train')
         return train_queue, valid_queue, _  # test_queue is not used in search currently
 
+
     @staticmethod
     def build_eval_dataloaders(config):
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode="val"
-        )
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(config, mode='val')
         return train_queue, valid_queue, test_queue
 
+
     @staticmethod
     def build_eval_optimizer(parameters, config):
         return torch.optim.SGD(
@@ -481,41 +466,49 @@ def build_eval_optimizer(parameters, config):
             weight_decay=config.evaluation.weight_decay,
         )
 
+
     @staticmethod
     def build_search_scheduler(optimizer, config):
         return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer,
-            T_max=config.search.epochs,
-            eta_min=config.search.learning_rate_min,
+            optimizer, 
+            T_max=config.search.epochs, 
+            eta_min=config.search.learning_rate_min
         )
 
+
     @staticmethod
     def build_eval_scheduler(optimizer, config):
         return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer,
-            T_max=config.evaluation.epochs,
-            eta_min=config.evaluation.learning_rate_min,
+            optimizer, 
+            T_max=config.evaluation.epochs, 
+            eta_min=config.evaluation.learning_rate_min
         )
 
-    def _log_and_reset_accuracies(self, epoch, writer=None):
-        logger.info(
-            "Epoch {} done. Train accuracy (top1, top5): {:.5f}, {:.5f}, Validation accuracy: {:.5f}, {:.5f}".format(
+
+    def _log_and_reset_accuracies(self, epoch, *args):
+        if args:
+            r, i = args[0], args[1]
+            logger.info("Process: {}, Round {} Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
+                i,
+                r,
                 epoch,
                 self.train_top1.avg,
-                self.train_top5.avg,
                 self.val_top1.avg,
-                self.val_top5.avg,
-            )
-        )
-
-        if writer is not None:
-            writer.add_scalar('Train accuracy (top 1)', self.train_top1.avg, epoch)
-            writer.add_scalar('Train accuracy (top 5)', self.train_top5.avg, epoch)
-            writer.add_scalar('Train loss', self.train_loss.avg, epoch)
-            writer.add_scalar('Validation accuracy (top 1)', self.val_top1.avg, epoch)
-            writer.add_scalar('Validation accuracy (top 5)', self.val_top5.avg, epoch)
-            writer.add_scalar('Validation loss', self.val_loss.avg, epoch)
-
+                self.errors_dict['runtime'][-1],
+                self.errors_dict['train_time'][-1],
+                sum(self.errors_dict['runtime']) +
+                sum(self.errors_dict['train_time'])
+            ))
+        else:
+            logger.info("Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
+                epoch,
+                self.train_top1.avg,
+                self.val_top1.avg,
+                self.errors_dict['runtime'][-1],
+                self.errors_dict['train_time'][-1],
+                sum(self.errors_dict['runtime']) +
+                sum(self.errors_dict['train_time']),
+            ))
         self.train_top1.reset()
         self.train_top5.reset()
         self.train_loss.reset()
@@ -523,6 +516,7 @@ def _log_and_reset_accuracies(self, epoch, writer=None):
         self.val_top5.reset()
         self.val_loss.reset()
 
+
     def _store_accuracies(self, logits, target, split):
         """Update the accuracy counters"""
         logits = logits.clone().detach().cpu()
@@ -530,38 +524,34 @@ def _store_accuracies(self, logits, target, split):
         prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
         n = logits.size(0)
 
-        if split == "train":
+        if split == 'train':
             self.train_top1.update(prec1.data.item(), n)
             self.train_top5.update(prec5.data.item(), n)
-        elif split == "val":
+        elif split == 'val':
             self.val_top1.update(prec1.data.item(), n)
             self.val_top5.update(prec5.data.item(), n)
         else:
             raise ValueError("Unknown split: {}. Expected either 'train' or 'val'")
 
-    def _prepare_dataloaders(self, config, mode="train"):
+
+    def _prepare_dataloaders(self, config, mode='train'):
         """
         Prepare train, validation, and test dataloaders with the splits defined
         in the config.
-
         Args:
             config (AttrDict): config from config file.
         """
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode
-        )
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(config, mode)
         self.train_queue = train_queue
         self.valid_queue = valid_queue
         self.test_queue = test_queue
+    
 
-    def _setup_checkpointers(
-        self, resume_from="", search=True, period=1, **add_checkpointables
-    ):
+    def _setup_checkpointers(self, resume_from="", search=True, period=1, **add_checkpointables):
         """
         Sets up a periodic chechkpointer which can be used to save checkpoints
         at every epoch. It will call optimizer's `get_checkpointables()` as objects
         to store.
-
         Args:
             resume_from (str): A checkpoint file to resume the search or evaluation from.
             search (bool): Whether search or evaluation phase is checkpointed. This is required
@@ -571,46 +561,38 @@ def _setup_checkpointers(
         """
         checkpointables = self.optimizer.get_checkpointables()
         checkpointables.update(add_checkpointables)
-        self.checkpointer = utils.Checkpointer( 
-            model=checkpointables.pop("model"),
-            save_dir=self.config.save + "/search"
-            if search
-            else self.config.save + "/eval",
-            # **checkpointables #NOTE: this is throwing an Error
+
+        checkpointer = utils.Checkpointer(
+            model=checkpointables.pop('model'),
+            save_dir=self.config.save + "/search" if search else self.config.save + "/eval",
+            #**checkpointables #NOTE: this is throwing an Error
         )
 
-    
+        self.periodic_checkpointer = PeriodicCheckpointer(
+            checkpointer,
+            period=period,
+            max_iter=1)
+        #max_iter=self.config.search.epochs if search else self.config.evaluation.epochs
+
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))
-            # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
-            checkpoint = self.checkpointer.resume_or_load("resume_from", resume=True)
-            if self.checkpointer.has_checkpoint():
+            checkpoint = checkpointer.resume_or_load(resume_from, resume=True)
+            if checkpointer.has_checkpoint():
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
-    def _log_optimizer_stats(self, filename="sh_stats.json"):
-        """Log extended statistics to json file"""
-        if not os.path.exists(self.config.save):
-            os.makedirs(self.config.save)
-        with codecs.open(
-            os.path.join(self.config.save, filename), "w", encoding="utf-8"
-        ) as file:
-            json.dump(self.optimizer.optimizer_stats, file, separators=(",", ":"))
 
     def _log_to_json(self):
         """log training statistics to json file"""
         if not os.path.exists(self.config.save):
             os.makedirs(self.config.save)
         if not self.lightweight_output:
-            with codecs.open(
-                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
-            ) as file:
-                json.dump(self.errors_dict, file, separators=(",", ":"))
+            with codecs.open(os.path.join(self.config.save, 'errors.json'), 'w', encoding='utf-8') as file:
+                json.dump(self.errors_dict, file, separators=(',', ':'))
         else:
-            with codecs.open(
-                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
-            ) as file:
+            with codecs.open(os.path.join(self.config.save, 'errors.json'), 'w', encoding='utf-8') as file:
                 lightweight_dict = copy.deepcopy(self.errors_dict)
-                for key in ["arch_eval", "train_loss", "valid_loss", "test_loss"]:
+                for key in ['arch_eval', 'train_loss', 'valid_loss', 'test_loss']:
                     lightweight_dict.pop(key)
-                json.dump([self.config, lightweight_dict], file, separators=(",", ":"))
+                json.dump([self.config, lightweight_dict], file, separators=(',', ':'))
+
diff --git a/naslib/defaults/trainer_multifidelity_old.py b/naslib/defaults/trainer_multifidelity_old.py
new file mode 100644
index 000000000..de547b048
--- /dev/null
+++ b/naslib/defaults/trainer_multifidelity_old.py
@@ -0,0 +1,615 @@
+import codecs
+from naslib.search_spaces.core.graph import Graph
+import time
+import json
+import logging
+import os
+import copy
+import torch
+import numpy as np
+
+from fvcore.common.checkpoint import Checkpointer
+
+from naslib.search_spaces.core.query_metrics import Metric
+
+from naslib.utils import utils
+from naslib.utils.logging import log_every_n_seconds, log_first_n
+
+from typing import Callable
+#from .additional_primitives import DropPathWrapper #Can be causes issues
+
+logger = logging.getLogger(__name__)
+
+
+class Trainer(object):
+    """
+    Default implementation that handles dataloading and preparing batches, the
+    train loop, gathering statistics, checkpointing and doing the final
+    final evaluation.
+
+    If this does not fulfil your needs free do subclass it and implement your
+    required logic.
+    """
+
+    def __init__(self, optimizer, config, lightweight_output=False):
+        """
+        Initializes the trainer.
+
+        Args:
+            optimizer: A NASLib optimizer
+            config (AttrDict): The configuration loaded from a yaml file, e.g
+                via  `utils.get_config_from_args()`
+        """
+        self.optimizer = optimizer
+        self.config = config
+        self.epochs = self.config.search.epochs
+        self.lightweight_output = lightweight_output
+
+        # preparations
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        # measuring stuff
+        self.train_top1 = utils.AverageMeter()
+        self.train_top5 = utils.AverageMeter()
+        self.train_loss = utils.AverageMeter()
+        self.val_top1 = utils.AverageMeter()
+        self.val_top5 = utils.AverageMeter()
+        self.val_loss = utils.AverageMeter()
+
+        n_parameters = optimizer.get_model_size()
+        logger.info("param size = %fMB", n_parameters)
+        self.errors_dict = utils.AttrDict(
+            {
+                "train_acc": [],
+                "train_loss": [],
+                "valid_acc": [],
+                "fidelity": [],
+                "arch": [],
+                "valid_loss": [],
+                "test_acc": [],
+                "test_loss": [],
+                "runtime": [],
+                "train_time": [],
+                "arch_eval": [],
+                "params": n_parameters,
+            }
+        )
+
+    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None] = None, report_incumbent=True):
+        """
+        Start the architecture search.
+
+        Generates a json file with training statistics.
+
+        Args:
+            resume_from (str): Checkpoint file to resume from. If not given then
+                train from scratch.
+        """
+        logger.info("Start training")
+
+        np.random.seed(self.config.search.seed)
+        torch.manual_seed(self.config.search.seed)
+
+        self.optimizer.before_training()
+        checkpoint_freq = self.config.search.checkpoint_freq
+        if self.optimizer.using_step_function:
+            self.scheduler = self.build_search_scheduler(
+                self.optimizer.op_optimizer, self.config
+            )
+
+            start_epoch = self._setup_checkpointers(
+                resume_from, period=checkpoint_freq, scheduler=self.scheduler
+            )
+        else:
+            start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq)
+
+        if self.optimizer.using_step_function:
+            self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(
+                self.config
+            )
+        e = start_epoch
+        # TODO: good name for "epochs"
+        while e < self.epochs:
+
+            start_time = time.time()
+            # return function_eqalisation difference time to run or number of elovations
+            used_budget = self.optimizer.new_epoch()
+
+            if self.optimizer.using_step_function:
+                for step, data_train in enumerate(self.train_queue):
+                    data_train = (
+                        data_train[0].to(self.device),
+                        data_train[1].to(self.device, non_blocking=True),
+                    )
+                    data_val = next(iter(self.valid_queue))
+                    data_val = (
+                        data_val[0].to(self.device),
+                        data_val[1].to(self.device, non_blocking=True),
+                    )
+
+                    stats = self.optimizer.step(data_train, data_val)
+                    logits_train, logits_val, train_loss, val_loss = stats
+
+                    self._store_accuracies(logits_train, data_train[1], "train")
+                    self._store_accuracies(logits_val, data_val[1], "val")
+
+                    log_every_n_seconds(
+                        logging.INFO,
+                        "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
+                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()
+                        ),
+                        n=5,
+                    )
+
+                    if torch.cuda.is_available():
+                        log_first_n(
+                            logging.INFO,
+                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                            n=3,
+                        )
+
+                    self.train_loss.update(float(train_loss.detach().cpu()))
+                    self.val_loss.update(float(val_loss.detach().cpu()))
+
+                self.scheduler.step()
+
+                end_time = time.time()
+
+                self.errors_dict.train_acc.append(self.train_top1.avg)
+                self.errors_dict.train_loss.append(self.train_loss.avg)
+                self.errors_dict.valid_acc.append(self.val_top1.avg)
+                self.errors_dict.valid_loss.append(self.val_loss.avg)
+                self.errors_dict.runtime.append(end_time - start_time)
+            else:
+                end_time = time.time()
+                # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
+                # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
+                # only for debugging is it but out
+                (
+                    train_acc,
+                    valid_acc,
+                    test_acc,
+                    train_time,
+                ) = self.optimizer.train_statistics()
+                train_loss, valid_loss, test_loss = -1, -1, -1
+
+                self.errors_dict.train_acc.append(train_acc)
+                self.errors_dict.train_loss.append(train_loss)
+                self.errors_dict.valid_acc.append(valid_acc)
+                self.errors_dict.valid_loss.append(valid_loss)
+                self.errors_dict.test_acc.append(test_acc)
+                self.errors_dict.test_loss.append(test_loss)
+                self.errors_dict.runtime.append(end_time - start_time)
+                self.errors_dict.train_time.append(train_time)
+                self.train_top1.avg = train_acc
+                self.val_top1.avg = valid_acc
+            self.checkpointer.save(e) # define the name in accurate , also 
+            # TODO: change step into save for checkpointer
+            anytime_results = self.optimizer.test_statistics()
+            if anytime_results:
+                # record anytime performance
+                self.errors_dict.arch_eval.append(anytime_results)
+                #log_every_n_seconds(
+                #    logging.INFO,
+                #    "Epoch {}, Anytime results: {}".format(e, anytime_results),
+                #    n=5,
+                #)
+
+            self._log_to_json()
+            self._log_and_reset_accuracies(e, summary_writer)
+            if after_epoch is not None:
+                after_epoch(e)
+
+            e += used_budget
+
+        self.optimizer.after_training()
+        self._log_optimizer_stats()
+
+        if summary_writer is not None:
+            summary_writer.close()
+
+        logger.info("Training finished")
+
+    def evaluate_oneshot(self, resume_from="", dataloader=None):
+        """
+        Evaluate the one-shot model on the specified dataset.
+
+        Generates a json file with training statistics.
+
+        Args:
+            resume_from (str): Checkpoint file to resume from. If not given then
+                evaluate with the current one-shot weights.
+        """
+        logger.info("Start one-shot evaluation")
+        self.optimizer.before_training()
+        self._setup_checkpointers(resume_from)
+
+        loss = torch.nn.CrossEntropyLoss()
+
+        if dataloader is None:
+            # load only the validation data
+            _, dataloader, _ = self.build_search_dataloaders(self.config)
+
+        self.optimizer.graph.eval()
+        with torch.no_grad():
+            start_time = time.time()
+            for step, data_val in enumerate(dataloader):
+                input_val = data_val[0].to(self.device)
+                target_val = data_val[1].to(self.device, non_blocking=True)
+
+                logits_val = self.optimizer.graph(input_val)
+                val_loss = loss(logits_val, target_val)
+
+                self._store_accuracies(logits_val, data_val[1], "val")
+                self.val_loss.update(float(val_loss.detach().cpu()))
+
+            end_time = time.time()
+
+            self.errors_dict.valid_acc.append(self.val_top1.avg)
+            self.errors_dict.valid_loss.append(self.val_loss.avg)
+            self.errors_dict.runtime.append(end_time - start_time)
+
+            self._log_to_json()
+
+        logger.info("Evaluation finished")
+        return self.val_top1.avg
+
+    def evaluate(
+        self,
+        retrain: bool = True,
+        search_model: str = "",
+        resume_from: str = "",
+        best_arch: Graph = None,
+        dataset_api: object = None,
+        metric: Metric = None,
+    ):
+        """
+        Evaluate the final architecture as given from the optimizer.
+
+        If the search space has an interface to a benchmark then query that.
+        Otherwise train as defined in the config.
+
+        Args:
+            retrain (bool)      : Reset the weights from the architecure search
+            search_model (str)  : Path to checkpoint file that was created during search. If not provided,
+                                  then try to load 'model_final.pth' from search
+            resume_from (str)   : Resume retraining from the given checkpoint file.
+            best_arch           : Parsed model you want to directly evaluate and ignore the final model
+                                  from the optimizer.
+            dataset_api         : Dataset API to use for querying model performance.
+            metric              : Metric to query the benchmark for.
+        """
+        logger.info("Start evaluation")
+        if not best_arch:
+
+            if not search_model:
+                search_model = os.path.join(
+                    self.config.save, "search", "model_final.pth"
+                )
+            self._setup_checkpointers(search_model)  # required to load the architecture
+
+            best_arch = self.optimizer.get_final_architecture()
+        logger.info("Final architecture:\n" + best_arch.modules_str())
+
+        if best_arch.QUERYABLE:
+            if metric is None:
+                metric = Metric.TEST_ACCURACY
+            result = best_arch.query(
+                metric=metric, dataset=self.config.dataset, dataset_api=dataset_api
+            )
+            logger.info("Queried results ({}): {}".format(metric, result))
+        else:
+            best_arch.to(self.device)
+            if retrain:
+                logger.info("Starting retraining from scratch")
+                best_arch.reset_weights(inplace=True)
+
+                (
+                    self.train_queue,
+                    self.valid_queue,
+                    self.test_queue,
+                ) = self.build_eval_dataloaders(self.config)
+
+                optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
+                scheduler = self.build_eval_scheduler(optim, self.config)
+
+                start_epoch = self._setup_checkpointers(
+                    resume_from,
+                    search=False,
+                    period=self.config.evaluation.checkpoint_freq,
+                    model=best_arch,  # checkpointables start here
+                    optim=optim,
+                    scheduler=scheduler,
+                )
+
+                grad_clip = self.config.evaluation.grad_clip
+                loss = torch.nn.CrossEntropyLoss()
+
+                self.train_top1.reset()
+                self.train_top5.reset()
+                self.val_top1.reset()
+                self.val_top5.reset()
+
+                # Enable drop path
+                best_arch.update_edges(
+                    update_func=lambda edge: edge.data.set(
+                        "op", DropPathWrapper(edge.data.op)
+                    ),
+                    scope=best_arch.OPTIMIZER_SCOPE,
+                    private_edge_data=True,
+                )
+
+                # train from scratch
+                epochs = self.config.evaluation.epochs
+                for e in range(start_epoch, epochs):
+                    best_arch.train()
+
+                    if torch.cuda.is_available():
+                        log_first_n(
+                            logging.INFO,
+                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                            n=20,
+                        )
+
+                    # update drop path probability
+                    drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
+                    best_arch.update_edges(
+                        update_func=lambda edge: edge.data.set(
+                            "drop_path_prob", drop_path_prob
+                        ),
+                        scope=best_arch.OPTIMIZER_SCOPE,
+                        private_edge_data=True,
+                    )
+
+                    # Train queue
+                    for i, (input_train, target_train) in enumerate(self.train_queue):
+                        input_train = input_train.to(self.device)
+                        target_train = target_train.to(self.device, non_blocking=True)
+
+                        optim.zero_grad()
+                        logits_train = best_arch(input_train)
+                        train_loss = loss(logits_train, target_train)
+                        if hasattr(
+                            best_arch, "auxilary_logits"
+                        ):  # darts specific stuff
+                            log_first_n(logging.INFO, "Auxiliary is used", n=10)
+                            auxiliary_loss = loss(
+                                best_arch.auxilary_logits(), target_train
+                            )
+                            train_loss += (
+                                self.config.evaluation.auxiliary_weight * auxiliary_loss
+                            )
+                        train_loss.backward()
+                        if grad_clip:
+                            torch.nn.utils.clip_grad_norm_(
+                                best_arch.parameters(), grad_clip
+                            )
+                        optim.step()
+
+                        self._store_accuracies(logits_train, target_train, "train")
+                        log_every_n_seconds(
+                            logging.INFO,
+                            "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
+                                e, i, train_loss, scheduler.get_last_lr()
+                            ),
+                            n=5,
+                        )
+
+                    # Validation queue
+                    if self.valid_queue:
+                        best_arch.eval()
+                        for i, (input_valid, target_valid) in enumerate(
+                            self.valid_queue
+                        ):
+
+                            input_valid = input_valid.to(self.device).float()
+                            target_valid = target_valid.to(self.device).float()
+
+                            # just log the validation accuracy
+                            with torch.no_grad():
+                                logits_valid = best_arch(input_valid)
+                                self._store_accuracies(
+                                    logits_valid, target_valid, "val"
+                                )
+
+                    scheduler.step()
+                    self.checkpointer.step(e)
+                    self._log_and_reset_accuracies(e)
+
+            # Disable drop path
+            best_arch.update_edges(
+                update_func=lambda edge: edge.data.set(
+                    "op", edge.data.op.get_embedded_ops()
+                ),
+                scope=best_arch.OPTIMIZER_SCOPE,
+                private_edge_data=True,
+            )
+
+            # measure final test accuracy
+            top1 = utils.AverageMeter()
+            top5 = utils.AverageMeter()
+
+            best_arch.eval()
+
+            for i, data_test in enumerate(self.test_queue):
+                input_test, target_test = data_test
+                input_test = input_test.to(self.device)
+                target_test = target_test.to(self.device, non_blocking=True)
+
+                n = input_test.size(0)
+
+                with torch.no_grad():
+                    logits = best_arch(input_test)
+
+                    prec1, prec5 = utils.accuracy(logits, target_test, topk=(1, 5))
+                    top1.update(prec1.data.item(), n)
+                    top5.update(prec5.data.item(), n)
+
+                log_every_n_seconds(
+                    logging.INFO,
+                    "Inference batch {} of {}.".format(i, len(self.test_queue)),
+                    n=5,
+                )
+
+            logger.info(
+                "Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(
+                    top1.avg, top5.avg
+                )
+            )
+
+    @staticmethod
+    def build_search_dataloaders(config):
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode="train"
+        )
+        return train_queue, valid_queue, _  # test_queue is not used in search currently
+
+    @staticmethod
+    def build_eval_dataloaders(config):
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode="val"
+        )
+        return train_queue, valid_queue, test_queue
+
+    @staticmethod
+    def build_eval_optimizer(parameters, config):
+        return torch.optim.SGD(
+            parameters,
+            lr=config.evaluation.learning_rate,
+            momentum=config.evaluation.momentum,
+            weight_decay=config.evaluation.weight_decay,
+        )
+
+    @staticmethod
+    def build_search_scheduler(optimizer, config):
+        return torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            T_max=config.search.epochs,
+            eta_min=config.search.learning_rate_min,
+        )
+
+    @staticmethod
+    def build_eval_scheduler(optimizer, config):
+        return torch.optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            T_max=config.evaluation.epochs,
+            eta_min=config.evaluation.learning_rate_min,
+        )
+
+    def _log_and_reset_accuracies(self, epoch, writer=None):
+        logger.info(
+            "Epoch {} done. Train accuracy (top1, top5): {:.5f}, {:.5f}, Validation accuracy: {:.5f}, {:.5f}".format(
+                epoch,
+                self.train_top1.avg,
+                self.train_top5.avg,
+                self.val_top1.avg,
+                self.val_top5.avg,
+            )
+        )
+
+        if writer is not None:
+            writer.add_scalar('Train accuracy (top 1)', self.train_top1.avg, epoch)
+            writer.add_scalar('Train accuracy (top 5)', self.train_top5.avg, epoch)
+            writer.add_scalar('Train loss', self.train_loss.avg, epoch)
+            writer.add_scalar('Validation accuracy (top 1)', self.val_top1.avg, epoch)
+            writer.add_scalar('Validation accuracy (top 5)', self.val_top5.avg, epoch)
+            writer.add_scalar('Validation loss', self.val_loss.avg, epoch)
+
+        self.train_top1.reset()
+        self.train_top5.reset()
+        self.train_loss.reset()
+        self.val_top1.reset()
+        self.val_top5.reset()
+        self.val_loss.reset()
+
+    def _store_accuracies(self, logits, target, split):
+        """Update the accuracy counters"""
+        logits = logits.clone().detach().cpu()
+        target = target.clone().detach().cpu()
+        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
+        n = logits.size(0)
+
+        if split == "train":
+            self.train_top1.update(prec1.data.item(), n)
+            self.train_top5.update(prec5.data.item(), n)
+        elif split == "val":
+            self.val_top1.update(prec1.data.item(), n)
+            self.val_top5.update(prec5.data.item(), n)
+        else:
+            raise ValueError("Unknown split: {}. Expected either 'train' or 'val'")
+
+    def _prepare_dataloaders(self, config, mode="train"):
+        """
+        Prepare train, validation, and test dataloaders with the splits defined
+        in the config.
+
+        Args:
+            config (AttrDict): config from config file.
+        """
+        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
+            config, mode
+        )
+        self.train_queue = train_queue
+        self.valid_queue = valid_queue
+        self.test_queue = test_queue
+
+    def _setup_checkpointers(
+        self, resume_from="", search=True, period=1, **add_checkpointables
+    ):
+        """
+        Sets up a periodic chechkpointer which can be used to save checkpoints
+        at every epoch. It will call optimizer's `get_checkpointables()` as objects
+        to store.
+
+        Args:
+            resume_from (str): A checkpoint file to resume the search or evaluation from.
+            search (bool): Whether search or evaluation phase is checkpointed. This is required
+                because the files are in different folders to not be overridden
+            add_checkpointables (object): Additional things to checkpoint together with the
+                optimizer's checkpointables.
+        """
+        checkpointables = self.optimizer.get_checkpointables()
+        checkpointables.update(add_checkpointables)
+        self.checkpointer = utils.Checkpointer( 
+            model=checkpointables.pop("model"),
+            save_dir=self.config.save + "/search"
+            if search
+            else self.config.save + "/eval",
+            # **checkpointables #NOTE: this is throwing an Error
+        )
+
+    
+        if resume_from:
+            logger.info("loading model from file {}".format(resume_from))
+            # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
+            checkpoint = self.checkpointer.resume_or_load("resume_from", resume=True)
+            if self.checkpointer.has_checkpoint():
+                return checkpoint.get("iteration", -1) + 1
+        return 0
+
+    def _log_optimizer_stats(self, filename="sh_stats.json"):
+        """Log extended statistics to json file"""
+        if not os.path.exists(self.config.save):
+            os.makedirs(self.config.save)
+        with codecs.open(
+            os.path.join(self.config.save, filename), "w", encoding="utf-8"
+        ) as file:
+            json.dump(self.optimizer.optimizer_stats, file, separators=(",", ":"))
+
+    def _log_to_json(self):
+        """log training statistics to json file"""
+        if not os.path.exists(self.config.save):
+            os.makedirs(self.config.save)
+        if not self.lightweight_output:
+            with codecs.open(
+                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
+            ) as file:
+                json.dump(self.errors_dict, file, separators=(",", ":"))
+        else:
+            with codecs.open(
+                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
+            ) as file:
+                lightweight_dict = copy.deepcopy(self.errors_dict)
+                for key in ["arch_eval", "train_loss", "valid_loss", "test_loss"]:
+                    lightweight_dict.pop(key)
+                json.dump([self.config, lightweight_dict], file, separators=(",", ":"))
diff --git a/naslib/optimizers/__init__.py b/naslib/optimizers/__init__.py
index bda761007..5b4a8f25f 100644
--- a/naslib/optimizers/__init__.py
+++ b/naslib/optimizers/__init__.py
@@ -5,11 +5,11 @@
 from .oneshot.drnas.optimizer import DrNASOptimizer
 from .discrete.rs.optimizer import RandomSearch
 from .discrete.sh.optimizer import SuccessiveHalving
-from .discrete.hb.optimizer import HyperBand
 from .discrete.re.optimizer import RegularizedEvolution
 from .discrete.ls.optimizer import LocalSearch
 from .discrete.bananas.optimizer import Bananas
 from .discrete.bp.optimizer import BasePredictor
 from .discrete.npenas.optimizer import Npenas
-from .discrete.hb.optimizer import HyperBand
+from .discrete.hb.optimizer import HB
 from .discrete.bohb.optimizer import BOHB
+from .discrete.dehb.optimizer import DEHB
diff --git a/naslib/optimizers/core/metaclasses.py b/naslib/optimizers/core/metaclasses.py
index b97ce83e8..f12aa5af6 100644
--- a/naslib/optimizers/core/metaclasses.py
+++ b/naslib/optimizers/core/metaclasses.py
@@ -38,6 +38,13 @@ def test_statistics(self):
         Return anytime test statistics if provided by the optimizer
         """
         pass
+    
+
+    def compute_epochs(self):
+        """
+        Return anytime epochs if provided by the optimizer
+        """
+        return None
 
     @abstractmethod
     def adapt_search_space(self, search_space, scope=None):
diff --git a/naslib/optimizers/discrete/bohb/optimizer._old.py b/naslib/optimizers/discrete/bohb/optimizer._old.py
new file mode 100644
index 000000000..5a5668920
--- /dev/null
+++ b/naslib/optimizers/discrete/bohb/optimizer._old.py
@@ -0,0 +1,192 @@
+import numpy as np
+import torch
+import copy
+import math
+from collections import defaultdict
+from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
+from naslib.search_spaces.core.query_metrics import Metric
+
+from collections import defaultdict
+
+
+class BOHB(MetaOptimizer):
+    """
+    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(BOHB, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+        self.sh_config = copy.deepcopy(config)
+        self.tpe_config = copy.deepcopy(config)
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+        self.method = config.search.method
+        #self.fidelit_min = config.search.min_fidelity
+        self.budget_max = config.search.budget_max
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.s_max = math.floor(math.log(self.budget_max, self.eta))
+        self.s = self.s_max + 1
+        self.sh = None
+        self.first = True # TODO: think about a more ellegant solution 
+        self.b = (self.s_max + 1)* self.budget_max
+        self.encoding_type = config.search.encoding_type
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+        self.ensemble = None
+        
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+        if self.method == "tpe":
+            #self.tpe_config.search_space = self.search_space.clone()
+            #self.tpe_config.dataset_api  = self.dataset_api
+            self.ensemble =   Ensemble(
+                    predictor_type= "tpe",  
+                    num_ensemble= 1,
+                    encoding_type= self.encoding_type,
+                    ss_type = self.tpe_config.search_space,
+                    config_tpe = [self.search_space.clone(),self.dataset_api, self.dataset],
+                    hyperparams = self.tpe_config,   #replace with config maybe or not 
+
+                    )
+
+    def new_epoch(self):
+        """
+        Sample a new architecture to train.
+        after https://arxiv.org/pdf/1603.06560.pdf
+        """
+        # TODO: rethink because of s
+        if  self.sh == None or self.sh.end:
+            #if sh is finish go to something diffrent as initial budget
+                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
+            self.s -= 1
+            if self.sh != None:
+                self.sampled_archs.extend(self.sh.sampled_archs)
+            if self.s < 0:
+                print("HB is finish, allready not defined what to do") # TODO define what to do 
+                return math.inf #end the thing
+            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+            r = self.budget_max * self.eta ** (-self.s)
+            self.sh_config.search.number_archs = n
+            self.sh_config.search.min_fidelity  = r 
+
+            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
+            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+        
+        budget = self.sh.new_epoch()
+
+        self.update_optimizer_stats()
+        return budget
+                
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
+        all_sampled.extend(self.sh.sampled_archs)
+        return max(all_sampled, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+        
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+    
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
+        """
+        self.optimizer_stats[self.s] = self.sh.optimizer_stats
+
+
+
+    def test_statistics(self):
+        return False
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 5a5668920..ac9516868 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -1,192 +1,275 @@
-import numpy as np
+import collections
+import os
+import math
+import logging
 import torch
 import copy
-import math
-from collections import defaultdict
-from naslib.optimizers import SuccessiveHalving as SH
+import numpy as np
+
 from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
-from naslib.search_spaces.core.query_metrics import Metric
 
-from collections import defaultdict
+from naslib.search_spaces.core.query_metrics import Metric
+import statsmodels.api as sm
 
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
+from naslib.utils.logging import log_every_n_seconds
 
+logger = logging.getLogger(__name__)
+    
+        
 class BOHB(MetaOptimizer):
-    """
-    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
-    """
-
+    
     # training the models is not implemented
     using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(BOHB, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-        self.sh_config = copy.deepcopy(config)
-        self.tpe_config = copy.deepcopy(config)
+    
+    def __init__(self, config):
+        super().__init__()
+        # Hyperband related stuff
+        self.config = config
+        self.rounds = []
+        self.round_sizes = []
+        self.fidelities = []
+        self.min_bandwidth = self.config.search.min_bandwith
+        self.kde_models = dict()
+        self._epsilon = float(self.config.search.epsilon)
+        self.min_budget = self.config.search.min_budget
+        self.max_budget = self.config.search.max_budget
+        self.eta = self.config.search.eta
+        self.min_points_in_model = self.config.search.min_points_in_model
+        self.top_n_percent = self.config.search.top_n_percent
+        s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
+        # set up round sizes, fidelities, and list of arches
+        for s in reversed(range(s_max + 1)):
+            self.rounds.append(s)
+            round_sizes = []
+            fidelities = []
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
+            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            for i in range(s + 1):
+                n_i = math.floor(n / self.eta ** i + self._epsilon)
+                r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
+                round_sizes.append(n_i)
+                fidelities.append(r_i)
+            self.round_sizes.append(round_sizes)
+            self.fidelities.append(fidelities)
+        for budget in self.fidelities[0][1:]:
+            budget = min(budget, config.search.fidelity)
+            self.kde_models[budget] = {}
+            self.kde_models[budget]['good'] = collections.deque(maxlen=300)
+            self.kde_models[budget]['bad'] = collections.deque(maxlen=300)
+            self.kde_models[budget]['minimize_kde'] = None
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-        self.method = config.search.method
-        #self.fidelit_min = config.search.min_fidelity
-        self.budget_max = config.search.budget_max
-        self.eta = config.search.eta
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
-        self.sampled_archs = []
         self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s = self.s_max + 1
-        self.sh = None
-        self.first = True # TODO: think about a more ellegant solution 
-        self.b = (self.s_max + 1)* self.budget_max
-        self.encoding_type = config.search.encoding_type
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-        self.ensemble = None
-        
+
+        self.epochs = self.compute_epochs()
+        self.current_round = []
+        self.current_round_ = []
+        self.next_round = []
+        self.round_number = 0
+        self.prev_round = 0
+        self.process = 0
+
+
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
+        assert search_space.QUERYABLE, "Hyperband_simple is currently only implemented for benchmarks."
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
-        if self.method == "tpe":
-            #self.tpe_config.search_space = self.search_space.clone()
-            #self.tpe_config.dataset_api  = self.dataset_api
-            self.ensemble =   Ensemble(
-                    predictor_type= "tpe",  
-                    num_ensemble= 1,
-                    encoding_type= self.encoding_type,
-                    ss_type = self.tpe_config.search_space,
-                    config_tpe = [self.search_space.clone(),self.dataset_api, self.dataset],
-                    hyperparams = self.tpe_config,   #replace with config maybe or not 
-
-                    )
-
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        after https://arxiv.org/pdf/1603.06560.pdf
-        """
-        # TODO: rethink because of s
-        if  self.sh == None or self.sh.end:
-            #if sh is finish go to something diffrent as initial budget
-                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-            self.s -= 1
-            if self.sh != None:
-                self.sampled_archs.extend(self.sh.sampled_archs)
-            if self.s < 0:
-                print("HB is finish, allready not defined what to do") # TODO define what to do 
-                return math.inf #end the thing
-            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-            r = self.budget_max * self.eta ** (-self.s)
-            self.sh_config.search.number_archs = n
-            self.sh_config.search.min_fidelity  = r 
-
-            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
-            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-        
-        budget = self.sh.new_epoch()
+        self.max_training_epoch = self.search_space.get_max_epochs()
+
+
+    def compute_epochs(self):
+        return self.round_sizes, self.rounds[::-1]
+
+
+    def impute_conditional_data(self, array):
+        return_array = np.zeros(array.shape)
+        for i in range(array.shape[0]):
+            datum = np.copy(array[i])
+            nan_indices = np.argwhere(np.isnan(datum)).flatten()
+            while np.any(nan_indices):
+                nan_idx = nan_indices[0]
+                valid_indices = np.argwhere(np.isfinite(array[:, nan_idx])).flatten()
+                if valid_indices:
+                    # pick one of them at random and overwrite all NaN values
+                    row_idx = np.random.choice(valid_indices)
+                    datum[nan_indices] = array[row_idx, nan_indices]
+                else:
+                    # no good point in the data has this value activated, so fill it with a valid but random value
+                    t = self.vartypes[nan_idx]
+                    if t == 0:
+                        datum[nan_idx] = np.random.rand()
+                    else:
+                        datum[nan_idx] = np.random.randint(t)
+                nan_indices = np.argwhere(np.isnan(datum)).flatten()
+            return_array[i, :] = datum
+        return return_array
+
+
+    def fit_kde(self, round):
+        budget = self.fidelities[round][0]
+        good_models = self.kde_models[budget]['good']
+        bad_models = self.kde_models[budget]['bad']
+        if self.config.search_space == 'nasbench101':
+            from naslib.predictors.utils.encodings_nb101 import encode_101
+            good_enc = np.array([encode_101(m.arch, encoding_type='adjacency_cat') for m in good_models])
+            bad_enc = np.array([encode_101(m.arch, encoding_type='adjacency_cat') for m in bad_models])
+            self.kde_vartypes = ""
+            self.vartypes = []
+            for _ in range(len(good_enc[0])-5): # adj encoding + one-hot ops list
+                self.kde_vartypes += 'u'
+                self.vartypes += [2]
+            for _ in range(len(good_enc[0])-5, len(good_enc[0])):  # adj encoding + one-hot ops list
+                self.kde_vartypes += 'u'
+                self.vartypes += [3]
+        elif self.config.search_space == "nasbench201":
+            from naslib.search_spaces.nasbench201.conversions import convert_naslib_to_op_indices
+            good_enc = np.array([convert_naslib_to_op_indices(m.arch) for m in good_models])
+            bad_enc = np.array([convert_naslib_to_op_indices(m.arch) for m in bad_models])
+            self.kde_vartypes = ""
+            self.vartypes = []
+            for _ in range(len(good_enc[0])):  # we use unordered discrete variable
+                self.kde_vartypes += 'u'
+                self.vartypes += [5]  # depend on the encoding of search spaces
+        elif self.config.search_space == "darts":
+            from naslib.search_spaces.darts.conversions import convert_naslib_to_compact, \
+                make_compact_mutable, convert_mutable_to_vector
+            good_enc = np.array([convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in good_models])
+            bad_enc = np.array([convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in bad_models])
+            self.kde_vartypes = ""
+            self.vartypes = []
+            for i in range(len(good_enc[0])):  # we use unordered discrete variable
+                self.kde_vartypes += 'u'
+                if i % 2 == 0:
+                    self.vartypes += [5]  # depend on the encoding of search spaces
+                else:
+                    self.vartypes += [7]
+        self.vartypes = np.array(self.vartypes, dtype=int)
+        good_enc = self.impute_conditional_data(good_enc)
+        bad_enc = self.impute_conditional_data(bad_enc)
+        self.good_kde = sm.nonparametric.KDEMultivariate(data=good_enc, var_type=self.kde_vartypes,
+                                                         bw='normal_reference')
+        self.bad_kde = sm.nonparametric.KDEMultivariate(data=bad_enc, var_type=self.kde_vartypes,
+                                                        bw='normal_reference')
+        self.bad_kde.bw = np.clip(self.bad_kde.bw, self.min_bandwidth, None)
+        self.good_kde.bw = np.clip(self.good_kde.bw, self.min_bandwidth, None)
+        l = self.good_kde.pdf
+        g = self.bad_kde.pdf
+        self.minimize_me = lambda x: max(1e-32, g(x) / max(l(x), 1e-32))
+
+
+    def new_epoch(self, epoch, round, i):
+        if self.process < i: # re-init for each new process
+            self.current_round = []
+            self.current_round_ = []
+            self.next_round = []
+            self.round_number = 0
+            self.prev_round = 0
+            self.process = i
+
+        if self.prev_round < round:  # reset round_number for each new round
+            self.prev_round = round
+            self.round_number = 0
+
+        if epoch < self.round_sizes[round][0]:
+            # sample random architectures
+            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            budget = self.fidelities[round][0]
+            if round == 0:
+                model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+            else:
+                print("budget: {}, the number of good enc: {}".format(budget, len(self.kde_models[budget]['good'])))
+                print("budget: {}, the number of bad enc: {}".format(budget, len(self.kde_models[budget]['bad'])))
+                if epoch == 0 and \
+                        len(self.kde_models[budget]['good']) >= self.min_points_in_model and \
+                        len(self.kde_models[budget]['bad']) >= self.min_points_in_model:
+                    self.fit_kde(round)
+                    self.kde_models[budget]['minimize_kde'] = True
+                if not self.kde_models[budget]['minimize_kde']:
+                    model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+                else:
+                    model.arch.model_based_sample_architecture(dataset_api=self.dataset_api,
+                                                               minimize_me=self.minimize_me,
+                                                               good_kde=self.good_kde,
+                                                               vartypes=self.vartypes
+                                                               )
+
+            model.epoch = min(self.fidelities[round][0], self.max_training_epoch)
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self._update_history(model)
+            self.next_round.append(model)
 
-        self.update_optimizer_stats()
-        return budget
-                
-    def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
         else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
+            if len(self.current_round) == 0:
+                # if we are at the end of a round of hyperband, continue training only the best
+                logger.info("Starting a new round: continuing to train the best arches")
+                cutoff = math.ceil(self.round_sizes[round][self.round_number] * self.top_n_percent)
+                self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
+                self.current_round_ = sorted(self.next_round, key=lambda x: -x.accuracy)[cutoff:]
+                self.round_number += 1
+                self.round_number = min(self.round_number, len(self.fidelities[round]) - 1)
+                while len(self.current_round_) > 0:
+                    self.kde_models[self.fidelities[round][self.round_number]]['bad'].append(self.current_round_.pop())
+                self.next_round = []
+            # train the next architecture
+            model = self.current_round.pop()
+            """
+            Note: technically we would just continue training this arch, but right now,
+            just for simplicity, we treat it as if we start to train it again from scratch
+            """
+            print(self.fidelities[round])
+            print(self.round_number)
+            print(self.fidelities[round][self.round_number])
+            model = copy.deepcopy(model)
+            model.epoch = min(self.fidelities[round][self.round_number], self.max_training_epoch)
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self.kde_models[self.fidelities[round][self.round_number]]['good'].append(model)
+            self._update_history(model)
+            self.next_round.append(model)
+
+    def _update_history(self, child):
+        self.history.append(child)
 
     def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
-        all_sampled.extend(self.sh.sampled_archs)
-        return max(all_sampled, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
         
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
+        # Returns the sampled architecture with the lowest validation error.
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        return best_arch.arch, best_arch.epoch
 
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
+    def get_latest_architecture(self):
 
-        )
-    def train_model_statistics(self, report_incumbent=True):
+        # Returns the architecture from the most recent epoch
+        latest_arch = self.history[-1]
+        return latest_arch.arch, latest_arch.epoch
 
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+    def train_statistics(self):
+        best_arch, best_arch_epoch = self.get_final_architecture()
+        latest_arch, latest_arch_epoch = self.get_latest_architecture()
         return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
         )
     
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
-        """
-        self.optimizer_stats[self.s] = self.sh.optimizer_stats
-
-
-
     def test_statistics(self):
-        return False
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+        best_arch, epoch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
+    
     def get_op_optimizer(self):
-        return self.weight_optimizer
+        raise NotImplementedError()
 
     def get_checkpointables(self):
-        return {"model": self.history}
+        return {'model': self.history}
+
+    def get_model_size(self):
+        return count_parameters_in_MB(self.history)
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 93297be29..932775fbb 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -1,312 +1,202 @@
-from logging import raiseExceptions
-import numpy as np
-import torch
-import copy
 import collections
 import math
-from collections import defaultdict
-from naslib.optimizers import SuccessiveHalving as SH
+import logging
+import torch
+import copy
+import numpy as np
+
 from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.search_spaces.core.query_metrics import Metric
 
-from collections import defaultdict
+from naslib.search_spaces.core.query_metrics import Metric
 
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
+from naslib.utils.logging import log_every_n_seconds
 
+logger = logging.getLogger(__name__)
+    
+        
 class DEHB(MetaOptimizer):
-    """
-    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
-    """
-
+    
     # training the models is not implemented
     using_step_function = False
+    
+    def __init__(self, config):
+        super().__init__()
+        # Hyperband related stuff
+        self.config = config
+        self.rounds = []
+        self.round_sizes = []
+        self.fidelities = []
+        self._epsilon = float(self.config.search.epsilon)
+        self.min_budget = self.config.search.min_budget
+        self.max_budget = self.config.search.max_budget
+        self.eta = self.config.search.eta
+        self.enc_dim = self.config.search.enc_dim
+        self.max_mutations = self.config.search.max_mutations
+        self.crossover_prob = self.config.search.crossover_prob
+        self.top_n_percent = self.config.search.top_n_percent
+        self.mutate_prob = self.config.search.mutate_prob
+        self.de = dict()
+        self.pop_size = {}
+        self.counter = 0
+        s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
+        # set up round sizes, fidelities, and list of arches
+        for s in reversed(range(s_max + 1)):
+            self.rounds.append(s)
+            round_sizes = []
+            fidelities = []
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
+            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            for i in range(s + 1):
+                n_i = math.floor(n / self.eta ** i + self._epsilon)
+                r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
+                round_sizes.append(n_i)
+                fidelities.append(r_i)
+                self.pop_size[r_i] = self.pop_size.get(r_i, 0) + n_i
+            self.round_sizes.append(round_sizes)
+            self.fidelities.append(fidelities)
+        for budget in self.fidelities[0][1:]:
+            budget = min(budget, config.search.fidelity)
+            self.de[budget] = {}
+            self.de[budget]['promotions'] = collections.deque(maxlen=100)
 
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(DEHB, self).__init__()
-        # DE related variables
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-        self.fidelity = config.search.min_fidelity
-        self.number_archs = config.search.number_archs
-        self.eta = config.search.eta
-        self.budget_max = config.search.budget_max 
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type  # is not for one query is overall
-        self.fidelity_counter = 0
         self.history = torch.nn.ModuleList()
-        self.end = False
-        self.old_fidelity = 0 
-        self.round_number = 0
-        self.top_n_percent = 0.3
-        self.method = config.search.method
-        self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.b = (self.s_max + 1)* self.budget_max
-        self.s = self.s_max
+
+
+        self.epochs = self.compute_epochs()
         self.current_round = []
         self.next_round = []
-        self.new_fidelity = False # TODO think of more ellegant sollution
-        self.de = dict()
-        self._epsilon = 1e-6
-        self.pop_size = {}       
-        self.de[self.fidelity] = {}
-        self.enc_dim = 6
-        self.max_mutations = 1
-        self.crossover_prob = 0.5
-        self.top_n_percent = 0.3
-        self.mutate_prob = 0.1
-        self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
-        # Global trackers
-        self.population = None
-        self.fitness = None
-        self.inc_score = np.inf
-        self.inc_config = None
-       
-        
+        self.round_number = 0
+        self.prev_round = 0
+        self.counter = 0
+        self.process = 0
+
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
+        assert search_space.QUERYABLE, "Hyperband_simple is currently only implemented for benchmarks."
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
-        #self.max_training_epoch = self.search_space.get_max_epochs() #deifne
-        self.max_training_epoch = 200
-       
-    def new_epoch(self):   #TODO: epoch, round and i have to be adappted 
-        #calculate s n,r not before but during like in bohb 
-        #
-        ##this mind help if we need to parraleise 
-        #if self.process < i: # re-init for each new process
-        #    self.current_round = []
-        #    self.next_round = []
-        #    self.round_number = 0
-        #    self.prev_round = 0
-        #    self.counter = 0
-        #    self.process = i
-       
-        print("bracket: {}, number_archs: {}, fidelity:{}, counter: {}".format(self.s,self.number_archs, self.fidelity, self.fidelity_counter))
-        if self.round_number >= self.s: # reset round_number for each new round
-            self.s -=1 
+        self.max_training_epoch = self.search_space.get_max_epochs()
+
+    def compute_epochs(self):
+        return self.round_sizes, self.rounds[::-1]
+
+    def new_epoch(self, epoch, round, i):
+        if self.process < i: # re-init for each new process
+            self.current_round = []
+            self.next_round = []
             self.round_number = 0
-            n = math.ceil(self.b / self.budget_max / (self.s + 1) * self.eta ** self.s)
-            r = self.budget_max * self.eta ** (-self.s)
-            self.number_archs = n
-            self.fidelity  = r
-            print("bracket: {}, number_archs: {}, fidelity:{}".format(self.s,self.number_archs, self.fidelity))
-        if self.s < 1:
-            raise NameError("this method is finish")
+            self.prev_round = 0
+            self.counter = 0
+            self.process = i
 
+        if self.prev_round < round:  # reset round_number for each new round
+            self.prev_round = round
+            self.round_number = 0
 
-        if self.fidelity_counter  <  self.number_archs:
+        if epoch < self.round_sizes[round][0]:
             # sample random architectures
             model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
-            budget = self.fidelity
-            if self.s == self.s_max:
-                model.arch.sample_random_architecture(dataset_api=self.dataset_api) #sample in the first round 
+            budget = self.fidelities[round][0]
+            if round == 0:
+                model.arch.sample_random_architecture(dataset_api=self.dataset_api)
             else:
-                if len(self.de[self.fidelity]['promotions']) > 0:  #if we have allready something mutate the best 
-                    print('promotion from budget: {}, length: {}'.format(self.fidelity, len(self.de[self.fidelity]['promotions'])))
-                    model = self.de[self.fidelity]['promotions'].pop()
+                if len(self.de[budget]['promotions']) > 0:
+                    print('promotion from budget: {}, length: {}'.format(budget, len(self.de[budget]['promotions'])))
+                    model = self.de[budget]['promotions'].pop()
                     model = copy.deepcopy(model)
                     arch = self.search_space.clone()
                     arch.mutate(model.arch, dataset_api=self.dataset_api)
                     model.arch = arch
                 else:
-                    model.arch.sample_random_architecture(dataset_api=self.dataset_api) #if not do something random
-            #model.epoch = self.fidelity # has to be changed
-            # TODO: this is really good idea to implemtent it in all optimizer (that we write or in other but not from us) 
-            model.epoch = int(min(self.fidelity, self.max_training_epoch)) #
+                    model.arch.sample_random_architecture(dataset_api=self.dataset_api)
+
+            model.epoch = min(self.fidelities[round][0], self.max_training_epoch)
             model.accuracy = model.arch.query(self.performance_metric,
                                               self.dataset,
                                               epoch=model.epoch,
                                               dataset_api=self.dataset_api)
-            budget = (self.fidelity - self.old_fidelity) / self.budget_max
-            # DONE: make query type secure
-            if self.budget_type == 'time':
-            #TODO also for predictions
-            # DONE: make dependent on performance_metric
-                model.time = model.arch.query(  # TODO: this is the time for training from screatch.
-                self.performance_metric,
-                self.dataset,
-                epoch=int(self.fidelity),
-                dataset_api=self.dataset_api,
-                )
-                budget = model.time
-            elif not(self.budget_type == "epoch"):
-                raise NameError("budget time should be time or epoch")
-            #Maybe this needs to the beginnig of this if 
-            self.fidelity_counter += 1
 
             self._update_history(model)
             self.next_round.append(model)
 
         else:
-           
-            #self.new_fidelity = True
-            if len(self.current_round) == 0: 
-                self.new_fidelity = False
-                self.old_fidelity = self.fidelity
-                self.fidelity = math.floor(self.eta*self.fidelity)
-                self.number_archs = math.floor(self.number_archs/self.eta) #maybe +1 or -1 not quite sure 
-            
-                self.de[self.fidelity] = {}
-                self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
-                self.round_number += 1
-                #n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-                #r = self.budget_max * self.eta ** (-self.s)
-                #self.number_archs = n
-                #self.fidelity  = r
-                self.fidelity_counter = 0
-                self.de[self.fidelity] = {}
-                self.de[self.fidelity]['promotions'] = collections.deque(maxlen=100)
+            if len(self.current_round) == 0:
                 # if we are at the end of a round of hyperband, continue training only the best
-                print("Starting a new round: continuing to train the best arches")
-                cutoff = math.ceil(self.number_archs * self.top_n_percent)
+                logger.info("Starting a new round: continuing to train the best arches")
+                self.counter = 0
+                cutoff = math.ceil(self.round_sizes[round][self.round_number] * self.top_n_percent)
                 self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
-                print("bracket: {}, number_archs: {}, fidelity:{}".format(self.s,self.number_archs, self.fidelity))
-                #self.round_number = min(self.round_number, len(self.fidelities[round]) - 1)
+                self.round_number += 1
+                self.round_number = min(self.round_number, len(self.fidelities[round]) - 1)
                 self.next_round = []
-        
-                
-            
-              
-               
+
             # train the next architecture
             model = self.current_round.pop()
-            self.fidelity_counter += 1
+            self.counter += 1
             """
             Note: technically we would just continue training this arch, but right now,
             just for simplicity, we treat it as if we start to train it again from scratch
             """
             model = copy.deepcopy(model)
 
-            if np.random.rand(1) < self.mutate_prob:  #mutate through any     
+            if np.random.rand(1) < self.mutate_prob:
                 candidate = model.arch.clone()
-                for _ in range(self.max_mutations): #here the mutation happens.
+                for _ in range(self.max_mutations):
                     arch_ = self.search_space.clone()
                     arch_.mutate(candidate, dataset_api=self.dataset_api)
                     candidate = arch_
                 mutant = candidate
                 arch = self.search_space.clone()
-                arch.crossover_bin(model.arch, mutant, self.enc_dim, self.crossover_prob, dataset_api=self.dataset_api) #this needs to be implemeted 
+                arch.crossover_bin(model.arch, mutant, self.enc_dim, self.crossover_prob, dataset_api=self.dataset_api)
                 model.arch = arch
-            #model.epoch = self.fidelity
-            model.epoch = int(min(self.fidelity, self.max_training_epoch))
+            model.epoch = min(self.fidelities[round][self.round_number], self.max_training_epoch)
             model.accuracy = model.arch.query(self.performance_metric,
                                               self.dataset,
                                               epoch=model.epoch,
                                               dataset_api=self.dataset_api)
-            budget = (self.fidelity - self.old_fidelity) / self.budget_max
-            # DONE: make query type secure
-            if self.budget_type == 'time':
-            #TODO also for predictions
-            # DONE: make dependent on performance_metric
-                model.time = model.arch.query(  # TODO: this is the time for training from screatch.
-                self.performance_metric,
-                self.dataset,
-                epoch=model.epoch,
-                dataset_api=self.dataset_api,
-                )
-                budget = model.time
-            elif not(self.budget_type == "epoch"):
-                raise NameError("budget time should be time or epoch")
-            self.de[self.fidelity]['promotions'].append(model)  #apened model if round is finsih 
+            self.de[self.fidelities[round][self.round_number]]['promotions'].append(model)
             self._update_history(model)
             self.next_round.append(model)
-        return budget
-    def _update_history(self, child): #not even sure if this is needed, but why not 
+
+    def _update_history(self, child):
         self.history.append(child)
 
     def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        best_arch = max(self.history, key=lambda x: x.accuracy)
-        return best_arch.arch
-
-    def train_statistics(self, report_incumbent=True):
         
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
-
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-
-        )
-
+        # Returns the sampled architecture with the lowest validation error.
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        return best_arch.arch, best_arch.epoch
 
-    
+    def get_latest_architecture(self):
 
-    def train_model_statistics(self, report_incumbent=True):
+        # Returns the architecture from the most recent epoch
+        latest_arch = self.history[-1]
+        return latest_arch.arch, latest_arch.epoch
 
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+    def train_statistics(self):
+        best_arch, best_arch_epoch = self.get_final_architecture()
+        latest_arch, latest_arch_epoch = self.get_latest_architecture()
         return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1),
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch),
         )
     
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
-        """
-        self.optimizer_stats[self.s] = self.sh.optimizer_stats
-
-
-
     def test_statistics(self):
-        return False
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+        best_arch, epoch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
+    
     def get_op_optimizer(self):
-        return self.weight_optimizer
+        raise NotImplementedError()
 
     def get_checkpointables(self):
-        return {"model": self.history}
+        return {'model': self.history}
+
+    def get_model_size(self):
+        return count_parameters_in_MB(self.history)
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/hb/old.py b/naslib/optimizers/discrete/hb/old.py
new file mode 100644
index 000000000..da8703a0f
--- /dev/null
+++ b/naslib/optimizers/discrete/hb/old.py
@@ -0,0 +1,176 @@
+import numpy as np
+import torch
+import copy
+import math
+from collections import defaultdict
+from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
+from naslib.search_spaces.core.query_metrics import Metric
+
+from collections import defaultdict
+
+
+class HyperBand(MetaOptimizer):
+    """
+    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
+    """
+
+    # training the models is not implemented
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+    ):
+        """
+        Initialize a Successive Halving  optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(HyperBand, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+        self.sh_config = copy.deepcopy(config)
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+        self.budget_max = config.search.budget_max
+        self.eta = config.search.eta
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type #is not for one query is overall
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.s_max = math.floor(math.log(self.budget_max, self.eta))
+        self.s = self.s_max + 1
+        self.sh = None
+        self.first = True # TODO: think about a more ellegant solution 
+        self.b = (self.s_max + 1)* self.budget_max
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+       
+        
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+
+    def new_epoch(self):
+        """
+        Sample a new architecture to train.
+        after https://arxiv.org/pdf/1603.06560.pdf
+        """
+        # TODO: rethink because of s
+        if  self.sh == None or self.sh.end:
+            #if sh is finish go to something diffrent as initial budget
+                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
+            self.s -= 1
+            if self.sh != None:
+                self.sampled_archs.extend(self.sh.sampled_archs)
+            if self.s < 0:
+                print("HB is finish, allready not defined what to do") # TODO define what to do 
+                return math.inf #end the thing
+            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
+            r = self.budget_max * self.eta ** (-self.s)
+            self.sh_config.search.number_archs = n
+            self.sh_config.search.min_fidelity  = r 
+
+            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
+            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
+        
+        budget = self.sh.new_epoch()
+
+        self.update_optimizer_stats()
+        return budget
+                
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
+        all_sampled.extend(self.sh.sampled_archs)
+        return max(all_sampled, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+        
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+
+        )
+    def train_model_statistics(self, report_incumbent=True):
+
+        
+        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
+        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+            self.fidelity,
+            best_arch_hash,
+        )
+    
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
+        """
+        self.optimizer_stats[self.s] = self.sh.optimizer_stats
+
+
+
+    def test_statistics(self):
+        return False
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+
+    def get_checkpointables(self):
+        return {"model": self.history}
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index da8703a0f..cc1cf19f6 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -1,176 +1,158 @@
-import numpy as np
+import collections
+import logging
+import math
 import torch
 import copy
-import math
-from collections import defaultdict
-from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
-from naslib.search_spaces.core.query_metrics import Metric
+import numpy as np
 
-from collections import defaultdict
+from naslib.optimizers.core.metaclasses import MetaOptimizer
 
+from naslib.search_spaces.core.query_metrics import Metric
 
-class HyperBand(MetaOptimizer):
-    """
-    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
-    """
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
+from naslib.utils.logging import log_every_n_seconds
 
+logger = logging.getLogger(__name__)
+    
+        
+class HB(MetaOptimizer):
+    
     # training the models is not implemented
     using_step_function = False
+    
+    def __init__(self, config):
+        super().__init__()
+        # Hyperband related stuff
+        self.config = config
+        self.rounds = []
+        self.round_sizes = []
+        self.fidelities = []
+        self.max_budget = self.config.search.max_budget
+        self.min_budget = self.config.search.min_budget
+        self.eta = self.config.search.eta 
+        self._epsilon = float(self.config.search.epsilon) 
+       
+        s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
+        # set up round sizes, fidelities, and list of arches
+        for s in reversed(range(s_max + 1)):
+            self.rounds.append(s)
+            round_sizes = []
+            fidelities = []
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
+            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            for i in range(s + 1):
+                n_i = math.floor(n / self.eta ** i + self._epsilon)
+                r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
+                round_sizes.append(n_i)
+                fidelities.append(r_i)
+            self.round_sizes.append(round_sizes)
+            self.fidelities.append(fidelities)
 
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(HyperBand, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-        self.sh_config = copy.deepcopy(config)
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-        self.budget_max = config.search.budget_max
-        self.eta = config.search.eta
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
-        self.sampled_archs = []
         self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s = self.s_max + 1
-        self.sh = None
-        self.first = True # TODO: think about a more ellegant solution 
-        self.b = (self.s_max + 1)* self.budget_max
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-       
-        
+
+        self.epochs = self.compute_epochs()
+        self.current_round = []
+        self.next_round = []
+        self.round_number = 0
+        self.prev_round = 0
+        self.process = 0
+
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
+        assert search_space.QUERYABLE, "Hyperband_simple is currently only implemented for benchmarks."
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
+        self.max_training_epoch = self.search_space.get_max_epochs()
+
+    def compute_epochs(self):
+        return self.round_sizes, self.rounds[::-1]
+
+    def new_epoch(self, epoch, round, i):
+        if self.process < i: # re-init for each new process
+            self.current_round = []
+            self.next_round = []
+            self.round_number = 0
+            self.prev_round = 0
+            self.process = i
+
+        if self.prev_round < round:  # reset round_number for each new round
+            self.prev_round = round
+            self.round_number = 0
+
+        if epoch < self.round_sizes[round][0]:
+            # sample random architectures
+            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api)   
+            model.epoch = self.fidelities[round][0]
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self._update_history(model)
+            self.next_round.append(model)
 
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        after https://arxiv.org/pdf/1603.06560.pdf
-        """
-        # TODO: rethink because of s
-        if  self.sh == None or self.sh.end:
-            #if sh is finish go to something diffrent as initial budget
-                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-            self.s -= 1
-            if self.sh != None:
-                self.sampled_archs.extend(self.sh.sampled_archs)
-            if self.s < 0:
-                print("HB is finish, allready not defined what to do") # TODO define what to do 
-                return math.inf #end the thing
-            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-            r = self.budget_max * self.eta ** (-self.s)
-            self.sh_config.search.number_archs = n
-            self.sh_config.search.min_fidelity  = r 
-
-            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
-            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-        
-        budget = self.sh.new_epoch()
+        else:
+            if len(self.current_round) == 0:
+                # if we are at the end of a round of hyperband, continue training only the best 
+                logger.info("Starting a new round: continuing to train the best arches")
+                self.round_number += 1
+                cutoff = self.round_sizes[round][self.round_number]
+                self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
+                self.next_round = []
+
+            # train the next architecture
+            model = self.current_round.pop()
+            """
+            Note: technically we would just continue training this arch, but right now,
+            just for simplicity, we treat it as if we start to train it again from scratch
+            """
+            model = copy.deepcopy(model)
+            model.epoch = self.fidelities[round][self.round_number]
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self._update_history(model)
+            self.next_round.append(model)
 
-        self.update_optimizer_stats()
-        return budget
-                
     def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
+        self.history.append(child)
 
     def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
-        all_sampled.extend(self.sh.sampled_archs)
-        return max(all_sampled, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
         
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
+        # Returns the sampled architecture with the lowest validation error.
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        return best_arch.arch, best_arch.epoch
 
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
+    def get_latest_architecture(self):
 
-        )
-    def train_model_statistics(self, report_incumbent=True):
+        # Returns the architecture from the most recent epoch
+        latest_arch = self.history[-1]
+        return latest_arch.arch, latest_arch.epoch
 
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
+    def train_statistics(self):
+        best_arch, best_arch_epoch = self.get_final_architecture()
+        latest_arch, latest_arch_epoch = self.get_latest_architecture()
         return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
         )
     
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
-        """
-        self.optimizer_stats[self.s] = self.sh.optimizer_stats
-
-
-
     def test_statistics(self):
-        return False
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+        best_arch, epoch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
+    
     def get_op_optimizer(self):
-        return self.weight_optimizer
+        raise NotImplementedError()
 
     def get_checkpointables(self):
-        return {"model": self.history}
+        return {'model': self.history}
+
+    def get_model_size(self):
+        return count_parameters_in_MB(self.history)
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index caf5dcff9..305f4a529 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -30,7 +30,6 @@ def __init__(
         ss_type=None,
         hpo_wrapper=True,
         config=None,
-        config_tpe = None,
         hyperparams = None,
     ):
         self.num_ensemble = num_ensemble
@@ -117,10 +116,7 @@ def get_ensemble(self):
                  max_zerocost=1000,
                  config=self.config,
              ),
-            "tpe": TreeParserEstimator(
-                #TODO review if more needed 
-                config_tpe  = self.config_tpe,
-                ),
+            
 
             
 
@@ -156,13 +152,6 @@ def query(self, xtest, info=None):
             prediction = self.ensemble[i].query(xtest, info) #added info dict
             predictions.append(prediction)
         return np.array(predictions)
-    def query_tpe(self, xtest, info=None):
-        predictions = []
-        for i in range(self.num_ensemble):
-            prediction, info_dict = self.ensemble[i].query(xtest, info) #added info dict
-            predictions.append(prediction)
-
-        return np.array(predictions), info_dict #added info dict
 
     def set_hyperparams(self, params):
         if self.ensemble is None:
diff --git a/naslib/predictors/tpe/__init__.py b/naslib/predictors/tpe/__init__.py
deleted file mode 100644
index 978b19b66..000000000
--- a/naslib/predictors/tpe/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .tpe import TreeParserEstimator
\ No newline at end of file
diff --git a/naslib/predictors/tpe/tpe.py b/naslib/predictors/tpe/tpe.py
deleted file mode 100644
index 9f3a6f0d9..000000000
--- a/naslib/predictors/tpe/tpe.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from logging import error
-
-import statsmodels.api as sm
-from collections import defaultdict
-import numpy as np
-
-
-from naslib.search_spaces.core.query_metrics import Metric
-from naslib.predictors.utils.encodings import encode
-import scipy.stats as sps
-import scipy.optimize as spo
-
-import torch #maybe not sure rigth now
-
-class TreeParserEstimator(): #TODO maybe this need 
-    def __init__(
-        self,
-        encoding_type="adjacency_one_hot",
-        ss_type="nasbench201",
-        config_tpe = [None,None,None],
-        search_params = None
-    ):
-        self.encoding_type = encoding_type
-        self.ss_type = ss_type
-        
-        
-        self.kde_models = defaultdict(lambda: defaultdict(list))
-        if (type(config_tpe) == list):
-            self.configspace = config_tpe[0]#thing about later
-            self.dataset_api = config_tpe[1]
-            self.dataset = config_tpe[2]
-    def set_hyperparams(self, hyperparams): #here set config 
-        self.num_samples = int(hyperparams.search.num_ensemble)
-        self.min_points_in_model = int(hyperparams.search.min_points_in_model)
-        self.top_n_percent = int(hyperparams.search.top_n_percent)
-        self.min_bandwith = float(hyperparams.search.min_bandwith)
-        self.random_fraction = float(hyperparams.search.random_fraction )
-        self.p = float(hyperparams.search.p)
-        self.N_min = int(hyperparams.search.N_min)
-
-
-
-    def fit(self, xtrain, ytrain, train_info):
-        
-        budget = train_info
-        """
-        if budget not in self.configs.keys():
-            self.configs[budget] = []
-            self.losses[budget] = []
-        print(model.arch.__str__)
-        self.configs[budget].append(model.arch.__str__) #which data has to be in train is to test
-        print(model.arch.__str__())
-        self.losses[budget].append(model.arch.query(
-            self.performance_metric,
-            self.dataset,
-            epoch=int(self.fidelity),
-            dataset_api=self.dataset_api,
-        ))
-        train_configs = np.array(self.configs[budget])
-        train_losses = np.array(self.losses[budget])
-        
-        # Refit KDE for the current budget
-        idx = np.argsort(train_losses)
-        """
-        if type(xtrain) is list:
-            # when used in itself, we use
-            xtrain = np.array(
-                [
-                    [arch.get_op_indices()]
-                    for arch in xtrain
-                ]
-            )
-       
-        n_good= max(self.min_points_in_model, (self.top_n_percent *len(xtrain)//100)) 
-        n_bad = max(self.min_points_in_model, ((100-self.top_n_percent)*len(xtrain)//100))
-       
-        idx = np.argsort(ytrain)
-
-        train_good_data = xtrain[idx[:n_good]]
-        train_bad_data = xtrain[idx[n_good:n_good+n_bad]]
-        #TODO check if this is only special for nasbench-201
-        self.kde_vartype = "".join("u" for i in range(len(list(train_good_data[0,0]))))
-        #train_good_data = train_good_data[0]
-        #train_bad_data = train_bad_data[0]self.vartypes = np.array(self.vartypes, dtype=int)
-        #TODO why is there an issue with opteration 4 Attention1x1 ???
-        self.vartypes = [5,5,5,5,5,5] #TODO I think this has to discuss with the autors
-        self.vartypes = np.array(self.vartypes, dtype=int)
-        print(self.vartypes)
-        #self.vartypes is needed 
-        # quick rule of thumb
-        bw_estimation = 'normal_reference'
-        good_kde = sm.nonparametric.KDEMultivariate(data=train_good_data,  var_type= self.kde_vartype, bw=bw_estimation) #var_type is type of hyperparameter
-        # c : continuous, u : unordered (discrete),  o : ordered (discrete)
-        bad_kde = sm.nonparametric.KDEMultivariate(data=train_bad_data, var_type=  self.kde_vartype, bw=bw_estimation) 
-        bad_kde.bw = np.clip(bad_kde.bw, float(self.min_bandwith),None)
-        good_kde.bw = np.clip(good_kde.bw, float(self.min_bandwith),None)
-
-        self.kde_models[budget] = {
-                'good': good_kde,
-                'bad' : bad_kde
-        }
-    
-    def query(self, _, budget):
-        """
-            Function to sample a new configuration
-            This function is called inside Hyperband to query a new configuration
-            Parameters:
-            -----------
-            budget: float
-                the budget for which this configuration is scheduled
-            returns: config
-                should return a valid configuration
-        """
-        sampled = False
-        info_dict = {}
-        sample =  torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-        sample.arch = self.configspace.clone()
-        # If no model is available, sample from prior
-        # also mix in a fraction of random configs
-        if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
-            sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            info_dict['model_based_pick'] = False
-            sampled = True
-        best = np.inf
-        best_vector = None
-
-        if not(sampled):
-            try:
-                
-                #sample from largest budget
-                budget = max(self.kde_models.keys())
-
-                l = self.kde_models[budget]['good'].pdf
-                g = self.kde_models[budget]['bad' ].pdf
-            
-                minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
-                
-                kde_good = self.kde_models[budget]['good']
-                kde_bad = self.kde_models[budget]['bad']
-
-                for i in range(self.num_samples):
-                    idx = np.random.randint(0, len(kde_good.data))
-                    datum = kde_good.data[idx]
-                    vector = []
-                    
-                    for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
-                        
-                        bw = max(bw, self.min_bandwith)
-                        if t == 0:
-                            bw = self.bw_factor*bw
-                            try:
-                                vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
-                            except:
-                                print("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
-                                print("data in the KDE:\n%s"%kde_good.data)
-                        else:
-                            
-                            if np.random.rand() < (1-bw):
-                                vector.append(int(m))
-                            else:
-                                vector.append(np.random.randint(t))
-                    val = minimize_me(vector)
-
-                    if not np.isfinite(val):
-                        print('sampled vector: %s has EI value %s'%(vector, val))
-                        print("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
-                        print("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
-                        print("l(x) = %s"%(l(vector)))
-                        print("g(x) = %s"%(g(vector)))
-
-                        # right now, this happens because a KDE does not contain all values for a categorical parameter
-                        # this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
-                        # if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
-                        if np.isfinite(l(vector)):
-                            best_vector = vector
-                            break
-
-                    if val < best:
-                        best = val
-                        best_vector = vector
-
-                if best_vector is None:
-                    print("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
-                    sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-                    info_dict['model_based_pick']  = False
-                else:
-                    print('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
-                    #for i, hp_value in enumerate(best_vector):
-                    #    if isinstance(
-                    #       self.configspace.get_hyperparameter(
-                    #            self.configspace.get_hyperparameter_by_idx(i)
-                    #       ),
-                    #        ConfigSpace.hyperparameters.CategoricalHyperparameter
-                    #   ):
-                    #       best_vector[i] = int(np.rint(best_vector[i]))
-                    sample.arch.set_op_indices(best_vector)
-            except Exception as e:
-                #print("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
-                #print("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc())
-                #sample.arch.sample_configuration() v v v 
-                print(e)
-                sample.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-                info_dict['model_based_pick']  = False
-        accuracy = sample.arch.query(
-                Metric.VAL_ACCURACY,
-                self.dataset,
-                epoch=int(budget),
-                dataset_api=self.dataset_api,
-            )
-        info_dict["model"] = sample
-        #print('done sampling a new configuration.')
-        return accuracy, info_dict
-    
-    
-#    "Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc())
-
- 
\ No newline at end of file
diff --git a/naslib/search_spaces/nasbench201/graph.py b/naslib/search_spaces/nasbench201/graph.py
index 04e4ba25f..a6ae49229 100644
--- a/naslib/search_spaces/nasbench201/graph.py
+++ b/naslib/search_spaces/nasbench201/graph.py
@@ -198,6 +198,41 @@ def query(
             # return the value of the metric only at the specified epoch
             return query_results[dataset][metric_to_nb201[metric]][epoch]
 
+    def model_based_sample_architecture(self, dataset_api=None, minimize_me=None, good_kde=None, vartypes=None):
+        """
+        This will perform a model-based architecture sampling and update the edges in the
+        naslib object accordingly.
+        """
+        num_samples = 128
+        random_fraction = 0.33
+        best = np.inf
+        best_vector = None
+        for i in range(num_samples):
+            idx = np.random.randint(0, len(good_kde.data))
+            datum = good_kde.data[idx]
+            vector = []
+            for m, bw, t in zip(datum, good_kde.bw, vartypes):
+                if np.random.rand() < (1 - bw):
+                    vector.append(int(m))
+                else:
+                    vector.append(np.random.randint(t))
+            val = minimize_me(vector)
+            if val < best:
+                best = val
+                best_vector = vector
+        if best_vector is None or np.random.rand() < random_fraction:
+            self.sample_random_architecture(dataset_api=dataset_api)
+        else:
+            for i in range(len(best_vector)):
+                best_vector[i] = int(np.rint(best_vector[i]))
+            self.set_op_indices(best_vector)
+
+
+
+    def get_max_epochs(self):
+        # Return the max number of epochs that can be queried
+        return 199
+
     def get_op_indices(self):
         if self.op_indices is None:
             self.op_indices = convert_naslib_to_op_indices(self)

From 936a7da2fd5dbef4d8c82f9628c8fd010f84084a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 17 Feb 2022 14:06:41 +0100
Subject: [PATCH 067/130] nas-bench-x11 clone

---
 .../nas_predictors/discrete_config_BOHB.yaml  |   3 +-
 .../nas_predictors/discrete_config_DEHB.yaml  |   1 +
 .../nas_predictors/discrete_config_HB.yaml    |   2 +
 .../nas_predictors/discrete_config_SH.yaml    |  39 +--
 naslib/defaults/trainer_multifidelity.py      |   2 +-
 naslib/optimizers/discrete/sh/optimizer.py    | 324 +++++++-----------
 .../optimizers/discrete/sh/optimizer_old.py   | 226 ++++++++++++
 naslib/predictors/ensemble.py                 |   1 -
 8 files changed, 367 insertions(+), 231 deletions(-)
 create mode 100644 naslib/optimizers/discrete/sh/optimizer_old.py

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 45d873288..67a930e1e 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -17,4 +17,5 @@ search:
   predictor_type: var_sparse_gp
   min_bandwith: 0.001
   top_n_percent: 0.1
-  min_points_in_model: 7
\ No newline at end of file
+  min_points_in_model: 7
+  n_process: 1
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
index f14bfc7df..ae8f025cb 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
@@ -20,3 +20,4 @@ search:
   max_mutations: 1
   crossover_prob: 0.5   
   mutate_prob: 0.1
+  n_process: 1
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index aa8809380..c4789355f 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -10,9 +10,11 @@ search:
   fidelity: 200
   budgets: 4000
   eta: 3
+  n_process: 100
   max_budget: 512
   min_budget: 12
   epsilon: 1e-6
   predictor_type: var_sparse_gp
+
  
   
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index bd5bd769f..5d7e03282 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -7,40 +7,11 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  epochs: 4 # TODO : this setting should be not neccessary!!!
   min_fidelity: 1
-  number_archs: 128
+  fidelity: 200
+  budgets: 4000
   eta: 2
-  budget_type: epoch 
-  method: "random"
-  budget_max: 128
-
-
+  max_budget: 200
+  min_budget: 1
+  n_process: 1
   predictor_type: var_sparse_gp
-  num_init: 10  
-  k: 10
-
-  # BANANAS
-  num_ensemble: 3
-  acq_fn_type: its
-  acq_fn_optimization: random_sampling
-  encoding_type: adjacency_one_hot
-  num_arches_to_mutate: 2
-  max_mutations: 1
-  num_candidates: 20
-  
-  # jacov data loader
-  batch_size: 256
-  data_size: 25000
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  train_portion: 0.7
-  
-  # other params
-  debug_predictor: False
-  sample_size: 10
-  population_size: 30
-
-  # TODO: write config file for successive halving
-
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 793804318..4a40ab5e4 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -39,7 +39,7 @@ def __init__(self, optimizer, config, lightweight_output=False):
         self.optimizer = optimizer
         self.config = config
         self.lightweight_output = lightweight_output
-        self.n_process = 512 #need to be a parameter
+        self.n_process = config.search.n_process #need to be a parameter
 
         # anytime
         computed_epochs = self.optimizer.compute_epochs()
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 777f4e2da..ada2571cd 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,226 +1,162 @@
-import numpy as np
+import collections
+import logging
+import math
 import torch
-import random
+import copy
+import numpy as np
+
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+
+from naslib.search_spaces.core.query_metrics import Metric
+
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
+from naslib.utils.logging import log_every_n_seconds
+
+logger = logging.getLogger(__name__)
+    
+        
+import collections
+import logging
 import math
+import torch
+import copy
+import numpy as np
 
-from collections import defaultdict
-from naslib.predictors import predictor
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
 from naslib.optimizers.core.metaclasses import MetaOptimizer
+
 from naslib.search_spaces.core.query_metrics import Metric
 
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
+from naslib.utils.logging import log_every_n_seconds
 
-class SuccessiveHalving(MetaOptimizer):
-    """
-    Optimizer is randomly sampling architectures and queries/trains on the corresponding fidelities.
-    After that, models will be discarded according to eta.
-    DONE: Implement training
-    """
+logger = logging.getLogger(__name__)
+    
+        
+class  SuccessiveHalving(MetaOptimizer):
+    
+    # training the models is not implemented
     using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-        esemble = None,
-    ):
-        """
-        Initialize a Successive Halving optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(SuccessiveHalving, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
+    
+    def __init__(self, config):
+        super().__init__()
+        # Hyperband related stuff
+        self.config = config
+        self.round_sizes = []
+        self.fidelities = []
+        self.max_budget = self.config.search.max_budget
+        self.min_budget = self.config.search.min_budget
+        self.eta = self.config.search.eta 
+        
+        times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta) )
+        # set up round sizes, fidelities, and list of arches
+        
+        n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (times_of_split + 1)) # initial number of configurations
+        r = self.max_budget / self.eta**times_of_split # initial number of iterations to run configurations for
+        for i in range(times_of_split):
+           
+            # n= 2*3 ** 1/2  for i in range(s + 1):
+            n = math.floor(n / self.eta)
+            r = min(math.floor(r * self.eta), config.search.fidelity)
+            self.round_sizes.append(n)
+            self.fidelities.append(r)
+        
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
-        self.end = False
-        self.fidelity = config.search.min_fidelity
-        self.number_archs = config.search.number_archs
-        self.eta = config.search.eta
-        self.budget_max = config.search.budget_max 
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type  # is not for one query is overall
-        self.fidelity_counter = 0
-        self.sampled_archs = []
         self.history = torch.nn.ModuleList()
-        self.end = False
-        self.old_fidelity = 0
-        self.method = config.search.method
-        #right now only for testing 
-        if self.method == "tpe":#
-            self.ss_type= config.search_space
-            self.encoding_type = config.search.encoding_type
-            self.N_min =  config.search.N_min #This has to be higher then 
-            self.ensemble = esemble
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+
+        self.epochs = self.compute_epochs()
+        self.current_round = []
+        self.next_round = []
+        self.round_number = 0
+        self.prev_round = 0
+        self.process = 0
 
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
+        assert search_space.QUERYABLE, "Hyperband_simple is currently only implemented for benchmarks."
         self.search_space = search_space.clone()
         self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
         self.dataset_api = dataset_api
-    
+        self.max_training_epoch = self.search_space.get_max_epochs()
+
+    def compute_epochs(self):
+        return [self.round_sizes], [0]
+
+    def new_epoch(self, epoch, round, i):
+        if self.process < i: # re-init for each new process
+            self.current_round = []
+            self.next_round = []
+            self.round_number = 0
+            self.prev_round = 0
+            self.process = i
+
+        if self.prev_round < round:  # reset round_number for each new round
+            self.prev_round = round
+            self.round_number = 0
+
+        if epoch < self.round_sizes[round]:
+            # sample random architectures
+            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api)   
+            model.epoch = self.fidelities[round]
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self._update_history(model)
+            self.next_round.append(model)
 
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        # TODO: with this kind of architekeur, in evaluation only the last fideltiy
-        """
-        # TODO: error is occuring on fidelity: 128 on architecture number: 1
-        # fidelity is not calculated corretly and therefore epoch is maybe not correct 
-        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-        #model.arch = self.search_space.clone()
-        #TODO is num_init needed 
-        if len(self.sampled_archs) < self.number_archs:
-            #model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            model = self.sample(self.method)
-            
         else:
-            model = self.sampled_archs[self.fidelity_counter]
+            # train the next architecture
+            model = self.current_round.pop()
+            """
+            Note: technically we would just continue training this arch, but right now,
+            just for simplicity, we treat it as if we start to train it again from scratch
+            """
+            model = copy.deepcopy(model)
+            model.epoch = self.fidelities[round]
+            model.accuracy = model.arch.query(self.performance_metric,
+                                              self.dataset, 
+                                              epoch=model.epoch, 
+                                              dataset_api=self.dataset_api)
+            self._update_history(model)
+            self.next_round.append(model)
 
-#             return(ranks < self.num_configs[self.stage])
-
-        model.accuracy = model.arch.query(
-            self.performance_metric,
-            self.dataset,
-            epoch=int(self.fidelity),
-            dataset_api=self.dataset_api,
-        )
-
-        budget = (self.fidelity - self.old_fidelity) / self.budget_max
-        # DONE: make query type secure
-        if self.budget_type == 'time':
-            #TODO also for predictions
-            # DONE: make dependent on performance_metric
-            model.time = model.arch.query(  # TODO: this is the time for training from screatch.
-                self.performance_metric,
-                self.dataset,
-                epoch=int(self.fidelity),
-                dataset_api=self.dataset_api,
-            )
-            budget = model.time
-        elif not(self.budget_type == "epoch"):
-            raise NameError("budget time should be time or epoch")
-        # TODO: make this more beautiful/more efficient
-        # DONE: we may need to track of all ever sampled archs
-        if len(self.sampled_archs) < self.number_archs:
-            self.sampled_archs.append(model)
-        else:
-            self.sampled_archs[self.fidelity_counter] = model
-        self.update_optimizer_stats()
-        self.fidelity_counter += 1
-        # DONE: fidelity is changed for new epoch, what make the wrong values in the dictonary
-        self._update_history(model)
-        if self.fidelity_counter == self.number_archs:
-            self.old_fidelity = self.fidelity
-            self.fidelity = math.floor(self.eta*self.fidelity)
-            self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
-            if self.fidelity > self.budget_max:
-                self.end = True
-            elif(math.floor(self.number_archs/self.eta)) != 0:
-                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)]
-
-            else:
-                self.end = True
-                self.sampled_archs = [self.sampled_archs[0]]  # but maybe there maybe a different way
-            self.number_archs = len(self.sampled_archs)
-            self.fidelity_counter = 0
-        # TODO: budget equals
-        return budget
-       
     def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
+        self.history.append(child)
 
     def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+        
+        # Returns the sampled architecture with the lowest validation error.
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        return best_arch.arch, best_arch.epoch
 
-    def train_statistics(self, report_incumbent=True):
+    def get_latest_architecture(self):
 
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
+        # Returns the architecture from the most recent epoch
+        latest_arch = self.history[-1]
+        return latest_arch.arch, latest_arch.epoch
 
+    def train_statistics(self):
+        best_arch, best_arch_epoch = self.get_final_architecture()
+        latest_arch, latest_arch_epoch = self.get_latest_architecture()
         return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
         )
-
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        """
-        arch = self.sampled_archs[self.fidelity_counter].arch
-        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
-        # this dict contains metrics to save
-        metrics = {
-            "train_acc": Metric.TRAIN_ACCURACY,
-            "val_acc": Metric.VAL_ACCURACY,
-            "test_acc": Metric.TEST_ACCURACY,
-            "train_time": Metric.TRAIN_TIME
-        }
-        for metric_name, metric in metrics.items():
-            metric_value = arch.query(
-                metric,
-                self.dataset,
-                dataset_api=self.dataset_api,
-                epoch=int(self.fidelity)
-            )
-            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
-        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity)
-
+    
     def test_statistics(self):
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+        best_arch, epoch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
-    def get_op_optimizer(self):
-        return self.weight_optimizer
     
+    def get_op_optimizer(self):
+        raise NotImplementedError()
+
     def get_checkpointables(self):
-        return {"model": self.history}
+        return {'model': self.history}
 
-    
-    def sample(self, method):
-        #is right now in tpe
-        if method == "random" or  len(self.sampled_archs) < self.N_min:
-            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-            model.arch = self.search_space.clone()
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-        else:        
-            xtrain = [m.arch for m in self.sampled_archs]
-            ytrain = [m.accuracy for m in self.sampled_archs]
-            train_error = self.ensemble.fit(xtrain,ytrain,self.fidelity)
-            _, info_dict  = self.ensemble.query_tpe("test", self.fidelity)
-            model = info_dict["model"]
-        return model
+    def get_model_size(self):
+        return count_parameters_in_MB(self.history)
diff --git a/naslib/optimizers/discrete/sh/optimizer_old.py b/naslib/optimizers/discrete/sh/optimizer_old.py
new file mode 100644
index 000000000..777f4e2da
--- /dev/null
+++ b/naslib/optimizers/discrete/sh/optimizer_old.py
@@ -0,0 +1,226 @@
+import numpy as np
+import torch
+import random
+import math
+
+from collections import defaultdict
+from naslib.predictors import predictor
+from naslib.predictors import ensemble
+from naslib.predictors.ensemble import Ensemble
+from naslib.optimizers.core.metaclasses import MetaOptimizer
+from naslib.search_spaces.core.query_metrics import Metric
+
+
+class SuccessiveHalving(MetaOptimizer):
+    """
+    Optimizer is randomly sampling architectures and queries/trains on the corresponding fidelities.
+    After that, models will be discarded according to eta.
+    DONE: Implement training
+    """
+    using_step_function = False
+
+    def __init__(
+        self,
+        config,
+        weight_optimizer=torch.optim.SGD,
+        loss_criteria=torch.nn.CrossEntropyLoss(),
+        grad_clip=None,
+        esemble = None,
+    ):
+        """
+        Initialize a Successive Halving optimizer.
+
+        Args:
+            config
+            weight_optimizer (torch.optim.Optimizer): The optimizer to
+                train the (convolutional) weights.
+            loss_criteria (TODO): The loss
+            grad_clip (float): Where to clip the gradients (default None).
+        """
+        super(SuccessiveHalving, self).__init__()
+        self.weight_optimizer = weight_optimizer
+        self.loss = loss_criteria
+        self.grad_clip = grad_clip
+        self.performance_metric = Metric.VAL_ACCURACY
+        self.dataset = config.dataset
+        self.end = False
+        self.fidelity = config.search.min_fidelity
+        self.number_archs = config.search.number_archs
+        self.eta = config.search.eta
+        self.budget_max = config.search.budget_max 
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.budget_type = config.search.budget_type  # is not for one query is overall
+        self.fidelity_counter = 0
+        self.sampled_archs = []
+        self.history = torch.nn.ModuleList()
+        self.end = False
+        self.old_fidelity = 0
+        self.method = config.search.method
+        #right now only for testing 
+        if self.method == "tpe":#
+            self.ss_type= config.search_space
+            self.encoding_type = config.search.encoding_type
+            self.N_min =  config.search.N_min #This has to be higher then 
+            self.ensemble = esemble
+        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
+
+    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
+        assert (
+            search_space.QUERYABLE
+        ), "Successsive Halving is currently only implemented for benchmarks."
+        self.search_space = search_space.clone()
+        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
+        self.dataset_api = dataset_api
+    
+
+    def new_epoch(self):
+        """
+        Sample a new architecture to train.
+        # TODO: with this kind of architekeur, in evaluation only the last fideltiy
+        """
+        # TODO: error is occuring on fidelity: 128 on architecture number: 1
+        # fidelity is not calculated corretly and therefore epoch is maybe not correct 
+        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+        #model.arch = self.search_space.clone()
+        #TODO is num_init needed 
+        if len(self.sampled_archs) < self.number_archs:
+            #model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+            model = self.sample(self.method)
+            
+        else:
+            model = self.sampled_archs[self.fidelity_counter]
+
+#             return(ranks < self.num_configs[self.stage])
+
+        model.accuracy = model.arch.query(
+            self.performance_metric,
+            self.dataset,
+            epoch=int(self.fidelity),
+            dataset_api=self.dataset_api,
+        )
+
+        budget = (self.fidelity - self.old_fidelity) / self.budget_max
+        # DONE: make query type secure
+        if self.budget_type == 'time':
+            #TODO also for predictions
+            # DONE: make dependent on performance_metric
+            model.time = model.arch.query(  # TODO: this is the time for training from screatch.
+                self.performance_metric,
+                self.dataset,
+                epoch=int(self.fidelity),
+                dataset_api=self.dataset_api,
+            )
+            budget = model.time
+        elif not(self.budget_type == "epoch"):
+            raise NameError("budget time should be time or epoch")
+        # TODO: make this more beautiful/more efficient
+        # DONE: we may need to track of all ever sampled archs
+        if len(self.sampled_archs) < self.number_archs:
+            self.sampled_archs.append(model)
+        else:
+            self.sampled_archs[self.fidelity_counter] = model
+        self.update_optimizer_stats()
+        self.fidelity_counter += 1
+        # DONE: fidelity is changed for new epoch, what make the wrong values in the dictonary
+        self._update_history(model)
+        if self.fidelity_counter == self.number_archs:
+            self.old_fidelity = self.fidelity
+            self.fidelity = math.floor(self.eta*self.fidelity)
+            self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
+            if self.fidelity > self.budget_max:
+                self.end = True
+            elif(math.floor(self.number_archs/self.eta)) != 0:
+                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)]
+
+            else:
+                self.end = True
+                self.sampled_archs = [self.sampled_archs[0]]  # but maybe there maybe a different way
+            self.number_archs = len(self.sampled_archs)
+            self.fidelity_counter = 0
+        # TODO: budget equals
+        return budget
+       
+    def _update_history(self, child):
+        if len(self.history) < 100:
+            self.history.append(child)
+        else:
+            for i, p in enumerate(self.history):
+                if child.accuracy > p.accuracy:
+                    self.history[i] = child
+                    break
+
+    def get_final_architecture(self):
+        """
+        Returns the sampled architecture with the lowest validation error.
+        """
+        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
+
+    def train_statistics(self, report_incumbent=True):
+
+        if report_incumbent:
+            best_arch = self.get_final_architecture()
+        else:
+            best_arch = self.sampled_archs[-1].arch
+
+        return (
+            best_arch.query(
+                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
+            ),
+            best_arch.query(
+                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
+            ),
+        )
+
+    def update_optimizer_stats(self):
+        """
+        Updates statistics of optimizer to be able to create useful plots
+        """
+        arch = self.sampled_archs[self.fidelity_counter].arch
+        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
+        # this dict contains metrics to save
+        metrics = {
+            "train_acc": Metric.TRAIN_ACCURACY,
+            "val_acc": Metric.VAL_ACCURACY,
+            "test_acc": Metric.TEST_ACCURACY,
+            "train_time": Metric.TRAIN_TIME
+        }
+        for metric_name, metric in metrics.items():
+            metric_value = arch.query(
+                metric,
+                self.dataset,
+                dataset_api=self.dataset_api,
+                epoch=int(self.fidelity)
+            )
+            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
+        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity)
+
+    def test_statistics(self):
+        best_arch = self.get_final_architecture()
+        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
+
+    def get_op_optimizer(self):
+        return self.weight_optimizer
+    
+    def get_checkpointables(self):
+        return {"model": self.history}
+
+    
+    def sample(self, method):
+        #is right now in tpe
+        if method == "random" or  len(self.sampled_archs) < self.N_min:
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
+            model.arch = self.search_space.clone()
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
+        else:        
+            xtrain = [m.arch for m in self.sampled_archs]
+            ytrain = [m.accuracy for m in self.sampled_archs]
+            train_error = self.ensemble.fit(xtrain,ytrain,self.fidelity)
+            _, info_dict  = self.ensemble.query_tpe("test", self.fidelity)
+            model = info_dict["model"]
+        return model
diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index 305f4a529..ec50b3b15 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -10,7 +10,6 @@
 from naslib.predictors.bonas import BonasPredictor
 from naslib.predictors.bnn import DNGOPredictor, BOHAMIANN, BayesianLinearRegression
 from naslib.predictors.seminas import SemiNASPredictor
-from naslib.predictors.tpe import TreeParserEstimator
 from naslib.predictors.gp import (
     GPPredictor,
     SparseGPPredictor,

From 1672c186c952a14523a1976fe4998b51c4e900b4 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Thu, 17 Feb 2022 14:13:49 +0100
Subject: [PATCH 068/130] add parameter for multi-fidelity methods

---
 naslib/benchmarks/mf/create_configs.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index fac2c7281..e7e888aac 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -134,7 +134,14 @@ def create_configs(
             for seed in range(start_seed, start_seed + trials):
                 np.random.seed(seed)
                 random.seed(seed)
-
+                eta = int(np.random.choice(range(2, 5)))
+                fidelity_range = [2**i for i in range(0, 9)]
+                max_fidelity = np.random.choice(fidelity_range)
+                # min fidelity has to be lower/equal to max_fidelity
+                min_fidelity = np.random.choice(
+                    list(
+                        filter(
+                            lambda n: n <= max_fidelity, fidelity_range)))
                 config = {
                     "seed": seed,
                     "search_space": search_space,
@@ -159,7 +166,12 @@ def create_configs(
                         "num_candidates": int(np.random.choice(range(5, 50))),
                         "predictor": predictor,
                         "debug_predictor": False,
-                        # config section for successive halving
+                        # config section for successive halving,
+                        "min_fidelity": min_fidelity,
+                        "max_fidelity": max_fidelity,
+                        "eta": eta,
+                        # config section for BOHB
+                        "tpe_bandwidth": float(np.random.choice(np.arange(0.01, 1.0, 0.01))), # TODO: what is a good range for tpe??
                     },
                 }
 

From a777c49531944d82f4b5f99644159c19cf0c35b8 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 18 Feb 2022 15:57:49 +0100
Subject: [PATCH 069/130] set up benchmark setup

---
 naslib/benchmarks/mf/config.yaml             | 13 ++++++-------
 naslib/benchmarks/mf/create_configs.py       |  9 +++++----
 naslib/benchmarks/mf/make_configs_nbxx.py    |  8 +++++---
 naslib/optimizers/discrete/bohb/optimizer.py |  2 +-
 naslib/optimizers/discrete/dehb/optimizer.py |  2 +-
 naslib/optimizers/discrete/hb/optimizer.py   |  2 +-
 naslib/optimizers/discrete/sh/optimizer.py   |  2 +-
 7 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/naslib/benchmarks/mf/config.yaml b/naslib/benchmarks/mf/config.yaml
index 21e17b142..089160555 100644
--- a/naslib/benchmarks/mf/config.yaml
+++ b/naslib/benchmarks/mf/config.yaml
@@ -1,17 +1,16 @@
-start_seed: 5
-trials: 1
-out_dir: run_m1
-out_dir_config: configs_m1
+start_seed: 0
+trials: 10
+out_dir: run
+out_dir_config: configs
 config_type: bbo-bs
 search_space: nasbench201
 datasets: 
   - cifar10
-
 optimizers: # sh, rs, ls, ...
-  - sh
+  - rs
 fidelity: -1
 
-HPO: False
+HPO: True
 # only relevant if HPO is enabled
 num_config: 100
 
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index e7e888aac..596d8cf90 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -80,7 +80,7 @@ def create_configs(
         # first generate the default config at config 0
         config_id = 0
         # TODO: add config out dir to config
-        folder = f"/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+        folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
         # folder = f"naslib/benchmarks/bbo/configs_cpu/{search_space}/{dataset}/{optimizer}/config_{config_id}"
         os.makedirs(folder, exist_ok=True)       
             
@@ -128,7 +128,7 @@ def create_configs(
                 yaml.dump(config, fh)
         num_config = num_config if HPO else 1
         for config_id in range(1, num_config):
-            folder = f"naslib/benchmarks/bbo/configs_m1/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+            folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
             os.makedirs(folder, exist_ok=True)
             
             for seed in range(start_seed, start_seed + trials):
@@ -174,8 +174,9 @@ def create_configs(
                         "tpe_bandwidth": float(np.random.choice(np.arange(0.01, 1.0, 0.01))), # TODO: what is a good range for tpe??
                     },
                 }
-
-                path = os.path.join(folder, f"/seed_{seed}.yaml")
+                print(f"folder: {folder}")
+                path = os.path.join(folder, f"seed_{seed}.yaml")
+                print(f"path: {path}")
                 with open(path, "w") as fh:
                     yaml.dump(config, fh)
     
diff --git a/naslib/benchmarks/mf/make_configs_nbxx.py b/naslib/benchmarks/mf/make_configs_nbxx.py
index 971c3c0ea..d6934f76b 100644
--- a/naslib/benchmarks/mf/make_configs_nbxx.py
+++ b/naslib/benchmarks/mf/make_configs_nbxx.py
@@ -13,14 +13,16 @@
     config = yaml.safe_load(stream)
 
 def check_config(search_space, dataset, optimizer, trials, HPO, num_config):
-    folder = os.path.join(".", "bbo", "configs_m1", search_space, dataset, optimizer)
+    folder = os.path.join(".", "configs", search_space, dataset, optimizer)
     test = os.path.join(folder, "**", "*.yaml")  
     files = glob(test, recursive=True)
+    print(f"{len(files)} files")
     num_config = num_config if HPO else 1
-    if len(files) == trials:
+    if len(files) == trials * num_config:
         print("Created config file(s) successfully")
         return
-    print("Config file(s) not successfully created")        
+    print("Config file(s) not successfully created")
+    exit(1)
 
 # convert such that config elements are accessiable via attributes
 config = Dict(config)
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index ac9516868..c82f4f0de 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -257,7 +257,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 932775fbb..50ac21439 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -184,7 +184,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1),
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch),
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index cc1cf19f6..e85c52fd3 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -140,7 +140,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API 
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index ada2571cd..df356edb3 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -144,7 +144,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API 
         )
     
     def test_statistics(self):

From d734183526afb77724cf31c1fe01a96eaca2a2f3 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 18 Feb 2022 16:53:29 +0100
Subject: [PATCH 070/130] sh works

---
 naslib/optimizers/discrete/sh/optimizer.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index ada2571cd..7da978cde 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -89,24 +89,31 @@ def new_epoch(self, epoch, round, i):
             self.prev_round = 0
             self.process = i
 
-        if self.prev_round < round:  # reset round_number for each new round
-            self.prev_round = round
-            self.round_number = 0
-
-        if epoch < self.round_sizes[round]:
+        
+        if epoch < self.round_sizes[self.round_number]:
             # sample random architectures
             model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             model.arch.sample_random_architecture(dataset_api=self.dataset_api)   
-            model.epoch = self.fidelities[round]
+            model.epoch = self.fidelities[self.round_number]
             model.accuracy = model.arch.query(self.performance_metric,
                                               self.dataset, 
                                               epoch=model.epoch, 
                                               dataset_api=self.dataset_api)
+            print(model.epoch)
             self._update_history(model)
             self.next_round.append(model)
 
         else:
+            if len(self.current_round) == 0:
+                # if we are at the end of a round of hyperband, continue training only the best 
+                logger.info("Starting a new round: continuing to train the best arches")
+                self.round_number += 1
+                cutoff = self.round_sizes[self.round_number]
+                self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
+                self.next_round = []
+
+
             # train the next architecture
             model = self.current_round.pop()
             """
@@ -114,11 +121,12 @@ def new_epoch(self, epoch, round, i):
             just for simplicity, we treat it as if we start to train it again from scratch
             """
             model = copy.deepcopy(model)
-            model.epoch = self.fidelities[round]
+            model.epoch = self.fidelities[self.round_number]
             model.accuracy = model.arch.query(self.performance_metric,
                                               self.dataset, 
                                               epoch=model.epoch, 
                                               dataset_api=self.dataset_api)
+            print(model.epoch)
             self._update_history(model)
             self.next_round.append(model)
 

From fa87aa65a2766776ce19041396801d62c7ac4ab5 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sun, 20 Feb 2022 17:36:08 +0100
Subject: [PATCH 071/130] preparing benchmarks

---
 naslib/benchmarks/mf/create_configs.py |  6 ++---
 naslib/benchmarks/mf/submit_configs.sh | 32 ++++++++++++++++++++++++++
 2 files changed, 35 insertions(+), 3 deletions(-)
 create mode 100644 naslib/benchmarks/mf/submit_configs.sh

diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 596d8cf90..9619d0da7 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -136,12 +136,12 @@ def create_configs(
                 random.seed(seed)
                 eta = int(np.random.choice(range(2, 5)))
                 fidelity_range = [2**i for i in range(0, 9)]
-                max_fidelity = np.random.choice(fidelity_range)
+                max_fidelity = int(np.random.choice(fidelity_range))
                 # min fidelity has to be lower/equal to max_fidelity
-                min_fidelity = np.random.choice(
+                min_fidelity = int(np.random.choice(
                     list(
                         filter(
-                            lambda n: n <= max_fidelity, fidelity_range)))
+                            lambda n: n <= max_fidelity, fidelity_range))))
                 config = {
                     "seed": seed,
                     "search_space": search_space,
diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
new file mode 100644
index 000000000..99cf0fa7c
--- /dev/null
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
+#SBATCH -t 0-15:00           # time (D-HH:MM)
+#SBATCH -o log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -e log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -J naslib-bbo-rs            # sets the job name. 
+#SBATCH --mem=7G  
+#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
+# Print some information about the job to STDOUT
+
+echo "Workingdir: $PWD";
+echo "Started at $(date)";
+echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
+
+config_dir=/work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/rs
+
+
+# python -u runner.py --config-file $1
+for config in $config_dir/*
+    do
+    for config_file_seed in $config/*
+        do
+            echo submitted ${config_file_seed}
+            python -u /work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/bbo/runner.py --config-file $config_file_seed
+        done
+    done
+ 
+# echo $COMMAND;
+# eval $COMMAND;
+
+echo "DONE";
+echo "Finished at $(date)";
\ No newline at end of file

From e3b4a1d79c147ecc9a20d7792d91afb505be1486 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Feb 2022 11:50:42 +0100
Subject: [PATCH 072/130] add new runner for mf

---
 naslib/benchmarks/mf/runner.py | 67 ++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 naslib/benchmarks/mf/runner.py

diff --git a/naslib/benchmarks/mf/runner.py b/naslib/benchmarks/mf/runner.py
new file mode 100644
index 000000000..3e6284761
--- /dev/null
+++ b/naslib/benchmarks/mf/runner.py
@@ -0,0 +1,67 @@
+import logging
+import sys
+#from nasbench import api
+
+from naslib.defaults.trainer import Trainer
+from naslib.defaults.trainer_multifidelity import Trainer as Trainer_MF
+
+from naslib.optimizers import RandomSearch, Npenas, \
+RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HB
+
+from naslib.search_spaces.core.query_metrics import Metric
+from naslib.search_spaces import NasBench101SearchSpace, NasBench201SearchSpace, \
+DartsSearchSpace, NasBenchNLPSearchSpace, TransBench101SearchSpace, NasBenchASRSearchSpace
+from naslib.utils import utils, setup_logger, get_dataset_api
+
+from torch.utils.tensorboard import SummaryWriter
+
+config = utils.get_config_from_args(config_type='bbo-bs')
+
+logger = setup_logger(config.save + "/log.log")
+logger.setLevel(logging.INFO)
+
+utils.log_args(config)
+
+# writer = SummaryWriter(config.save)
+
+supported_optimizers = {
+    'rs': RandomSearch(config),
+    're': RegularizedEvolution(config),
+    'bananas': Bananas(config),
+    'npenas': Npenas(config),
+    'ls': LocalSearch(config),
+    # 'sh': SuccessiveHalving(config),
+    # 'hb': HyperBand(config),
+}
+
+supported_search_spaces = {
+    'nasbench101': NasBench101SearchSpace(),
+    'nasbench201': NasBench201SearchSpace(),
+    'darts': DartsSearchSpace(),
+    'nlp': NasBenchNLPSearchSpace(),
+    'transbench101_micro': TransBench101SearchSpace(),
+    'transbench101_macro': TransBench101SearchSpace(),
+    'asr': NasBenchASRSearchSpace(),
+}
+
+dataset_api = get_dataset_api(config.search_space, config.dataset)
+utils.set_seed(config.seed)
+
+search_space = supported_search_spaces[config.search_space]
+if config.search_space == 'transbench101_macro':
+    search_space.space = 'macro'
+
+metric = Metric.VAL_ACCURACY if config.search_space == 'darts' else None
+
+optimizer = supported_optimizers[config.optimizer]
+optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
+
+trainer = Trainer(optimizer, config, lightweight_output=True)
+multi_fidelity_optimizers = {'sh', 'hb'}
+if config.optimizer in multi_fidelity_optimizers:
+    trainer = Trainer_MF(optimizer, config, lightweight_output=True)
+# trainer.search(resume_from="", summary_writer=writer, report_incumbent=False)
+trainer.search(resume_from="", report_incumbent=False)
+trainer.evaluate(resume_from="", dataset_api=dataset_api, metric=metric)
+
+# error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'

From 16604a681a6617daa8774ef4acff656f36066f44 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Feb 2022 12:01:50 +0100
Subject: [PATCH 073/130] improvements in sbatch submit for mf experiments

---
 naslib/benchmarks/mf/submit_configs.sh | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
index 99cf0fa7c..233f91dcb 100644
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -1,18 +1,22 @@
 #!/bin/bash
+# define variable names
+workspace=/work/dlclarge2/gernel-naslib-experiments/
+
+
 #SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
 #SBATCH -t 0-15:00           # time (D-HH:MM)
-#SBATCH -o log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -e log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -o $workspace/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -e $workspace/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -J naslib-bbo-rs            # sets the job name. 
 #SBATCH --mem=7G  
-#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
+#SBATCH --chdir=$workspace/NASLib
 # Print some information about the job to STDOUT
 
 echo "Workingdir: $PWD";
 echo "Started at $(date)";
 echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
 
-config_dir=/work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/rs
+config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/rs
 
 
 # python -u runner.py --config-file $1
@@ -21,7 +25,7 @@ for config in $config_dir/*
     for config_file_seed in $config/*
         do
             echo submitted ${config_file_seed}
-            python -u /work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/bbo/runner.py --config-file $config_file_seed
+            /home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/bbo/runner.py --config-file $config_file_seed
         done
     done
  

From d67e7c10d7e2388811f4f72c169034befeb9bf74 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Feb 2022 12:04:21 +0100
Subject: [PATCH 074/130] fix bug in sbatch

---
 naslib/benchmarks/mf/submit_configs.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
index 233f91dcb..f492ffb6a 100644
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 # define variable names
-workspace=/work/dlclarge2/gernel-naslib-experiments/
+workspace=/work/dlclarge2/gernel-naslib-experiments
 
 
 #SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)

From 97c18e8d5e5e059c29795aabe0758306fd7aa0a2 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Feb 2022 12:09:17 +0100
Subject: [PATCH 075/130] fix variable

---
 naslib/benchmarks/mf/submit_configs.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
index f492ffb6a..1f254a6e5 100644
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -5,11 +5,11 @@ workspace=/work/dlclarge2/gernel-naslib-experiments
 
 #SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
 #SBATCH -t 0-15:00           # time (D-HH:MM)
-#SBATCH -o $workspace/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -e $workspace/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -J naslib-bbo-rs            # sets the job name. 
 #SBATCH --mem=7G  
-#SBATCH --chdir=$workspace/NASLib
+#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
 # Print some information about the job to STDOUT
 
 echo "Workingdir: $PWD";

From 871003f620f6df289e468a04d09c70bbcd565d90 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Feb 2022 12:19:52 +0100
Subject: [PATCH 076/130] fix runner for sbatch

---
 naslib/benchmarks/mf/submit_configs.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
index 1f254a6e5..443bc3a9e 100644
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -25,7 +25,7 @@ for config in $config_dir/*
     for config_file_seed in $config/*
         do
             echo submitted ${config_file_seed}
-            /home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/bbo/runner.py --config-file $config_file_seed
+            /home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed
         done
     done
  

From ef2cb263e0c1dda371af742d9d33f9cf5d77bf8c Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 22 Feb 2022 14:21:31 +0100
Subject: [PATCH 077/130] config change

---
 naslib/benchmarks/nas_predictors/discrete_config_SH.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index 5d7e03282..4c1e096fe 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -13,5 +13,5 @@ search:
   eta: 2
   max_budget: 200
   min_budget: 1
-  n_process: 1
+  n_process: 3
   predictor_type: var_sparse_gp

From b9df6cc6cc121b04a4b09a67b13e5bb593652245 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Tue, 22 Feb 2022 16:52:16 +0100
Subject: [PATCH 078/130] adapt sh configs

---
 naslib/benchmarks/mf/create_configs.py | 18 ++++++++++++++----
 naslib/benchmarks/mf/runner.py         |  2 +-
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 9619d0da7..429a0fd98 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -113,14 +113,19 @@ def create_configs(
                     "budget_type": "epoch",
                     "budget_max": 128,
                     "method": "random",
-                    "eta": 2,
+                    "eta": 3,
                     "num_ensemble": 3,
                     "acq_fn_type": "its",
                     "acq_fn_optimization": acq_fn_optimization,
                     "encoding_type": "path",
                     "predictor": predictor,
                     "debug_predictor": False,
-                    # config secton for successive halving
+                    # config secton for successive halving,
+                    "min_budget": 1,
+                    "max_budget": 200,
+                    "fidelity": 200,
+                    "n_process": 1_000_000,
+                    "budgets": 360_000, 
                 },
             }
             path = os.path.join(folder, f"seed_{seed}.yaml")
@@ -134,6 +139,7 @@ def create_configs(
             for seed in range(start_seed, start_seed + trials):
                 np.random.seed(seed)
                 random.seed(seed)
+                # TODO: max_fidelity should be dependent on eta and min_fidelity
                 eta = int(np.random.choice(range(2, 5)))
                 fidelity_range = [2**i for i in range(0, 9)]
                 max_fidelity = int(np.random.choice(fidelity_range))
@@ -167,8 +173,12 @@ def create_configs(
                         "predictor": predictor,
                         "debug_predictor": False,
                         # config section for successive halving,
-                        "min_fidelity": min_fidelity,
-                        "max_fidelity": max_fidelity,
+                        # config secton for successive halving,
+                        "min_budget": min_fidelity,
+                        "max_budget": max_fidelity,
+                        "fidelity": 200,
+                        "n_process": 1_000_000,
+                        "budgets": 360_000, 
                         "eta": eta,
                         # config section for BOHB
                         "tpe_bandwidth": float(np.random.choice(np.arange(0.01, 1.0, 0.01))), # TODO: what is a good range for tpe??
diff --git a/naslib/benchmarks/mf/runner.py b/naslib/benchmarks/mf/runner.py
index 3e6284761..9e253e11d 100644
--- a/naslib/benchmarks/mf/runner.py
+++ b/naslib/benchmarks/mf/runner.py
@@ -30,7 +30,7 @@
     'bananas': Bananas(config),
     'npenas': Npenas(config),
     'ls': LocalSearch(config),
-    # 'sh': SuccessiveHalving(config),
+    'sh': SuccessiveHalving(config),
     # 'hb': HyperBand(config),
 }
 

From c6b3443e85ded9d22a46a454e1075d9b479ba07a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 22 Feb 2022 16:53:14 +0100
Subject: [PATCH 079/130] claculate training time

---
 naslib/optimizers/discrete/sh/optimizer.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 7da978cde..a66a805a2 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -12,6 +12,8 @@
 from naslib.utils.utils import AttrDict, count_parameters_in_MB
 from naslib.utils.logging import log_every_n_seconds
 
+from naslib.search_spaces.nasbench201.conversions import convert_naslib_to_str
+
 logger = logging.getLogger(__name__)
     
         
@@ -29,6 +31,8 @@
 from naslib.utils.utils import AttrDict, count_parameters_in_MB
 from naslib.utils.logging import log_every_n_seconds
 
+
+
 logger = logging.getLogger(__name__)
     
         
@@ -145,14 +149,20 @@ def get_latest_architecture(self):
         latest_arch = self.history[-1]
         return latest_arch.arch, latest_arch.epoch
 
+
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
+        models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch)]
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
+        train_time_scaled = train_time * latest_arch_epoch
+        if len(models) > 1:
+            train_time_scaled = train_time_scaled - train_time * models[-2].epoch
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), 
+            train_time_scaled, 
         )
     
     def test_statistics(self):

From ef8db00ef6643edd39a8d9f2c5e56e455ab7aada Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 25 Feb 2022 15:17:36 +0100
Subject: [PATCH 080/130] ram little bit memory opt

---
 .../nas_predictors/discrete_config_BOHB.yaml  |  6 ++---
 .../nas_predictors/discrete_config_DEHB.yaml  |  9 ++++---
 .../nas_predictors/discrete_config_HB.yaml    |  1 -
 .../nas_predictors/discrete_config_SH.yaml    | 14 +++++------
 naslib/defaults/trainer_multifidelity.py      |  3 ++-
 naslib/optimizers/discrete/bohb/optimizer.py  | 10 +++++++-
 naslib/optimizers/discrete/dehb/optimizer.py  | 11 +++++++++
 naslib/optimizers/discrete/hb/optimizer.py    | 12 ++++++++++
 naslib/optimizers/discrete/sh/optimizer.py    | 24 +++++++++++++++----
 naslib/search_spaces/nasbench201/graph.py     |  2 +-
 10 files changed, 68 insertions(+), 24 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
index 67a930e1e..a7f435c51 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
@@ -15,7 +15,7 @@ search:
   min_budget: 12
   epsilon: 1e-6
   predictor_type: var_sparse_gp
-  min_bandwith: 0.001
-  top_n_percent: 0.1
-  min_points_in_model: 7
+  min_bandwith: 0.001  #bandwidth is done by algorithm, but we want also a minimum bandwidth at the end train 0 to 0.01
+  top_n_percent: 0.1   #number of archiekteurs that are consider as good train 0.05 to 0.6 but I think log scale 
+  min_points_in_model: 7  #number of points where we start to train a model min dimesnsions + 1, here 7. Can be trained or fix. 
   n_process: 1
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
index ae8f025cb..3307473f8 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
@@ -7,7 +7,6 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  min_fidelity: 1
   fidelity: 200
   budgets: 4000
   eta: 3
@@ -16,8 +15,8 @@ search:
   epsilon: 1e-6
   predictor_type: var_sparse_gp
   top_n_percent: 0.3
-  enc_dim: 6
-  max_mutations: 1
-  crossover_prob: 0.5   
-  mutate_prob: 0.1
+  enc_dim: 6   #dimensions of the output fix.
+  max_mutations: 1 #nummber of gens that mutate if it is mutating range(0 to enc_dim) but maybe to make it better (1 to 4)??
+  crossover_prob: 0.5   #probabilty that a gen is choosen from mutate range(0.4 to 0.9)
+  mutate_prob: 0.1 #probability of mutation  range(0.05 to 0.7)
   n_process: 1
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index c4789355f..d4bc754e3 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -6,7 +6,6 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  min_fidelity: 1
   fidelity: 200
   budgets: 4000
   eta: 3
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index 4c1e096fe..d29c562e5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -7,11 +7,11 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  min_fidelity: 1
-  fidelity: 200
-  budgets: 4000
-  eta: 2
-  max_budget: 200
-  min_budget: 1
-  n_process: 3
+  fidelity: 200 #max fidelity of the benchmark fix 
+  budgets: 400000 #budget inseconds the hole algorithm have
+  eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
+  max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
+  min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)
+  n_process: 1000
+  epsilon: 1e-6 #small param that is added to prevend that floor(x.9999999999999999) is x not x+1 fix 
   predictor_type: var_sparse_gp
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 4a40ab5e4..175daf531 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -6,7 +6,7 @@
 import copy
 import torch
 import numpy as np
-
+import gc
 from fvcore.common.checkpoint import PeriodicCheckpointer
 
 from naslib.search_spaces.core.query_metrics import Metric
@@ -241,6 +241,7 @@ def search(self, resume_from=""):
                 self._log_to_json()
                 self._log_and_reset_accuracies(e)
                 if self.search_time > self.budgets:
+                    gc.collect() 
                     return
 
             self.optimizer.after_training()
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index ac9516868..59df70138 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -161,12 +161,14 @@ def fit_kde(self, round):
 
     def new_epoch(self, epoch, round, i):
         if self.process < i: # re-init for each new process
-            self.current_round = []
+            del self.current_round
+            del self.next_round
             self.current_round_ = []
             self.next_round = []
             self.round_number = 0
             self.prev_round = 0
             self.process = i
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -237,6 +239,12 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
+    
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = []
+        self.history.append(best_arch)
+
 
     def get_final_architecture(self):
         
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 932775fbb..2ee3a08bd 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -85,12 +85,19 @@ def compute_epochs(self):
 
     def new_epoch(self, epoch, round, i):
         if self.process < i: # re-init for each new process
+            #to save ram for experiements
+            del self.current_round
+            del  self.next_round
+            del self.round_number
+            del self.prev_round
+            del self.process
             self.current_round = []
             self.next_round = []
             self.round_number = 0
             self.prev_round = 0
             self.counter = 0
             self.process = i
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -164,6 +171,10 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = []
+        self.history.append(best_arch)
 
     def get_final_architecture(self):
         
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index cc1cf19f6..687413e5e 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -71,11 +71,17 @@ def compute_epochs(self):
 
     def new_epoch(self, epoch, round, i):
         if self.process < i: # re-init for each new process
+            del self.current_round
+            del  self.next_round
+            del self.round_number
+            del self.prev_round
+            del self.process
             self.current_round = []
             self.next_round = []
             self.round_number = 0
             self.prev_round = 0
             self.process = i
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -120,6 +126,12 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
+    
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = []
+        self.history.append(best_arch)
+
 
     def get_final_architecture(self):
         
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index a66a805a2..2783c9cd4 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -50,11 +50,12 @@ def __init__(self, config):
         self.max_budget = self.config.search.max_budget
         self.min_budget = self.config.search.min_budget
         self.eta = self.config.search.eta 
+        self._epsilon = float(self.config.search.epsilon) 
         
-        times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta) )
+        times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta)  + self._epsilon )
         # set up round sizes, fidelities, and list of arches
         
-        n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (times_of_split + 1)) # initial number of configurations
+        n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (times_of_split + 1) + self._epsilon) # initial number of configurations
         r = self.max_budget / self.eta**times_of_split # initial number of iterations to run configurations for
         for i in range(times_of_split):
            
@@ -86,7 +87,14 @@ def compute_epochs(self):
         return [self.round_sizes], [0]
 
     def new_epoch(self, epoch, round, i):
+        
         if self.process < i: # re-init for each new process
+            del self.current_round
+            del  self.next_round
+            del self.round_number
+            del self.prev_round
+            del self.process
+            self.clean_history()
             self.current_round = []
             self.next_round = []
             self.round_number = 0
@@ -104,7 +112,7 @@ def new_epoch(self, epoch, round, i):
                                               self.dataset, 
                                               epoch=model.epoch, 
                                               dataset_api=self.dataset_api)
-            print(model.epoch)
+            
             self._update_history(model)
             self.next_round.append(model)
 
@@ -115,6 +123,7 @@ def new_epoch(self, epoch, round, i):
                 self.round_number += 1
                 cutoff = self.round_sizes[self.round_number]
                 self.current_round = sorted(self.next_round, key=lambda x: -x.accuracy)[:cutoff]
+                del self.next_round
                 self.next_round = []
 
 
@@ -130,13 +139,18 @@ def new_epoch(self, epoch, round, i):
                                               self.dataset, 
                                               epoch=model.epoch, 
                                               dataset_api=self.dataset_api)
-            print(model.epoch)
             self._update_history(model)
             self.next_round.append(model)
+        logger.info("fidelity: {}".format(self.fidelities[self.round_number]))
 
     def _update_history(self, child):
         self.history.append(child)
 
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = []
+        self.history.append(best_arch)
+
     def get_final_architecture(self):
         
         # Returns the sampled architecture with the lowest validation error.
@@ -153,7 +167,7 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch)]
+        models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch) and x.epoch < self.history[-1].epoch]
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
         train_time_scaled = train_time * latest_arch_epoch
         if len(models) > 1:
diff --git a/naslib/search_spaces/nasbench201/graph.py b/naslib/search_spaces/nasbench201/graph.py
index a6ae49229..52f6cc35d 100644
--- a/naslib/search_spaces/nasbench201/graph.py
+++ b/naslib/search_spaces/nasbench201/graph.py
@@ -269,7 +269,7 @@ def crossover_bin(self, parent, mutant, dim, prob, dataset_api=None):
         target_indices = np.array(convert_naslib_to_op_indices(parent))
         mutant_indices = np.array(convert_naslib_to_op_indices(mutant))
         cross_points = np.random.rand(dim) < prob
-        if not np.any(cross_points):
+        if not np.any(cross_points):  #at leat one gen is a crosspoint
             cross_points[np.random.randint(0, dim)] = True
 
         offspring = np.where(cross_points, mutant_indices, target_indices)

From a83b171e99044efea591b8a618e15477872df970 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 28 Feb 2022 15:36:49 +0100
Subject: [PATCH 081/130] adapt sbatch submission

---
 naslib/benchmarks/mf/submit_configs.sh | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 naslib/benchmarks/mf/submit_configs.sh

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
old mode 100644
new mode 100755
index 443bc3a9e..c3f736cf3
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -8,7 +8,7 @@ workspace=/work/dlclarge2/gernel-naslib-experiments
 #SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -J naslib-bbo-rs            # sets the job name. 
-#SBATCH --mem=7G  
+#SBATCH --mem=7G
 #SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
 # Print some information about the job to STDOUT
 
@@ -17,15 +17,26 @@ echo "Started at $(date)";
 echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
 
 config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/rs
-
-
+if [[ $# -eq 0 ]] ; then
+    echo 'some message'
+    exit 0
+fi
+start=${args[0]} 
+end=${args[1]}
+echo "Starting at config $start"
 # python -u runner.py --config-file $1
+i=-1
 for config in $config_dir/*
     do
     for config_file_seed in $config/*
         do
+            ((i=i+1))
+            echo $i
+            if [[ $i -lt $start ]] && [[$i -ge $end]]; then # &&  $i -gt $end ]] ; then
+                continue
+            fi
             echo submitted ${config_file_seed}
-            /home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed
+            echo "/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed"
         done
     done
  

From a7543a77f42a81040113c2c9f1d91b0c4ddd1c28 Mon Sep 17 00:00:00 2001
From: gernel <gernel@informatik.uni-freiburg.de>
Date: Mon, 28 Feb 2022 15:41:15 +0100
Subject: [PATCH 082/130] fixed runner

---
 naslib/benchmarks/mf/runner.py      |  4 ++--
 naslib/benchmarks/mf/submit_test.sh | 28 ++++++++++++++++++++++++++++
 2 files changed, 30 insertions(+), 2 deletions(-)
 create mode 100755 naslib/benchmarks/mf/submit_test.sh

diff --git a/naslib/benchmarks/mf/runner.py b/naslib/benchmarks/mf/runner.py
index 9e253e11d..e7733d9d4 100644
--- a/naslib/benchmarks/mf/runner.py
+++ b/naslib/benchmarks/mf/runner.py
@@ -61,7 +61,7 @@
 if config.optimizer in multi_fidelity_optimizers:
     trainer = Trainer_MF(optimizer, config, lightweight_output=True)
 # trainer.search(resume_from="", summary_writer=writer, report_incumbent=False)
-trainer.search(resume_from="", report_incumbent=False)
-trainer.evaluate(resume_from="", dataset_api=dataset_api, metric=metric)
+trainer.search(resume_from="")
+trainer.evaluate(resume_from="", dataset_api=dataset_api)
 
 # error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'
diff --git a/naslib/benchmarks/mf/submit_test.sh b/naslib/benchmarks/mf/submit_test.sh
new file mode 100755
index 000000000..0838baf9f
--- /dev/null
+++ b/naslib/benchmarks/mf/submit_test.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+# define variable names
+workspace=/work/dlclarge2/gernel-naslib-experiments
+
+
+#SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
+#SBATCH -t 0-15:00           # time (D-HH:MM)
+#SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
+#SBATCH -J naslib-bbo-rs            # sets the job name. 
+#SBATCH --mem=7G  
+#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
+# Print some information about the job to STDOUT
+
+echo "Workingdir: $PWD";
+echo "Started at $(date)";
+echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
+
+config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh
+
+config_file_seed=/work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh/config_0/seed_0.yaml
+/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed
+ 
+# echo $COMMAND;
+# eval $COMMAND;
+
+echo "DONE";
+echo "Finished at $(date)";

From 6c5289ab94c6c9fa27b1972bd9aa64dd4539fbd8 Mon Sep 17 00:00:00 2001
From: gernel <gernel@informatik.uni-freiburg.de>
Date: Tue, 1 Mar 2022 23:16:05 +0100
Subject: [PATCH 083/130] fix sbatch

---
 naslib/benchmarks/mf/submit_configs.sh | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
index c3f736cf3..9693dccb4 100755
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ b/naslib/benchmarks/mf/submit_configs.sh
@@ -4,11 +4,12 @@ workspace=/work/dlclarge2/gernel-naslib-experiments
 
 
 #SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
-#SBATCH -t 0-15:00           # time (D-HH:MM)
+#SBATCH -t 0-1:00           # time (D-HH:MM)
 #SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
 #SBATCH -J naslib-bbo-rs            # sets the job name. 
-#SBATCH --mem=7G
+#SBATCH --mem=40G
+#SBATCH -c=8
 #SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
 # Print some information about the job to STDOUT
 
@@ -16,13 +17,13 @@ echo "Workingdir: $PWD";
 echo "Started at $(date)";
 echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
 
-config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/rs
+config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh
 if [[ $# -eq 0 ]] ; then
     echo 'some message'
     exit 0
 fi
-start=${args[0]} 
-end=${args[1]}
+start=$1
+end=$2
 echo "Starting at config $start"
 # python -u runner.py --config-file $1
 i=-1
@@ -31,12 +32,11 @@ for config in $config_dir/*
     for config_file_seed in $config/*
         do
             ((i=i+1))
-            echo $i
-            if [[ $i -lt $start ]] && [[$i -ge $end]]; then # &&  $i -gt $end ]] ; then
-                continue
-            fi
-            echo submitted ${config_file_seed}
-            echo "/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed"
+            if [[ "$i" -ge "$start" ]] && [[ "$i" -lt "$end" ]]; then # &&  $i -gt $end ]] ; then
+		echo $i
+            	echo submitted ${config_file_seed}
+		echo "/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed"
+	    fi
         done
     done
  
@@ -44,4 +44,4 @@ for config in $config_dir/*
 # eval $COMMAND;
 
 echo "DONE";
-echo "Finished at $(date)";
\ No newline at end of file
+echo "Finished at $(date)";

From 39d5b4223023dcb71622694d69bdb011faddd7ab Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 13 Mar 2022 15:23:51 +0100
Subject: [PATCH 084/130] test

---
 examples/mf_demo.py                             |  3 ++-
 examples/mf_demo.yaml                           |  2 +-
 examples/mf_plot.py                             |  1 -
 .../nas_predictors/discrete_config.yaml         | 17 +++++++----------
 .../nas_predictors/discrete_config_SH.yaml      |  2 +-
 5 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 4a87e6f1f..2a439ed84 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -11,6 +11,7 @@
 from naslib.optimizers import RandomSearch as RS
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
+from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers import HB
 from naslib.optimizers import BOHB 
 from naslib.optimizers import DEHB
@@ -97,4 +98,4 @@
     exit()
 
 # TODO: Make this dependent on optimizer type, currently statistics are just for SH available
-plot_func(predictor)
+#plot_func(predictor)
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 5cb9eb678..2a773b180 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: BOHB
+optimizer: SH
 # Activates/deactivates plotting
 plot: FAlSE
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
index b5cb44945..0e63ed9a6 100644
--- a/examples/mf_plot.py
+++ b/examples/mf_plot.py
@@ -23,7 +23,6 @@ def plot_sh(predictor):
     folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
     predictor = predictor
     results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
-
     for arch, stats in results.items():
         x = stats['fidelity']
         values = stats['val_acc']
diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 45d873288..d29c562e5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -7,14 +7,11 @@ out_dir: run
 search:
   seed: 0
   checkpoint_freq: 10 # 1000
-  min_fidelity: 1
-  fidelity: 200
-  budgets: 4000
-  eta: 3
-  max_budget: 512
-  min_budget: 12
-  epsilon: 1e-6
+  fidelity: 200 #max fidelity of the benchmark fix 
+  budgets: 400000 #budget inseconds the hole algorithm have
+  eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
+  max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
+  min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)
+  n_process: 1000
+  epsilon: 1e-6 #small param that is added to prevend that floor(x.9999999999999999) is x not x+1 fix 
   predictor_type: var_sparse_gp
-  min_bandwith: 0.001
-  top_n_percent: 0.1
-  min_points_in_model: 7
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index d29c562e5..7b5f5cc24 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -8,7 +8,7 @@ search:
   seed: 0
   checkpoint_freq: 10 # 1000
   fidelity: 200 #max fidelity of the benchmark fix 
-  budgets: 400000 #budget inseconds the hole algorithm have
+  budgets: 400 #budget inseconds the hole algorithm have
   eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
   max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
   min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)

From 4c7da87b69f7f4687df4341981c1b8b520555339 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 13 Mar 2022 15:24:27 +0100
Subject: [PATCH 085/130] config

---
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index d29c562e5..7b5f5cc24 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -8,7 +8,7 @@ search:
   seed: 0
   checkpoint_freq: 10 # 1000
   fidelity: 200 #max fidelity of the benchmark fix 
-  budgets: 400000 #budget inseconds the hole algorithm have
+  budgets: 400 #budget inseconds the hole algorithm have
   eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
   max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
   min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)

From c9218ef6705cc917b27ff064d90d2de91f29f9dd Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 13 Mar 2022 15:24:49 +0100
Subject: [PATCH 086/130] config two

---
 naslib/benchmarks/nas_predictors/discrete_config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index 7b5f5cc24..d29c562e5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -8,7 +8,7 @@ search:
   seed: 0
   checkpoint_freq: 10 # 1000
   fidelity: 200 #max fidelity of the benchmark fix 
-  budgets: 400 #budget inseconds the hole algorithm have
+  budgets: 400000 #budget inseconds the hole algorithm have
   eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
   max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
   min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)

From a8d8dc470dee97a0dcdec7e98c32853189d7f12f Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 13 Mar 2022 16:41:03 +0100
Subject: [PATCH 087/130] clean history remove

---
 naslib/optimizers/discrete/bohb/optimizer.py | 10 +++++-----
 naslib/optimizers/discrete/dehb/optimizer.py | 10 +++++-----
 naslib/optimizers/discrete/hb/optimizer.py   | 10 +++++-----
 naslib/optimizers/discrete/sh/optimizer.py   | 10 +++++-----
 4 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 59df70138..300628a1b 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -168,7 +168,7 @@ def new_epoch(self, epoch, round, i):
             self.round_number = 0
             self.prev_round = 0
             self.process = i
-            self.clean_history()
+            #self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -240,10 +240,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
     
-    def clean_history(self):
-        best_arch = max(self.history, key=lambda x: x.accuracy)
-        self.history = []
-        self.history.append(best_arch)
+    #def clean_history(self):
+    #    best_arch = max(self.history, key=lambda x: x.accuracy)
+    #    self.history = []
+    #    self.history.append(best_arch)
 
 
     def get_final_architecture(self):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 2ee3a08bd..490a8c624 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -97,7 +97,7 @@ def new_epoch(self, epoch, round, i):
             self.prev_round = 0
             self.counter = 0
             self.process = i
-            self.clean_history()
+            #self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -171,10 +171,10 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
-    def clean_history(self):
-        best_arch = max(self.history, key=lambda x: x.accuracy)
-        self.history = []
-        self.history.append(best_arch)
+    #def clean_history(self):
+    #    best_arch = max(self.history, key=lambda x: x.accuracy)
+    #    self.history = []
+    #    self.history.append(best_arch)
 
     def get_final_architecture(self):
         
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 687413e5e..33546afe4 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -81,7 +81,7 @@ def new_epoch(self, epoch, round, i):
             self.round_number = 0
             self.prev_round = 0
             self.process = i
-            self.clean_history()
+            #self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -127,10 +127,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
     
-    def clean_history(self):
-        best_arch = max(self.history, key=lambda x: x.accuracy)
-        self.history = []
-        self.history.append(best_arch)
+    #def clean_history(self):
+    #    best_arch = max(self.history, key=lambda x: x.accuracy)
+    #    self.history = []
+    #    self.history.append(best_arch)
 
 
     def get_final_architecture(self):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 2783c9cd4..14e6835a6 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -94,7 +94,7 @@ def new_epoch(self, epoch, round, i):
             del self.round_number
             del self.prev_round
             del self.process
-            self.clean_history()
+            #self.clean_history()
             self.current_round = []
             self.next_round = []
             self.round_number = 0
@@ -146,10 +146,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
 
-    def clean_history(self):
-        best_arch = max(self.history, key=lambda x: x.accuracy)
-        self.history = []
-        self.history.append(best_arch)
+    #def clean_history(self):
+    #    best_arch = max(self.history, key=lambda x: x.accuracy)
+    #    self.history = []
+    #    self.history.append(best_arch)
 
     def get_final_architecture(self):
         

From 815566581de04fab7e54440f88dde594d780c003 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 14 Mar 2022 11:41:15 +0100
Subject: [PATCH 088/130] add submission script

---
 naslib/benchmarks/mf/submit_mf.sh | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100755 naslib/benchmarks/mf/submit_mf.sh

diff --git a/naslib/benchmarks/mf/submit_mf.sh b/naslib/benchmarks/mf/submit_mf.sh
new file mode 100755
index 000000000..d7cb9c9ce
--- /dev/null
+++ b/naslib/benchmarks/mf/submit_mf.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+num_nodes=21
+num_configs=1000
+#testOne=$(( ($num_configs + ($stepsize - 1)) / $step_size))
+step_size=$(( ($num_configs + ($num_nodes - 1)) / $num_nodes))
+echo Stepsize: $step_size
+for ((i=0;i<=num_configs;i=i+step_size)); do
+    echo ./submit_configs.sh $i $(( $i + $step_size))
+    echo $i
+done
\ No newline at end of file

From a2d172b2edf1cf9b29f5ef8763fc4e1cd0ad1603 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 14 Mar 2022 12:57:13 +0100
Subject: [PATCH 089/130] clean history back in buisness

---
 naslib/optimizers/discrete/bohb/optimizer.py | 10 +++++-----
 naslib/optimizers/discrete/dehb/optimizer.py | 10 +++++-----
 naslib/optimizers/discrete/hb/optimizer.py   | 10 +++++-----
 naslib/optimizers/discrete/sh/optimizer.py   | 10 +++++-----
 4 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 300628a1b..f07660a30 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -168,7 +168,7 @@ def new_epoch(self, epoch, round, i):
             self.round_number = 0
             self.prev_round = 0
             self.process = i
-            #self.clean_history()
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -240,10 +240,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
     
-    #def clean_history(self):
-    #    best_arch = max(self.history, key=lambda x: x.accuracy)
-    #    self.history = []
-    #    self.history.append(best_arch)
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = torch.nn.ModuleList()
+        self.history.append(best_arch)
 
 
     def get_final_architecture(self):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 490a8c624..257ee0c87 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -97,7 +97,7 @@ def new_epoch(self, epoch, round, i):
             self.prev_round = 0
             self.counter = 0
             self.process = i
-            #self.clean_history()
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -171,10 +171,10 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
-    #def clean_history(self):
-    #    best_arch = max(self.history, key=lambda x: x.accuracy)
-    #    self.history = []
-    #    self.history.append(best_arch)
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = torch.nn.ModuleList()
+        self.history.append(best_arch)
 
     def get_final_architecture(self):
         
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 33546afe4..5737352eb 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -81,7 +81,7 @@ def new_epoch(self, epoch, round, i):
             self.round_number = 0
             self.prev_round = 0
             self.process = i
-            #self.clean_history()
+            self.clean_history()
 
         if self.prev_round < round:  # reset round_number for each new round
             self.prev_round = round
@@ -127,10 +127,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
     
-    #def clean_history(self):
-    #    best_arch = max(self.history, key=lambda x: x.accuracy)
-    #    self.history = []
-    #    self.history.append(best_arch)
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = torch.nn.ModuleList()
+        self.history.append(best_arch)
 
 
     def get_final_architecture(self):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 14e6835a6..3d01b9d7b 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -94,7 +94,7 @@ def new_epoch(self, epoch, round, i):
             del self.round_number
             del self.prev_round
             del self.process
-            #self.clean_history()
+            self.clean_history()
             self.current_round = []
             self.next_round = []
             self.round_number = 0
@@ -146,10 +146,10 @@ def new_epoch(self, epoch, round, i):
     def _update_history(self, child):
         self.history.append(child)
 
-    #def clean_history(self):
-    #    best_arch = max(self.history, key=lambda x: x.accuracy)
-    #    self.history = []
-    #    self.history.append(best_arch)
+    def clean_history(self):
+        best_arch = max(self.history, key=lambda x: x.accuracy)
+        self.history = torch.nn.ModuleList()
+        self.history.append(best_arch)
 
     def get_final_architecture(self):
         

From f62d4d256b95471db6df4d28d9c5efa577eab987 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 14 Mar 2022 12:57:35 +0100
Subject: [PATCH 090/130] doubble import

---
 examples/mf_demo.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 2a439ed84..1aa26fb35 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -11,7 +11,6 @@
 from naslib.optimizers import RandomSearch as RS
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers import HB
 from naslib.optimizers import BOHB 
 from naslib.optimizers import DEHB

From a1a54fcb426da277a24d01358e63f8708b73e4fe Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 14 Mar 2022 18:27:23 +0100
Subject: [PATCH 091/130] fixed config script

---
 naslib/benchmarks/mf/config.yaml       |  4 ++--
 naslib/benchmarks/mf/create_configs.py | 19 +++++++++++++------
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/naslib/benchmarks/mf/config.yaml b/naslib/benchmarks/mf/config.yaml
index 089160555..0c15b1f5a 100644
--- a/naslib/benchmarks/mf/config.yaml
+++ b/naslib/benchmarks/mf/config.yaml
@@ -7,8 +7,8 @@ search_space: nasbench201
 datasets: 
   - cifar10
 optimizers: # sh, rs, ls, ...
-  - rs
-fidelity: -1
+  - sh
+fidelity: 200
 
 HPO: True
 # only relevant if HPO is enabled
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 429a0fd98..6110661d7 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -113,7 +113,11 @@ def create_configs(
                     "budget_type": "epoch",
                     "budget_max": 128,
                     "method": "random",
-                    "eta": 3,
+                    "eta": 2,
+                    "max_budget": 200,
+                    "min_budget": 1,
+                    "n_process": 1000,
+                    "epsilon": 1e-6,
                     "num_ensemble": 3,
                     "acq_fn_type": "its",
                     "acq_fn_optimization": acq_fn_optimization,
@@ -142,12 +146,14 @@ def create_configs(
                 # TODO: max_fidelity should be dependent on eta and min_fidelity
                 eta = int(np.random.choice(range(2, 5)))
                 fidelity_range = [2**i for i in range(0, 9)]
-                max_fidelity = int(np.random.choice(fidelity_range))
+                # max_fidelity = int(np.random.choice(fidelity_range))
+                max_fidelity = int(np.random.choice(100, 200))
                 # min fidelity has to be lower/equal to max_fidelity
-                min_fidelity = int(np.random.choice(
-                    list(
-                        filter(
-                            lambda n: n <= max_fidelity, fidelity_range))))
+                min_fidelity = int(np.random.choice(range(1, 10)))
+                # min_fidelity = int(np.random.choice(
+                #     list(
+                #         filter(
+                #             lambda n: n <= max_fidelity, fidelity_range))))
                 config = {
                     "seed": seed,
                     "search_space": search_space,
@@ -180,6 +186,7 @@ def create_configs(
                         "n_process": 1_000_000,
                         "budgets": 360_000, 
                         "eta": eta,
+                        "epsilon": 1e-6,
                         # config section for BOHB
                         "tpe_bandwidth": float(np.random.choice(np.arange(0.01, 1.0, 0.01))), # TODO: what is a good range for tpe??
                     },

From ac8e3d85bb5fcce903f56dede6ef3c4d7d4eb5f5 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 14 Mar 2022 18:32:05 +0100
Subject: [PATCH 092/130] fix range in config

---
 naslib/benchmarks/mf/create_configs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 6110661d7..211398d89 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -147,7 +147,7 @@ def create_configs(
                 eta = int(np.random.choice(range(2, 5)))
                 fidelity_range = [2**i for i in range(0, 9)]
                 # max_fidelity = int(np.random.choice(fidelity_range))
-                max_fidelity = int(np.random.choice(100, 200))
+                max_fidelity = int(np.random.choice(range(100, 200)))
                 # min fidelity has to be lower/equal to max_fidelity
                 min_fidelity = int(np.random.choice(range(1, 10)))
                 # min_fidelity = int(np.random.choice(

From 47239d60ae44660888187f8dc3b3194486104909 Mon Sep 17 00:00:00 2001
From: gernel <gernel@informatik.uni-freiburg.de>
Date: Wed, 16 Mar 2022 12:07:33 +0100
Subject: [PATCH 093/130] fix old runner

---
 naslib/benchmarks/bbo/runner.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/naslib/benchmarks/bbo/runner.py b/naslib/benchmarks/bbo/runner.py
index b457dfa74..f22d49550 100644
--- a/naslib/benchmarks/bbo/runner.py
+++ b/naslib/benchmarks/bbo/runner.py
@@ -6,7 +6,7 @@
 from naslib.defaults.trainer_multifidelity import Trainer as Trainer_MF
 
 from naslib.optimizers import RandomSearch, Npenas, \
-RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HyperBand
+RegularizedEvolution, LocalSearch, Bananas, BasePredictor
 
 from naslib.search_spaces.core.query_metrics import Metric
 from naslib.search_spaces import NasBench101SearchSpace, NasBench201SearchSpace, \
@@ -30,8 +30,6 @@
     'bananas': Bananas(config),
     'npenas': Npenas(config),
     'ls': LocalSearch(config),
-    'sh': SuccessiveHalving(config),
-    'hb': HyperBand(config),
 }
 
 supported_search_spaces = {
@@ -64,4 +62,4 @@
 trainer.search(resume_from="", report_incumbent=False)
 trainer.evaluate(resume_from="", dataset_api=dataset_api, metric=metric)
 
-# error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'
\ No newline at end of file
+# error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'

From 98070416f90193117cbda423216d8a9bdbecade6 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Thu, 17 Mar 2022 10:17:14 +0100
Subject: [PATCH 094/130] add plots for mf

---
 examples/optimizers.ipynb     | 529 ++++++++++++++++++++++++++++++++++
 examples/optimizers_HPO.ipynb | 501 ++++++++++++++++++++++++++++++++
 2 files changed, 1030 insertions(+)
 create mode 100644 examples/optimizers.ipynb
 create mode 100644 examples/optimizers_HPO.ipynb

diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
new file mode 100644
index 000000000..49176c169
--- /dev/null
+++ b/examples/optimizers.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
+    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this defines all of the default colors and markers for the plots\n",
+    "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
+    "predictors = ['rs', 'sh']\n",
+    "\n",
+    "# TODO: What of this am I really needing?\n",
+    "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+    "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+    "            (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+    "            (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+    "            (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+    "            (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+    "            (0.09019607843137255, 0.7450980392156863, 0.8117647058823529),\n",
+    "            (0.0, 0.0, 0.0),\n",
+    "            (0.7058823529411765, 0.12156862745098039, 0.4666666666666667),\n",
+    "            (0.4666666666666667, 0.8901960784313725, 0.7607843137254902,),\n",
+    "           ]\n",
+    "\n",
+    "fmts = ['-', '--', ':']\n",
+    "markers = ['^', 'v', 'o']\n",
+    "defaults = [np.array(d) for d in defaults]\n",
+    "\n",
+    "# \n",
+    "pred_plot_dict={\n",
+    "    'ls':{'color':defaults[0], 'fmt':fmts[0]},\n",
+    "    # 'ls_lce':{'color':defaults[0], 'fmt':fmts[1]},\n",
+    "    # 'ls_svr':{'color':defaults[0], 'fmt':fmts[2]},\n",
+    "    # 're':{'color':defaults[1], 'fmt':fmts[0]},\n",
+    "    # 'rea_lce':{'color':defaults[1], 'fmt':fmts[1]},\n",
+    "    # 'rea_svr':{'color':defaults[1], 'fmt':fmts[2]},\n",
+    "    'bananas/bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    # 'bananas_lce/bananas':{'color':defaults[2], 'fmt':fmts[1]},\n",
+    "    # 'bananas_svr/bananas':{'color':defaults[2], 'fmt':fmts[2]},    \n",
+    "    'rs':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    'sh': {'color': defaults[1], 'fmt':fmts[0]},\n",
+    "    'hb':{'color':defaults[4], 'fmt':fmts[0]},\n",
+    "    # 'bohb':{'color':defaults[5], 'fmt':fmts[0]},\n",
+    "    # 'dehb':{'color':defaults[6], 'fmt':fmts[0]},\n",
+    "}\n",
+    "\n",
+    "# define colors, formats, and markers\n",
+    "c_max = 10\n",
+    "colors = [*defaults[:c_max], *defaults[:c_max], *defaults[:c_max]]\n",
+    "fmts = [*['-']*c_max, *['--']*c_max, *[':']*c_max]\n",
+    "markers = [*['^']*c_max, *['v']*c_max, *['o']*c_max]\n",
+    "\n",
+    "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "\n",
+    "# some of them are not used\n",
+    "pred_label_dict={\n",
+    "    'bananas/gcn':'BANANAS-GCN', 'bananas/bananas':'BANANAS', 'bananas/xgb':'BANANAS-XGB', \n",
+    "    'npenas/gcn':'NPENAS-GCN', 'npenas/bananas':'NPENAS-BANANAS', 'npenas/xgb':'NPENAS-XGB', \n",
+    "    'rs':'RS',\n",
+    "    're': 'REA',\n",
+    "    'ls': 'LS',\n",
+    "    'hb': 'HB',\n",
+    "    'sh': 'SH',\n",
+    "    'bohb': 'BOHB',\n",
+    "    'dehb': 'DEHB',\n",
+    "    'rea_svr': 'REA-SVR',\n",
+    "    'bananas_svr/bananas':'BANANAS-SVR',\n",
+    "    'rea_lce': 'REA-WPM',\n",
+    "    'bananas_lce/bananas':'BANANAS-WPM',\n",
+    "    'ls_svr': 'LS-SVR',\n",
+    "    'ls_lce': 'LS-WPM',\n",
+    "    'ls_svr_hp': 'LS-SVR-HP',\n",
+    "    'rea_svr_hp': 'REA-SVR-HP',\n",
+    "    'bananas_svr_hp/bananas': 'BANANAS-SVR-HP',\n",
+    "}\n",
+    "\n",
+    "val_gts = {'cifar10': 91.61, \n",
+    "           'cifar100': 73.49, \n",
+    "           'ImageNet16-120': 46.77, \n",
+    "           'nasbench311': 95.8, \n",
+    "           'nasbench111':95.6, \n",
+    "           'nasbenchnlp':100.7}\n",
+    "\n",
+    "datasets = {'cifar10': 'cifar10',\n",
+    "           'cifar100': 'cifar100', \n",
+    "           'ImageNet16-120': 'ImageNet16-120', \n",
+    "           'nasbench311': 'cifar10', \n",
+    "           'nasbench111':'cifar10', \n",
+    "           'nasbenchnlp':'ptb'\n",
+    "           }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_and_fill_trajectories(pandas_data_frames, default_value=None):\n",
+    "    # merge all trajectories keeping all time steps\n",
+    "    df = pd.DataFrame().join(pandas_data_frames, how='outer')\n",
+    "\n",
+    "    # forward fill to make it a propper step function\n",
+    "    df = df.fillna(method='ffill')\n",
+    "    if default_value is None:\n",
+    "        # backward fill to replace the NaNs for the early times by the\n",
+    "        # performance of a random configuration\n",
+    "        df = df.fillna(method='bfill')\n",
+    "    else:\n",
+    "        df = df.fillna(default_value)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_trajectories(losses, iterations):\n",
+    "    dfs = []\n",
+    "    for i in range(len(losses)):\n",
+    "        loss = losses[i]\n",
+    "        iteration = iterations[i]\n",
+    "        # print('Run %d, Min: %f'%(i, loss))\n",
+    "        df = pd.DataFrame({str(i): loss}, index=iteration)\n",
+    "        dfs.append(df)\n",
+    "\n",
+    "    df = merge_and_fill_trajectories(dfs, default_value=None)\n",
+    "\n",
+    "    return np.array(df.T), np.array(df.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_results(predictor, path, metric='valid_acc', key_choice=None):\n",
+    "    output = []\n",
+    "    time = []\n",
+    "    nan_count = 0\n",
+    "    missing_results_count = 0\n",
+    "    file_count = 0\n",
+    "    for folder in os.listdir(path):\n",
+    "        if not(folder == datasets[key_choice] and os.path.isdir(os.path.join(path, folder))):\n",
+    "            continue\n",
+    "        # TODO: This needs to be done better !!\n",
+    "        # Because config_0 is just one config, with multiple seed.\n",
+    "        # But more than one config with multiple seed should be possible. \n",
+    "        algo_path = os.path.join(path, folder, predictor, 'config_0')\n",
+    "        if not os.path.isdir(algo_path):\n",
+    "            continue\n",
+    "        seed_dirs = list(os.listdir(algo_path))\n",
+    "        \n",
+    "        print(seed_dirs)\n",
+    "        # seed_dirs = sorted(seed_dirs, key=lambda x: int(x))\n",
+    "        seed_dirs = sorted(seed_dirs)\n",
+    "\n",
+    "        for seed_dir in seed_dirs:\n",
+    "            '''if not os.path.isdir(os.path.join(algo_path, seed_dir)):\n",
+    "                continue\n",
+    "            result_file = os.path.join(algo_path, seed_dir, 'errors.json')\n",
+    "\n",
+    "            if not (os.path.isfile(result_file) and file_count < 100):\n",
+    "                continue\n",
+    "            file_count += 1'''\n",
+    "            result_file = os.path.join(algo_path, seed_dir)\n",
+    "            result = json.load(open(result_file))                        \n",
+    "            # config = result[0]\n",
+    "            epochs = len(result[1]['train_time'])\n",
+    "\n",
+    "            val_acc = result[1]['valid_acc'][:epochs]\n",
+    "\n",
+    "            if key_choice == 'nasbench111':\n",
+    "                val_incumbent = [(val_gts[key_choice] - val_acc[0] *  100.) / 100.]\n",
+    "                for  ind in range(1, len(val_acc)):\n",
+    "                    val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
+    "            elif key_choice == 'nasbenchnlp':\n",
+    "                val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
+    "            else:\n",
+    "                val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
+    "                for ind in range(1, len(val_acc)):\n",
+    "                    val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])) / 100)\n",
+    "\n",
+    "\n",
+    "            runtime = result[1]['runtime']\n",
+    "            train_time = result[1]['train_time']\n",
+    "            runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
+    "            train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
+    "            total_time = [i+j for i,j in zip(runtime, train_time)]\n",
+    "            if predictor == 'sh':\n",
+    "                total_time[:] = [x / 200.0 for x in total_time]\n",
+    "            # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
+    "            if metric == 'valid_acc':\n",
+    "                incumbent = val_incumbent\n",
+    "                #incumbent = val_acc\n",
+    "            elif metric == 'test_acc':\n",
+    "                test_err = [100 - x for x in result[1]['test_acc']]\n",
+    "                inc_idx, best, best_idx = [], np.inf, 0\n",
+    "                for i, err in enumerate(val_err):\n",
+    "                    if err < best:\n",
+    "                        best, best_idx = err, i\n",
+    "                    inc_idx.append(best_idx)\n",
+    "                incumbent = [test_err[idx] for idx in inc_idx]\n",
+    "\n",
+    "            if len(incumbent) == epochs:\n",
+    "                output.append(incumbent)\n",
+    "                time.append(total_time)\n",
+    "            else:\n",
+    "                nan_count += 1\n",
+    "\n",
+    "    output = np.array(output)\n",
+    "    time = np.array(time)\n",
+    "    \n",
+    "    output, time = get_trajectories(output, time)\n",
+    "    \n",
+    "    print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
+    "    mean = np.mean(output, axis=0)\n",
+    "    std = np.std(output, axis=0)\n",
+    "    std_error = np.sqrt(np.var(output, axis=0, ddof=1)/np.asarray(output).shape[0])\n",
+    "    print('first mean', mean[0], 'last mean', mean[-1])\n",
+    "    print('first std', std[0], 'last std', std[-1])\n",
+    "    return mean, std, std_error, time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_results_to_dict(folder, predictors, key_choice):\n",
+    "    results_dict = {}\n",
+    "    print('\\n saving', key_choice)\n",
+    "    for i, predictor in enumerate(predictors):\n",
+    "        mean, std, std_error, runtime = get_results(predictor, folder, metric='valid_acc', key_choice=key_choice)\n",
+    "        key = predictor\n",
+    "        results_dict[key] = {'label':pred_label_dict[predictor], \n",
+    "                             'key':key, 'mean':mean, 'std':std, \n",
+    "                             'std_error': std_error, 'runtime': runtime}\n",
+    "    return results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_all_results():\n",
+    "    \n",
+    "    all_results = {}\n",
+    "     \n",
+    "    # that was/is very very shity code!\n",
+    "    key_choice = 'nasbench311'\n",
+    "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
+    "    all_results[key_choice] = save_results_to_dict(folder, predictors, key_choice)       \n",
+    "    return all_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load results into dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " saving nasbench311\n",
+      "['errors.json']\n",
+      "rs output shape (1, 200) nans 0 missing files 0\n",
+      "first mean 0.08989999999999995 last mean 0.0536999999999999\n",
+      "first std 0.0 last std 0.0\n",
+      "['errors.json']\n",
+      "sh output shape (1, 2953) nans 0 missing files 0\n",
+      "first mean 0.3997999999267578 last mean 0.04564000014648428\n",
+      "first std 0.0 last std 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "base_path = '/Users/lars/Projects/NASLib-mf/naslib/benchmarks/mf/run/'\n",
+    "\n",
+    "results = save_all_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_folder = 'plots_naslib/'\n",
+    "if not os.path.exists(result_folder):\n",
+    "    os.makedirs(result_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kS4EpblmItZ4FDZAdHaWiWuaJthR3/gQzmyupL8GIjib0hRZF1cedHzp0g6RKzFUthizoqISGldOnU/vAgwBsWd/Aps1cr6OfRVY0QHZ0lIKqjztvZnPj5wfAnwndBs3WVfK7cjo93RP/yFbVvtmBSpyOoKrjzkuqkbRlTO8GfA54saW6ynRvTiem6yabUDNgAAANb7zRwWqcSlPVcecl9QEeiga0K/AocF08p7m6HKfkdB86lDULF7LqDW+JdjaqOu68mS0nzAMtdK2CdTlOOei+w1BWTJrEqjfewMzYyCaCOC1Q1Ou8pANjv2J+eo2kA0svy3Gqix5DgyetxhUrWLNwYQercSpJsX2iTwCFlmNsFvMcp1PTPeGOcNWb/krfmSjWiAooNCjTn8r5IHaczNJjh3Uj9D641LlosU9UUm703IBbJCWXY3QFdgP+UyZtjlM11Gy1FerZE6uv98GlTkZrA0u5eZUC3mWdkw+AVcDTrBsNd5xOi7p0ofvQoTTMnMkqb4l2Klo0omb2NQBJtcDlcTTccZwC9IhGdMWUKdQeu/6MuvqGBmrLHDamNbKgIY2OwTePp0v37mVQlI6ipjiZ2UUAkvYGdgTuNbPlcZ5mg5mtKaNGx6kKeuy0E9x/P1Zfz8oXXtggf2WBcypNFjRAO3VkNIJDUUZU0kDg74QllUZcMQT8CqgHziyXQMepFjY/9hhWzZnDmgULNshbuXIlvXpVJpR2c2RBQyodXbLpQ77YyfZXAAsIo/FzEul/BX5TalGOU43UbLEFW//k0oJ5tbW1DM6AM5aO1pAlHaWiWCN6CHCImb2btxLjdWBwyVU5juNUCcW2j3sRRuPz+RDhdd5xHKdTUqwRfQoYkzi26FXpB8BjpRblOI5TLRT7On8u8E9Jw4EewC+BXQnLPvcrkzbHcZzMU1RLNAaT252wOulhoCdhUOnjZvZ6+eQ5juNkm1ZbotFX59PAiWZ2YfklOY7jVA+ttkTNbDUwlMIOSNqEpMMlvSJplqTzCuT3kHRbzJ8U480jaYiklZKmxu2amN5b0n2SXpb0kqTLEnWNkbQocc4pafU7juPkU+zA0k3AN9JcKBF3/ghgF+B4SbvkFWuKFU+Ym5oM4v26me0Rt9MT6Zeb2UeBjwP7SToikXdb4pzr0+h3HMcpRLEDS32AEyQdCjxHnvs7M/tOEXU0xZ0HkJSLOz8jUWYUMC7u3wFc3VKseDNbQfRnamarJE0hBMBzHMepCMUa0Z2BKXF/h7y8Yl/zyxl3HkmbA58Hfp1I/lL0vP8qcLaZJa+fO69q485nWQNkQ0cWNEA2dGRBA2RHR6ko1gHJQeUW0gotxZ3PhUSeAFyVa+kC/wAmmFmDpNMIXRIH51dc9XHnM6wBsqEjCxogGzqyoAGyo6MUVH3c+cgfgNfM7MpcgpktMbOcE+nraSagneM4ThqK9eL0BIVf242w7HMWcJOZTSlQJkdT3HmCsTwO+GpemVys+InkxZ0H3jGztcm481HbJQRju97ou6RBZjYvHh4FzCzmXh3HcdpCsS3RmcCewNaEvsw6YFBMWwgcAEySdEhzFUSfo7m48zOB23Nx5yUdFYvdAPSPseK/C+SmQR0ITJM0lTDglIs7vy1wPmG0f0reVKbvxGlPLwDfYf1lq47jOCWh2IGleuBGMzsrmSjpl4CZ2Z6Sfg1cQgtr6csQd76OELqk0LXGAmObvyXHcZz0FNsSPYkwxzOfa4Gvxf3rCC1Cx3GcTkNbQibvWiB9F9a1BFcB2fTf7ziOUyaKfZ2/CbhB0jDCABHAcIIrvBvj8aeAF0uqznEcJ+MUa0TPIYQHORvYKqbNB34BXB6PHwIeKKk6x3GcjFPsZPu1wGXAZZI2jWlL88rMKXSu4zjOxkybJtvHkMlHAGvjcZ84Kd5xHKdT4iGTHcdxUlBsSzQZMnlFIv2vwGGlFuU4jlMteMhkx3GcFHjIZMdxnBR4yGTHcZwUeMhkx3GcFHjIZMdxnBR4yGTHcZwUVHXI5Ji3l6Tp8ZyrcoHtJPWT9Iik1+LnFmn1O47j5LMxhEz+fdQ2LG6Hx/TzgMfMbBhh8GsDo+04jpOWqg6ZLGkQsKmZPROPxwNfIDhCGQWMjEVvAp4kzCZwHMcpGdUeMnmbWE+yzm3i/sBEjKX5wMAidTqO4xRNVYdMLvbkGOyuoLH3uPPlIws6sqABsqEjCxogOzpKRSU9MLUlZHJdXshkAxoghEyWlAuZPDfWU6jOBbmIn/G1f2EhUR53vrxkQUcWNEA2dGRBA2RHRymoZNz5ppDJkroTQibfk1cmFzIZ8kImx4EpkiGT4+v6UkkjYt/piQRvU/l1nZRIdxzHKRkVa4nGPs5cyOSuwB9zIZOByWZ2DyFk8s0xZPI7BEMLIWTyxZJWE+I4nW5m78S8/yOEKOlFGFDKede/DLhd0snAbOCYct+j4zidj4o6VC51yOSYNxnYrUD6EoL3KcdxnLJRydd5x3GcjQ43oo7jOClIZUQlzZS0plRiHMdxqo20faK/Zd1k+OrnnTdhwlfbf35Nd3oMPgo2oukbjuO0TCojamZXl0pIJqh/D165L1UV/ebNgH2PLo0ex3Eyj4c7TtKtFwzcvX3nvv8W1L9H14alpdXkOE6madaISnqCItfFm9nBJVPUkXzoo3DG0+079+/fhOdvAVtbWk2O42SallqiLyb2uwInEBx5TIpp+wCDgFvKI63KCAuqwFK7XXUcp4po1oia2bdz+5KuILiTOzOuY8+lXwk066quU6Ew0UHeEnWcTkWxU5xOBK5OGtDI74DRpZVUpXTxlqjjdEaKNaIiBKrLp52jMBshsSXqfaKO07kodnT+j8D1koYBz8S0EYRQyn8qh7CqI/aJyho7WIjjOJWkLXHnFwJnAj+JafMInpJ+WQZd1UdTS9SNqON0Jor1bN8I/Bz4uaRNY5pPiEzSxY2o43RG2jzZ3o1nM/jrvON0SpodWJI0LRerPcZ1n9bcVuzF2ht3PpE/WNIySefE448kYtFPlbRU0lkxb5ykuYm8I4vV2S78dd5xOiUttUTvJMY1IoQvTkUi7vyhhKicz0q6x8ySIZOb4s5LOo4Qd/7YRP6vWOe5HjN7BdgjUf9c4G+J8leY2eVptRdFbooTbkQdpzPR0mT7iwrtp6DdcedjnKUvAG+SF/M+wSHA62Y2uwRa285/gi8WWSP8/VugjluD0P+DZTBtk9JX3HMz2Pd02Gzb1ss6Tiehkg5I2h13XlI98ANCK/acZuo/DpiQl/YtSScCk4Hvmdm7+SeVKmTykDUr1x08f3O76igVfctY9weL5rJkvwuLKpuF0LhZ0ADZ0JEFDZAdHaWiJQck0yneAcnHSqaoMOMIr+bLVKCFF6OHHgWMTST/Hvgx4R5+TJiK9fX8c0sWMnnTbWFpXdjvO6h9dZSINWvXUNO1xP8fly+CxjX07VJP3zY8oyyExs2CBsiGjixogOzoKAUt/dJS94Pm0e6484QW65cl/RzYHGiUVJ/wZ3oEMMXMFuQqSu5Lug64t7S3k8d3XwKgtra2w78gdeXQcM0BMH+aD5w5Th5F9YmWiKa48wRjeRyQ70Y+Fyt+Iom488ABuQKSxgHL8hxCH0/eq7ykQTEuPcAXWd8rldNWmmYfuG8Ax0lSLXHnm0VSH0Jf6Wl5WT+XtAfhdb62QL7TFnLdKN4SdZz1KNqISvoaocU3GOiezDOzHYqpo71x5/PKj8s7Xk6BOE9m5t6lSkmuJVpcN7njdBqK8uIk6fuEgZnngCHA3YTX434E5yTOxo4vJnCcghTrCu8bwKlmNhZYTfAtehTBsG5fLnFOlvDXeccpRLFGdFvgv3F/JbBp3J8AfKnUopwM4gNLjlOQYo3ofGDLuD8b+ETc/zDeSdY58IElxylIsUb0ccJkdggj6L+K0UBvA+4qhzAnY6jYr4rjdC5aHJ2X9Gkze5SwLLILgJldI+ldYD+Ck5Jry67S6Xh8YMlxCtLaFKeHJdUSWp9/At4GMLPbCK1Qp7PhRtRx1qO1d7RdCa/r3wZmS7pP0hej2zmnM+EDS45TkBaNqJnNNLNzCKPzxxIGkW4H5kr6maSPVECjkwV8YMlxClLUaIGZrTGzu8zsc4R5oVcBRwMzJD1VToFORvAVS45TkPbEWHpb0u+ADwgu6vYrtSgng+SM6Jp6WDqv5bKRrisWwtIeZRRVHRqyoiMLGpp0NA5eF9yxymmTEZX0aYJPzi8A9YTJ9teXXpaTPeLr/Pzp8KuPFnXGdq0XKTtZ0ADZ0JEFDRB1nDcnRErYCGj1X0EMDnehpDeBh4FBhClPW5vZN83s+XKLdDLAlsM6WoHjZJLW5ok+CowEFgI3ATeY2awK6HKyxiEXwLZ7Q33xEbMXL1nClv03cLBVUbKgISs6sqChSUdNr46WUTJae51fThhAus/M1lZAj5NVuvWC3drmJmFZbS1bdrCX/yxoyIqOLGho0lHTvfWCVUJrU5xGmdk9pTKgpY47H9NqJU2PseUnJ9L7SXpE0mvxc4tS3IPjOE6Sig2PJeLOHwHsAhwvaZe8Yk1x54ErCHHnk6wXdz7BQWa2h5ntnUg7D3jMzIYBj8Vjx3GcklLJOQZNcefNbBWQizufZBSh7xVCoLxDFMN7JuLOv1Tk9ZJ13USYUeA4jlNSNoa480ZY42/AtTEEMsDARKC6+cDAQqJKFXc+RxZiamdBA2RDRxY0QDZ0ZEEDZEdHqaikEU3DOJqPO7+/mc2VNAB4RNLLZrbeKiozs2hkN6BkcecTdHTI5KxogGzoyIIGyIaOLGiA7OgoBZU0omWJO29mcwHMbKGkvxG6DZ4CFuTCJksaRJim5TiOU1Iq2SfaFHdeUndCOOR78srk4s5DIu68mR1gZkPMbAhwJfATM7taUh9JfaEpdPJhrIsvn6zrJODvZbovx3E6MdUed34g8Lf4il8D/NnMHox5lwG3SzqZENLkmJLflOM4nZ6K9omWOu68mb0B/E8z5ZYAh6SQ6ziO0yobhxsVx3GcDsKNqOM4TgrciDqO46TAjajjOE4K3Ig6juOkwI2o4zhOCtyIOo7jpMCNqOM4TgrciDqO46TAjajjOE4K3Ig6juOkwI2o4zhOCtyIOo7jpMCNqOM4TgrciDqO46Sgoka01HHnJW0n6QlJMyS9JOnMRNlxkubGePRTJR1Z9ht0HKfTUe1x59cA3zOzXYARwDfz6rwixqPfIzqEdhzHKSlVHXfezOaZ2ZS4/wEwkxB22XEcpyJsDHHnAYiv/h8HJiWSvyXpRGAyocX6boHzPO58mciCjixogGzoyIIGyI6OUrExxJ1H0ibAncBZZrY0Jv8e+DFg8fOXwNfzz/W48+UlCzqyoAGyoSMLGiA7OkpB1cedl9SNYEBvNbO7chWZ2YLcvqTrgHtLf0uO43R2KmlEm+LOE4zlccBX88rkYsVPJBF3HjggV0DSOGBZNKAihFmeaWa/SlYkaZCZzYuHX2RdPHrHcZySUe1x5/cDRgPTJU2NaT+MI/E/l7QH4XW+FjitrZpXr15NXV0d9fX1RZ+zZs0aZs6c2dZLlZQsaChGR8+ePdl2223p1q1bBVU5Tmmp9rjzTwMbdpKGvNFptALU1dXRt29fhgwZQqG+2EI0NDTQo0ePtJdORRY0tKbDzFiyZAl1dXUMHTq0wsocp3T4iqUWqK+vp3///kUbUKd4JNG/f/82tfIdJ4u4EW0FN6Dlw5+tszHgRjQDPPnkk2y//fYcdNBBHHrooSxZsoRbb72VESNGcMABB3Dqqad2tMRWWbp0KZ/97GcZOXIkI0aMYPLkyYwZM6Ypf9y4cTz66KMdJ9BxykS1zBPd6Bk9ejSXXHIJt9xyCxMmTGD8+PH85z//oaamhnff3WCNQOYYP348Rx99NCeffHJmBrYcpxJ4SzRjvPfeewAsX76cSZMm0djYyBZbbNGxooqgd+/eTJw4kcWLF1NTU0Pfvn07WpLjVARviRbB/J/8hIaZLxdVttEa6aLC/5t67PxRtvrhDwvm3XzzzTz44IOsWLGCiRMnsu+++3LppZcyffp0zj33XE47rc0ztNbngfNg/vR0dQBstTsccdkGyaNHj6auro6DDjqIgQMHcskll/D4448zcuRIAGpra9l///3TX99xMoYb0SJomPkyK559tqzXyL3Ojxkzhjlz5jB8+HDuvvtuVqxYwUEHHcQJJ5zAJpts0v4LzJ8Os58uneA8unXrxgUXXMAFF1zAhAkTuPLKKzn44IOZMGECEPpEHWdjxI1oEfTY+aNFl22tJdoaY8eOZdy4cVx88cUMGzaM3r1706tXr6Kv3yxb7Z6+jhbqmT17NltvvTXdunVjwIABNDY20rVr19Jc03EyjBvRImjuFbwQaSe6f+QjH2HRokWcccYZrFy5EoAvf/nL6VqhUPAVvJRMnTqVY445hl69etGtWzcuvPBCfvvb35b1mo6TBdyIZoCRI0c29R0CVTkVaNSoUYwatb572OHDhzft++u8s7Hio/OO4zgpcCPqOI6TAjeirRA88TnlwJ+tszHgRrQFevbsyZIlS/zHXgZyXpx69uzZ0VIcJxUVHViSdDjwa4I/0evN7LK8/B7AeGAvgkf7Y82sNpE/GJgBjDOzy1uqMzp//gvQH3gOGB0D5BXNtttuS11dHYsWLSr6nDVr1lBT07HjdVnQUIyOnD9Rx6lmKvZLS4RMPpQQpO5ZSfeY2YxEsaaQyZKOI4RMPjaRv17I5Fbq/BkhLtNfJF0T6/59WzR369atzb4ua2trOzx+TBY0ZEmH45STqg6Z3Fyd8ZyDYx3EOr9Q8jtyHKfTU0kjWihkcn6M+PVCJgO5kMmbEEImX1Rknf2B92IdzV3LcRwnNR3fcVYc42ghZHIaknHngWWSXklZ5ZbA4pR1pCULGiAbOrKgAbKhIwsaIBs6PlKqiqo6ZDJhwKhQnUuAzSXVxNZooWsB68edLwWSJpvZ3qWqr1o1ZEVHFjRkRUcWNGRFh6TJpaqr2kMm1xSq08xM0hOxjr/EOv9ezptzHKdzUrE+0dgizIVMngncnguZLOmoWOwGQh/oLOC7wHntqTNm/wD4bqyrf6zbcRynpFR1yOTm6ozpbxBG7ytNyboGUpAFDZANHVnQANnQkQUNkA0dpevC89U4juM47ceXfTqO46TAjWgbkbS5pDskvSxppqRPSOon6RFJr8XPLWJZSbpK0ixJ0yTtWUIdZ0t6SdKLkiZI6ilpqKRJ8Xq3Seoey/aIx7Ni/pB2XvOPkhZKejGR1uZ7l3RSLP+apJNKpOMX8W8yTdLfJG2eyBsbdbwi6TOJ9MNj2ixJLfa/F6Mhkfc9SSZpy3hc0WcR078dn8dLcVZLLr0iz0LSHpKekTRV0mRJ+5TzWUjaTtITkmbEez4zppf/+2lmvrVhI6x+OiXudydMufo5cF5MOw/4Wdw/krBMVcAIYFKJNGxDWL3VKx7fDoyJn8fFtGuAM+L+/wHXxP3jgNvaed0DgT2BFxNpbbp3oB/wRvzcIu5vUQIdhwE1cf9nCR27AC8APYChwOsEPwtd4/4O8e/4ArBLGg0xfTvCQOdsYMsOehYHAY8CPeLxgEo/C+Bh4IjE/T9ZzmcBDAL2jPt9gVfj/Zb9++kt0TYgaTPCF+YGADNbZWbvsf5y1eQS01HAeAs8Q5i7OqhEcmqAXgrTvHoD82h+qWuzy2nbgpk9BbyTl9zWe/8M8IiZvWNm7wKPAIen1WFmD9u6FWrPEOYG53T8xcwazOxNYBZhwLGYZcht0hC5AjgXSA42VPRZAGcAl5lZQyyzMKGjUs/CgE3j/mbA2wkNJX8WZjbPzKbE/Q8Is3W2oQLfTzeibWMosAj4k6TnJV0vqQ8w0MzmxTLzgYFxv5ilrm3GzOYClwNzCMbzfcLCg/es8FLXgstp0+qItPXey/JM8vg66xzVVEyHpFHAXDN7IS+r0s9iJ+CA2HXzT0m5OC2V1HEW8AtJbxG+q2MrpUGhu+rjwCQq8P10I9o2agivLb83s48Dy8mby2rhnaCsUx5iv84oglHfGuhDG1sw5aAS994aks4H1gC3Vvi6vYEfAhe0VrYC1BBeR0cA3wdub8+bR0rOAM42s+2As6nQPG0FPxt3AmeZ2dJkXrm+n25E20YdUGdmk+LxHQSjuiD3mh4/c69PxSx1bQ+fBt40s0Vmthq4C9iPuNS1wLWadGj95bSloK33Xq5ngqQxwOeAE+IPppI6diT8U3tBUm2sb4qkrSqoIUcdcFd8Vf0v0EhYr15JHScRvpcAf2XdnO2yaZDUjWBAbzWz3LXL/v10I9oGzGw+8JaknPOCQwhOonPLVWH9Jab3ACfGkcARwPuJV4s0zAFGSOodWxg5HbmlroV05PQll9OWgrbe+0PAYZK2iC3qw2JaKhScc58LHGVmK/L0HacwQ2EoMAz4L4llyAqzGI6LZduFmU03swFmNsTMhhAM2Z7xO1PRZwHcTRhcQtJOhMGixVToWUTeBj4V9w8GXov7ZXkW8XdwAzDTzH6VyCr/97PY0S/fmkYB9wAmA9MIX9YtCP2Lj8UvyqNAv1hWBKfRrwPTgb1LqOMi4GXgReBmwojrDoQfxSzCf//c6GzPeDwr5u/QzmtOIPTBriYYiZPbc++EPstZcftaiXTMIvRlTY3bNYny50cdrxBHjGP6kYRR3NeB89NqyMuvZd3ofKWfRXfglvjdmAIcXOlnAexP6Kd/gdA3uVc5n0W8nhF+l7nvwJGV+H76iiXHcZwU+Ou84zhOCtyIOo7jpMCNqOM4TgrciDqO46TAjajjOE4K3Ig6juOkwI2o06FIGqLgNq7DApdFF2ondtT1S4WkZXHVVjFl/yrpe2WW1ClwI9rJkXRjNGImaY2kOZJ+n/O7WOJrPSnp6rzktwhuzKaW+nrFIOmzhGV+FV1rnwEuBs6PnsmcFLgRdSCs5BgEDAFOAT4P/K4SFzaztWY239Z5n6o0ZwI3mtnaDrp+h2Bm0wm+Mv+3o7VUO25EHYCGaMjqzOxh4DbCmmGgqbV6b/IESeO0vifzGyXdK+lMSXMlvSvpT9G7EZJuJKyl/mai5Tsk/3Ve0sh4fISk5yStlPQvSdtK+pSkF+Jr672S+udp+pqCZ/N6Sa8qeP9v9jsu6UMEZy7/yEs/LZ5fL2mxpIe0zrFLq9eRtFlszc+LZWZKOjaRf7Sk6ZIaJL0l6fy49juXXyvpR5KulbRUUp2k7+dp/HBs2dcreKT/XIH7u0DS7Hid+ZLG5xW5Bzi+uefjFEdFo3062UfSDgS3eqvbcfoBhDXUnya8It9OWI/9U0KLbyfCev8fxvKLWN9jTpKLCD4p3wf+TDDs9cCpwFqCL4BxwLej7m8QXlG/TVizvRtwXbyP/C6EHPsDDYQ15sR69iasqT4JeJoQueDgRH6L14nG8H6CT4Wvxfv/CMF/AZL2itovIXQhDAeuBZYCv0loOxu4EPgFcARwlaSnzWxiNNh/A94FPkFwyv1rgv+EnM4vAecQjOR0YADBNV6S/wI/ktTLzFY284yc1kjjBMO36t+AGwn+N5cBKwlOHIzgCzJZ5t6888axfjiIGwn9m10TadcBjyaOnwSuzqtnSLze3vF4ZDz+TKLMt2Lani1cfw4wOq/us4AZLdz7WcDsvLSjCYa7bzPntHgd4FCC67mdmzn/VoIXrfxnWZc4rgUm5JV5DfhR3D+M8I9kcCI/54BjTDz+LsHJSLcW7v9j8ZwdO/p7WM2bv847AE8RvFPtQ2gN3Q9c1Y56Ztj6fYtvE1pA7WFaYn9B/JyelzYAml7LtwOuja/6yyQtAy4j+Plsjl6E1m2SRwjxkd6UdKtC0LK+bbjOx4F5ZjazmWvuDPw7L+1pYBtJmybSpuWVST7LnQke9Ock8icRjHeOvxJav29KukHSVyT1YH1yrc9ezWh1isCNqAOwwsxmWfCJ+R3C6+H/S+Q3ElyHJelWoJ78LgCj/d+xZF0GYMEBdaG6c5+nE/4Z5LbdgF1buMZiwmv3ukpDfJ49gWMIrc6xwMuStk5xnWJJulRL9SzN7C1CN8JphK6CXwLPKYSzydEvfi5qu1QnhxtRpxAXAT+IhgPCjyw/wN4e7ah3FSGyZEkxswWEltqO8Z/BelsLpz4PfEgxtHGivjVm9riZjSW88vYBPlfkdZ4HBknauZlrziREIUiyP+F1/oMib3kmoeWa7E/eh7zfs5nVm9l9ZnY2oe9117xr70Zo0S7AaTc+sORsgJk9KWkG8CNCuOXHgXMlfZ3w6n804cdY18aqa4F9FAKJLaNwtMz2ciHwG0nvEbojuhFalNuY2U+bOed5QriI/QkOtomj3DsS7vMdgof4vgTDVcx1HiO8Wt8p6WzCwNKHgT5mdjehRfispHGEAbPhwPdYN9hWDI8SBujGx2v0IkQZbZompjDpviZqWQYcS2jdvpao5wBK40m/U+MtUac5fgmcLGl7M3uI0Dq9lDAiPYT2zSO9nNAanUFo3Q4ujVQws+sJHslHE7yp/4swkv9mC+esBf4InJBIfo8QVjdnqM4BTjGzfxVzHTNrJIym/5vgXX4mYeS8e8yfAnwF+BJhVsBlcWtuBkEh3Y3AFwm/30nAeMJof0PefZwc9b0Yr3e0hVDJSOoZ67iu2Os6hXHP9k6nRtIAglEfnjMwnQFJ3wRGmdlhrRZ2WsRbok6nxswWElqWJWsVVwmriXNsnXR4S9RxHCcF3hJ1HMdJgRtRx3GcFLgRdRzHSYEbUcdxnBS4EXUcx0mBG1HHcZwU/H9M8KPUzWE+ZgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 324x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot performance vs runtime\n",
+    "\n",
+    "for key_choice in results.keys():\n",
+    "    results_dict = results[key_choice]\n",
+    "\n",
+    "    keys = predictors\n",
+    "\n",
+    "\n",
+    "    keys = sorted(keys)\n",
+    "\n",
+    "    plt.rcParams['axes.grid'] = True\n",
+    "    plt.rcParams['grid.linestyle'] = 'dotted'\n",
+    "    add_sub_axes = False\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=[4.5, 3.5])\n",
+    "    if add_sub_axes:\n",
+    "        sub_axes = plt.axes([.6, .5, .3, .38]) \n",
+    "\n",
+    "\n",
+    "    for i, key in enumerate(keys):\n",
+    "        mean = results_dict[key]['mean']\n",
+    "        sem = results_dict[key]['std_error']\n",
+    "        label = results_dict[key]['label']\n",
+    "        x = results_dict[key]['runtime']\n",
+    "        #tag = '_'.join(key.split('_')[:-1])\n",
+    "        tag = key\n",
+    "        color = pred_plot_dict[tag]['color']\n",
+    "        fmt = pred_plot_dict[tag]['fmt']\n",
+    "        ax.plot(x, mean, label=label, color=color, linestyle=fmt, linewidth=2.5)\n",
+    "        ax.fill_between(x, mean-2*sem, mean+2*sem,\n",
+    "                        color=color, alpha=0.2)\n",
+    "\n",
+    "        if add_sub_axes:\n",
+    "            n = 450\n",
+    "            X_detail = x[n:]\n",
+    "            Y_detail = mean[n:]\n",
+    "            sem_detail = sem[n:]\n",
+    "            sub_axes.plot(X_detail, Y_detail, color=color, linestyle=fmt)\n",
+    "            sub_axes.tick_params(labelbottom=False) \n",
+    "\n",
+    "    ax.set_xlabel('Runtime (seconds)', fontsize=14)\n",
+    "\n",
+    "    if key_choice == 'nasbenchnlp':\n",
+    "        ax.set_ylabel('Perplexity regret', fontsize=14) #nas201\n",
+    "    else:\n",
+    "        ax.set_ylabel('Valid. regret', fontsize=14) #nas201\n",
+    "\n",
+    "    ax.grid(True, which=\"both\",ls=\"-\", alpha=.5)\n",
+    "\n",
+    "    if key_choice == 'cifar10':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([.001, .19])\n",
+    "        ax.set_xlim([1e4, 4*10e4])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'cifar100':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([0.002, 0.3])\n",
+    "        ax.set_xlim([1e4, 8.1e5])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'ImageNet16-120':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([.002, .4])\n",
+    "        ax.set_xlim([1e4, 1.0e6])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
+    "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'nasbench311':    \n",
+    "        # ax.set_xscale('log')\n",
+    "        # ax.set_yscale('log')\n",
+    "        ax.set_ylim([.04, .06])\n",
+    "        ax.set_xlim([500, 2000])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas311_c10.pdf')\n",
+    "        plt.show()\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "\n",
+    "    elif key_choice == 'nasbench111':\n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([4e-4, 8e-2])\n",
+    "        ax.set_xlim([2e4, 5.8*10e4])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-111 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas111_c10.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "\n",
+    "    elif key_choice == 'nasbenchnlp':\n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([.5, 4.2e1])\n",
+    "        ax.set_xlim([5e4, 1.0*10e5])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-NLP11 PTB', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nasn11_ptb.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "37e59fccee3cee055d0c30896f8ae2ee545f4691b0b28f0762ae05c70393af3f"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.9 64-bit ('jupyter-venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
new file mode 100644
index 000000000..c48a01310
--- /dev/null
+++ b/examples/optimizers_HPO.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting for NAS-Bench-Suite predictors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "from scipy.stats import kendalltau\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "11/14: added extra matrix experiments for the rebuttal. \n",
+    "BBO has a lot more weird nan issues than pred, so I made some adjustments.\n",
+    "\n",
+    "This notebook is similar to plot_pred_nov12.ipynb but with black-box optimizer (NAS) \n",
+    "results instead of performance predictor results.\n",
+    "\n",
+    "plot_pred_nov12.ipynb is slightly cleaner and better commented than this notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# method that gathers all of the results\n",
+    "\n",
+    "# example result path:\n",
+    "# ~/results_nas_bench_suite/bbo_results_oct1/bbo_results_oct1_0/nasbench201/cifar100/npenas/config_28/7\n",
+    "\n",
+    "# and the result dict structure:\n",
+    "# results_dict[28][nb201_c10][npenas] = [np.mean(values), np.std(values)]\n",
+    "\n",
+    "def get_hpo_results(optimizers, search_spaces, root):\n",
+    "    errors = 0\n",
+    "    results_dict = {}\n",
+    "    root = os.path.expanduser(root)\n",
+    "    for seed_header in os.listdir(root):\n",
+    "        for search_space in search_spaces:\n",
+    "            for optimizer in optimizers:\n",
+    "                optimizer_path = os.path.join(root, seed_header, file_dict[search_space], optimizer)\n",
+    "                if not os.path.exists(optimizer_path):\n",
+    "                    continue\n",
+    "                for hpo_folder in os.listdir(optimizer_path):\n",
+    "                    hpo_seed = int(hpo_folder.split('_')[-1])\n",
+    "                    values = []\n",
+    "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
+    "                        result_path = optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
+    "                        if not os.path.isfile(result_path):\n",
+    "                            continue\n",
+    "                        result = json.load(open(result_path))\n",
+    "                        valid_accs = result[1]['valid_acc']\n",
+    "                        value = np.max(valid_accs)\n",
+    "                        if not np.isnan(value):\n",
+    "                            continue\n",
+    "                        values.append(value)\n",
+    "                    if len(values) > 3:\n",
+    "                        # had to add these if statements because file structure is in a different order\n",
+    "                        if hpo_seed not in results_dict:\n",
+    "                            results_dict[hpo_seed] = {}\n",
+    "                        if search_space not in results_dict[hpo_seed]:\n",
+    "                            results_dict[hpo_seed][search_space] = {}\n",
+    "                        if optimizer not in results_dict[hpo_seed][search_space]:\n",
+    "                            results_dict[hpo_seed][search_space][optimizer] = {}\n",
+    "                        results_dict[hpo_seed][search_space][optimizer] = [np.mean(values), np.std(values)]\n",
+    "                    else:\n",
+    "                        print(len(values), 'not enough values', hpo_seed, search_space, optimizer)\n",
+    "                        errors += 1\n",
+    "                        if errors < 100:\n",
+    "                            continue\n",
+    "                        print('ending early due to excess errors')\n",
+    "                        return results_dict\n",
+    "    return results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define constants that will be used throughout the notebook\n",
+    "\n",
+    "\"\"\"\n",
+    "Set up colors. There are 11 colors defined in 'defaults', and 3 markers/formats for the plot lines.\n",
+    "So there are 33 color/markers total.\n",
+    "\"\"\"\n",
+    "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+    "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+    "            (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+    "            (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+    "            (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+    "            (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+    "            (0.09019607843137255, 0.7450980392156863, 0.8117647058823529),\n",
+    "            (0.0, 0.0, 0.0)\n",
+    "           ]\n",
+    "\n",
+    "fmts = ['-', '--', ':', '-.']\n",
+    "markers = ['^', 'v', 'o']\n",
+    "defaults = [np.array(d) for d in defaults]\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "color_dict={\n",
+    "    'rs':{'color':defaults[0], 'fmt':fmts[0]},\n",
+    "    're':{'color':defaults[1], 'fmt':fmts[0]},\n",
+    "    'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
+    "}\n",
+    "\n",
+    "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "\n",
+    "# how the optimizer names will be displayed in the plot legends:\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "pred_label_dict={\n",
+    "    'rs': 'Rand. Search', 're':'Reg. Evo.', 'ls':'Local Search', \n",
+    "    'bananas':'BANANAS', 'npenas':'NPENAS'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# search space constants\n",
+    "\n",
+    "# how the search space name will be displayed in the plot titles:\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "ss_dict_lined = {'nb101':'NB-101\\n CIFAR10', \n",
+    "                 'nb201_c10':'NB-201\\n CIFAR10', \n",
+    "                 'nb201_c100':'NB-201\\n CIFAR100', \n",
+    "                 'nb201_im':'NB-201\\n ImageNet', \n",
+    "                 'darts':'DARTS\\n CIFAR10',\n",
+    "                 'nlp':'NB-NLP\\n TreeBank',\n",
+    "                 'asr':'NB-ASR\\n TIMIT',\n",
+    "                 'mr_3ddet':'NB-MR\\n KITTI',\n",
+    "                 'mr_cls':'NB-MR\\n ImageNet',\n",
+    "                 'mr_seg':'NB-MR\\n City',\n",
+    "                 'mr_video':'NB-MR\\n HMDB51',\n",
+    "                 'transmicro_obj':'TNB-Micro\\n Object', \n",
+    "                 'transmicro_scene':'TNB-Micro\\n Scene', \n",
+    "                 'transmicro_jigsaw':'TNB-Micro\\n Jigsaw', \n",
+    "                 'transmicro_room':'TNB-Micro\\n Room',\n",
+    "                 'transmicro_segment':'TNB-Micro\\n Semantic', \n",
+    "                 'transmicro_normal':'TNB-Micro\\n Surface', \n",
+    "                 'transmicro_auto':'TNB-Micro\\n Autoenc.', \n",
+    "                 'transmacro_obj':'TNB-Macro\\n Object', \n",
+    "                 'transmacro_scene':'TNB-Macro\\n Scene', \n",
+    "                 'transmacro_jigsaw':'TNB-Macro\\n Jigsaw', \n",
+    "                 'transmacro_room':'TNB-Macro\\n Room',\n",
+    "                 'transmacro_segment':'TNB-Macro\\n Semantic', \n",
+    "                 'transmacro_normal':'TNB-Macro\\n Surface', \n",
+    "                 'transmacro_auto':'TNB-Macro\\n Autoenc.', \n",
+    "          }\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "ss_dict_oneline = {'nb101':'NB-101 CIFAR10', \n",
+    "                 'nb201_c10':'NB-201 CIFAR10', \n",
+    "                 'nb201_c100':'NB-201 CIFAR100', \n",
+    "                 'nb201_im':'NB-201 ImageNet', \n",
+    "                 'darts':'DARTS CIFAR10',\n",
+    "                 'nlp':'NB-NLP TreeBank',\n",
+    "                 'asr':'NB-ASR TIMIT',\n",
+    "                 'mr_3ddet':'NB-MR KITTI',\n",
+    "                 'mr_cls':'NB-MR ImageNet',\n",
+    "                 'mr_seg':'NB-MR City',\n",
+    "                 'mr_video':'NB-MR HMDB51',\n",
+    "                 'transmicro_obj':'TNB-Micro Object', \n",
+    "                 'transmicro_scene':'TNB-Micro Scene', \n",
+    "                 'transmicro_jigsaw':'TNB-Micro Jigsaw', \n",
+    "                 'transmicro_room':'TNB-Micro Room',\n",
+    "                 'transmicro_segment':'TNB-Micro Semantic', \n",
+    "                 'transmicro_normal':'TNB-Micro Surface', \n",
+    "                 'transmicro_auto':'TNB-Micro Autoenc.', \n",
+    "                 'transmacro_obj':'TNB-Macro Object', \n",
+    "                 'transmacro_scene':'TNB-Macro Scene', \n",
+    "                 'transmacro_jigsaw':'TNB-Macro Jigsaw', \n",
+    "                 'transmacro_room':'TNB-Macro Room',\n",
+    "                 'transmacro_segment':'TNB-Macro Semantic', \n",
+    "                 'transmacro_normal':'TNB-Macro Surface', \n",
+    "                 'transmacro_auto':'TNB-Macro Autoenc.', \n",
+    "          }\n",
+    "\n",
+    "# these are the filenames as of sep24\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "file_dict = {\n",
+    "    'nb101':'nasbench101/cifar10', \n",
+    "    'nb201_c10':'nasbench201/cifar10', \n",
+    "    'nb201_c100':'nasbench201/cifar100', \n",
+    "    'nb201_im':'nasbench201/ImageNet16-120', \n",
+    "    'darts':'darts/cifar10', \n",
+    "    'nlp':'nlp/LM-task', \n",
+    "    'asr':'asr/TIMIT', \n",
+    "    'transmicro_obj':'transbench101_micro/class_object',\n",
+    "    'transmicro_scene':'transbench101_micro/class_scene',\n",
+    "    'transmicro_jigsaw':'transbench101_micro/jigsaw',\n",
+    "    'transmicro_room':'transbench101_micro/room_layout',\n",
+    "    'transmicro_segment':'transbench101_micro/segmentsemantic',\n",
+    "    'transmicro_normal':'transbench101_micro/normal',\n",
+    "    'transmicro_auto':'transbench101_micro/autoencoder',\n",
+    "    'transmacro_obj':'transbench101_macro/class_object',\n",
+    "    'transmacro_scene':'transbench101_macro/class_scene',\n",
+    "    'transmacro_jigsaw':'transbench101_macro/jigsaw',\n",
+    "    'transmacro_room':'transbench101_macro/room_layout',\n",
+    "    'transmacro_segment':'transbench101_macro/segmentsemantic',\n",
+    "    'transmacro_normal':'transbench101_macro/normal',\n",
+    "    'transmacro_auto':'transbench101_macro/autoencoder',\n",
+    "    'mr_3ddet':'mr/3ddet',\n",
+    "    'mr_cls':'mr/cls',\n",
+    "    'mr_seg':'mr/seg',\n",
+    "    'mr_video':'mr/video',\n",
+    "            }\n",
+    "# TODO: adapt this for multi-fidelity\n",
+    "ss_dict_mat = {'nb101':'NB-101', \n",
+    "                 'nb201_c10':'NB-201', \n",
+    "                 'nb201_c100':'NB-201', \n",
+    "                 'nb201_im':'NB-201', \n",
+    "                 'darts':'DARTS',\n",
+    "                 'nlp':'NB-NLP',\n",
+    "                 'asr':'NB-ASR',\n",
+    "                 'mr_3ddet':'NB-MR',\n",
+    "                 'mr_cls':'NB-MR',\n",
+    "                 'mr_seg':'NB-MR',\n",
+    "                 'mr_video':'NB-MR',\n",
+    "                 'transmicro_obj':'TNB-Micro', \n",
+    "                 'transmicro_scene':'TNB-Micro', \n",
+    "                 'transmicro_jigsaw':'TNB-Micro', \n",
+    "                 'transmicro_room':'TNB-Micro',\n",
+    "                 'transmicro_segment':'TNB-Micro', \n",
+    "                 'transmicro_normal':'TNB-Micro', \n",
+    "                 'transmicro_auto':'TNB-Micro', \n",
+    "                 'transmacro_obj':'TNB-Macro', \n",
+    "                 'transmacro_scene':'TNB-Macro', \n",
+    "                 'transmacro_jigsaw':'TNB-Macro', \n",
+    "                 'transmacro_room':'TNB-Macro',\n",
+    "                 'transmacro_segment':'TNB-Macro', \n",
+    "                 'transmacro_normal':'TNB-Macro', \n",
+    "                 'transmacro_auto':'TNB-Macro', \n",
+    "          }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now load all of the data\n",
+    " - Each algorithm is tagged as \"algorithm_date\" and then later we can pick which ones we want to plot\n",
+    " - The next cell will create a dictionary will all of the results from the specified search space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# do not accidentally run this twice.\n",
+    "# takes >30 min to load all these results\n",
+    "if True:\n",
+    "    search_spaces = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp',\n",
+    "                     'transmicro_obj', 'transmicro_scene', 'transmicro_jigsaw', \n",
+    "                     'transmicro_room', 'transmicro_segment', 'transmicro_normal', \n",
+    "                     'transmicro_auto', \n",
+    "                     'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
+    "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
+    "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
+    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas')\n",
+    "    root = '~/plotting_data/bbo'\n",
+    "    results_dicts = {}\n",
+    "\n",
+    "    results = get_hpo_results(optimizers, search_spaces, root=root)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n",
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute how many of each we have\n",
+    "print(len(results.keys()))\n",
+    "keys = results.keys()\n",
+    "keys = list(keys)\n",
+    "keys.sort()\n",
+    "print(keys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss_group_full = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp',\n",
+    "                 'mr_3ddet', 'mr_video', 'mr_cls', 'mr_seg',\n",
+    "            'transmicro_obj', 'transmicro_scene', 'transmicro_jigsaw', \n",
+    "            'transmicro_room', 'transmicro_segment', 'transmicro_normal', 'transmicro_auto', \n",
+    "            'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
+    "            'transmacro_room', 'transmacro_segment', 'transmacro_normal', 'transmacro_auto']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now plot everything"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 435,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_folder = 'plots_nov10/'\n",
+    "# specify the seed that represents the default config:\n",
+    "default_config = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot default and hpo side by side\n",
+    "def plot_default_hpo(results, ss_group, optimizers, offset=0.1, scale_type='0-1', one_line=False, label=False, save=None):\n",
+    "\n",
+    "    if not one_line:\n",
+    "        plot_width = .7 * len(ss_group)\n",
+    "    else:\n",
+    "        plot_width = .42 * len(ss_group)        \n",
+    "    fig, ax = plt.subplots(figsize=(plot_width,2))\n",
+    "    width = 0\n",
+    "    ss_order = []\n",
+    "    for search_space in ss_group:\n",
+    "        if one_line:\n",
+    "            ss_order.append(ss_dict_oneline[search_space])\n",
+    "        else:\n",
+    "            ss_order.append(ss_dict_lined[search_space])\n",
+    "        width += 1\n",
+    "        means = []\n",
+    "        for optimizer in optimizers:\n",
+    "            mean = results[default_config][search_space][optimizer][0]\n",
+    "            means.append(mean)\n",
+    "\n",
+    "            hpo_means = []\n",
+    "            for hpo_seed in results.keys():\n",
+    "                if optimizer not in results[hpo_seed][search_space]:\n",
+    "                    continue\n",
+    "                hpo_means.append(results[hpo_seed][search_space][optimizer][0])\n",
+    "            mean = np.max(hpo_means)\n",
+    "            means.append(mean)\n",
+    "\n",
+    "        #print(search_space, means[::2])\n",
+    "        if scale_type == '0-1':\n",
+    "            min_acc, max_acc = np.min(means), np.max(means)\n",
+    "            means = [(m - min_acc) / (max_acc - min_acc) for m in means]\n",
+    "\n",
+    "        i = 0\n",
+    "        for optimizer in optimizers:\n",
+    "            color = color_dict[optimizer]['color']\n",
+    "            \n",
+    "            # to average nbmr\n",
+    "            first, second = i, i+1\n",
+    "            if optimizer == 'rs' and 'mr' in search_space:\n",
+    "                second = i\n",
+    "\n",
+    "            if width == 1:\n",
+    "                ax.scatter(width-offset, means[first], label=pred_label_dict[optimizer], color=color, marker='o')\n",
+    "                ax.scatter(width+offset, means[second], label=pred_label_dict[optimizer]+'+HPO', color=color, marker='x')\n",
+    "            else:\n",
+    "                ax.scatter(width-offset, means[first], color=color, marker='o')    \n",
+    "                ax.scatter(width+offset, means[second], color=color, marker='x')\n",
+    "            i = i + 2\n",
+    "\n",
+    "    ax.set_xticks(range(1, len(ss_order)+1))\n",
+    "    if label:\n",
+    "        ax.set_xticklabels(ss_order, fontsize=12, rotation=90)\n",
+    "    else:\n",
+    "        ax.set_xticklabels(['' for _ in range(len(ss_order))], fontsize=12, rotation=90)\n",
+    "\n",
+    "    if one_line:\n",
+    "        ax.legend(loc=(1.01,-.37))\n",
+    "    else:\n",
+    "        ax.legend(loc=(1.01,0))\n",
+    "    #ax.set_xlabel('NAS Benchmark Task', fontsize=14)    \n",
+    "    ax.set_ylabel('Scaled Accuracy', fontsize=12)   \n",
+    "    #ax.set_title('Scaled Accuracy of NAS Algorithms', fontsize=14)\n",
+    "    ax.set_title('NAS Algorithms', fontsize=14)\n",
+    "    \n",
+    "    if save:\n",
+    "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 756x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 756x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
+    "\n",
+    "ss_group = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp', \\\n",
+    "            'mr_3ddet', 'mr_video', 'mr_cls', 'mr_seg', \\\n",
+    "            'transmicro_scene', 'transmicro_segment', 'transmacro_scene', 'transmacro_segment']\n",
+    "\n",
+    "plot_default_hpo(results, ss_group, optimizers, scale_type='0-1', label=False) #, save='optimizers.pdf')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From cb5f3f547b46b00b0ede2ee28ababc3dae5cd332 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Thu, 17 Mar 2022 10:45:04 +0100
Subject: [PATCH 095/130] tpe remove

---
 naslib/predictors/ensemble.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/naslib/predictors/ensemble.py b/naslib/predictors/ensemble.py
index ec50b3b15..77aec30be 100644
--- a/naslib/predictors/ensemble.py
+++ b/naslib/predictors/ensemble.py
@@ -37,7 +37,6 @@ def __init__(
         self.ss_type = ss_type
         self.hpo_wrapper = hpo_wrapper
         self.config = config
-        self.config_tpe = config_tpe
         self.hyperparams = hyperparams
         self.ensemble = None
         

From c1d30b0cd6c15bcb846fa900d5d2d10037e56c42 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 20 Mar 2022 12:00:19 +0100
Subject: [PATCH 096/130] plt opti

---
 examples/optimizers_HPO.ipynb | 375 ++++++++++++++++++++++++++++------
 1 file changed, 311 insertions(+), 64 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index c48a01310..9187133f5 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,9 +9,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -20,11 +29,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
+    "import random\n",
     "import numpy as np\n",
     "import json\n",
     "import matplotlib.pyplot as plt\n",
@@ -48,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,13 +74,15 @@
     "    errors = 0\n",
     "    results_dict = {}\n",
     "    root = os.path.expanduser(root)\n",
-    "    for seed_header in os.listdir(root):\n",
-    "        for search_space in search_spaces:\n",
+    "    for search_space in search_spaces:\n",
+    "            #print(search_space)\n",
     "            for optimizer in optimizers:\n",
-    "                optimizer_path = os.path.join(root, seed_header, file_dict[search_space], optimizer)\n",
+    "                optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
+    "                #print(optimizer_path)\n",
     "                if not os.path.exists(optimizer_path):\n",
     "                    continue\n",
-    "                for hpo_folder in os.listdir(optimizer_path):\n",
+    "                for hpo_folder in os.listdir(optimizer_path):  #change seed and hpo_folder\n",
+    "                    print(hpo_folder)\n",
     "                    hpo_seed = int(hpo_folder.split('_')[-1])\n",
     "                    values = []\n",
     "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
@@ -80,31 +92,32 @@
     "                        result = json.load(open(result_path))\n",
     "                        valid_accs = result[1]['valid_acc']\n",
     "                        value = np.max(valid_accs)\n",
-    "                        if not np.isnan(value):\n",
+    "                        \n",
+    "                        #print(value)\n",
+    "                        if  np.isnan(value):\n",
+    "                            print(type(value))\n",
     "                            continue\n",
+    "                        value = float(value)\n",
+    "                        #print(value)\n",
     "                        values.append(value)\n",
-    "                    if len(values) > 3:\n",
-    "                        # had to add these if statements because file structure is in a different order\n",
-    "                        if hpo_seed not in results_dict:\n",
-    "                            results_dict[hpo_seed] = {}\n",
-    "                        if search_space not in results_dict[hpo_seed]:\n",
-    "                            results_dict[hpo_seed][search_space] = {}\n",
-    "                        if optimizer not in results_dict[hpo_seed][search_space]:\n",
-    "                            results_dict[hpo_seed][search_space][optimizer] = {}\n",
-    "                        results_dict[hpo_seed][search_space][optimizer] = [np.mean(values), np.std(values)]\n",
-    "                    else:\n",
-    "                        print(len(values), 'not enough values', hpo_seed, search_space, optimizer)\n",
-    "                        errors += 1\n",
-    "                        if errors < 100:\n",
-    "                            continue\n",
-    "                        print('ending early due to excess errors')\n",
-    "                        return results_dict\n",
+    "                   \n",
+    "                   \n",
+    "                       \n",
+    "                    # had to add these if statements because file structure is in a different order\n",
+    "                    if hpo_seed not in results_dict:\n",
+    "                        results_dict[hpo_seed] = {}\n",
+    "                    if search_space not in results_dict[hpo_seed]:\n",
+    "                        results_dict[hpo_seed][search_space] = {}\n",
+    "                    if optimizer not in results_dict[hpo_seed][search_space]:\n",
+    "                        results_dict[hpo_seed][search_space][optimizer] = {}\n",
+    "                    results_dict[hpo_seed][search_space][optimizer] = [np.mean(values), np.std(values)]\n",
+    "                   \n",
     "    return results_dict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,6 +150,8 @@
     "    'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
     "    'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
     "    'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
+    "    'sh': {'color': defaults[5], 'fmt': fmts[0]},\n",
+    "    'hb': {'color': defaults[6], 'fmt': fmts[0]}\n",
     "}\n",
     "\n",
     "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
@@ -145,13 +160,13 @@
     "# TODO: adapt this for multi-fidelity\n",
     "pred_label_dict={\n",
     "    'rs': 'Rand. Search', 're':'Reg. Evo.', 'ls':'Local Search', \n",
-    "    'bananas':'BANANAS', 'npenas':'NPENAS'\n",
-    "}"
+    "    'bananas':'BANANAS', 'npenas':'NPENAS', 'sh': 'Succesive Halving', 'hb': 'Hyberband'\n",
+    "}\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,6 +174,9 @@
     "\n",
     "# how the search space name will be displayed in the plot titles:\n",
     "# TODO: adapt this for multi-fidelity\n",
+    "\n",
+    "ss_dict_lined = {'nb201_c10':'NB-201\\n CIFAR10'}\n",
+    "\"\"\"\n",
     "ss_dict_lined = {'nb101':'NB-101\\n CIFAR10', \n",
     "                 'nb201_c10':'NB-201\\n CIFAR10', \n",
     "                 'nb201_c100':'NB-201\\n CIFAR100', \n",
@@ -185,6 +203,7 @@
     "                 'transmacro_normal':'TNB-Macro\\n Surface', \n",
     "                 'transmacro_auto':'TNB-Macro\\n Autoenc.', \n",
     "          }\n",
+    "\"\"\"\n",
     "# TODO: adapt this for multi-fidelity\n",
     "ss_dict_oneline = {'nb101':'NB-101 CIFAR10', \n",
     "                 'nb201_c10':'NB-201 CIFAR10', \n",
@@ -282,11 +301,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/till/plotting_data/bbo\n",
+      "['nb201_c10']\n",
+      "config_0\n",
+      "[90.43]\n",
+      "0\n",
+      "config_1\n",
+      "[90.43]\n",
+      "1\n",
+      "config_2\n",
+      "[90.43]\n",
+      "2\n",
+      "config_8\n",
+      "[90.43]\n",
+      "8\n",
+      "config_3\n",
+      "[90.43]\n",
+      "3\n",
+      "config_4\n",
+      "[90.43]\n",
+      "4\n",
+      "config_5\n",
+      "[90.43]\n",
+      "5\n",
+      "config_7\n",
+      "[90.43]\n",
+      "7\n",
+      "config_9\n",
+      "[90.43]\n",
+      "9\n",
+      "config_6\n",
+      "[90.43]\n",
+      "6\n",
+      "config_0\n",
+      "[91.57]\n",
+      "0\n",
+      "config_1\n",
+      "[91.57]\n",
+      "1\n",
+      "config_2\n",
+      "[91.57]\n",
+      "2\n",
+      "config_8\n",
+      "[91.57]\n",
+      "8\n",
+      "config_3\n",
+      "[91.57]\n",
+      "3\n",
+      "config_4\n",
+      "[91.57]\n",
+      "4\n",
+      "config_5\n",
+      "[91.57]\n",
+      "5\n",
+      "config_7\n",
+      "[91.57]\n",
+      "7\n",
+      "config_9\n",
+      "[91.57]\n",
+      "9\n",
+      "config_6\n",
+      "[91.57]\n",
+      "6\n",
+      "config_0\n",
+      "[91.57]\n",
+      "0\n",
+      "config_1\n",
+      "[91.57]\n",
+      "1\n",
+      "config_2\n",
+      "[91.57]\n",
+      "2\n",
+      "config_8\n",
+      "[91.57]\n",
+      "8\n",
+      "config_3\n",
+      "[91.57]\n",
+      "3\n",
+      "config_4\n",
+      "[91.57]\n",
+      "4\n",
+      "config_5\n",
+      "[91.57]\n",
+      "5\n",
+      "config_7\n",
+      "[91.57]\n",
+      "7\n",
+      "config_9\n",
+      "[91.57]\n",
+      "9\n",
+      "config_6\n",
+      "[91.57]\n",
+      "6\n",
+      "config_0\n",
+      "[91.31]\n",
+      "0\n",
+      "config_1\n",
+      "[90.21]\n",
+      "1\n",
+      "config_2\n",
+      "[90.21]\n",
+      "2\n",
+      "config_8\n",
+      "[90.21]\n",
+      "8\n",
+      "config_3\n",
+      "[90.21]\n",
+      "3\n",
+      "config_4\n",
+      "[90.21]\n",
+      "4\n",
+      "config_5\n",
+      "[90.21]\n",
+      "5\n",
+      "config_7\n",
+      "[90.21]\n",
+      "7\n",
+      "config_9\n",
+      "[90.21]\n",
+      "9\n",
+      "config_6\n",
+      "[90.21]\n",
+      "6\n",
+      "config_0\n",
+      "[91.14]\n",
+      "0\n",
+      "config_1\n",
+      "[90.21]\n",
+      "1\n",
+      "config_2\n",
+      "[90.21]\n",
+      "2\n",
+      "config_8\n",
+      "[90.21]\n",
+      "8\n",
+      "config_3\n",
+      "[90.21]\n",
+      "3\n",
+      "config_4\n",
+      "[90.21]\n",
+      "4\n",
+      "config_5\n",
+      "[90.21]\n",
+      "5\n",
+      "config_7\n",
+      "[90.21]\n",
+      "7\n",
+      "config_9\n",
+      "[90.21]\n",
+      "9\n",
+      "config_6\n",
+      "[90.21]\n",
+      "6\n",
+      "config_0\n",
+      "[91.35599998535156]\n",
+      "0\n",
+      "config_1\n",
+      "[89.29999998535156]\n",
+      "1\n",
+      "config_2\n",
+      "[89.29999998535156]\n",
+      "2\n",
+      "config_8\n",
+      "[89.29999998535156]\n",
+      "8\n",
+      "config_3\n",
+      "[89.29999998535156]\n",
+      "3\n",
+      "config_4\n",
+      "[89.29999998535156]\n",
+      "4\n",
+      "config_5\n",
+      "[89.29999998535156]\n",
+      "5\n",
+      "config_7\n",
+      "[89.29999998535156]\n",
+      "7\n",
+      "config_9\n",
+      "[89.29999998535156]\n",
+      "9\n",
+      "config_6\n",
+      "[89.29999998535156]\n",
+      "6\n",
+      "config_0\n",
+      "[90.9]\n",
+      "0\n",
+      "config_1\n",
+      "[89.54800001464844]\n",
+      "1\n",
+      "config_2\n",
+      "[89.54800001464844]\n",
+      "2\n",
+      "config_8\n",
+      "[89.54800001464844]\n",
+      "8\n",
+      "config_3\n",
+      "[89.54800001464844]\n",
+      "3\n",
+      "config_4\n",
+      "[89.54800001464844]\n",
+      "4\n",
+      "config_5\n",
+      "[89.54800001464844]\n",
+      "5\n",
+      "config_7\n",
+      "[89.54800001464844]\n",
+      "7\n",
+      "config_9\n",
+      "[89.54800001464844]\n",
+      "9\n",
+      "config_6\n",
+      "[89.54800001464844]\n",
+      "6\n"
+     ]
+    }
+   ],
    "source": [
     "# do not accidentally run this twice.\n",
     "# takes >30 min to load all these results\n",
@@ -298,7 +543,8 @@
     "                     'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
     "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
     "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
-    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas')\n",
+    "    search_spaces = ['nb201_c10']\n",
+    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb')\n",
     "    root = '~/plotting_data/bbo'\n",
     "    results_dicts = {}\n",
     "\n",
@@ -307,30 +553,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100\n",
-      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# compute how many of each we have\n",
-    "print(len(results.keys()))\n",
-    "keys = results.keys()\n",
-    "keys = list(keys)\n",
-    "keys.sort()\n",
-    "print(keys)"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +569,9 @@
     "            'transmicro_obj', 'transmicro_scene', 'transmicro_jigsaw', \n",
     "            'transmicro_room', 'transmicro_segment', 'transmicro_normal', 'transmicro_auto', \n",
     "            'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
-    "            'transmacro_room', 'transmacro_segment', 'transmacro_normal', 'transmacro_auto']"
+    "            'transmacro_room', 'transmacro_segment', 'transmacro_normal', 'transmacro_auto']\n",
+    "\n",
+    "ss_group_full = [\"nb201_c10\"]"
    ]
   },
   {
@@ -351,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 435,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,13 +594,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "# plot default and hpo side by side\n",
     "def plot_default_hpo(results, ss_group, optimizers, offset=0.1, scale_type='0-1', one_line=False, label=False, save=None):\n",
-    "\n",
     "    if not one_line:\n",
     "        plot_width = .7 * len(ss_group)\n",
     "    else:\n",
@@ -394,10 +625,12 @@
     "                hpo_means.append(results[hpo_seed][search_space][optimizer][0])\n",
     "            mean = np.max(hpo_means)\n",
     "            means.append(mean)\n",
-    "\n",
+    "        \n",
     "        #print(search_space, means[::2])\n",
     "        if scale_type == '0-1':\n",
     "            min_acc, max_acc = np.min(means), np.max(means)\n",
+    "            print(min_acc)\n",
+    "            print(max_acc)\n",
     "            means = [(m - min_acc) / (max_acc - min_acc) for m in means]\n",
     "\n",
     "        i = 0\n",
@@ -433,19 +666,32 @@
     "    ax.set_title('NAS Algorithms', fontsize=14)\n",
     "    \n",
     "    if save:\n",
-    "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)"
+    "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    print(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "90.43\n",
+      "91.57\n",
+      "{0: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [91.31, 0.0], 'npenas': [91.14, 0.0], 'sh': [91.35599998535156, 0.0], 'hb': [90.9, 0.0]}}, 1: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 2: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 8: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 3: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 4: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 5: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 7: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 9: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 6: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}}\n",
+      "90.43\n",
+      "91.57\n",
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+     ]
+    },
     {
      "data": {
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",
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",
       "text/plain": [
-       "<Figure size 756x144 with 1 Axes>"
+       "<Figure size 30.24x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -455,9 +701,9 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 756x144 with 1 Axes>"
+       "<Figure size 50.4x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -472,6 +718,7 @@
     "ss_group = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp', \\\n",
     "            'mr_3ddet', 'mr_video', 'mr_cls', 'mr_seg', \\\n",
     "            'transmicro_scene', 'transmicro_segment', 'transmacro_scene', 'transmacro_segment']\n",
+    "ss_group = ['nb201_c10']\n",
     "\n",
     "plot_default_hpo(results, ss_group, optimizers, scale_type='0-1', label=False) #, save='optimizers.pdf')"
    ]
@@ -493,7 +740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.7.11"
   }
  },
  "nbformat": 4,

From 40860738ce3799c38c9052cb3cd53537103fab59 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 21 Mar 2022 17:11:43 +0100
Subject: [PATCH 097/130] fix random seed & num_config from config

---
 naslib/benchmarks/create_configs.py    | 8 ++++----
 naslib/benchmarks/mf/config.yaml       | 4 ++--
 naslib/benchmarks/mf/create_configs.py | 9 ++++-----
 3 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index c30219dcb..1f23c0109 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -19,8 +19,8 @@ def main(args):
         os.makedirs(folder, exist_ok=True)       
             
         for seed in range(args.start_seed, args.start_seed + args.trials):
-            np.random.seed(seed)
-            random.seed(seed)
+            # np.random.seed(seed)
+            # random.seed(seed)
 
             config = {
                 "seed": seed,
@@ -63,8 +63,8 @@ def main(args):
             
             
             for seed in range(args.start_seed, args.start_seed + args.trials):
-                np.random.seed(seed)
-                random.seed(seed)
+                # np.random.seed(seed)
+                # random.seed(seed)
 
                 config = {
                     "seed": seed,
diff --git a/naslib/benchmarks/mf/config.yaml b/naslib/benchmarks/mf/config.yaml
index 0c15b1f5a..6288a25b1 100644
--- a/naslib/benchmarks/mf/config.yaml
+++ b/naslib/benchmarks/mf/config.yaml
@@ -1,5 +1,5 @@
 start_seed: 0
-trials: 10
+trials: 1
 out_dir: run
 out_dir_config: configs
 config_type: bbo-bs
@@ -12,7 +12,7 @@ fidelity: 200
 
 HPO: True
 # only relevant if HPO is enabled
-num_config: 100
+num_config: 10
 
 # for SH: 4
 # for default BBOs: 200
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 211398d89..2082f5b9e 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -75,7 +75,6 @@ def create_configs(
     if config_type == 'bbo-bs':
         start_seed = int(start_seed)
         trials = int(trials)
-        num_config = 100 
         
         # first generate the default config at config 0
         config_id = 0
@@ -85,8 +84,8 @@ def create_configs(
         os.makedirs(folder, exist_ok=True)       
             
         for seed in range(start_seed, start_seed + trials):
-            np.random.seed(seed)
-            random.seed(seed)
+            # np.random.seed(seed)
+            # random.seed(seed)
 
             config = {
                 "seed": seed,
@@ -141,8 +140,8 @@ def create_configs(
             os.makedirs(folder, exist_ok=True)
             
             for seed in range(start_seed, start_seed + trials):
-                np.random.seed(seed)
-                random.seed(seed)
+                # np.random.seed(seed)
+                # random.seed(seed)
                 # TODO: max_fidelity should be dependent on eta and min_fidelity
                 eta = int(np.random.choice(range(2, 5)))
                 fidelity_range = [2**i for i in range(0, 9)]

From 308c24a359e1edc47f835ffaca4a5c14517f681f Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 25 Mar 2022 16:45:39 +0100
Subject: [PATCH 098/130] bug fix in bohb & change config creation

---
 naslib/benchmarks/create_configs.py          |  3 ++-
 naslib/benchmarks/mf/create_configs.py       | 23 +++++++++++++++++---
 naslib/optimizers/discrete/bohb/optimizer.py |  1 +
 3 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index 1f23c0109..38626ce43 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -11,7 +11,8 @@ def main(args):
     if args.config_type == 'bbo-bs':
         args.start_seed = int(args.start_seed)
         args.trials = int(args.trials)
-        num_config = 100 
+        num_config = int(args.num_config)
+        # num_config = 100 
         
         # first generate the default config at config 0
         config_id = 0
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 2082f5b9e..7f9cc64ad 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -123,12 +123,21 @@ def create_configs(
                     "encoding_type": "path",
                     "predictor": predictor,
                     "debug_predictor": False,
-                    # config secton for successive halving,
+                    # config secton for successive halving/ hyperband,
                     "min_budget": 1,
                     "max_budget": 200,
                     "fidelity": 200,
                     "n_process": 1_000_000,
                     "budgets": 360_000, 
+                    # config section for bohb
+                    "min_bandwith": 0.001,
+                    "top_n_percent": 0.1,
+                    "min_points_in_model": 7,
+                    # config section for dehb
+                    "enc_dim": 6,
+                    "max_mutations": 1,
+                    "crossover_prob": 0.5,
+                    "mutate_prob": 0.1,
                 },
             }
             path = os.path.join(folder, f"seed_{seed}.yaml")
@@ -178,7 +187,7 @@ def create_configs(
                         "predictor": predictor,
                         "debug_predictor": False,
                         # config section for successive halving,
-                        # config secton for successive halving,
+                        # config secton for Hyperband,
                         "min_budget": min_fidelity,
                         "max_budget": max_fidelity,
                         "fidelity": 200,
@@ -187,7 +196,15 @@ def create_configs(
                         "eta": eta,
                         "epsilon": 1e-6,
                         # config section for BOHB
-                        "tpe_bandwidth": float(np.random.choice(np.arange(0.01, 1.0, 0.01))), # TODO: what is a good range for tpe??
+                        "min_bandwith": float(np.random.choice(np.arange(0.0, 0.01, 0.001))),
+                        "top_n_percent": float(np.random.choice(np.arange(0.05, 0.6, 0.01))),
+                        "min_points_in_model": 7,
+                        # config section for DEHB
+                        # config section for dehb
+                        "enc_dim": 6,
+                        "max_mutations": int(np.random.choice(range(1, 5))),
+                        "crossover_prob": float(np.random.choice(np.arange(0.4, 1.0, 0.01))),
+                        "mutate_prob": float(np.random.choice(np.arange(0.05, 0.71, 0.01))),
                     },
                 }
                 print(f"folder: {folder}")
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index e73fe0846..44de6bf04 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -164,6 +164,7 @@ def new_epoch(self, epoch, round, i):
             del self.current_round
             del self.next_round
             self.current_round_ = []
+            self.current_round = []
             self.next_round = []
             self.round_number = 0
             self.prev_round = 0

From 27d3a9ba5187194944e94e38ece10ab415c2bcd3 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 1 Apr 2022 10:15:47 +0200
Subject: [PATCH 099/130] fix experimental setup

---
 naslib/benchmarks/mf/create_configs.py | 33 ++++++++++++++++----------
 1 file changed, 21 insertions(+), 12 deletions(-)

diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index 7f9cc64ad..d00be4584 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -137,7 +137,7 @@ def create_configs(
                     "enc_dim": 6,
                     "max_mutations": 1,
                     "crossover_prob": 0.5,
-                    "mutate_prob": 0.1,
+                    "mutate_prob": 0.5,
                 },
             }
             path = os.path.join(folder, f"seed_{seed}.yaml")
@@ -147,17 +147,26 @@ def create_configs(
         for config_id in range(1, num_config):
             folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
             os.makedirs(folder, exist_ok=True)
-            
+
+            # SH/HB
+            max_fidelity = 200 # int(np.random.choice(range(100, 200)))
+            min_budget = int(np.random.choice(range(1, 50)))
+            eta = int(np.random.choice(range(2, 5)))
+            # BOHB
+            min_bandwith = float(np.random.choice(np.arange(0.0, 0.011, 0.001)))
+            top_n_percent = float(np.random.choice(np.arange(0.05, 0.31, 0.01)))
+            # DEHB
+            max_mutations = int(np.random.choice(range(1, 5)))
+            crossover_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
+            mutate_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.01)))
+
             for seed in range(start_seed, start_seed + trials):
                 # np.random.seed(seed)
                 # random.seed(seed)
                 # TODO: max_fidelity should be dependent on eta and min_fidelity
-                eta = int(np.random.choice(range(2, 5)))
-                fidelity_range = [2**i for i in range(0, 9)]
+                # fidelity_range = [2**i for i in range(0, 9)]
                 # max_fidelity = int(np.random.choice(fidelity_range))
-                max_fidelity = int(np.random.choice(range(100, 200)))
                 # min fidelity has to be lower/equal to max_fidelity
-                min_fidelity = int(np.random.choice(range(1, 10)))
                 # min_fidelity = int(np.random.choice(
                 #     list(
                 #         filter(
@@ -188,7 +197,7 @@ def create_configs(
                         "debug_predictor": False,
                         # config section for successive halving,
                         # config secton for Hyperband,
-                        "min_budget": min_fidelity,
+                        "min_budget": min_budget,
                         "max_budget": max_fidelity,
                         "fidelity": 200,
                         "n_process": 1_000_000,
@@ -196,15 +205,15 @@ def create_configs(
                         "eta": eta,
                         "epsilon": 1e-6,
                         # config section for BOHB
-                        "min_bandwith": float(np.random.choice(np.arange(0.0, 0.01, 0.001))),
-                        "top_n_percent": float(np.random.choice(np.arange(0.05, 0.6, 0.01))),
+                        "min_bandwith": min_bandwith,
+                        "top_n_percent": top_n_percent,
                         "min_points_in_model": 7,
                         # config section for DEHB
                         # config section for dehb
                         "enc_dim": 6,
-                        "max_mutations": int(np.random.choice(range(1, 5))),
-                        "crossover_prob": float(np.random.choice(np.arange(0.4, 1.0, 0.01))),
-                        "mutate_prob": float(np.random.choice(np.arange(0.05, 0.71, 0.01))),
+                        "max_mutations": max_mutations,
+                        "crossover_prob": crossover_prob,
+                        "mutate_prob": mutate_prob,
                     },
                 }
                 print(f"folder: {folder}")

From f01cbbab7f95b09eb7791fc368e08105a11027d0 Mon Sep 17 00:00:00 2001
From: Lars Gerne <gernel@informatik.uni-freiburg.de>
Date: Fri, 1 Apr 2022 10:23:50 +0200
Subject: [PATCH 100/130] bug fix in dehb optimizer & mf runner

---
 naslib/benchmarks/mf/runner.py               | 10 +++++-----
 naslib/optimizers/discrete/bohb/optimizer.py |  3 ++-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/naslib/benchmarks/mf/runner.py b/naslib/benchmarks/mf/runner.py
index e7733d9d4..51f77e2be 100644
--- a/naslib/benchmarks/mf/runner.py
+++ b/naslib/benchmarks/mf/runner.py
@@ -6,7 +6,7 @@
 from naslib.defaults.trainer_multifidelity import Trainer as Trainer_MF
 
 from naslib.optimizers import RandomSearch, Npenas, \
-RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HB
+RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HB, BOHB, DEHB
 
 from naslib.search_spaces.core.query_metrics import Metric
 from naslib.search_spaces import NasBench101SearchSpace, NasBench201SearchSpace, \
@@ -31,7 +31,9 @@
     'npenas': Npenas(config),
     'ls': LocalSearch(config),
     'sh': SuccessiveHalving(config),
-    # 'hb': HyperBand(config),
+    'hb': HB(config),
+    'bohb': BOHB(config),
+    'dehb': DEHB(config),
 }
 
 supported_search_spaces = {
@@ -57,11 +59,9 @@
 optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
 
 trainer = Trainer(optimizer, config, lightweight_output=True)
-multi_fidelity_optimizers = {'sh', 'hb'}
+multi_fidelity_optimizers = {'sh', 'hb', 'bohb', 'dehb'}
 if config.optimizer in multi_fidelity_optimizers:
     trainer = Trainer_MF(optimizer, config, lightweight_output=True)
 # trainer.search(resume_from="", summary_writer=writer, report_incumbent=False)
 trainer.search(resume_from="")
 trainer.evaluate(resume_from="", dataset_api=dataset_api)
-
-# error: FileNotFoundError: [Errno 2] No such file or directory: '/Users/lars/Projects/NASLib/naslib/data/nasbench_only108.pkl'
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index e73fe0846..4c8bd21d4 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -164,6 +164,7 @@ def new_epoch(self, epoch, round, i):
             del self.current_round
             del self.next_round
             self.current_round_ = []
+            self.current_round = []
             self.next_round = []
             self.round_number = 0
             self.prev_round = 0
@@ -280,4 +281,4 @@ def get_checkpointables(self):
         return {'model': self.history}
 
     def get_model_size(self):
-        return count_parameters_in_MB(self.history)
\ No newline at end of file
+        return count_parameters_in_MB(self.history)

From 08d0a0668e6ba594a1abc6fa8426bef681ac64ae Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 1 Apr 2022 10:31:43 +0200
Subject: [PATCH 101/130] plot further work

---
 examples/optimizers_HPO.ipynb | 310 +++++++---------------------------
 1 file changed, 63 insertions(+), 247 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 9187133f5..e9c2cd60c 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,25 +77,27 @@
     "    for search_space in search_spaces:\n",
     "            #print(search_space)\n",
     "            for optimizer in optimizers:\n",
-    "                optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
+    "                print(optimizer)\n",
+    "                optimizer_path = os.path.join(root, file_dict[search_space],'100', optimizer)\n",
     "                #print(optimizer_path)\n",
     "                if not os.path.exists(optimizer_path):\n",
     "                    continue\n",
     "                for hpo_folder in os.listdir(optimizer_path):  #change seed and hpo_folder\n",
-    "                    print(hpo_folder)\n",
+    "                    #print(hpo_folder)\n",
     "                    hpo_seed = int(hpo_folder.split('_')[-1])\n",
     "                    values = []\n",
     "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
-    "                        result_path = optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
+    "                        result_path =   optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
     "                        if not os.path.isfile(result_path):\n",
     "                            continue\n",
+    "                        #print(result_path)\n",
     "                        result = json.load(open(result_path))\n",
     "                        valid_accs = result[1]['valid_acc']\n",
     "                        value = np.max(valid_accs)\n",
     "                        \n",
     "                        #print(value)\n",
     "                        if  np.isnan(value):\n",
-    "                            print(type(value))\n",
+    "                            #print(type(value))\n",
     "                            continue\n",
     "                        value = float(value)\n",
     "                        #print(value)\n",
@@ -117,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,11 +130,11 @@
     "So there are 33 color/markers total.\n",
     "\"\"\"\n",
     "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
-    "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
-    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+    "            (1.0, 0.4980392156862745, 0.054901960784313725),               \n",
+    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), \n",
     "            (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
-    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
-    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),    \n",
+    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354), \n",
     "            (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
     "            (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
     "            (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
@@ -148,10 +150,13 @@
     "    'rs':{'color':defaults[0], 'fmt':fmts[0]},\n",
     "    're':{'color':defaults[1], 'fmt':fmts[0]},\n",
     "    'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
-    "    'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    #'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
     "    'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
     "    'sh': {'color': defaults[5], 'fmt': fmts[0]},\n",
-    "    'hb': {'color': defaults[6], 'fmt': fmts[0]}\n",
+    "    'hb': {'color': defaults[6], 'fmt': fmts[0]},\n",
+    "    'bohb': {'color': defaults[3], 'fmt': fmts[0]},\n",
+    "    'dehb': {'color': defaults[8], 'fmt': fmts[0]},\n",
+    "\n",
     "}\n",
     "\n",
     "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
@@ -160,13 +165,14 @@
     "# TODO: adapt this for multi-fidelity\n",
     "pred_label_dict={\n",
     "    'rs': 'Rand. Search', 're':'Reg. Evo.', 'ls':'Local Search', \n",
-    "    'bananas':'BANANAS', 'npenas':'NPENAS', 'sh': 'Succesive Halving', 'hb': 'Hyberband'\n",
+    "    'bananas':'BANANAS', 'npenas':'NPENAS', 'sh': 'Succesive Halving', 'hb': 'Hyberband',\n",
+    "    'bohb': 'BOHB', 'dehb': 'DEHB'\n",
     "}\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 93,
    "metadata": {
     "scrolled": true
    },
@@ -317,218 +323,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/till/plotting_data/bbo\n",
-      "['nb201_c10']\n",
-      "config_0\n",
-      "[90.43]\n",
-      "0\n",
-      "config_1\n",
-      "[90.43]\n",
-      "1\n",
-      "config_2\n",
-      "[90.43]\n",
-      "2\n",
-      "config_8\n",
-      "[90.43]\n",
-      "8\n",
-      "config_3\n",
-      "[90.43]\n",
-      "3\n",
-      "config_4\n",
-      "[90.43]\n",
-      "4\n",
-      "config_5\n",
-      "[90.43]\n",
-      "5\n",
-      "config_7\n",
-      "[90.43]\n",
-      "7\n",
-      "config_9\n",
-      "[90.43]\n",
-      "9\n",
-      "config_6\n",
-      "[90.43]\n",
-      "6\n",
-      "config_0\n",
-      "[91.57]\n",
-      "0\n",
-      "config_1\n",
-      "[91.57]\n",
-      "1\n",
-      "config_2\n",
-      "[91.57]\n",
-      "2\n",
-      "config_8\n",
-      "[91.57]\n",
-      "8\n",
-      "config_3\n",
-      "[91.57]\n",
-      "3\n",
-      "config_4\n",
-      "[91.57]\n",
-      "4\n",
-      "config_5\n",
-      "[91.57]\n",
-      "5\n",
-      "config_7\n",
-      "[91.57]\n",
-      "7\n",
-      "config_9\n",
-      "[91.57]\n",
-      "9\n",
-      "config_6\n",
-      "[91.57]\n",
-      "6\n",
-      "config_0\n",
-      "[91.57]\n",
-      "0\n",
-      "config_1\n",
-      "[91.57]\n",
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-      "config_2\n",
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-      "config_3\n",
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-      "3\n",
-      "config_4\n",
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-      "config_5\n",
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-      "[89.54800001464844]\n",
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-      "config_9\n",
-      "[89.54800001464844]\n",
-      "9\n",
-      "config_6\n",
-      "[89.54800001464844]\n",
-      "6\n"
+      "rs\n",
+      "re\n",
+      "ls\n",
+      "npenas\n",
+      "hb\n",
+      "bohb\n",
+      "dehb\n"
      ]
     }
    ],
@@ -544,8 +345,9 @@
     "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
     "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
     "    search_spaces = ['nb201_c10']\n",
-    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb')\n",
-    "    root = '~/plotting_data/bbo'\n",
+    "    #optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb', 'bohb','dehb')\n",
+    "    optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
+    "    root = '~/plotting_data/bbo/'\n",
     "    results_dicts = {}\n",
     "\n",
     "    results = get_hpo_results(optimizers, search_spaces, root=root)"
@@ -556,11 +358,13 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "results[4]"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,7 +445,7 @@
     "            first, second = i, i+1\n",
     "            if optimizer == 'rs' and 'mr' in search_space:\n",
     "                second = i\n",
-    "\n",
+    "            print()\n",
     "            if width == 1:\n",
     "                ax.scatter(width-offset, means[first], label=pred_label_dict[optimizer], color=color, marker='o')\n",
     "                ax.scatter(width+offset, means[second], label=pred_label_dict[optimizer]+'+HPO', color=color, marker='x')\n",
@@ -657,7 +461,7 @@
     "        ax.set_xticklabels(['' for _ in range(len(ss_order))], fontsize=12, rotation=90)\n",
     "\n",
     "    if one_line:\n",
-    "        ax.legend(loc=(1.01,-.37))\n",
+    "        ax.legend(loc=(1.5,-.6))\n",
     "    else:\n",
     "        ax.legend(loc=(1.01,0))\n",
     "    #ax.set_xlabel('NAS Benchmark Task', fontsize=14)    \n",
@@ -667,29 +471,41 @@
     "    \n",
     "    if save:\n",
     "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "    print(results)"
+    "    #print(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "90.43\n",
-      "91.57\n",
-      "{0: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [91.31, 0.0], 'npenas': [91.14, 0.0], 'sh': [91.35599998535156, 0.0], 'hb': [90.9, 0.0]}}, 1: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 2: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 8: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 3: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 4: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 5: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 7: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 9: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 6: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}}\n",
-      "90.43\n",
-      "91.57\n",
-      "{0: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [91.31, 0.0], 'npenas': [91.14, 0.0], 'sh': [91.35599998535156, 0.0], 'hb': [90.9, 0.0]}}, 1: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 2: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 8: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 3: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 4: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 5: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 7: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 9: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}, 6: {'nb201_c10': {'rs': [90.43, 0.0], 're': [91.57, 0.0], 'ls': [91.57, 0.0], 'bananas': [90.21, 0.0], 'npenas': [90.21, 0.0], 'sh': [89.29999998535156, 0.0], 'hb': [89.54800001464844, 0.0]}}}\n"
+      "90.367\n",
+      "91.51299999999999\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "90.367\n",
+      "91.51299999999999\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
      ]
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 30.24x144 with 1 Axes>"
       ]
@@ -701,7 +517,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 50.4x144 with 1 Axes>"
       ]

From 79c7423df37813923c357f5eb7ed02834fe336ed Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 1 Apr 2022 10:40:43 +0200
Subject: [PATCH 102/130] fix seed

---
 naslib/benchmarks/create_configs.py | 23 +++++++++++++++--------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index 38626ce43..b7b4d85c1 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -62,7 +62,14 @@ def main(args):
             folder = f"naslib/benchmarks/bbo/configs_cpu/{args.search_space}/{args.dataset}/{args.optimizer}/config_{config_id}"
             os.makedirs(folder, exist_ok=True)
             
-            
+            sample_size = int(np.random.choice(range(5, 100)))
+            population_size = int(np.random.choice(range(5, 100)))
+            num_init = int(np.random.choice(range(5, 100)))
+            k = int(np.random.choice(range(10, 50)))
+            num_arches_to_mutate = int(np.random.choice(range(1, 20)))
+            max_mutations = int(np.random.choice(range(1, 20)))
+            num_candidates = int(np.random.choice(range(5, 50)))
+
             for seed in range(args.start_seed, args.start_seed + args.trials):
                 # np.random.seed(seed)
                 # random.seed(seed)
@@ -78,17 +85,17 @@ def main(args):
                         "checkpoint_freq": args.checkpoint_freq,
                         "epochs": args.epochs,
                         "fidelity": args.fidelity,
-                        "sample_size": int(np.random.choice(range(5, 100))),
-                        "population_size": int(np.random.choice(range(5, 100))),
-                        "num_init": int(np.random.choice(range(5, 100))),
-                        "k":int(np.random.choice(range(10, 50))),
+                        "sample_size": sample_size,
+                        "population_size": population_size,
+                        "num_init": num_init,
+                        "k": k,
                         "num_ensemble": 3,
                         "acq_fn_type": "its",
                         "acq_fn_optimization": args.acq_fn_optimization,
                         "encoding_type": "path",
-                        "num_arches_to_mutate": int(np.random.choice(range(1, 20))),
-                        "max_mutations": int(np.random.choice(range(1, 20))),
-                        "num_candidates": int(np.random.choice(range(5, 50))),
+                        "num_arches_to_mutate": num_arches_to_mutate,
+                        "max_mutations": max_mutations,
+                        "num_candidates": num_candidates,
                         "predictor": args.predictor,
                         "debug_predictor": False,
                     },

From 23f86e14a5daaa3ccbe9c3ce6b13be95491de4f1 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 1 Apr 2022 10:52:04 +0200
Subject: [PATCH 103/130] fix range for mutate_prob

---
 naslib/benchmarks/create_configs.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index 38626ce43..52b1fa9a9 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -63,6 +63,7 @@ def main(args):
             os.makedirs(folder, exist_ok=True)
             
             
+            
             for seed in range(args.start_seed, args.start_seed + args.trials):
                 # np.random.seed(seed)
                 # random.seed(seed)

From 64992b7e7fe01be85fa748661a8a3aedc9b2f3d5 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 1 Apr 2022 10:57:31 +0200
Subject: [PATCH 104/130] fix number

---
 naslib/benchmarks/create_configs.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index b7b4d85c1..9e91fbc69 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -11,8 +11,8 @@ def main(args):
     if args.config_type == 'bbo-bs':
         args.start_seed = int(args.start_seed)
         args.trials = int(args.trials)
-        num_config = int(args.num_config)
-        # num_config = 100 
+        #num_config = int(args.num_config)
+        num_config = 100 
         
         # first generate the default config at config 0
         config_id = 0

From 85a8ba9ab146f756c80eb438ab0ade7d3c3f1982 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 1 Apr 2022 10:58:40 +0200
Subject: [PATCH 105/130] merge

---
 naslib/benchmarks/create_configs.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/naslib/benchmarks/create_configs.py b/naslib/benchmarks/create_configs.py
index 7b075acae..9e91fbc69 100644
--- a/naslib/benchmarks/create_configs.py
+++ b/naslib/benchmarks/create_configs.py
@@ -62,10 +62,6 @@ def main(args):
             folder = f"naslib/benchmarks/bbo/configs_cpu/{args.search_space}/{args.dataset}/{args.optimizer}/config_{config_id}"
             os.makedirs(folder, exist_ok=True)
             
-<<<<<<< HEAD
-            
-            
-=======
             sample_size = int(np.random.choice(range(5, 100)))
             population_size = int(np.random.choice(range(5, 100)))
             num_init = int(np.random.choice(range(5, 100)))
@@ -74,7 +70,6 @@ def main(args):
             max_mutations = int(np.random.choice(range(1, 20)))
             num_candidates = int(np.random.choice(range(5, 50)))
 
->>>>>>> 79c7423df37813923c357f5eb7ed02834fe336ed
             for seed in range(args.start_seed, args.start_seed + args.trials):
                 # np.random.seed(seed)
                 # random.seed(seed)

From aa2b49d5679ee49edd5580f7ef9fb217babe5fe2 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 11 Apr 2022 13:27:54 +0200
Subject: [PATCH 106/130] hpo block

---
 examples/optimizers_HPO.ipynb | 27135 +++++++++++++++++++++++++++++++-
 1 file changed, 27092 insertions(+), 43 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index e9c2cd60c..3b6c16397 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,18 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -29,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,10 +66,10 @@
     "    results_dict = {}\n",
     "    root = os.path.expanduser(root)\n",
     "    for search_space in search_spaces:\n",
-    "            #print(search_space)\n",
+    "            print(search_space)\n",
     "            for optimizer in optimizers:\n",
-    "                print(optimizer)\n",
-    "                optimizer_path = os.path.join(root, file_dict[search_space],'100', optimizer)\n",
+    "                #print(optimizer)\n",
+    "                optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
     "                #print(optimizer_path)\n",
     "                if not os.path.exists(optimizer_path):\n",
     "                    continue\n",
@@ -90,7 +81,7 @@
     "                        result_path =   optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
     "                        if not os.path.isfile(result_path):\n",
     "                            continue\n",
-    "                        #print(result_path)\n",
+    "                        print(result_path)\n",
     "                        result = json.load(open(result_path))\n",
     "                        valid_accs = result[1]['valid_acc']\n",
     "                        value = np.max(valid_accs)\n",
@@ -119,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +141,7 @@
     "    'rs':{'color':defaults[0], 'fmt':fmts[0]},\n",
     "    're':{'color':defaults[1], 'fmt':fmts[0]},\n",
     "    'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
-    "    #'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
     "    'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
     "    'sh': {'color': defaults[5], 'fmt': fmts[0]},\n",
     "    'hb': {'color': defaults[6], 'fmt': fmts[0]},\n",
@@ -172,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -323,13 +314,27008 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rs\n",
-      "re\n",
-      "ls\n",
-      "npenas\n",
-      "hb\n",
-      "bohb\n",
-      "dehb\n"
+      "nb201_c10\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_88/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_41/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_78/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_54/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_71/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_56/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_79/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_28/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_87/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/9/errors.json\n",
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+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_14/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_45/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_42/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_59/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_0/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/4/errors.json\n",
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+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_31/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/cifar10/rs/config_77/2/errors.json\n",
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+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/2/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/5/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/4/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/7/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/6/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/3/errors.json\n"
      ]
     }
    ],
@@ -344,9 +27330,9 @@
     "                     'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
     "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
     "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
-    "    search_spaces = ['nb201_c10']\n",
-    "    #optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb', 'bohb','dehb')\n",
-    "    optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
+    "    search_spaces = ['nb201_c10','nb201_c100','nb201_im']\n",
+    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb', 'bohb','dehb')\n",
+    "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
     "    root = '~/plotting_data/bbo/'\n",
     "    results_dicts = {}\n",
     "\n",
@@ -355,16 +27341,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "  're': [91.47999999999999, 0.14778362561528577],\n",
+       "  'ls': [91.31, 0.22445489524623813],\n",
+       "  'bananas': [91.147, 0.14893287078411946],\n",
+       "  'npenas': [90.21600000000001, 0.6384857085323041],\n",
+       "  'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "  'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "  'bohb': [91.15879998730468, 0.2170349333983711],\n",
+       "  'dehb': [91.28919998828125, 0.2226893839048166]},\n",
+       " 'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "  're': [73.22799994995117, 0.04118249115978971],\n",
+       "  'ls': [72.83599997070311, 0.7234804416656028],\n",
+       "  'bananas': [71.44399994934082, 1.085976078817879],\n",
+       "  'npenas': [73.1399999206543, 0.1335664597491603],\n",
+       "  'sh': [71.942, 0.889649369133707],\n",
+       "  'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "  'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "  'dehb': [71.11, 0.8461560139832375]},\n",
+       " 'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "  're': [47.169999918619794, 0.19857828352987805],\n",
+       "  'ls': [46.94999994710287, 0.2891558226092122],\n",
+       "  'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "  'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "  'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "  'hb': [45.13666662190755, 0.658525305689547],\n",
+       "  'bohb': [44.48666665547689, 1.0413879592348234],\n",
+       "  'dehb': [45.20833331807454, 0.8498447548008442]}}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results[4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +27398,7 @@
     "            'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
     "            'transmacro_room', 'transmacro_segment', 'transmacro_normal', 'transmacro_auto']\n",
     "\n",
-    "ss_group_full = [\"nb201_c10\"]"
+    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im']"
    ]
   },
   {
@@ -387,7 +27410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,7 +27421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +27499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +27507,31 @@
      "output_type": "stream",
      "text": [
       "90.367\n",
-      "91.51299999999999\n",
+      "91.52499999999998\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "70.61499999999998\n",
+      "73.25799993530272\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "43.38166665344239\n",
+      "47.196666573079426\n",
+      "\n",
+      "\n",
       "\n",
       "\n",
       "\n",
@@ -493,7 +27540,9 @@
       "\n",
       "\n",
       "90.367\n",
-      "91.51299999999999\n",
+      "91.52499999999998\n",
+      "\n",
+      "\n",
       "\n",
       "\n",
       "\n",
@@ -505,9 +27554,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 30.24x144 with 1 Axes>"
+       "<Figure size 90.72x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -517,7 +27566,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 50.4x144 with 1 Axes>"
       ]

From baa3b1b411c03527ed153ab6d614b7f31125de20 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Mon, 11 Apr 2022 13:31:55 +0200
Subject: [PATCH 107/130] hpo

---
 examples/optimizers_HPO.ipynb | 2733 ++++++++++++++++++++++++++++++++-
 1 file changed, 2703 insertions(+), 30 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 3b6c16397..c278f6489 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -27341,48 +27341,2721 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "  're': [91.47999999999999, 0.14778362561528577],\n",
-       "  'ls': [91.31, 0.22445489524623813],\n",
-       "  'bananas': [91.147, 0.14893287078411946],\n",
-       "  'npenas': [90.21600000000001, 0.6384857085323041],\n",
-       "  'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "  'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "  'bohb': [91.15879998730468, 0.2170349333983711],\n",
-       "  'dehb': [91.28919998828125, 0.2226893839048166]},\n",
-       " 'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "  're': [73.22799994995117, 0.04118249115978971],\n",
-       "  'ls': [72.83599997070311, 0.7234804416656028],\n",
-       "  'bananas': [71.44399994934082, 1.085976078817879],\n",
-       "  'npenas': [73.1399999206543, 0.1335664597491603],\n",
-       "  'sh': [71.942, 0.889649369133707],\n",
-       "  'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "  'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "  'dehb': [71.11, 0.8461560139832375]},\n",
-       " 'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "  're': [47.169999918619794, 0.19857828352987805],\n",
-       "  'ls': [46.94999994710287, 0.2891558226092122],\n",
-       "  'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "  'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "  'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "  'hb': [45.13666662190755, 0.658525305689547],\n",
-       "  'bohb': [44.48666665547689, 1.0413879592348234],\n",
-       "  'dehb': [45.20833331807454, 0.8498447548008442]}}"
+       "{88: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.33099999999999, 0.2147300631024888],\n",
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+       "   'hb': [91.02000000000001, 0.237191905426807],\n",
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+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
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+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.3666666615804, 0.8423841895332618]}},\n",
+       " 41: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.322, 0.16521501142450623],\n",
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+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
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+       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
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+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
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+       " 78: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.35559998828124, 0.12936089274082296]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [71.16100000000002, 0.95128807413948],\n",
+       "   'dehb': [71.66, 0.8673061743121623]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.49333329467773, 0.5932584095143788],\n",
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+       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
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+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.40499998575846, 1.1509669871312727]}},\n",
+       " 54: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.34100000000001, 0.18960221517692954],\n",
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+       "   'hb': [91.01200000000001, 0.2691765220073962],\n",
+       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
+       "   'dehb': [91.3303999934082, 0.1405611621851642]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1339999572754, 0.22218010256014134],\n",
+       "   'ls': [72.90599997436523, 0.7195025715409926],\n",
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+       "   'sh': [72.212, 0.8472638314008226],\n",
+       "   'hb': [71.78999997070312, 1.036387972192968],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.118, 0.7002256779067739]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.91333325907389, 0.26674998192399213],\n",
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+       "   'bananas': [44.04999994099934, 1.4740156691356903],\n",
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+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [45.231666649373366, 0.6954794557159045]}},\n",
+       " 71: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.42999999999999, 0.12393546707863573],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
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+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
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+       "   'dehb': [44.49999998779297, 1.3177421762189991]}},\n",
+       " 56: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   're': [47.0099999399821, 0.311644368094445],\n",
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+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
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+       " 79: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   'dehb': [44.52333332519531, 0.9745141264064098]}},\n",
+       " 28: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
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+       "   'bohb': [45.57666665039062, 0.6869902055743355],\n",
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+       " 87: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.273, 0.2031280384388106],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
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+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [45.29666665649414, 0.8071830287075519]}},\n",
+       " 14: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.37499999999999, 0.20284230328015748],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.96399997070313, 0.3975726094994739],\n",
+       "   'ls': [73.07999997802735, 0.22715628417767605],\n",
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+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.99666659342448, 0.2841947420244036],\n",
+       "   'ls': [46.836666599527994, 0.4822285980209421],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [46.216666606648765, 0.4955916790039053],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [44.55333331502278, 0.7721470779666786]}},\n",
+       " 45: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.366, 0.21574058496258602],\n",
+       "   'ls': [91.313, 0.22072833982069376],\n",
+       "   'bananas': [90.71, 0.30410524493997293],\n",
+       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
+       "   'sh': [91.022, 0.23625410049351406],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.32799998657227, 0.24643376487943966]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.13599993530273, 0.27982853108044814],\n",
+       "   'ls': [72.93399996704102, 0.6142996147202994],\n",
+       "   'bananas': [72.68799996582031, 0.7742970958782901],\n",
+       "   'npenas': [71.44399995178223, 1.085976077010389],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.95, 0.6726217361935323]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.14666662699382, 0.28836701896157313],\n",
+       "   'ls': [46.91666663004557, 0.303406571821439],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [45.196666635131834, 0.7421365378565281],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.25833332316081, 1.0197834767756673],\n",
+       "   'dehb': [44.83333332010905, 0.610373286196737]}},\n",
+       " 42: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.375, 0.15964022049596213],\n",
+       "   'ls': [91.331, 0.19299999999999676],\n",
+       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
+       "   'npenas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [91.20599999999999, 0.18440173534975068],\n",
+       "   'bohb': [91.06719998901367, 0.3696259781464282],\n",
+       "   'dehb': [91.25119998583983, 0.17848966314169798]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.20399995971681, 0.08845335425469975],\n",
+       "   'ls': [73.22999994506836, 0.08999998168945353],\n",
+       "   'bananas': [72.71799993530274, 0.45461633795041795],\n",
+       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.612, 0.5207072113961919]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.86666662699381, 0.2569046389024295],\n",
+       "   'ls': [46.87999993591309, 0.4643274524076278],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [44.51166664428711, 0.7321600389476588]}},\n",
+       " 59: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.31899999999999, 0.21454370184183588],\n",
+       "   'ls': [91.22999999999999, 0.2846752535785277],\n",
+       "   'bananas': [90.71, 0.30410524493997293],\n",
+       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.3523999831543, 0.21592183863656533],\n",
+       "   'dehb': [91.40719999047852, 0.20210729756953177]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.12199993896486, 0.2641893425015655],\n",
+       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
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+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
+       "   'dehb': [71.433, 0.8784082194515275]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
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+       "   'bananas': [46.66999997558594, 0.27262099588524663],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.576666621907556, 1.0037762152394802],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.86999998677572, 0.9859513109560508]}},\n",
+       " 0: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.47099999999998, 0.1223478647136882],\n",
+       "   'ls': [91.35900000000001, 0.18721378154398627],\n",
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+       "   'bohb': [90.73359998535156, 0.6473310114046523],\n",
+       "   'dehb': [90.82279999096679, 0.46860490917418735]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.19999995239257, 0.08625540828832842],\n",
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+       "   'sh': [72.611, 0.36530672044187684],\n",
+       "   'hb': [71.78999997070312, 1.036387972192968],\n",
+       "   'bohb': [70.61499999999998, 0.7569841477864636],\n",
+       "   'dehb': [70.652, 1.4735861019974363]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.13666659851075, 0.2681003181753849],\n",
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+       "   'sh': [45.63499999186198, 0.6830670204276367],\n",
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+       "   'bohb': [44.27166665445964, 1.534819026705123],\n",
+       "   'dehb': [43.38166665344239, 2.1492175641832474]}},\n",
+       " 31: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.23799999999999, 0.19928873525615837],\n",
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+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
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+       "   'dehb': [91.00679998706055, 0.3960325269994897]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.47800000000001, 0.793722873552225],\n",
+       "   'dehb': [70.75500000000001, 0.7333246211603685]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.829999943033854, 0.33778689474328394],\n",
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+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
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+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
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+       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
+       "   'dehb': [43.19333331807455, 3.4767577464827206]}},\n",
+       " 77: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.405, 0.16644819013734966],\n",
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+       "   'sh': [90.87, 0.20610676844781026],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
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+       " 66: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.52299999999998, 0.09613012014971856],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       " 1: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       " 84: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
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+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
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+       " 46: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
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+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.22399994262693, 0.04715928916649512],\n",
+       "   'ls': [73.23999995849609, 0.03999996093749587],\n",
+       "   'bananas': [72.82999998901369, 0.4038068876513289],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "   'dehb': [71.31700000000001, 0.6804270717718384]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.01666660054524, 0.1939358057911294],\n",
+       "   'ls': [46.90333326822916, 0.4842978752022971],\n",
+       "   'bananas': [44.04999994099934, 1.4740156691356903],\n",
+       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [43.68333332417805, 1.0264826576505077]}},\n",
+       " 82: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.435, 0.16752611736681397],\n",
+       "   'ls': [91.42699999999999, 0.11925183436743828],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [89.65, 0.7737053702799261],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.27759998999025, 0.18733669908749703],\n",
+       "   'dehb': [91.29199998583985, 0.15469195443081413]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.15799993530274, 0.2737078911053187],\n",
+       "   'ls': [72.90599997436523, 0.7195025715409926],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
+       "   'bohb': [71.965, 0.770340833657415],\n",
+       "   'dehb': [72.578, 0.45446231967018114]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.9933332631429, 0.2760032089464641],\n",
+       "   'ls': [46.869999930826815, 0.3701200864094226],\n",
+       "   'bananas': [42.3799999613444, 2.732528330244475],\n",
+       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
+       "   'bohb': [45.116666654459635, 1.0610791149790055],\n",
+       "   'dehb': [44.346666663614904, 1.3120128795746278]}},\n",
+       " 47: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46400000000001, 0.10753604047015765],\n",
+       "   'ls': [91.28199999999998, 0.23029546239559182],\n",
+       "   'bananas': [89.789, 0.6473553892569365],\n",
+       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.006, 0.27302747114530307],\n",
+       "   'bohb': [90.21519998999023, 0.849831604773715],\n",
+       "   'dehb': [91.37959998413086, 0.11661149323498196]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.18399994628905, 0.17036427320873365],\n",
+       "   'ls': [73.18799995605468, 0.18508374165707397],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.024, 0.7636779425909858],\n",
+       "   'dehb': [71.471, 0.3226282690651896]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.729999932861325, 0.15878003444423305],\n",
+       "   'ls': [46.82999995015462, 0.45933525204638725],\n",
+       "   'bananas': [45.196666635131834, 0.7421365378565281],\n",
+       "   'npenas': [45.196666635131834, 0.7421365378565281],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.87499999287923, 1.3897091970654065],\n",
+       "   'dehb': [43.91833332417806, 0.88833333331234]}},\n",
+       " 61: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.348, 0.21989997726238733],\n",
+       "   'ls': [91.336, 0.2266803917413226],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [91.35999999999999, 0.19313207915827818],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
+       "   'dehb': [91.22759998901367, 0.181261803136699]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.13399995361328, 0.2671404334093051],\n",
+       "   'ls': [73.05799996459959, 0.3661092570406024],\n",
+       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'npenas': [72.27199998168945, 0.7594840138443747],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.027, 0.7369810038257442],\n",
+       "   'dehb': [71.612, 0.5207072113961919]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.90999993082682, 0.3577863919355146],\n",
+       "   'ls': [46.83999995015462, 0.49436826421063906],\n",
+       "   'bananas': [46.66666662292481, 0.26288991417376534],\n",
+       "   'npenas': [46.00666661987304, 0.5023942676992026],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [43.98499998982747, 2.5176096328086137]}},\n",
+       " 27: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.405, 0.16644819013734966],\n",
+       "   'ls': [91.308, 0.2271035006335206],\n",
+       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'npenas': [90.32600000000001, 0.4875284607076801],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.3555999819336, 0.20602485508617766]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.05399994995116, 0.2790053888666053],\n",
+       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
+       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.965, 0.770340833657415],\n",
+       "   'dehb': [71.02199999999999, 0.7515557198238868]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.98666661478679, 0.4973708343821461],\n",
+       "   'ls': [47.00999993896484, 0.29403322438384805],\n",
+       "   'bananas': [45.22333328959147, 0.6909011879452681],\n",
+       "   'npenas': [46.70666664835612, 0.3210399583220378],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [44.1383333211263, 0.6309186407275083]}},\n",
+       " 58: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.376, 0.16001249951175475],\n",
+       "   'ls': [91.47399999999999, 0.12034949106663831],\n",
+       "   'bananas': [90.71, 0.30410524493997293],\n",
+       "   'npenas': [90.686, 0.34115099296352724],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [90.95839998803712, 0.18921479786025383],\n",
+       "   'dehb': [91.49319998876953, 0.14836764696397006]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.05599994506835, 0.3963634373436752],\n",
+       "   'ls': [73.17799994384765, 0.17121915315817512],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.41, 1.2734284432193266],\n",
+       "   'dehb': [71.64699999999999, 0.6873870816359591]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.97666659444173, 0.2902297747714774],\n",
+       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
+       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
+       "   'npenas': [46.39999999694824, 0.5393411427589887],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.48666665547689, 1.0413879592348234],\n",
+       "   'dehb': [44.9316666615804, 0.7875930287576318]}},\n",
+       " 74: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.26599999999999, 0.1807871676862037],\n",
+       "   'ls': [91.365, 0.17246738822165503],\n",
+       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
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+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.26679999072266, 0.1798581636850016],\n",
+       "   'dehb': [91.21239998852539, 0.16232756978259588]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.24199993408203, 0.028913637907266736],\n",
+       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
+       "   'bananas': [71.72399996582033, 1.1192426223066643],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.64500000000001, 0.8852824408063242]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.93333327128093, 0.42531035640443504],\n",
+       "   'ls': [46.75666660461426, 0.5264239721612104],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
+       "   'dehb': [43.99499998982747, 1.535018087914917]}},\n",
+       " 43: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.38699999999999, 0.22418965185752593],\n",
+       "   'ls': [91.40700000000001, 0.16601505955785859],\n",
+       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
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+       "   'sh': [91.075, 0.1410850807137311],\n",
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+       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.13999991577148, 0.2705550005096594],\n",
+       "   'ls': [72.91799994628907, 0.7058300185027062],\n",
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+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [72.017, 0.579776681145421]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.05999992675782, 0.17938164140943463],\n",
+       "   'ls': [46.86333328755697, 0.37400235099756535],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.6733333211263, 0.7913139531269915],\n",
+       "   'dehb': [44.66333332621256, 1.010962143481567]}},\n",
+       " 92: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.27799999999999, 0.22846443924602056],\n",
+       "   'ls': [91.27899999999998, 0.23649312886424345],\n",
+       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.1711999897461, 0.31614578936312165]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.05399994262694, 0.3590042063959192],\n",
+       "   'ls': [73.08599994628905, 0.29732810038297064],\n",
+       "   'bananas': [72.72799996948243, 0.5428222427792193],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [72.502, 0.5230831673835432]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.996666610717774, 0.2983100545599037],\n",
+       "   'ls': [46.869999930826815, 0.3701200864094226],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
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+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
+       "   'bohb': [44.91499999186199, 1.050567304176101],\n",
+       "   'dehb': [44.97333332621257, 0.8100480110329846]}},\n",
+       " 64: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.485, 0.1069813067783309],\n",
+       "   'ls': [91.16400000000002, 0.30137020423392685],\n",
+       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.21959998901369, 0.17942530649368174]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.02199994873047, 0.4058028987534716],\n",
+       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
+       "   'bananas': [71.27999995239259, 1.1918724970886854],\n",
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+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.72799997314453, 1.1008796620930725],\n",
+       "   'bohb': [71.372, 0.8890309330951311],\n",
+       "   'dehb': [71.80799999999999, 0.8310691908619895]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.08999992167155, 0.4765734390749889],\n",
+       "   'ls': [46.829999938964846, 0.41297027213245097],\n",
+       "   'bananas': [43.87333329366048, 1.6771536434658265],\n",
+       "   'npenas': [46.39999994913737, 0.5729068277445918],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.87333331604004, 0.9071383422339475]}},\n",
+       " 2: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.47499999999998, 0.16384443841644183],\n",
+       "   'ls': [91.413, 0.10714942836991305],\n",
+       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.10199998657228, 0.2676968414510213]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.81599995971679, 0.7018147623583206],\n",
+       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
+       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
+       "   'npenas': [72.31799996337892, 0.7207468500286193],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.41, 1.2734284432193266],\n",
+       "   'dehb': [71.40600000000002, 0.6725503698608749]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.916666605631505, 0.32702359667147973],\n",
+       "   'ls': [46.92666660970052, 0.4263019350533028],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [41.469999971008306, 2.9911517777397147],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.87166664835612, 0.8069851319578044]}},\n",
+       " 8: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.32, 0.21227340860314964],\n",
+       "   'ls': [91.31, 0.22746428291052306],\n",
+       "   'bananas': [90.21600000000001, 0.6384857085323041],\n",
+       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.006, 0.27302747114530307],\n",
+       "   'bohb': [91.16959998828125, 0.1618908246319105],\n",
+       "   'dehb': [91.30079998901367, 0.181256067700162]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.04599993164064, 0.38987689361866185],\n",
+       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
+       "   'bananas': [72.27199994873048, 0.564496212204965],\n",
+       "   'npenas': [71.68799994750978, 0.8179584333220017],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.96900000000001, 0.7439952956840534]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.0299999420166, 0.27505552803844197],\n",
+       "   'ls': [46.83999992675781, 0.5272148850007627],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
+       "   'dehb': [44.926666662597654, 0.9440515432025652]}},\n",
+       " 29: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.41799999999998, 0.17639727889057452],\n",
+       "   'ls': [91.344, 0.2026425424238447],\n",
+       "   'bananas': [90.507, 0.48381918109971644],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.44279999291993, 0.15688008083343494]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.88999996337888, 0.7210131564861866],\n",
+       "   'ls': [73.00999995239258, 0.342023390263453],\n",
+       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.91999999999999, 0.9142756695876798]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.10999991251627, 0.300388639618315],\n",
+       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
+       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [45.523333323160806, 0.7968758469919937],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.51999999287923, 0.7036334231553507]}},\n",
+       " 48: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.32199999999999, 0.2232397814010728],\n",
+       "   'ls': [91.203, 0.28823774908918187],\n",
+       "   'bananas': [90.108, 0.723696068802366],\n",
+       "   'npenas': [91.09299999999999, 0.19370338148829244],\n",
+       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.14719999047851, 0.3476586789752243]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.10999994506835, 0.30675723352088197],\n",
+       "   'ls': [73.06399995605469, 0.408881393445887],\n",
+       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.86333330790202, 0.28104961436623016],\n",
+       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.58666666158041, 0.6115917295586587]}},\n",
+       " 52: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.39399999999999, 0.09551963149007406],\n",
+       "   'ls': [91.42699999999999, 0.12008746812219366],\n",
+       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
+       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
+       "   'sh': [91.022, 0.23625410049351406],\n",
+       "   'hb': [91.003, 0.25060127693210343],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.21639998754884, 0.26287305106602116]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.16199998046875, 0.1462736949288101],\n",
+       "   'ls': [73.03599996215819, 0.38613987386373666],\n",
+       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "   'bohb': [71.12700000000001, 0.9840939995752443],\n",
+       "   'dehb': [71.531, 0.8652219368462648]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.063333261108404, 0.37281212744718006],\n",
+       "   'ls': [46.896666606648765, 0.5147706974218812],\n",
+       "   'bananas': [46.17666664835612, 0.4150100453010071],\n",
+       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.67999999796549, 0.41209492938083875]}},\n",
+       " 68: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.409, 0.15661736813009963],\n",
+       "   'ls': [91.38899999999998, 0.20349201458533675],\n",
+       "   'bananas': [90.108, 0.723696068802366],\n",
+       "   'npenas': [90.65, 0.3594440151122292],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.14639998779296, 0.5504622012880396],\n",
+       "   'dehb': [91.36119999194337, 0.2501330858688809]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.0899999597168, 0.27338618987337304],\n",
+       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.22, 0.9473119866232049],\n",
+       "   'dehb': [70.752, 0.6318829005440775]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.04999992167156, 0.3106444834264413],\n",
+       "   'ls': [46.80666657714844, 0.5197862540946532],\n",
+       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [44.76833332824707, 0.7160249107071831]}},\n",
+       " 51: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.40299999999999, 0.11498260738041606],\n",
+       "   'ls': [91.33500000000001, 0.19946177578673782],\n",
+       "   'bananas': [89.96700000000001, 0.6205328355534462],\n",
+       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.1275999897461, 0.2865935098400215],\n",
+       "   'dehb': [91.21239998486327, 0.13909938755285042]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.00199996215818, 0.6814953801594783],\n",
+       "   'ls': [73.12599997192383, 0.3115188412004214],\n",
+       "   'bananas': [72.48199993408204, 0.5818900266756044],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [72.12600001586914, 0.7874033289220081],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.91, 0.8083934685535257]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.7966666229248, 0.3689173505116282],\n",
+       "   'ls': [46.7666665822347, 0.48739672968011166],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.661666663614916, 0.9038205913677476]}},\n",
+       " 57: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.33600000000001, 0.20698792235296923],\n",
+       "   'ls': [91.30699999999999, 0.2433536521197062],\n",
+       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
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+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.26679998974608, 0.2231702524900759]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.01799996459961, 0.3770888512192757],\n",
+       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
+       "   'bananas': [71.8499999633789, 1.0354226419727222],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.556, 0.8598395199105476]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.99333325703939, 0.3032417354689357],\n",
+       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
+       "   'bananas': [45.586666631062826, 0.5177300850692277],\n",
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+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
+       "   'dehb': [44.3899999786377, 1.2362847559972756]}},\n",
+       " 80: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.42999999999999, 0.16334013591275956],\n",
+       "   'ls': [91.31, 0.22746428291052306],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.33199998852538, 0.1721952420729978]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.01799995483398, 0.5925841369709894],\n",
+       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
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+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
+       "   'dehb': [71.858, 0.8328601323151431]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.083333262125656, 0.33474699086362947],\n",
+       "   'ls': [46.75666660461426, 0.5264239721612104],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
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+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.79499999593098, 0.9498552510203311]}},\n",
+       " 70: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.405, 0.14298601330199853],\n",
+       "   'ls': [91.43, 0.20479257799050818],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
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+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.28599999047852, 0.18409780426224426]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.0499999633789, 0.32412962178541077],\n",
+       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
+       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
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+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.81400000000001, 0.6737981893712698]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.059999905395514, 0.30760726724524773],\n",
+       "   'ls': [46.809999940999354, 0.46834221009228677],\n",
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+       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.11166664733887, 1.3045231193656752]}},\n",
+       " 53: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.36999999999999, 0.21042813500099883],\n",
+       "   'ls': [91.23499999999999, 0.26926752496355655],\n",
+       "   'bananas': [90.507, 0.48381918109971644],\n",
+       "   'npenas': [90.98800000000001, 0.2613350340080735],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.11839998754883, 0.27413398431540836],\n",
+       "   'dehb': [91.24239998974609, 0.12001599788544079]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.19199994140624, 0.07704545216442857],\n",
+       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
+       "   'bananas': [73.04799997314453, 0.2852648081717301],\n",
+       "   'npenas': [69.41199996093751, 2.010138300507699],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.66600000000001, 0.6460061919207903]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.973333266194665, 0.3006659156355776],\n",
+       "   'ls': [46.933333263142906, 0.500444231640458],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [46.19999994608561, 0.42972857008585347],\n",
+       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.633333329264325, 0.8220908236812496]}},\n",
+       " 94: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.40799999999999, 0.13636715146984302],\n",
+       "   'ls': [91.15899999999999, 0.3093687120573093],\n",
+       "   'bananas': [90.91, 0.23315231073270615],\n",
+       "   'npenas': [91.132, 0.20994284936620106],\n",
+       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
+       "   'hb': [91.006, 0.27302747114530307],\n",
+       "   'bohb': [91.05039999145508, 0.32003099571870103],\n",
+       "   'dehb': [91.22599998657228, 0.23659078733409855]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.02399993530273, 0.4573663834081752],\n",
+       "   'ls': [72.93399996704102, 0.6142996147202994],\n",
+       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'npenas': [71.27999995239259, 1.1918724970886854],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "   'dehb': [72.01899999999999, 0.8726562897269456]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.93666660257976, 0.32777021321277366],\n",
+       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.921666649373364, 0.7827675252240144]}},\n",
+       " 49: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.356, 0.21077001684299962],\n",
+       "   'ls': [91.37799999999999, 0.19051509126575586],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'npenas': [91.334, 0.10499523798725151],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.006, 0.27302747114530307],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.32839998803712, 0.2085488937111959]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23999994384765, 0.030983843130847382],\n",
+       "   'ls': [73.03399996582031, 0.38544001214065354],\n",
+       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'npenas': [72.88799997680664, 0.501693134934719],\n",
+       "   'sh': [72.535, 0.35964565894780276],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [72.32300000000001, 0.6667840729951504]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.92333327229817, 0.28792358319467587],\n",
+       "   'ls': [46.836666599527994, 0.4822285980209421],\n",
+       "   'bananas': [46.54999991455078, 0.4763868603504092],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
+       "   'dehb': [45.069999985758464, 0.7163099974909092]}},\n",
+       " 67: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.36699999999999, 0.16589454481687857],\n",
+       "   'ls': [91.27, 0.24943937139112365],\n",
+       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.003, 0.25060127693210343],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.08679998999023, 0.21682841368657332]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.12999994873047, 0.22877934718253454],\n",
+       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
+       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
+       "   'npenas': [71.8499999633789, 1.0354226419727222],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.595, 0.6773514597312098]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.946666599528, 0.45927475259040246],\n",
+       "   'ls': [46.79666662394206, 0.467487355491559],\n",
+       "   'bananas': [46.21666658630372, 0.5506057456849347],\n",
+       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.415000000000006, 1.2946996658027377]}},\n",
+       " 17: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.42099999999999, 0.17455371666051453],\n",
+       "   'ls': [91.381, 0.15952742710894352],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'npenas': [91.17699999999999, 0.14533065746771975],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.08359998754882, 0.2166098771134104]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2039999597168, 0.08845335425469975],\n",
+       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
+       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [70.58500000000001, 0.6309556244301178]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.87333326721191, 0.42838199276747696],\n",
+       "   'ls': [46.72666658732096, 0.5681940138884105],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'sh': [45.523333323160806, 0.7968758469919937],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.373333330281575, 1.6571595502251262]}},\n",
+       " 86: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.274, 0.2106751053162178],\n",
+       "   'ls': [91.252, 0.3223290244455159],\n",
+       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
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+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.18199993530273, 0.11608614782058224],\n",
+       "   'ls': [73.14399995605467, 0.3160759188618709],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.27799999999999, 0.5411432342735151]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.73333330383301, 0.2628899224286895],\n",
+       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
+       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
+       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [43.66000000101725, 1.394377599998569]}},\n",
+       " 23: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.16399999999999, 0.12579348154813053],\n",
+       "   'ls': [91.448, 0.1297536126664655],\n",
+       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.2563999934082, 0.24209386386542095],\n",
+       "   'dehb': [91.1803999934082, 0.26013811665669984]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.02199993652343, 0.592314086907299],\n",
+       "   'ls': [73.23999995849609, 0.03999996093749587],\n",
+       "   'bananas': [69.58599997680665, 1.7337139051941306],\n",
+       "   'npenas': [69.58599997680665, 1.7337139051941306],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [70.87499999999999, 0.7759413637640394]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.02666660563151, 0.3398692326613129],\n",
+       "   'ls': [46.933333263142906, 0.500444231640458],\n",
+       "   'bananas': [46.30999995625813, 0.5420229323362055],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.58333331197103, 0.7776388644728909],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [45.03666665649415, 0.7074523974708331]}},\n",
+       " 12: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.297, 0.24409219569662294],\n",
+       "   'ls': [91.31, 0.22746428291052306],\n",
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+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [91.003, 0.25060127693210343],\n",
+       "   'bohb': [90.92119999169923, 0.27704757625045284],\n",
+       "   'dehb': [91.26079998681641, 0.28508413159539336]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1319999597168, 0.23481055142091806],\n",
+       "   'ls': [72.98599995483399, 0.45695074125831625],\n",
+       "   'bananas': [69.58599997680665, 1.7337139051941306],\n",
+       "   'npenas': [71.39799995788574, 1.1144308062423007],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.184, 0.7111568226257754],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.43199999999999, 0.6469590404345532]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.01999995117187, 0.40993219730296676],\n",
+       "   'ls': [47.00666661478679, 0.32138586040782224],\n",
+       "   'bananas': [45.22333328959147, 0.6909011879452681],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [45.05166667378744, 1.18622768273718]}},\n",
+       " 65: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.41999999999999, 0.1687601848778277],\n",
+       "   'ls': [91.306, 0.26431799030712894],\n",
+       "   'bananas': [91.132, 0.1655777762865499],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.26239999145508, 0.27149482506865885],\n",
+       "   'dehb': [91.03244443196614, 0.2862939845812309]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23799994018555, 0.030265467781614545],\n",
+       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
+       "   'bananas': [72.57599998657227, 0.48971830220809554],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.43299999999999, 0.7531673120894177]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.87666658630372, 0.4343705612392511],\n",
+       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
+       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
+       "   'hb': [45.31333332417806, 0.5057887203043718],\n",
+       "   'bohb': [43.541666662597656, 1.713301708439184],\n",
+       "   'dehb': [44.008333328247076, 0.9754842129860548]}},\n",
+       " 24: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.453, 0.11807201192492535],\n",
+       "   'ls': [91.31, 0.22746428291052306],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
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+       "   'sh': [91.022, 0.23625410049351406],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.25559998999023, 0.19524303164031448]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23599993652343, 0.04543127138791328],\n",
+       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
+       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.385, 0.9288191427829203],\n",
+       "   'dehb': [71.03699999999999, 0.825748751134386]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.13999992370606, 0.2462158228979491],\n",
+       "   'ls': [46.809999940999354, 0.46834221009228677],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
+       "   'dehb': [44.948333319091795, 1.120774783881138]}},\n",
+       " 62: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.403, 0.08567963585356549],\n",
+       "   'ls': [91.413, 0.19042321287069813],\n",
+       "   'bananas': [91.225, 0.20963062753328493],\n",
+       "   'npenas': [90.65, 0.3594440151122292],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.17959999389647, 0.18826747468020216]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.18999995483398, 0.14649228503644],\n",
+       "   'ls': [73.11999994873047, 0.28227644224013915],\n",
+       "   'bananas': [70.70399995361329, 1.2985468673780534],\n",
+       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [71.027, 0.7369810038257442],\n",
+       "   'dehb': [71.604, 0.7328055676644397]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.10666657613119, 0.2520140906268146],\n",
+       "   'ls': [46.789999944051104, 0.5006773160029646],\n",
+       "   'bananas': [45.586666631062826, 0.5177300850692277],\n",
+       "   'npenas': [45.06999995422363, 0.9393200879432245],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.92499998982747, 1.1357388704929123],\n",
+       "   'dehb': [43.73833330790202, 1.1883707359173892]}},\n",
+       " 89: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.34700000000001, 0.15133076356114636],\n",
+       "   'ls': [91.31, 0.22746428291052306],\n",
+       "   'bananas': [90.21600000000001, 0.6384857085323041],\n",
+       "   'npenas': [90.78099999999999, 0.2897395382063003],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [91.053, 0.23639162421710094],\n",
+       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
+       "   'dehb': [91.21919998999024, 0.25761242538832047]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.22399993408203, 0.08845335734601689],\n",
+       "   'ls': [72.87799997192383, 0.5940673165120702],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [72.312, 0.4521238768302327],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.505, 0.672610585703199]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.93999992167154, 0.32448248773548183],\n",
+       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
+       "   'bananas': [46.16666663818359, 0.3762386797796466],\n",
+       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.79666665649414, 0.6369981679366045]}},\n",
+       " 3: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.44399999999999, 0.1774936618586682],\n",
+       "   'ls': [91.221, 0.28532262440962985],\n",
+       "   'bananas': [90.755, 0.3000083332175982],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [91.022, 0.23625410049351406],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.16199998901367, 0.14852070453241303]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.15399994018554, 0.1627390359240187],\n",
+       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "   'dehb': [71.756, 0.7842601609160081]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.16999996337891, 0.2040969003820301],\n",
+       "   'ls': [46.77333328552246, 0.5318311622421547],\n",
+       "   'bananas': [46.16000001525879, 0.5242560982798979],\n",
+       "   'npenas': [46.78999995625814, 0.3580967864297857],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.47166665547689, 1.0465034372858832],\n",
+       "   'dehb': [44.87499998982747, 0.7711913615281554]}},\n",
+       " 15: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.35499999999999, 0.22646191732827542],\n",
+       "   'ls': [91.207, 0.2838326972003031],\n",
+       "   'bananas': [90.71, 0.30410524493997293],\n",
+       "   'npenas': [90.686, 0.34115099296352724],\n",
+       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.10759997705078, 0.27653542565428774],\n",
+       "   'dehb': [91.1143999819336, 0.2306040718883427]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.24399993041992, 0.023323793343668523],\n",
+       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
+       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.62, 0.8167863857827218],\n",
+       "   'dehb': [71.96199999999999, 0.7496505852728994]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.01333325500489, 0.48309187068330456],\n",
+       "   'ls': [46.79666662394206, 0.467487355491559],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'npenas': [46.49333323669434, 0.563481230212473],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.576666621907556, 1.0037762152394802],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.831666659545895, 0.5728801461874484]}},\n",
+       " 72: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.45500000000001, 0.1253195914452297],\n",
+       "   'ls': [91.24299999999998, 0.3198765386832835],\n",
+       "   'bananas': [90.94699999999999, 0.1976891499298823],\n",
+       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.32959998901367, 0.20578201776622312],\n",
+       "   'dehb': [91.05119999267579, 0.32466500134092596]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.08599995239258, 0.3073825044437118],\n",
+       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
+       "   'bananas': [72.10399997436522, 0.6933859086894885],\n",
+       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.33300000000001, 0.5587315992495866]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.17666661885579, 0.2246231122157868],\n",
+       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
+       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [44.396666657511396, 1.0466295124789269]}},\n",
+       " 81: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.325, 0.19247077700263657],\n",
+       "   'ls': [91.15899999999999, 0.3093687120573093],\n",
+       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
+       "   'npenas': [88.98700000000001, 1.2792267195458367],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.1715999868164, 0.2510741716604863]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2439999304199, 0.023323793343668527],\n",
+       "   'ls': [72.85399997436522, 0.7095378407774434],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [72.22799999023437, 0.8239999970430587],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.916666540527345, 0.2941087993010106],\n",
+       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
+       "   'bananas': [46.81666661987305, 0.39306487315307015],\n",
+       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.98166665039063, 0.9729522596171338]}},\n",
+       " 75: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.26499999999999, 0.2260199106273591],\n",
+       "   'ls': [91.274, 0.29428557558942375],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'npenas': [91.032, 0.261526289309503],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.1715999909668, 0.23684898142600977],\n",
+       "   'dehb': [91.21039998510741, 0.2072926439824124]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.14799991699218, 0.2723527273871788],\n",
+       "   'ls': [73.08599994628905, 0.29732810038297064],\n",
+       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'npenas': [70.0899999560547, 1.3856478538407666],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.79999999999998, 0.8612316761475969]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.039999950154616, 0.3598765041126613],\n",
+       "   'ls': [46.9799999053955, 0.38244826113431935],\n",
+       "   'bananas': [46.14666664326986, 0.4529409840372371],\n",
+       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.98666664530437, 0.8390404577916358],\n",
+       "   'dehb': [44.4566666615804, 0.9982262156934935]}},\n",
+       " 93: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.338, 0.14260434775980418],\n",
+       "   'ls': [91.345, 0.19210674116230142],\n",
+       "   'bananas': [89.58200000000001, 0.8309969915709677],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
+       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.07199994384766, 0.46589268152990576],\n",
+       "   'ls': [73.03599994750975, 0.43733739631192065],\n",
+       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
+       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
+       "   'dehb': [72.06700000000001, 0.613156586852004]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.933333267211914, 0.4659518520237985],\n",
+       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
+       "   'bananas': [45.74666662190755, 0.49937738128039566],\n",
+       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
+       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
+       "   'dehb': [44.37499998474121, 1.1225034045437063]}},\n",
+       " 4: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.47999999999999, 0.14778362561528577],\n",
+       "   'ls': [91.31, 0.22445489524623813],\n",
+       "   'bananas': [91.147, 0.14893287078411946],\n",
+       "   'npenas': [90.21600000000001, 0.6384857085323041],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.15879998730468, 0.2170349333983711],\n",
+       "   'dehb': [91.28919998828125, 0.2226893839048166]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.22799994995117, 0.04118249115978971],\n",
+       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
+       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
+       "   'npenas': [73.1399999206543, 0.1335664597491603],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.11, 0.8461560139832375]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.169999918619794, 0.19857828352987805],\n",
+       "   'ls': [46.94999994710287, 0.2891558226092122],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [45.13666662190755, 0.658525305689547],\n",
+       "   'bohb': [44.48666665547689, 1.0413879592348234],\n",
+       "   'dehb': [45.20833331807454, 0.8498447548008442]}},\n",
+       " 85: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.38899999999998, 0.20685502169393585],\n",
+       "   'ls': [91.41799999999999, 0.1275774274705309],\n",
+       "   'bananas': [89.789, 0.6473553892569365],\n",
+       "   'npenas': [90.686, 0.34115099296352724],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.23959998266601, 0.17976384913236232]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.20599997558595, 0.04103653276637411],\n",
+       "   'ls': [73.03599994750975, 0.43733739631192065],\n",
+       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.42199997070313, 0.5972905783047683],\n",
+       "   'bohb': [71.024, 0.7636779425909858],\n",
+       "   'dehb': [71.443, 1.1093786549235571]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.913333249918615, 0.47192274766763537],\n",
+       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
+       "   'bananas': [44.3433332804362, 1.3207783721457254],\n",
+       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
+       "   'dehb': [44.5566666595459, 0.9461089888183467]}},\n",
+       " 18: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.445, 0.10385085459445893],\n",
+       "   'ls': [91.232, 0.2774094446842062],\n",
+       "   'bananas': [89.927, 0.5511633151798111],\n",
+       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.92119999169923, 0.27704757625045284],\n",
+       "   'dehb': [91.06999998535156, 0.3004702965703844]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23799994750978, 0.03515675951021431],\n",
+       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [72.09400000000001, 0.7941435638472438],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
+       "   'dehb': [71.897, 0.7753457293362738]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.90666659240723, 0.5454457672166005],\n",
+       "   'ls': [46.949999954223635, 0.30849092020267993],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [44.51499999084473, 0.9429990533019659]}},\n",
+       " 96: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.376, 0.1955095905575952],\n",
+       "   'ls': [91.41299999999998, 0.23516164653276148],\n",
+       "   'bananas': [90.686, 0.34115099296352724],\n",
+       "   'npenas': [89.789, 0.6473553892569365],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.21559998510742, 0.18816546114878685]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.02599994384765, 0.5899186579421641],\n",
+       "   'ls': [73.06399995605469, 0.408881393445887],\n",
+       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'npenas': [70.8579999584961, 1.2019800200639044],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.857, 0.7124471910254133]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.073333251953116, 0.21281185676476877],\n",
+       "   'ls': [46.91999994913737, 0.4425180077250209],\n",
+       "   'bananas': [46.249999894205736, 0.43262763292350354],\n",
+       "   'npenas': [45.586666631062826, 0.5177300850692277],\n",
+       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [45.119999990844725, 1.0011327006407615]}},\n",
+       " 40: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.357, 0.22122612865572278],\n",
+       "   'ls': [91.31899999999999, 0.2185612042426535],\n",
+       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [91.37399999511719, 0.14371360640529274],\n",
+       "   'hb': [91.006, 0.27302747114530307],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.44599998681642, 0.14224766822424434]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.84799998901369, 0.6752895406425103],\n",
+       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
+       "   'dehb': [71.834, 1.091505382487874]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.16666659952799, 0.23094009798522308],\n",
+       "   'ls': [46.986666602579746, 0.27936434351666317],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [46.756666600545245, 0.26248806222081056],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [45.16499998677572, 0.6560170279957473]}},\n",
+       " 25: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.41799999999998, 0.248225703745599],\n",
+       "   'ls': [91.34399999999998, 0.242577822564223],\n",
+       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
+       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
+       "   'dehb': [91.44039998706054, 0.19235342365143787]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1299999609375, 0.2665708371717949],\n",
+       "   'ls': [73.14399995605467, 0.3160759188618709],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'sh': [72.312, 0.4521238768302327],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.675, 0.493725632310092]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.12999988708496, 0.25053279798882916],\n",
+       "   'ls': [47.05666658020019, 0.2060474406308672],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [43.8699999572754, 1.6782563415101452],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.85166664733886, 0.8143658033995257]}},\n",
+       " 5: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.42299999999999, 0.16763352886579266],\n",
+       "   'ls': [91.24299999999998, 0.3198765386832835],\n",
+       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'npenas': [91.35999999999999, 0.2077498495787624],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.23159998876953, 0.1981470151776915]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.17599994873046, 0.15919796947929366],\n",
+       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
+       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.287, 0.9833824281529533]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.92999994812011, 0.3658931134180845],\n",
+       "   'ls': [46.79666662394206, 0.467487355491559],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.551666652425126, 1.1621687212379304]}},\n",
+       " 90: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.277, 0.1921483801649101],\n",
+       "   'ls': [91.252, 0.3223290244455159],\n",
+       "   'bananas': [90.71, 0.30410524493997293],\n",
+       "   'npenas': [91.26399999999998, 0.13654303350958258],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.15279998754883, 0.19886719122692054]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.15599993530273, 0.22694491448143697],\n",
+       "   'ls': [73.06799995117187, 0.28596503931588974],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [72.29199995971679, 0.8251157543320088],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.72799997314453, 1.1008796620930725],\n",
+       "   'bohb': [70.91499999999999, 0.7585940943614059],\n",
+       "   'dehb': [71.181, 0.5073351949155495]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.00666659444173, 0.31756013995985427],\n",
+       "   'ls': [46.9699999206543, 0.4895462747637796],\n",
+       "   'bananas': [46.73333332010905, 0.45946824583320334],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
+       "   'dehb': [44.19499998677571, 0.771364233834982]}},\n",
+       " 16: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.30699999999999, 0.21508370463612164],\n",
+       "   'ls': [91.27899999999998, 0.23649312886424345],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'npenas': [91.069, 0.30615192307088085],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.17519998754882, 0.2500483142877078]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23199993286133, 0.03249612582079108],\n",
+       "   'ls': [73.0759999609375, 0.36538195084049135],\n",
+       "   'bananas': [72.42799999511718, 0.6395123114637266],\n",
+       "   'npenas': [71.72399996582033, 1.1192426223066643],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.16800000000002, 1.2521565397345489],\n",
+       "   'dehb': [71.199, 1.126502996001343]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.13999992268881, 0.3241398844843741],\n",
+       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
+       "   'bananas': [45.74666662190755, 0.49937738128039566],\n",
+       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [43.399999994913735, 2.1122394738016497]}},\n",
+       " 7: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.326, 0.25542905081450473],\n",
+       "   'ls': [91.37299999999999, 0.20288173895153644],\n",
+       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
+       "   'npenas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.42879998486328, 0.16628457177193315]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.139999954834, 0.26996298058932716],\n",
+       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
+       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'npenas': [72.69999998291016, 0.468444236995005],\n",
+       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [72.004, 0.8930419922937567],\n",
+       "   'dehb': [71.26800000000001, 0.8609041758523415]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.196666573079426, 0.2405780072258597],\n",
+       "   'ls': [46.80666657714844, 0.5197862540946532],\n",
+       "   'bananas': [46.279999991861985, 0.49714740034066196],\n",
+       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
+       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
+       "   'dehb': [44.90999999084473, 0.7270641024554297]}},\n",
+       " 44: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.361, 0.19856736892047192],\n",
+       "   'ls': [91.28799999999998, 0.27967123556061235],\n",
+       "   'bananas': [89.96700000000001, 0.6205328355534462],\n",
+       "   'npenas': [90.686, 0.34115099296352724],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
+       "   'bohb': [91.27759998999025, 0.18733669908749703],\n",
+       "   'dehb': [90.90519998364259, 0.27159999997878453]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.13399993286131, 0.22218011805379909],\n",
+       "   'ls': [72.85399997436522, 0.7095378407774434],\n",
+       "   'bananas': [72.87199997680663, 0.5133575844779364],\n",
+       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [70.765, 0.7578819169237366]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.08333325805664, 0.2717228864603964],\n",
+       "   'ls': [46.90333326822916, 0.4842978752022971],\n",
+       "   'bananas': [46.8299999613444, 0.32436258075569047],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.6733333211263, 0.7913139531269915],\n",
+       "   'dehb': [44.599999995930986, 0.7114929463416747]}},\n",
+       " 10: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.48299999999999, 0.10909170454255103],\n",
+       "   'ls': [91.361, 0.1783507779629783],\n",
+       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'npenas': [91.39699999999998, 0.17000294115102588],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.30359999072266, 0.15294914066855703]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.25799993530272, 0.006000010986325321],\n",
+       "   'ls': [73.1679999328613, 0.27600001831054616],\n",
+       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
+       "   'npenas': [70.00799994750977, 1.4137100085040557],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
+       "   'dehb': [71.612, 0.5207072113961919]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.95333328653971, 0.2940899169161976],\n",
+       "   'ls': [46.91999994913737, 0.4425180077250209],\n",
+       "   'bananas': [43.94999995015463, 1.173906104664524],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.81833331604004, 1.3276639885096027]}},\n",
+       " 50: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46900000000001, 0.10123734488813656],\n",
+       "   'ls': [91.274, 0.29428557558942375],\n",
+       "   'bananas': [91.244, 0.12092973166264599],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [91.37039998999023, 0.14425755204992768],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.18199998657227, 0.2862460478524256],\n",
+       "   'dehb': [91.3175999868164, 0.1613760878739472]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2079999621582, 0.04118247811762278],\n",
+       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
+       "   'bananas': [70.6419999584961, 1.37475668804989],\n",
+       "   'npenas': [72.06399996459962, 0.9854054985002976],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.291, 0.5339559906958629]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.02666660970052, 0.29694741735997576],\n",
+       "   'ls': [46.94999992065429, 0.2880007534723706],\n",
+       "   'bananas': [44.3433332804362, 1.3207783721457254],\n",
+       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.86333332926432, 1.035583579202776]}},\n",
+       " 55: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.40899999999998, 0.15852129194527512],\n",
+       "   'ls': [91.18299999999999, 0.3113856130266761],\n",
+       "   'bananas': [90.755, 0.3000083332175982],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.26079999096677, 0.2398919746322682],\n",
+       "   'dehb': [91.03079998828125, 0.2769320476415453]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.08399997558595, 0.31251237342691646],\n",
+       "   'ls': [73.17399994750977, 0.23255966263428118],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [68.56599994750977, 2.193318024787204],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.648, 1.0161574681121048],\n",
+       "   'dehb': [72.16900000000001, 0.7129719489573216]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.68666661987305, 0.32359096198300696],\n",
+       "   'ls': [46.833333276367185, 0.45971003488685935],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
+       "   'dehb': [44.839999996948244, 1.136520042787577]}},\n",
+       " 36: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.45599999999999, 0.1699529346613337],\n",
+       "   'ls': [91.261, 0.24772767306055807],\n",
+       "   'bananas': [90.98799999999999, 0.17971087891388332],\n",
+       "   'npenas': [90.65, 0.3594440151122292],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.29799998535157, 0.18453184219316485]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23999994384766, 0.030983843130847382],\n",
+       "   'ls': [73.24999994873046, 0.02999997070312191],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [72.004, 0.8930419922937567],\n",
+       "   'dehb': [72.03899999999999, 0.7531062342060382]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.969999937947584, 0.458124175324279],\n",
+       "   'ls': [46.9799999053955, 0.38244826113431935],\n",
+       "   'bananas': [46.24666660664876, 0.40857476400546877],\n",
+       "   'npenas': [43.10666662902832, 2.2594394686448602],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.70333327433268, 0.8579627423500099],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.52833332316081, 0.7983333363065136]}},\n",
+       " 22: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46, 0.11198214143335324],\n",
+       "   'ls': [91.27, 0.24943937139112365],\n",
+       "   'bananas': [88.98700000000001, 1.2792267195458367],\n",
+       "   'npenas': [90.78099999999999, 0.2897395382063003],\n",
+       "   'sh': [91.37399999511719, 0.14371360640529274],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.13359998901367, 0.28254387779751905],\n",
+       "   'dehb': [91.33799999023435, 0.1773426030524605]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.15199997436522, 0.16351143368374998],\n",
+       "   'ls': [72.8579999584961, 0.6428343626872822],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
+       "   'dehb': [71.672, 0.5082086185809914]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.9233332631429, 0.38007309562413083],\n",
+       "   'ls': [46.81999996032715, 0.5302409812619554],\n",
+       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.46833333129884, 0.6893011726929609]}},\n",
+       " 97: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.345, 0.24042670400768532],\n",
+       "   'ls': [91.308, 0.2271035006335206],\n",
+       "   'bananas': [89.363, 1.132616881385759],\n",
+       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [90.367, 0.841166452017671],\n",
+       "   'bohb': [91.25919998535156, 0.19695928734409582],\n",
+       "   'dehb': [91.38839998388671, 0.16294735698871998]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.94399997558594, 0.4586763290747864],\n",
+       "   'ls': [73.03599996215819, 0.38613987386373666],\n",
+       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'npenas': [71.3139999621582, 1.165711812381764],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.246, 0.604337653965067]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.93999993591309, 0.4154782823909908],\n",
+       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
+       "   'bananas': [46.27333329366048, 0.6051262299981502],\n",
+       "   'npenas': [47.12999995625813, 0.22776690312589737],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.97166665445964, 0.9714264997224785]}},\n",
+       " 37: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.36799999999998, 0.24326117651610174],\n",
+       "   'ls': [91.40700000000001, 0.16601505955785859],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [89.52700000000002, 0.8955227523631091],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.29439998632813, 0.2009194849603194]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1279999609375, 0.31128120323950825],\n",
+       "   'ls': [73.05799994873045, 0.38277408358260157],\n",
+       "   'bananas': [73.10599997680666, 0.1750542768368422],\n",
+       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
+       "   'sh': [72.54899999999999, 0.45654025014230665],\n",
+       "   'hb': [72.06399999633788, 0.8158823457637169],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
+       "   'dehb': [71.524, 0.6692264190840049]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.10999989318848, 0.2027861550933387],\n",
+       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
+       "   'bananas': [46.84333327840169, 0.45681258035956873],\n",
+       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.81999998982748, 1.0729709564636138],\n",
+       "   'dehb': [44.34999998677572, 1.0750452170628257]}},\n",
+       " 30: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.34700000000001, 0.2050390206765506],\n",
+       "   'ls': [91.361, 0.1783507779629783],\n",
+       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'npenas': [90.507, 0.48381918109971644],\n",
+       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.21879998925782, 0.17937714589744344],\n",
+       "   'dehb': [91.23319999365233, 0.190721155317251]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.00799994873049, 0.6838245016898179],\n",
+       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
+       "   'bananas': [72.36599995117187, 0.5598607256095786],\n",
+       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.723, 0.9278043974890402],\n",
+       "   'dehb': [71.413, 0.8972073338978016]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.89999995117188, 0.26791371582685164],\n",
+       "   'ls': [46.896666606648765, 0.5147706974218812],\n",
+       "   'bananas': [46.696666621907546, 0.28341176804307383],\n",
+       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.77999999287924, 0.8537108583323764]}},\n",
+       " 95: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.33900000000001, 0.2031477294975227],\n",
+       "   'ls': [91.26699999999998, 0.2572955499032189],\n",
+       "   'bananas': [90.755, 0.3000083332175982],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.10479998754883, 0.3791012490814029],\n",
+       "   'dehb': [91.20239999389648, 0.1735022786346223]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1539999279785, 0.27233069669844234],\n",
+       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
+       "   'bananas': [70.8579999584961, 1.2019800200639044],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.184, 0.7111568226257754],\n",
+       "   'bohb': [71.024, 0.7636779425909858],\n",
+       "   'dehb': [71.868, 0.8223600184833896]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.92333324991862, 0.41124472727077055],\n",
+       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
+       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
+       "   'dehb': [44.49999998779297, 1.3177421762189991]}},\n",
+       " 38: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.38399999999999, 0.1941751786403173],\n",
+       "   'ls': [91.28199999999998, 0.23029546239559182],\n",
+       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
+       "   'npenas': [90.71, 0.30410524493997293],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.27519998706056, 0.18240877118567775],\n",
+       "   'dehb': [91.37239999072266, 0.1789073493405069]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23999994384765, 0.03098384313084738],\n",
+       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
+       "   'bananas': [69.98199995117189, 1.4119617528819854],\n",
+       "   'npenas': [72.86199997558593, 0.36512466901838325],\n",
+       "   'sh': [72.611, 0.36530672044187684],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.09100000000001, 0.7481236528809931]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.05333327941895, 0.28409699915032105],\n",
+       "   'ls': [46.949999954223635, 0.30849092020267993],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.65499999389648, 0.7799946632919993]}},\n",
+       " 98: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.41599999999998, 0.19698730923589636],\n",
+       "   'ls': [91.381, 0.15952742710894352],\n",
+       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
+       "   'npenas': [90.108, 0.723696068802366],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.27239998779297, 0.1608621736218432]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.01999993530274, 0.45571922036765133],\n",
+       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
+       "   'bananas': [70.8579999584961, 1.2019800200639044],\n",
+       "   'npenas': [71.27999995239259, 1.1918724970886854],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.26599999999999, 0.6790905683338548]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.8666665802002, 0.2992583250500575],\n",
+       "   'ls': [46.76666659444173, 0.4777260668624399],\n",
+       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [42.84999999084473, 2.6502410795007316]}},\n",
+       " 13: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.49499999999998, 0.11689739090329977],\n",
+       "   'ls': [91.22, 0.28544701785094784],\n",
+       "   'bananas': [91.298, 0.21599999999999786],\n",
+       "   'npenas': [90.755, 0.3000083332175982],\n",
+       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.3295999892578, 0.11911944091665896]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.18199995239257, 0.17215106194817362],\n",
+       "   'ls': [72.82799997680664, 0.7191772759739727],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [72.11399998291014, 0.8568337240411942],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.965, 0.770340833657415],\n",
+       "   'dehb': [71.819, 0.7527343488907612]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.039999933878576, 0.27153883209318436],\n",
+       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
+       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.276666659545896, 0.5789933833108567]}},\n",
+       " 34: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.37799999999999, 0.19093454375779909],\n",
+       "   'ls': [91.35199999999999, 0.19197916553626207],\n",
+       "   'bananas': [90.686, 0.34115099296352724],\n",
+       "   'npenas': [89.96100000000001, 0.6287360336421008],\n",
+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.27039998510743, 0.1907224177592602]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2419999267578, 0.022715618336443765],\n",
+       "   'ls': [73.24799994506836, 0.029933231363229132],\n",
+       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.41, 1.2734284432193266],\n",
+       "   'dehb': [70.934, 0.667760436084675]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.89333326619466, 0.6246421025405501],\n",
+       "   'ls': [47.01333326212565, 0.3938978746038199],\n",
+       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [45.02833333231608, 1.0689052008193356]}},\n",
+       " 99: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.32900000000001, 0.2216506259860291],\n",
+       "   'ls': [91.299, 0.2411825035113437],\n",
+       "   'bananas': [90.507, 0.48381918109971644],\n",
+       "   'npenas': [90.971, 0.1949076704493708],\n",
+       "   'sh': [91.34759998876953, 0.182694943973596],\n",
+       "   'hb': [91.003, 0.25060127693210343],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.20439998437499, 0.2561652619701485]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.08799998779298, 0.33801772298903365],\n",
+       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
+       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.12600001586914, 0.7874033289220081],\n",
+       "   'bohb': [71.851, 0.9484139391636973],\n",
+       "   'dehb': [71.61999999999999, 0.810234533946807]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.86666659037272, 0.26956751089620473],\n",
+       "   'ls': [47.02333325703939, 0.49081562974454335],\n",
+       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'npenas': [42.203333310953774, 2.9346379440313997],\n",
+       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.749999985758464, 0.8784139794635262]}},\n",
+       " 91: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.327, 0.14360013927569598],\n",
+       "   'ls': [91.221, 0.28532262440962985],\n",
+       "   'bananas': [91.18, 0.1490637447537122],\n",
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+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.22999999829102, 0.1882402676274257]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.189999954834, 0.14649228503644002],\n",
+       "   'ls': [72.95599997314453, 0.5759721892784991],\n",
+       "   'bananas': [70.1479999621582, 1.3200969521427772],\n",
+       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.965, 0.770340833657415],\n",
+       "   'dehb': [71.423, 0.7924651411891871]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.056666611735025, 0.41447420927951434],\n",
+       "   'ls': [46.8966666208903, 0.37459604712308325],\n",
+       "   'bananas': [45.196666635131834, 0.7421365378565281],\n",
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+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [45.04999998168945, 0.9312178370031363]}},\n",
+       " 32: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.28299999999999, 0.2599634589706778],\n",
+       "   'ls': [91.413, 0.19042321287069813],\n",
+       "   'bananas': [90.755, 0.3000083332175982],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.20479998608398, 0.28419598794023915]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.21999992675781, 0.0769415511601034],\n",
+       "   'ls': [72.87799997192383, 0.5940673165120702],\n",
+       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.529, 1.0231368432423888],\n",
+       "   'dehb': [71.96400000000001, 0.852187772735563]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.886666620890296, 0.444522179565979],\n",
+       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
+       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
+       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.846666666666664, 0.7154563534659095]}},\n",
+       " 26: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46399999999998, 0.10753604047015765],\n",
+       "   'ls': [91.38899999999998, 0.20349201458533675],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.3383999880371, 0.22156678124125423]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.25599993164062, 0.008000014648433761],\n",
+       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
+       "   'bananas': [71.8499999633789, 1.0354226419727222],\n",
+       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
+       "   'bohb': [70.846, 0.5039087218931632],\n",
+       "   'dehb': [71.715, 0.9484329180284714]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.17333325297037, 0.2563851463888792],\n",
+       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.73333332722982, 1.1692590257993514]}},\n",
+       " 39: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.28099999999999, 0.18880942773071246],\n",
+       "   'ls': [91.33500000000001, 0.19946177578673782],\n",
+       "   'bananas': [90.686, 0.34115099296352724],\n",
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+       "   'sh': [90.87, 0.20610676844781026],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.02639998168945, 0.29246647341578763]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.22799994140624, 0.08953209838167313],\n",
+       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
+       "   'bananas': [70.73399994506836, 1.2853341902554598],\n",
+       "   'npenas': [71.44399995178223, 1.085976077010389],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.643, 0.628920503720462]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.97999996439616, 0.3297136685737497],\n",
+       "   'ls': [46.829999938964846, 0.41297027213245097],\n",
+       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'npenas': [43.8699999572754, 1.6782563415101452],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.58333331197103, 0.7776388644728909],\n",
+       "   'bohb': [44.97166665140788, 0.9442530779799753],\n",
+       "   'dehb': [45.04833332417806, 0.690830828018462]}},\n",
+       " 33: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.41099999999999, 0.19180458805774037],\n",
+       "   'ls': [91.27, 0.24943937139112365],\n",
+       "   'bananas': [89.363, 1.132616881385759],\n",
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+       "   'hb': [91.01200000000001, 0.2691765220073962],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.36839999121094, 0.16965329153687703]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.9399999621582, 0.5026330706498948],\n",
+       "   'ls': [73.22999994506836, 0.08999998168945353],\n",
+       "   'bananas': [73.11599994506835, 0.16292326831188456],\n",
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+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
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+       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.10333327128093, 0.3291233944909905],\n",
+       "   'ls': [46.82333330790202, 0.4494564285556264],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [46.473333266194665, 0.4464177873787461],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
+       "   'bohb': [44.17999997151693, 0.9853876854992182],\n",
+       "   'dehb': [44.42999998270671, 0.8524735311257914]}},\n",
+       " 69: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.39699999999999, 0.16401524319403626],\n",
+       "   'ls': [91.261, 0.24772767306055807],\n",
+       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
+       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23799994750976, 0.03515675951021431],\n",
+       "   'ls': [72.8579999584961, 0.6428343626872822],\n",
+       "   'bananas': [72.03199995239258, 0.8140368606327091],\n",
+       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [69.85, 0.6900724599634448],\n",
+       "   'dehb': [71.59700000000001, 1.0034844293759613]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.046666596476236, 0.21354155497329988],\n",
+       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
+       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
+       "   'bohb': [44.19166666056315, 0.9899284469486298],\n",
+       "   'dehb': [43.97666664530436, 1.2357229116540107]}},\n",
+       " 21: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.303, 0.28032302795167985],\n",
+       "   'ls': [91.37299999999999, 0.20288173895153644],\n",
+       "   'bananas': [90.961, 0.19582900704441078],\n",
+       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
+       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [90.517199986084, 0.6501496427132121],\n",
+       "   'dehb': [91.39199999462889, 0.1714036177612395]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.1499999597168, 0.22684795826058213],\n",
+       "   'ls': [73.15799994262694, 0.2742918336816008],\n",
+       "   'bananas': [71.39799995788574, 1.1144308062423007],\n",
+       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [70.58999999999999, 0.4154515615568193]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.086666599528, 0.35030145259925016],\n",
+       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
+       "   'bananas': [46.73333325093587, 0.5131601637679889],\n",
+       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.58499998779297, 1.0919770695844846]}},\n",
+       " 73: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.52499999999998, 0.09135097153287174],\n",
+       "   'ls': [91.361, 0.20954474462510225],\n",
+       "   'bananas': [90.686, 0.34115099296352724],\n",
+       "   'npenas': [88.98700000000001, 1.2792267195458367],\n",
+       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
+       "   'hb': [91.02000000000001, 0.237191905426807],\n",
+       "   'bohb': [91.01399998828126, 0.3617656750717998],\n",
+       "   'dehb': [91.18679998999023, 0.16384432169996163]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.05599994506835, 0.3100709899342914],\n",
+       "   'ls': [73.05399995361327, 0.364971212451024],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.721, 0.610564492907999]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.84999993998209, 0.47404875829399257],\n",
+       "   'ls': [47.01666661783854, 0.24596745339994966],\n",
+       "   'bananas': [47.01333327636719, 0.5304505139615243],\n",
+       "   'npenas': [44.3433332804362, 1.3207783721457254],\n",
+       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
+       "   'dehb': [44.616666655476884, 0.7671737510966764]}},\n",
+       " 9: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.408, 0.15810123339176987],\n",
+       "   'ls': [91.35300000000001, 0.1945276329984993],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
+       "   'hb': [91.20599999999999, 0.18440173534975068],\n",
+       "   'bohb': [91.11799999047852, 0.28588249281863337],\n",
+       "   'dehb': [91.37599998999022, 0.1469584951969324]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23399994018554, 0.033526075221060235],\n",
+       "   'ls': [73.03399996582031, 0.38544001214065354],\n",
+       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'npenas': [72.797999954834, 0.4221327246514823],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.184, 0.7111568226257754],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [72.731, 0.41795813187447345]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.156666601562506, 0.21962086459282246],\n",
+       "   'ls': [46.82999995015462, 0.45933525204638725],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
+       "   'dehb': [44.49166665649414, 1.1667202360661062]}},\n",
+       " 35: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.368, 0.219535874061618],\n",
+       "   'ls': [91.27, 0.24943937139112365],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
+       "   'dehb': [91.19279998339843, 0.2719282237005244]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2179999194336, 0.08072176678451039],\n",
+       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
+       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
+       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [72.221, 0.795266621957693]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.01999995117187, 0.3063041060614205],\n",
+       "   'ls': [46.833333278401696, 0.4133870958208716],\n",
+       "   'bananas': [46.786666619873046, 0.3019197574365747],\n",
+       "   'npenas': [45.72666663309733, 0.4127953260079985],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
+       "   'dehb': [43.83833331197103, 1.1768142151694443]}},\n",
+       " 20: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.23400000000001, 0.21827505583552106],\n",
+       "   'ls': [91.207, 0.2838326972003031],\n",
+       "   'bananas': [90.291, 0.6996063178674113],\n",
+       "   'npenas': [89.363, 1.132616881385759],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.053, 0.23639162421710094],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.37559999267577, 0.19945886851307323]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.13799993408203, 0.31463627372903924],\n",
+       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
+       "   'bananas': [72.21999996948243, 0.8076633099662458],\n",
+       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
+       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "   'dehb': [71.63199999999999, 0.7419811318355743]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.98666662394206, 0.2769676761427817],\n",
+       "   'ls': [46.8966666208903, 0.37459604712308325],\n",
+       "   'bananas': [46.09999997660319, 0.35276683099805695],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [44.24666666768392, 1.0534546359944508]}},\n",
+       " 83: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46099999999998, 0.1381629472760308],\n",
+       "   'ls': [91.207, 0.2838326972003031],\n",
+       "   'bananas': [89.789, 0.6473553892569365],\n",
+       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
+       "   'sh': [91.37039998999023, 0.14425755204992768],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.13359998901367, 0.28254387779751905],\n",
+       "   'dehb': [91.24079998950195, 0.24974739756213982]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.23199993652342, 0.047497363824885924],\n",
+       "   'ls': [73.21999994750976, 0.08988879903195432],\n",
+       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
+       "   'npenas': [69.58599997680665, 1.7337139051941306],\n",
+       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "   'dehb': [71.64, 0.6272957835024887]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.1899999399821, 0.14685590322162423],\n",
+       "   'ls': [46.77333328552246, 0.5318311622421547],\n",
+       "   'bananas': [46.55999992777507, 0.3614169606260508],\n",
+       "   'npenas': [46.55999997253418, 0.39603587690841624],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.35666666870118, 0.9129987122664861],\n",
+       "   'dehb': [44.84333331095378, 0.9943507088142549]}},\n",
+       " 19: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.46199999999999, 0.15866946776238697],\n",
+       "   'ls': [91.361, 0.20954474462510225],\n",
+       "   'bananas': [90.108, 0.723696068802366],\n",
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+       "   'sh': [91.075, 0.1410850807137311],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.17279999145508, 0.24490193947973699]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.2299999572754, 0.04219001178866624],\n",
+       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
+       "   'bananas': [68.56599994750977, 2.193318024787204],\n",
+       "   'npenas': [73.14399997924804, 0.17106721988945178],\n",
+       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.85999999999999, 1.032085267795254]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.86666662699381, 0.33763885911754793],\n",
+       "   'ls': [46.92666657613118, 0.3937850572225648],\n",
+       "   'bananas': [46.803333261108406, 0.3177874672089124],\n",
+       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
+       "   'bohb': [44.06499997965494, 1.4572129057732124],\n",
+       "   'dehb': [45.049999993896485, 0.9598900372080954]}},\n",
+       " 63: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.42999999999998, 0.13319159132617717],\n",
+       "   'ls': [91.39099999999999, 0.185765981815828],\n",
+       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
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+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [91.053, 0.23639162421710094],\n",
+       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
+       "   'dehb': [91.15119998217773, 0.18645149713003412]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.24199994750975, 0.03155944392693056],\n",
+       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
+       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
+       "   'npenas': [70.70399995361329, 1.2985468673780534],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [72.184, 0.7111568226257754],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.64199999999998, 0.6843946230063468]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.916666617838544, 0.41452779674086915],\n",
+       "   'ls': [46.72666658732096, 0.5681940138884105],\n",
+       "   'bananas': [45.06999995422363, 0.9393200879432245],\n",
+       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
+       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
+       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
+       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
+       "   'dehb': [45.03666665649415, 0.7074523974708331]}},\n",
+       " 76: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.337, 0.16112417571550106],\n",
+       "   'ls': [91.32399999999998, 0.20543612145871257],\n",
+       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
+       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.816, 0.27492544443903416],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.20079998754883, 0.23347668404484928]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [72.96999993041993, 0.36334559917312764],\n",
+       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
+       "   'bananas': [72.93399995727539, 0.6165095197278333],\n",
+       "   'npenas': [72.74599995483398, 0.35066225564520687],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [71.146, 0.8489782093787815],\n",
+       "   'dehb': [71.74100000000001, 0.8927984094967917]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.04666658528646, 0.2844878423618782],\n",
+       "   'ls': [47.00666657613119, 0.2640706906448812],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [42.203333310953774, 2.9346379440313997],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
+       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "   'dehb': [45.16833332214355, 0.8223087492835989]}},\n",
+       " 60: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.393, 0.18477283350102805],\n",
+       "   'ls': [91.34599999999999, 0.2226746505554651],\n",
+       "   'bananas': [90.507, 0.48381918109971644],\n",
+       "   'npenas': [91.217, 0.1533655763201092],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.891, 0.23144977856977764],\n",
+       "   'bohb': [91.32959998901367, 0.20578201776622312],\n",
+       "   'dehb': [91.20119998803712, 0.20173686188876364]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.24999994140624, 0.024083172937765445],\n",
+       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
+       "   'bananas': [72.40999996704102, 0.5606603503391208],\n",
+       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
+       "   'sh': [71.942, 0.889649369133707],\n",
+       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "   'bohb': [72.071, 0.7846712687488944],\n",
+       "   'dehb': [70.91600000000001, 0.43241646592145294]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [47.09333326009114, 0.2988124416726923],\n",
+       "   'ls': [46.833333278401696, 0.4133870958208716],\n",
+       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
+       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
+       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
+       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
+       "   'bohb': [44.35666666870118, 0.9129987122664861],\n",
+       "   'dehb': [44.606666661580405, 0.638540014553371]}},\n",
+       " 6: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.401, 0.2603247971285076],\n",
+       "   'ls': [91.26699999999998, 0.2572955499032189],\n",
+       "   'bananas': [90.65, 0.3594440151122292],\n",
+       "   'npenas': [90.755, 0.3000083332175982],\n",
+       "   'sh': [91.022, 0.23625410049351406],\n",
+       "   'hb': [91.003, 0.25060127693210343],\n",
+       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "   'dehb': [91.35559998828124, 0.12936089274082296]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.17799995605468, 0.1693398539988335],\n",
+       "   'ls': [73.07999996826172, 0.36452704830412275],\n",
+       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
+       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
+       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
+       "   'dehb': [71.542, 0.8452431602799263]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.74999998372395, 0.37006004547351595],\n",
+       "   'ls': [46.82333330790202, 0.4494564285556264],\n",
+       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
+       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
+       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
+       "   'dehb': [44.11333332417806, 1.4124210983303769]}},\n",
+       " 11: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
+       "   're': [91.523, 0.0946625585963078],\n",
+       "   'ls': [91.32999999999998, 0.18477012745571122],\n",
+       "   'bananas': [90.686, 0.34115099296352724],\n",
+       "   'npenas': [89.363, 1.132616881385759],\n",
+       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "   'dehb': [91.21919998999024, 0.25761242538832047]},\n",
+       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
+       "   're': [73.12799992065429, 0.2733788756798343],\n",
+       "   'ls': [73.11999994873047, 0.28227644224013915],\n",
+       "   'bananas': [70.70399995361329, 1.2985468673780534],\n",
+       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
+       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
+       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
+       "   'dehb': [71.282, 0.6934954938570262]},\n",
+       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
+       "   're': [46.8299999613444, 0.3423610956362203],\n",
+       "   'ls': [46.90999992472331, 0.4568125874112534],\n",
+       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
+       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
+       "   'sh': [44.55666663004557, 1.101216496499461],\n",
+       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
+       "   'dehb': [44.62499997965495, 1.2411743974236171]}}}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results[4]"
+    "results"
    ]
   },
   {

From 45d5276be2c9b81d8769f0cf9f0d5b0530165cda Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Wed, 11 May 2022 14:33:45 +0200
Subject: [PATCH 108/130] changes overt time

---
 examples/multifidelity             |     6 +
 examples/optimizers_HPO.ipynb      | 19665 +++------------------------
 naslib/analysis/analyze_results.py |    95 +
 3 files changed, 1980 insertions(+), 17786 deletions(-)
 create mode 100644 examples/multifidelity
 create mode 100644 naslib/analysis/analyze_results.py

diff --git a/examples/multifidelity b/examples/multifidelity
new file mode 100644
index 000000000..a62e8d54b
--- /dev/null
+++ b/examples/multifidelity
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
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"/>
+    </g>
+</svg>
\ No newline at end of file
diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index c278f6489..3f477c39a 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,9 +9,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -20,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,14 +68,14 @@
     "# ~/results_nas_bench_suite/bbo_results_oct1/bbo_results_oct1_0/nasbench201/cifar100/npenas/config_28/7\n",
     "\n",
     "# and the result dict structure:\n",
-    "# results_dict[28][nb201_c10][npenas] = [np.mean(values), np.std(values)]\n",
+    "# results_dict[28][nb201_c10_c10][npenas] = [np.mean(values), np.std(values)]\n",
     "\n",
     "def get_hpo_results(optimizers, search_spaces, root):\n",
     "    errors = 0\n",
     "    results_dict = {}\n",
     "    root = os.path.expanduser(root)\n",
     "    for search_space in search_spaces:\n",
-    "            print(search_space)\n",
+    "            #print(search_space)\n",
     "            for optimizer in optimizers:\n",
     "                #print(optimizer)\n",
     "                optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
@@ -74,14 +83,15 @@
     "                if not os.path.exists(optimizer_path):\n",
     "                    continue\n",
     "                for hpo_folder in os.listdir(optimizer_path):  #change seed and hpo_folder\n",
-    "                    #print(hpo_folder)\n",
+    "                    print(hpo_folder)\n",
     "                    hpo_seed = int(hpo_folder.split('_')[-1])\n",
     "                    values = []\n",
     "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
     "                        result_path =   optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
+    "                        print(result_path)\n",
     "                        if not os.path.isfile(result_path):\n",
+    "                            print(\"what?\")\n",
     "                            continue\n",
-    "                        print(result_path)\n",
     "                        result = json.load(open(result_path))\n",
     "                        valid_accs = result[1]['valid_acc']\n",
     "                        value = np.max(valid_accs)\n",
@@ -110,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,15 +148,15 @@
     "defaults = [np.array(d) for d in defaults]\n",
     "# TODO: adapt this for multi-fidelity\n",
     "color_dict={\n",
-    "    'rs':{'color':defaults[0], 'fmt':fmts[0]},\n",
-    "    're':{'color':defaults[1], 'fmt':fmts[0]},\n",
-    "    'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
-    "    'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
-    "    'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
-    "    'sh': {'color': defaults[5], 'fmt': fmts[0]},\n",
-    "    'hb': {'color': defaults[6], 'fmt': fmts[0]},\n",
+    "    #'rs':{'color':defaults[0], 'fmt':fmts[0]},\n",
+    "    #'re':{'color':defaults[1], 'fmt':fmts[0]},\n",
+    "    #'ls':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    #'bananas':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    #'npenas':{'color':defaults[4], 'fmt':fmts[0]},\n",
+    "    'sh': {'color': defaults[1], 'fmt': fmts[0]},\n",
+    "    'hb': {'color': defaults[2], 'fmt': fmts[0]},\n",
     "    'bohb': {'color': defaults[3], 'fmt': fmts[0]},\n",
-    "    'dehb': {'color': defaults[8], 'fmt': fmts[0]},\n",
+    "    'dehb': {'color': defaults[4], 'fmt': fmts[0]},\n",
     "\n",
     "}\n",
     "\n",
@@ -163,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 42,
    "metadata": {
     "scrolled": true
    },
@@ -314,5007 +324,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_60/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_6/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/npenas/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/5/errors.json\n",
@@ -5325,6 +335,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/5/errors.json\n",
@@ -5335,6 +346,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/5/errors.json\n",
@@ -5345,6 +357,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/5/errors.json\n",
@@ -5355,6 +368,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/5/errors.json\n",
@@ -5365,6 +379,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/5/errors.json\n",
@@ -5375,6 +390,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/5/errors.json\n",
@@ -5385,6 +401,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/5/errors.json\n",
@@ -5395,6 +412,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/5/errors.json\n",
@@ -5405,6 +423,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/5/errors.json\n",
@@ -5415,6 +434,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/5/errors.json\n",
@@ -5425,6 +445,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/5/errors.json\n",
@@ -5435,6 +456,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/5/errors.json\n",
@@ -5445,6 +467,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/5/errors.json\n",
@@ -5455,6 +478,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/5/errors.json\n",
@@ -5465,6 +489,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/5/errors.json\n",
@@ -5475,6 +500,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/5/errors.json\n",
@@ -5485,6 +511,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/5/errors.json\n",
@@ -5495,6 +522,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/5/errors.json\n",
@@ -5505,6 +533,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/5/errors.json\n",
@@ -5515,6 +544,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/5/errors.json\n",
@@ -5525,6 +555,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/5/errors.json\n",
@@ -5535,6 +566,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/5/errors.json\n",
@@ -5545,6 +577,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/5/errors.json\n",
@@ -5555,6 +588,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/5/errors.json\n",
@@ -5565,6 +599,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/5/errors.json\n",
@@ -5575,6 +610,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/5/errors.json\n",
@@ -5585,6 +621,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/5/errors.json\n",
@@ -5595,6 +632,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/5/errors.json\n",
@@ -5605,6 +643,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/5/errors.json\n",
@@ -5615,6 +654,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/5/errors.json\n",
@@ -5625,6 +665,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/5/errors.json\n",
@@ -5635,6 +676,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/5/errors.json\n",
@@ -5645,6 +687,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/5/errors.json\n",
@@ -5655,6 +698,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/5/errors.json\n",
@@ -5665,6 +709,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/5/errors.json\n",
@@ -5675,6 +720,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/5/errors.json\n",
@@ -5685,6 +731,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/5/errors.json\n",
@@ -5695,6 +742,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/5/errors.json\n",
@@ -5705,6 +753,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/5/errors.json\n",
@@ -5715,6 +764,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/5/errors.json\n",
@@ -5725,6 +775,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/5/errors.json\n",
@@ -5735,6 +786,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/5/errors.json\n",
@@ -5745,6 +797,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/5/errors.json\n",
@@ -5755,6 +808,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/5/errors.json\n",
@@ -5765,6 +819,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/5/errors.json\n",
@@ -5775,6 +830,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/5/errors.json\n",
@@ -5785,6 +841,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/5/errors.json\n",
@@ -5795,6 +852,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/5/errors.json\n",
@@ -5805,6 +863,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/5/errors.json\n",
@@ -5815,6 +874,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/5/errors.json\n",
@@ -5825,6 +885,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/5/errors.json\n",
@@ -5835,6 +896,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/5/errors.json\n",
@@ -5845,6 +907,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/5/errors.json\n",
@@ -5855,6 +918,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/5/errors.json\n",
@@ -5865,6 +929,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/5/errors.json\n",
@@ -5875,6 +940,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/5/errors.json\n",
@@ -5885,6 +951,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/5/errors.json\n",
@@ -5895,6 +962,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/5/errors.json\n",
@@ -5905,6 +973,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/5/errors.json\n",
@@ -5915,6 +984,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/5/errors.json\n",
@@ -5925,6 +995,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/5/errors.json\n",
@@ -5935,6 +1006,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/5/errors.json\n",
@@ -5945,6 +1017,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/5/errors.json\n",
@@ -5955,6 +1028,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/5/errors.json\n",
@@ -5965,6 +1039,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/5/errors.json\n",
@@ -5975,6 +1050,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/5/errors.json\n",
@@ -5985,6 +1061,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/5/errors.json\n",
@@ -5995,6 +1072,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/5/errors.json\n",
@@ -6005,6 +1083,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/5/errors.json\n",
@@ -6015,6 +1094,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/5/errors.json\n",
@@ -6025,6 +1105,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/5/errors.json\n",
@@ -6035,6 +1116,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/5/errors.json\n",
@@ -6045,6 +1127,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/5/errors.json\n",
@@ -6055,6 +1138,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/5/errors.json\n",
@@ -6065,6 +1149,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/5/errors.json\n",
@@ -6075,6 +1160,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/5/errors.json\n",
@@ -6085,6 +1171,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/5/errors.json\n",
@@ -6095,6 +1182,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/5/errors.json\n",
@@ -6105,6 +1193,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/5/errors.json\n",
@@ -6115,6 +1204,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/5/errors.json\n",
@@ -6125,6 +1215,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/5/errors.json\n",
@@ -6135,6 +1226,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/5/errors.json\n",
@@ -6145,6 +1237,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/5/errors.json\n",
@@ -6155,6 +1248,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/5/errors.json\n",
@@ -6165,6 +1259,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/5/errors.json\n",
@@ -6175,6 +1270,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/5/errors.json\n",
@@ -6185,6 +1281,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/5/errors.json\n",
@@ -6195,6 +1292,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/5/errors.json\n",
@@ -6205,6 +1303,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/5/errors.json\n",
@@ -6215,6 +1314,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/5/errors.json\n",
@@ -6225,6 +1325,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/5/errors.json\n",
@@ -6235,6 +1336,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/5/errors.json\n",
@@ -6245,6 +1347,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/5/errors.json\n",
@@ -6255,6 +1358,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/5/errors.json\n",
@@ -6265,6 +1369,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/5/errors.json\n",
@@ -6275,6 +1380,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/5/errors.json\n",
@@ -6285,6 +1391,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/5/errors.json\n",
@@ -6295,6 +1402,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/5/errors.json\n",
@@ -6305,6 +1413,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/5/errors.json\n",
@@ -6315,6 +1424,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/5/errors.json\n",
@@ -6325,6 +1435,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/5/errors.json\n",
@@ -6335,6 +1446,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/5/errors.json\n",
@@ -6345,6 +1457,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/5/errors.json\n",
@@ -6355,6 +1468,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/5/errors.json\n",
@@ -6365,6 +1479,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/5/errors.json\n",
@@ -6375,6 +1490,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/5/errors.json\n",
@@ -6385,6 +1501,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/5/errors.json\n",
@@ -6395,6 +1512,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/5/errors.json\n",
@@ -6405,6 +1523,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/5/errors.json\n",
@@ -6415,6 +1534,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/5/errors.json\n",
@@ -6425,6 +1545,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/5/errors.json\n",
@@ -6435,6 +1556,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/5/errors.json\n",
@@ -6445,6 +1567,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/5/errors.json\n",
@@ -6455,6 +1578,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/5/errors.json\n",
@@ -6465,6 +1589,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/5/errors.json\n",
@@ -6475,6 +1600,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/5/errors.json\n",
@@ -6485,6 +1611,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/5/errors.json\n",
@@ -6495,6 +1622,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/5/errors.json\n",
@@ -6505,6 +1633,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/5/errors.json\n",
@@ -6515,6 +1644,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/5/errors.json\n",
@@ -6525,6 +1655,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/5/errors.json\n",
@@ -6535,6 +1666,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/5/errors.json\n",
@@ -6545,6 +1677,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/5/errors.json\n",
@@ -6555,6 +1688,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/5/errors.json\n",
@@ -6565,6 +1699,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/5/errors.json\n",
@@ -6575,6 +1710,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/5/errors.json\n",
@@ -6585,6 +1721,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/5/errors.json\n",
@@ -6595,6 +1732,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/5/errors.json\n",
@@ -6605,6 +1743,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/5/errors.json\n",
@@ -6615,6 +1754,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/5/errors.json\n",
@@ -6625,6 +1765,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/5/errors.json\n",
@@ -6635,6 +1776,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/5/errors.json\n",
@@ -6645,6 +1787,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/5/errors.json\n",
@@ -6655,6 +1798,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/5/errors.json\n",
@@ -6665,6 +1809,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/5/errors.json\n",
@@ -6675,6 +1820,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/5/errors.json\n",
@@ -6685,6 +1831,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/5/errors.json\n",
@@ -6695,6 +1842,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/5/errors.json\n",
@@ -6705,6 +1853,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/5/errors.json\n",
@@ -6715,6 +1864,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/5/errors.json\n",
@@ -6725,6 +1875,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/5/errors.json\n",
@@ -6735,6 +1886,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/5/errors.json\n",
@@ -6745,6 +1897,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/5/errors.json\n",
@@ -6755,6 +1908,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/5/errors.json\n",
@@ -6765,6 +1919,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/5/errors.json\n",
@@ -6775,6 +1930,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/5/errors.json\n",
@@ -6785,6 +1941,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/5/errors.json\n",
@@ -6795,6 +1952,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/5/errors.json\n",
@@ -6805,6 +1963,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/5/errors.json\n",
@@ -6815,6 +1974,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/5/errors.json\n",
@@ -6825,6 +1985,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/5/errors.json\n",
@@ -6835,6 +1996,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/5/errors.json\n",
@@ -6845,6 +2007,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/5/errors.json\n",
@@ -6855,6 +2018,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/5/errors.json\n",
@@ -6865,6 +2029,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/5/errors.json\n",
@@ -6875,6 +2040,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/5/errors.json\n",
@@ -6885,6 +2051,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/5/errors.json\n",
@@ -6895,6 +2062,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/5/errors.json\n",
@@ -6905,6 +2073,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/5/errors.json\n",
@@ -6915,6 +2084,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/5/errors.json\n",
@@ -6925,6 +2095,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/5/errors.json\n",
@@ -6935,6 +2106,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/5/errors.json\n",
@@ -6945,6 +2117,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/5/errors.json\n",
@@ -6955,6 +2128,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/5/errors.json\n",
@@ -6965,6 +2139,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/5/errors.json\n",
@@ -6975,6 +2150,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/5/errors.json\n",
@@ -6985,6 +2161,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/5/errors.json\n",
@@ -6995,6 +2172,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/5/errors.json\n",
@@ -7005,6 +2183,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/5/errors.json\n",
@@ -7015,6 +2194,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/5/errors.json\n",
@@ -7025,6 +2205,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/5/errors.json\n",
@@ -7035,6 +2216,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/5/errors.json\n",
@@ -7045,6 +2227,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/5/errors.json\n",
@@ -7055,6 +2238,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/5/errors.json\n",
@@ -7065,6 +2249,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/5/errors.json\n",
@@ -7075,6 +2260,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/5/errors.json\n",
@@ -7085,6 +2271,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/5/errors.json\n",
@@ -7095,6 +2282,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/5/errors.json\n",
@@ -7105,6 +2293,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/5/errors.json\n",
@@ -7115,6 +2304,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/5/errors.json\n",
@@ -7125,6 +2315,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/5/errors.json\n",
@@ -7135,6 +2326,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/5/errors.json\n",
@@ -7145,6 +2337,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/5/errors.json\n",
@@ -7155,6 +2348,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/5/errors.json\n",
@@ -7165,6 +2359,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/5/errors.json\n",
@@ -7175,6 +2370,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/5/errors.json\n",
@@ -7185,6 +2381,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/5/errors.json\n",
@@ -7195,6 +2392,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/5/errors.json\n",
@@ -7205,6 +2403,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/5/errors.json\n",
@@ -7215,6 +2414,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/5/errors.json\n",
@@ -7225,6 +2425,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/5/errors.json\n",
@@ -7235,6 +2436,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/5/errors.json\n",
@@ -7245,6 +2447,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/5/errors.json\n",
@@ -7255,6 +2458,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/5/errors.json\n",
@@ -7265,6 +2469,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/5/errors.json\n",
@@ -7275,6 +2480,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/5/errors.json\n",
@@ -7285,6 +2491,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/5/errors.json\n",
@@ -7295,6 +2502,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/5/errors.json\n",
@@ -7305,6 +2513,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/5/errors.json\n",
@@ -7315,6 +2524,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/5/errors.json\n",
@@ -7325,6 +2535,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/5/errors.json\n",
@@ -7335,6 +2546,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/5/errors.json\n",
@@ -7345,6 +2557,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/5/errors.json\n",
@@ -7355,6 +2568,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/5/errors.json\n",
@@ -7365,6 +2579,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/5/errors.json\n",
@@ -7375,6 +2590,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/5/errors.json\n",
@@ -7385,6 +2601,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/5/errors.json\n",
@@ -7395,6 +2612,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/5/errors.json\n",
@@ -7405,6 +2623,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/5/errors.json\n",
@@ -7415,6 +2634,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/5/errors.json\n",
@@ -7425,6 +2645,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/5/errors.json\n",
@@ -7435,6 +2656,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/5/errors.json\n",
@@ -7445,6 +2667,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/5/errors.json\n",
@@ -7455,6 +2678,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/5/errors.json\n",
@@ -7465,6 +2689,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/5/errors.json\n",
@@ -7475,6 +2700,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/5/errors.json\n",
@@ -7485,6 +2711,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/5/errors.json\n",
@@ -7495,6 +2722,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/5/errors.json\n",
@@ -7505,6 +2733,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/5/errors.json\n",
@@ -7515,6 +2744,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/5/errors.json\n",
@@ -7525,6 +2755,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/5/errors.json\n",
@@ -7535,6 +2766,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/5/errors.json\n",
@@ -7545,6 +2777,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/5/errors.json\n",
@@ -7555,6 +2788,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/5/errors.json\n",
@@ -7565,6 +2799,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/5/errors.json\n",
@@ -7575,6 +2810,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/5/errors.json\n",
@@ -7585,6 +2821,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/5/errors.json\n",
@@ -7595,6 +2832,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/5/errors.json\n",
@@ -7605,6 +2843,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/5/errors.json\n",
@@ -7615,6 +2854,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/5/errors.json\n",
@@ -7625,6 +2865,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/5/errors.json\n",
@@ -7635,6 +2876,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/5/errors.json\n",
@@ -7645,6 +2887,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/5/errors.json\n",
@@ -7655,6 +2898,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/5/errors.json\n",
@@ -7665,6 +2909,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/5/errors.json\n",
@@ -7675,6 +2920,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/5/errors.json\n",
@@ -7685,6 +2931,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/5/errors.json\n",
@@ -7695,6 +2942,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/5/errors.json\n",
@@ -7705,6 +2953,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/5/errors.json\n",
@@ -7715,6 +2964,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/5/errors.json\n",
@@ -7725,6 +2975,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/5/errors.json\n",
@@ -7735,6 +2986,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/5/errors.json\n",
@@ -7745,6 +2997,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/5/errors.json\n",
@@ -7755,6 +3008,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/5/errors.json\n",
@@ -7765,6 +3019,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/5/errors.json\n",
@@ -7775,6 +3030,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/5/errors.json\n",
@@ -7785,6 +3041,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/5/errors.json\n",
@@ -7795,6 +3052,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/5/errors.json\n",
@@ -7805,6 +3063,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/5/errors.json\n",
@@ -7815,6 +3074,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/5/errors.json\n",
@@ -7825,6 +3085,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/5/errors.json\n",
@@ -7835,6 +3096,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/5/errors.json\n",
@@ -7845,6 +3107,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/5/errors.json\n",
@@ -7855,6 +3118,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/5/errors.json\n",
@@ -7865,6 +3129,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/5/errors.json\n",
@@ -7875,6 +3140,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/5/errors.json\n",
@@ -7885,6 +3151,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/5/errors.json\n",
@@ -7895,6 +3162,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/5/errors.json\n",
@@ -7905,6 +3173,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/5/errors.json\n",
@@ -7915,6 +3184,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/5/errors.json\n",
@@ -7925,6 +3195,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/5/errors.json\n",
@@ -7935,6 +3206,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/5/errors.json\n",
@@ -7945,6 +3217,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/5/errors.json\n",
@@ -7955,6 +3228,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/5/errors.json\n",
@@ -7965,6 +3239,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/5/errors.json\n",
@@ -7975,6 +3250,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/5/errors.json\n",
@@ -7985,6 +3261,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/5/errors.json\n",
@@ -7995,6 +3272,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/5/errors.json\n",
@@ -8005,6 +3283,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/5/errors.json\n",
@@ -8015,6 +3294,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/5/errors.json\n",
@@ -8025,6 +3305,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/5/errors.json\n",
@@ -8035,6 +3316,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/5/errors.json\n",
@@ -8045,6 +3327,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/5/errors.json\n",
@@ -8055,6 +3338,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/5/errors.json\n",
@@ -8065,6 +3349,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/5/errors.json\n",
@@ -8075,6 +3360,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/5/errors.json\n",
@@ -8085,6 +3371,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/5/errors.json\n",
@@ -8095,6 +3382,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/5/errors.json\n",
@@ -8105,6 +3393,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/5/errors.json\n",
@@ -8115,6 +3404,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/5/errors.json\n",
@@ -8125,6 +3415,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/5/errors.json\n",
@@ -8135,6 +3426,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/5/errors.json\n",
@@ -8145,6 +3437,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/5/errors.json\n",
@@ -8155,6 +3448,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/5/errors.json\n",
@@ -8165,6 +3459,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/5/errors.json\n",
@@ -8175,6 +3470,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/5/errors.json\n",
@@ -8185,6 +3481,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/5/errors.json\n",
@@ -8195,6 +3492,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/5/errors.json\n",
@@ -8205,6 +3503,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/5/errors.json\n",
@@ -8215,6 +3514,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/5/errors.json\n",
@@ -8225,6 +3525,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/5/errors.json\n",
@@ -8235,6 +3536,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/5/errors.json\n",
@@ -8245,6 +3547,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/5/errors.json\n",
@@ -8255,6 +3558,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/5/errors.json\n",
@@ -8265,6 +3569,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/5/errors.json\n",
@@ -8275,6 +3580,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/5/errors.json\n",
@@ -8285,6 +3591,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/5/errors.json\n",
@@ -8295,6 +3602,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/5/errors.json\n",
@@ -8305,6 +3613,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/5/errors.json\n",
@@ -8315,6 +3624,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/5/errors.json\n",
@@ -8325,6 +3635,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/5/errors.json\n",
@@ -8335,6 +3646,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/5/errors.json\n",
@@ -8345,6 +3657,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/5/errors.json\n",
@@ -8355,6 +3668,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/5/errors.json\n",
@@ -8365,6 +3679,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/5/errors.json\n",
@@ -8375,6 +3690,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/5/errors.json\n",
@@ -8385,6 +3701,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/5/errors.json\n",
@@ -8395,6 +3712,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/5/errors.json\n",
@@ -8405,6 +3723,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/5/errors.json\n",
@@ -8415,6 +3734,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/5/errors.json\n",
@@ -8425,6 +3745,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/5/errors.json\n",
@@ -8435,6 +3756,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/5/errors.json\n",
@@ -8445,6 +3767,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/5/errors.json\n",
@@ -8455,6 +3778,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/5/errors.json\n",
@@ -8465,6 +3789,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/5/errors.json\n",
@@ -8475,6 +3800,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/5/errors.json\n",
@@ -8485,6 +3811,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/5/errors.json\n",
@@ -8495,6 +3822,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/5/errors.json\n",
@@ -8505,6 +3833,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/5/errors.json\n",
@@ -8515,6 +3844,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/5/errors.json\n",
@@ -8525,6 +3855,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/5/errors.json\n",
@@ -8535,6 +3866,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/5/errors.json\n",
@@ -8545,6 +3877,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/5/errors.json\n",
@@ -8555,6 +3888,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/5/errors.json\n",
@@ -8565,6 +3899,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/5/errors.json\n",
@@ -8575,6 +3910,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/5/errors.json\n",
@@ -8585,6 +3921,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/5/errors.json\n",
@@ -8595,6 +3932,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/5/errors.json\n",
@@ -8605,6 +3943,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/5/errors.json\n",
@@ -8615,6 +3954,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/5/errors.json\n",
@@ -8625,6 +3965,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/5/errors.json\n",
@@ -8635,6 +3976,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/5/errors.json\n",
@@ -8645,6 +3987,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/5/errors.json\n",
@@ -8655,6 +3998,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/5/errors.json\n",
@@ -8665,6 +4009,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/5/errors.json\n",
@@ -8675,6 +4020,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/5/errors.json\n",
@@ -8685,6 +4031,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/5/errors.json\n",
@@ -8695,6 +4042,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/5/errors.json\n",
@@ -8705,6 +4053,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/5/errors.json\n",
@@ -8715,6 +4064,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/5/errors.json\n",
@@ -8725,6 +4075,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/5/errors.json\n",
@@ -8735,6 +4086,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/5/errors.json\n",
@@ -8745,6 +4097,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/5/errors.json\n",
@@ -8755,6 +4108,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/5/errors.json\n",
@@ -8765,6 +4119,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/5/errors.json\n",
@@ -8775,6 +4130,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/5/errors.json\n",
@@ -8785,6 +4141,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/5/errors.json\n",
@@ -8794,6 +4151,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/5/errors.json\n",
@@ -8804,6 +4162,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/5/errors.json\n",
@@ -8814,6 +4173,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/5/errors.json\n",
@@ -8824,6 +4184,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/5/errors.json\n",
@@ -8834,6 +4195,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/5/errors.json\n",
@@ -8844,6 +4206,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/5/errors.json\n",
@@ -8854,6 +4217,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/5/errors.json\n",
@@ -8864,6 +4228,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/5/errors.json\n",
@@ -8874,6 +4239,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/5/errors.json\n",
@@ -8884,6 +4250,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/5/errors.json\n",
@@ -8894,6 +4261,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/5/errors.json\n",
@@ -8904,6 +4272,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/5/errors.json\n",
@@ -8914,6 +4283,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/5/errors.json\n",
@@ -8924,6 +4294,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/5/errors.json\n",
@@ -8934,6 +4305,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/5/errors.json\n",
@@ -8944,6 +4316,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/5/errors.json\n",
@@ -8954,6 +4327,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/5/errors.json\n",
@@ -8964,6 +4338,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/5/errors.json\n",
@@ -8974,6 +4349,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/5/errors.json\n",
@@ -8984,6 +4360,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/5/errors.json\n",
@@ -8994,6 +4371,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/5/errors.json\n",
@@ -9004,6 +4382,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/5/errors.json\n",
@@ -9014,6 +4393,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/5/errors.json\n",
@@ -9024,6 +4404,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/5/errors.json\n",
@@ -9034,6 +4415,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/5/errors.json\n",
@@ -9044,6 +4426,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/5/errors.json\n",
@@ -9054,6 +4437,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/5/errors.json\n",
@@ -9064,6 +4448,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/5/errors.json\n",
@@ -9074,6 +4459,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/5/errors.json\n",
@@ -9084,6 +4470,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/5/errors.json\n",
@@ -9094,6 +4481,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/5/errors.json\n",
@@ -9104,6 +4492,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/5/errors.json\n",
@@ -9114,6 +4503,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/5/errors.json\n",
@@ -9124,6 +4514,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/5/errors.json\n",
@@ -9134,6 +4525,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/5/errors.json\n",
@@ -9144,6 +4536,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/5/errors.json\n",
@@ -9154,6 +4547,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/5/errors.json\n",
@@ -9164,6 +4558,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/5/errors.json\n",
@@ -9174,6 +4569,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/5/errors.json\n",
@@ -9184,6 +4580,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/5/errors.json\n",
@@ -9194,6 +4591,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/5/errors.json\n",
@@ -9204,6 +4602,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/5/errors.json\n",
@@ -9214,6 +4613,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/5/errors.json\n",
@@ -9224,6 +4624,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/5/errors.json\n",
@@ -9234,6 +4635,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/5/errors.json\n",
@@ -9244,6 +4646,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/5/errors.json\n",
@@ -9254,6 +4657,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/5/errors.json\n",
@@ -9264,6 +4668,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/5/errors.json\n",
@@ -9274,6 +4679,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/5/errors.json\n",
@@ -9284,6 +4690,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/5/errors.json\n",
@@ -9294,6 +4701,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/5/errors.json\n",
@@ -9304,6 +4712,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/5/errors.json\n",
@@ -9314,5007 +4723,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/3/errors.json\n",
-      "nb201_c100\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_88/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_41/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_78/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_54/9/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_71/9/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_56/2/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_79/2/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/cifar100/rs/config_28/2/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_63/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_76/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_60/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_6/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/npenas/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/5/errors.json\n",
@@ -14325,6 +4734,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/5/errors.json\n",
@@ -14335,6 +4745,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/5/errors.json\n",
@@ -14345,6 +4756,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/5/errors.json\n",
@@ -14355,6 +4767,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/5/errors.json\n",
@@ -14365,6 +4778,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/5/errors.json\n",
@@ -14375,6 +4789,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/5/errors.json\n",
@@ -14385,6 +4800,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/5/errors.json\n",
@@ -14395,6 +4811,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/5/errors.json\n",
@@ -14405,6 +4822,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/5/errors.json\n",
@@ -14415,6 +4833,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/5/errors.json\n",
@@ -14425,6 +4844,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/5/errors.json\n",
@@ -14435,6 +4855,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/5/errors.json\n",
@@ -14445,6 +4866,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/5/errors.json\n",
@@ -14455,6 +4877,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/5/errors.json\n",
@@ -14465,6 +4888,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/5/errors.json\n",
@@ -14475,6 +4899,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/5/errors.json\n",
@@ -14485,6 +4910,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/5/errors.json\n",
@@ -14495,6 +4921,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/5/errors.json\n",
@@ -14505,6 +4932,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/5/errors.json\n",
@@ -14515,6 +4943,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/5/errors.json\n",
@@ -14525,6 +4954,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/5/errors.json\n",
@@ -14535,6 +4965,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/5/errors.json\n",
@@ -14545,6 +4976,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/5/errors.json\n",
@@ -14555,6 +4987,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/5/errors.json\n",
@@ -14565,6 +4998,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/5/errors.json\n",
@@ -14575,6 +5009,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/5/errors.json\n",
@@ -14585,6 +5020,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/5/errors.json\n",
@@ -14595,6 +5031,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/5/errors.json\n",
@@ -14605,6 +5042,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/5/errors.json\n",
@@ -14615,6 +5053,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/5/errors.json\n",
@@ -14625,6 +5064,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/5/errors.json\n",
@@ -14635,6 +5075,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/5/errors.json\n",
@@ -14645,6 +5086,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/5/errors.json\n",
@@ -14655,6 +5097,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/5/errors.json\n",
@@ -14665,6 +5108,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/5/errors.json\n",
@@ -14675,6 +5119,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/5/errors.json\n",
@@ -14685,6 +5130,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/5/errors.json\n",
@@ -14695,6 +5141,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/5/errors.json\n",
@@ -14705,6 +5152,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/5/errors.json\n",
@@ -14715,6 +5163,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/5/errors.json\n",
@@ -14725,6 +5174,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/5/errors.json\n",
@@ -14735,6 +5185,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/5/errors.json\n",
@@ -14745,6 +5196,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/5/errors.json\n",
@@ -14755,6 +5207,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/5/errors.json\n",
@@ -14765,6 +5218,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/5/errors.json\n",
@@ -14775,6 +5229,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/5/errors.json\n",
@@ -14785,6 +5240,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/5/errors.json\n",
@@ -14795,6 +5251,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/5/errors.json\n",
@@ -14805,6 +5262,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/5/errors.json\n",
@@ -14815,6 +5273,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/5/errors.json\n",
@@ -14825,6 +5284,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/5/errors.json\n",
@@ -14835,6 +5295,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/5/errors.json\n",
@@ -14845,6 +5306,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/5/errors.json\n",
@@ -14855,6 +5317,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/5/errors.json\n",
@@ -14865,6 +5328,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/5/errors.json\n",
@@ -14875,6 +5339,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/5/errors.json\n",
@@ -14885,6 +5350,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/5/errors.json\n",
@@ -14895,6 +5361,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/5/errors.json\n",
@@ -14905,6 +5372,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/5/errors.json\n",
@@ -14915,6 +5383,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/5/errors.json\n",
@@ -14925,6 +5394,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/5/errors.json\n",
@@ -14935,6 +5405,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/5/errors.json\n",
@@ -14945,6 +5416,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/5/errors.json\n",
@@ -14955,6 +5427,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/5/errors.json\n",
@@ -14965,6 +5438,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/5/errors.json\n",
@@ -14975,6 +5449,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/5/errors.json\n",
@@ -14985,6 +5460,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/5/errors.json\n",
@@ -14995,6 +5471,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/5/errors.json\n",
@@ -15005,6 +5482,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/5/errors.json\n",
@@ -15015,6 +5493,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/5/errors.json\n",
@@ -15025,6 +5504,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/5/errors.json\n",
@@ -15035,6 +5515,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/5/errors.json\n",
@@ -15045,6 +5526,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/5/errors.json\n",
@@ -15055,6 +5537,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/5/errors.json\n",
@@ -15065,6 +5548,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/5/errors.json\n",
@@ -15075,6 +5559,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/5/errors.json\n",
@@ -15085,6 +5570,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/5/errors.json\n",
@@ -15095,6 +5581,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/5/errors.json\n",
@@ -15105,6 +5592,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/5/errors.json\n",
@@ -15115,6 +5603,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/5/errors.json\n",
@@ -15125,6 +5614,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/5/errors.json\n",
@@ -15135,6 +5625,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/5/errors.json\n",
@@ -15145,6 +5636,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/5/errors.json\n",
@@ -15155,6 +5647,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/5/errors.json\n",
@@ -15165,6 +5658,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/5/errors.json\n",
@@ -15175,6 +5669,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/5/errors.json\n",
@@ -15185,6 +5680,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/5/errors.json\n",
@@ -15195,6 +5691,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/5/errors.json\n",
@@ -15205,6 +5702,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/5/errors.json\n",
@@ -15215,6 +5713,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/5/errors.json\n",
@@ -15225,6 +5724,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/5/errors.json\n",
@@ -15235,6 +5735,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/5/errors.json\n",
@@ -15245,6 +5746,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/5/errors.json\n",
@@ -15255,6 +5757,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/5/errors.json\n",
@@ -15265,6 +5768,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/5/errors.json\n",
@@ -15275,6 +5779,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/5/errors.json\n",
@@ -15285,6 +5790,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/5/errors.json\n",
@@ -15295,6 +5801,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/5/errors.json\n",
@@ -15305,6 +5812,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/5/errors.json\n",
@@ -15315,6 +5823,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/5/errors.json\n",
@@ -15325,6 +5834,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/5/errors.json\n",
@@ -15335,6 +5845,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/5/errors.json\n",
@@ -15345,6 +5856,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/5/errors.json\n",
@@ -15355,6 +5867,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/5/errors.json\n",
@@ -15365,6 +5878,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/5/errors.json\n",
@@ -15375,6 +5889,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/5/errors.json\n",
@@ -15385,6 +5900,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/5/errors.json\n",
@@ -15395,6 +5911,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/5/errors.json\n",
@@ -15405,6 +5922,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/5/errors.json\n",
@@ -15415,6 +5933,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/5/errors.json\n",
@@ -15425,6 +5944,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/5/errors.json\n",
@@ -15435,6 +5955,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/5/errors.json\n",
@@ -15445,6 +5966,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/5/errors.json\n",
@@ -15455,6 +5977,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/5/errors.json\n",
@@ -15465,6 +5988,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/5/errors.json\n",
@@ -15475,6 +5999,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/5/errors.json\n",
@@ -15485,6 +6010,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/5/errors.json\n",
@@ -15495,6 +6021,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/5/errors.json\n",
@@ -15505,6 +6032,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/5/errors.json\n",
@@ -15515,6 +6043,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/5/errors.json\n",
@@ -15525,6 +6054,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/5/errors.json\n",
@@ -15535,6 +6065,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/5/errors.json\n",
@@ -15545,6 +6076,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/5/errors.json\n",
@@ -15555,6 +6087,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/5/errors.json\n",
@@ -15565,6 +6098,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/5/errors.json\n",
@@ -15575,6 +6109,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/5/errors.json\n",
@@ -15585,6 +6120,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/5/errors.json\n",
@@ -15595,6 +6131,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/5/errors.json\n",
@@ -15605,6 +6142,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/5/errors.json\n",
@@ -15615,6 +6153,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/5/errors.json\n",
@@ -15625,6 +6164,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/5/errors.json\n",
@@ -15635,6 +6175,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/5/errors.json\n",
@@ -15645,6 +6186,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/5/errors.json\n",
@@ -15655,6 +6197,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/5/errors.json\n",
@@ -15665,6 +6208,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/5/errors.json\n",
@@ -15675,6 +6219,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/5/errors.json\n",
@@ -15685,6 +6230,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/5/errors.json\n",
@@ -15695,6 +6241,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/5/errors.json\n",
@@ -15705,6 +6252,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/5/errors.json\n",
@@ -15715,6 +6263,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/5/errors.json\n",
@@ -15725,6 +6274,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/5/errors.json\n",
@@ -15735,6 +6285,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/5/errors.json\n",
@@ -15745,6 +6296,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/5/errors.json\n",
@@ -15755,6 +6307,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/5/errors.json\n",
@@ -15765,6 +6318,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/5/errors.json\n",
@@ -15775,6 +6329,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/5/errors.json\n",
@@ -15785,6 +6340,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/5/errors.json\n",
@@ -15795,6 +6351,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/5/errors.json\n",
@@ -15805,6 +6362,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/5/errors.json\n",
@@ -15815,6 +6373,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/5/errors.json\n",
@@ -15825,6 +6384,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/5/errors.json\n",
@@ -15835,6 +6395,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/5/errors.json\n",
@@ -15845,6 +6406,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/5/errors.json\n",
@@ -15855,6 +6417,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/5/errors.json\n",
@@ -15865,6 +6428,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/5/errors.json\n",
@@ -15875,6 +6439,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/5/errors.json\n",
@@ -15885,6 +6450,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/5/errors.json\n",
@@ -15895,6 +6461,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/5/errors.json\n",
@@ -15905,6 +6472,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/5/errors.json\n",
@@ -15915,6 +6483,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/5/errors.json\n",
@@ -15925,6 +6494,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/5/errors.json\n",
@@ -15935,6 +6505,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/5/errors.json\n",
@@ -15945,6 +6516,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/5/errors.json\n",
@@ -15955,6 +6527,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/5/errors.json\n",
@@ -15965,6 +6538,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/5/errors.json\n",
@@ -15975,6 +6549,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/5/errors.json\n",
@@ -15985,6 +6560,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/5/errors.json\n",
@@ -15995,6 +6571,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/5/errors.json\n",
@@ -16005,6 +6582,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/5/errors.json\n",
@@ -16015,6 +6593,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/5/errors.json\n",
@@ -16025,6 +6604,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/5/errors.json\n",
@@ -16035,6 +6615,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/5/errors.json\n",
@@ -16045,6 +6626,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/5/errors.json\n",
@@ -16055,6 +6637,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/5/errors.json\n",
@@ -16065,6 +6648,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/5/errors.json\n",
@@ -16075,6 +6659,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/5/errors.json\n",
@@ -16085,6 +6670,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/5/errors.json\n",
@@ -16095,6 +6681,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/5/errors.json\n",
@@ -16105,6 +6692,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/5/errors.json\n",
@@ -16115,6 +6703,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/5/errors.json\n",
@@ -16125,6 +6714,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/5/errors.json\n",
@@ -16135,6 +6725,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/5/errors.json\n",
@@ -16145,6 +6736,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/5/errors.json\n",
@@ -16155,6 +6747,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/5/errors.json\n",
@@ -16165,6 +6758,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/5/errors.json\n",
@@ -16175,6 +6769,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/5/errors.json\n",
@@ -16185,6 +6780,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/5/errors.json\n",
@@ -16195,6 +6791,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/5/errors.json\n",
@@ -16205,6 +6802,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/5/errors.json\n",
@@ -16215,6 +6813,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/5/errors.json\n",
@@ -16225,6 +6824,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/5/errors.json\n",
@@ -16235,6 +6835,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/5/errors.json\n",
@@ -16245,6 +6846,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/5/errors.json\n",
@@ -16255,6 +6857,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/5/errors.json\n",
@@ -16265,6 +6868,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/5/errors.json\n",
@@ -16275,6 +6879,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/5/errors.json\n",
@@ -16285,6 +6890,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/5/errors.json\n",
@@ -16295,6 +6901,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/5/errors.json\n",
@@ -16305,6 +6912,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/5/errors.json\n",
@@ -16315,6 +6923,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/5/errors.json\n",
@@ -16325,6 +6934,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/5/errors.json\n",
@@ -16335,6 +6945,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/5/errors.json\n",
@@ -16345,6 +6956,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/5/errors.json\n",
@@ -16355,6 +6967,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/5/errors.json\n",
@@ -16365,6 +6978,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/5/errors.json\n",
@@ -16375,6 +6989,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/5/errors.json\n",
@@ -16385,6 +7000,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/5/errors.json\n",
@@ -16395,6 +7011,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/5/errors.json\n",
@@ -16405,6 +7022,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/5/errors.json\n",
@@ -16415,6 +7033,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/5/errors.json\n",
@@ -16425,6 +7044,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/5/errors.json\n",
@@ -16435,6 +7055,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/5/errors.json\n",
@@ -16445,6 +7066,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/5/errors.json\n",
@@ -16455,6 +7077,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/5/errors.json\n",
@@ -16465,6 +7088,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/5/errors.json\n",
@@ -16475,6 +7099,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/5/errors.json\n",
@@ -16485,6 +7110,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/5/errors.json\n",
@@ -16495,6 +7121,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/5/errors.json\n",
@@ -16505,6 +7132,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/5/errors.json\n",
@@ -16515,6 +7143,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/5/errors.json\n",
@@ -16525,6 +7154,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/5/errors.json\n",
@@ -16535,6 +7165,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/5/errors.json\n",
@@ -16545,6 +7176,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/5/errors.json\n",
@@ -16555,6 +7187,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/5/errors.json\n",
@@ -16565,6 +7198,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/5/errors.json\n",
@@ -16575,6 +7209,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/5/errors.json\n",
@@ -16585,6 +7220,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/5/errors.json\n",
@@ -16595,6 +7231,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/5/errors.json\n",
@@ -16605,6 +7242,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/5/errors.json\n",
@@ -16615,6 +7253,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/5/errors.json\n",
@@ -16625,6 +7264,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/5/errors.json\n",
@@ -16635,6 +7275,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/5/errors.json\n",
@@ -16645,6 +7286,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/5/errors.json\n",
@@ -16655,6 +7297,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/5/errors.json\n",
@@ -16665,6 +7308,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/5/errors.json\n",
@@ -16675,6 +7319,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/5/errors.json\n",
@@ -16685,6 +7330,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/5/errors.json\n",
@@ -16695,6 +7341,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/5/errors.json\n",
@@ -16705,6 +7352,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/5/errors.json\n",
@@ -16715,6 +7363,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/5/errors.json\n",
@@ -16725,6 +7374,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/5/errors.json\n",
@@ -16735,6 +7385,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/5/errors.json\n",
@@ -16745,6 +7396,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/5/errors.json\n",
@@ -16755,6 +7407,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/5/errors.json\n",
@@ -16765,6 +7418,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/5/errors.json\n",
@@ -16775,6 +7429,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/5/errors.json\n",
@@ -16785,6 +7440,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/5/errors.json\n",
@@ -16795,6 +7451,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/5/errors.json\n",
@@ -16805,6 +7462,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/5/errors.json\n",
@@ -16815,6 +7473,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/5/errors.json\n",
@@ -16825,6 +7484,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/5/errors.json\n",
@@ -16835,6 +7495,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/5/errors.json\n",
@@ -16845,6 +7506,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/5/errors.json\n",
@@ -16855,6 +7517,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/5/errors.json\n",
@@ -16865,6 +7528,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/5/errors.json\n",
@@ -16875,6 +7539,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/5/errors.json\n",
@@ -16885,6 +7550,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/5/errors.json\n",
@@ -16895,6 +7561,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/5/errors.json\n",
@@ -16905,6 +7572,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/5/errors.json\n",
@@ -16915,6 +7583,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/5/errors.json\n",
@@ -16925,6 +7594,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/5/errors.json\n",
@@ -16935,6 +7605,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/5/errors.json\n",
@@ -16945,6 +7616,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/5/errors.json\n",
@@ -16955,6 +7627,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/5/errors.json\n",
@@ -16965,6 +7638,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/5/errors.json\n",
@@ -16975,6 +7649,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/5/errors.json\n",
@@ -16985,6 +7660,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/5/errors.json\n",
@@ -16995,6 +7671,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/5/errors.json\n",
@@ -17005,6 +7682,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/5/errors.json\n",
@@ -17015,6 +7693,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/5/errors.json\n",
@@ -17025,6 +7704,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/5/errors.json\n",
@@ -17035,6 +7715,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/5/errors.json\n",
@@ -17045,6 +7726,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/5/errors.json\n",
@@ -17055,6 +7737,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/5/errors.json\n",
@@ -17065,6 +7748,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/5/errors.json\n",
@@ -17075,6 +7759,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/5/errors.json\n",
@@ -17085,6 +7770,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/5/errors.json\n",
@@ -17095,6 +7781,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/5/errors.json\n",
@@ -17105,6 +7792,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/5/errors.json\n",
@@ -17115,6 +7803,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/5/errors.json\n",
@@ -17125,6 +7814,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/5/errors.json\n",
@@ -17135,6 +7825,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/5/errors.json\n",
@@ -17145,6 +7836,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/5/errors.json\n",
@@ -17155,6 +7847,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/5/errors.json\n",
@@ -17165,6 +7858,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/5/errors.json\n",
@@ -17175,6 +7869,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/5/errors.json\n",
@@ -17185,6 +7880,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/5/errors.json\n",
@@ -17195,6 +7891,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/5/errors.json\n",
@@ -17205,6 +7902,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/5/errors.json\n",
@@ -17215,6 +7913,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/5/errors.json\n",
@@ -17225,6 +7924,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/5/errors.json\n",
@@ -17235,6 +7935,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/5/errors.json\n",
@@ -17245,6 +7946,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/5/errors.json\n",
@@ -17255,6 +7957,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/5/errors.json\n",
@@ -17265,6 +7968,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/5/errors.json\n",
@@ -17275,6 +7979,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/5/errors.json\n",
@@ -17285,6 +7990,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/5/errors.json\n",
@@ -17295,6 +8001,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/5/errors.json\n",
@@ -17305,6 +8012,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/5/errors.json\n",
@@ -17315,6 +8023,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/5/errors.json\n",
@@ -17325,6 +8034,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/5/errors.json\n",
@@ -17335,6 +8045,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/5/errors.json\n",
@@ -17345,6 +8056,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/5/errors.json\n",
@@ -17355,6 +8067,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/5/errors.json\n",
@@ -17365,6 +8078,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/5/errors.json\n",
@@ -17375,6 +8089,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/5/errors.json\n",
@@ -17385,6 +8100,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/5/errors.json\n",
@@ -17395,6 +8111,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/5/errors.json\n",
@@ -17405,6 +8122,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/5/errors.json\n",
@@ -17415,6 +8133,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/5/errors.json\n",
@@ -17425,6 +8144,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/5/errors.json\n",
@@ -17435,6 +8155,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/5/errors.json\n",
@@ -17445,6 +8166,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/5/errors.json\n",
@@ -17455,6 +8177,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/5/errors.json\n",
@@ -17465,6 +8188,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/5/errors.json\n",
@@ -17475,6 +8199,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/5/errors.json\n",
@@ -17485,6 +8210,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/5/errors.json\n",
@@ -17495,6 +8221,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/5/errors.json\n",
@@ -17505,6 +8232,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/5/errors.json\n",
@@ -17515,6 +8243,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/5/errors.json\n",
@@ -17525,6 +8254,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/5/errors.json\n",
@@ -17535,6 +8265,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/5/errors.json\n",
@@ -17545,6 +8276,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/5/errors.json\n",
@@ -17555,6 +8287,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/5/errors.json\n",
@@ -17565,6 +8298,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/5/errors.json\n",
@@ -17575,6 +8309,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/5/errors.json\n",
@@ -17585,6 +8320,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/5/errors.json\n",
@@ -17595,6 +8331,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/5/errors.json\n",
@@ -17605,6 +8342,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/5/errors.json\n",
@@ -17615,6 +8353,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/5/errors.json\n",
@@ -17625,6 +8364,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/5/errors.json\n",
@@ -17635,6 +8375,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/5/errors.json\n",
@@ -17645,6 +8386,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/5/errors.json\n",
@@ -17655,6 +8397,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/5/errors.json\n",
@@ -17665,6 +8408,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/5/errors.json\n",
@@ -17675,6 +8419,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/5/errors.json\n",
@@ -17685,6 +8430,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/5/errors.json\n",
@@ -17695,6 +8441,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/5/errors.json\n",
@@ -17705,6 +8452,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/5/errors.json\n",
@@ -17715,6 +8463,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/5/errors.json\n",
@@ -17725,6 +8474,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/5/errors.json\n",
@@ -17735,6 +8485,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/5/errors.json\n",
@@ -17745,6 +8496,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/5/errors.json\n",
@@ -17755,6 +8507,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/5/errors.json\n",
@@ -17765,6 +8518,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/5/errors.json\n",
@@ -17775,6 +8529,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/5/errors.json\n",
@@ -17785,6 +8540,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/5/errors.json\n",
@@ -17795,6 +8551,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/5/errors.json\n",
@@ -17805,6 +8562,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/5/errors.json\n",
@@ -17815,6 +8573,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/5/errors.json\n",
@@ -17825,6 +8584,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/5/errors.json\n",
@@ -17835,6 +8595,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/5/errors.json\n",
@@ -17845,6 +8606,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/5/errors.json\n",
@@ -17855,6 +8617,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/5/errors.json\n",
@@ -17865,6 +8628,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/5/errors.json\n",
@@ -17875,6 +8639,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/5/errors.json\n",
@@ -17885,6 +8650,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/5/errors.json\n",
@@ -17895,6 +8661,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/5/errors.json\n",
@@ -17905,6 +8672,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/5/errors.json\n",
@@ -17915,6 +8683,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/5/errors.json\n",
@@ -17925,6 +8694,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/5/errors.json\n",
@@ -17935,6 +8705,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/5/errors.json\n",
@@ -17945,6 +8716,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/5/errors.json\n",
@@ -17955,6 +8727,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/5/errors.json\n",
@@ -17965,6 +8738,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/5/errors.json\n",
@@ -17975,6 +8749,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/5/errors.json\n",
@@ -17985,6 +8760,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/5/errors.json\n",
@@ -17995,6 +8771,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/5/errors.json\n",
@@ -18005,6 +8782,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/5/errors.json\n",
@@ -18015,6 +8793,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/5/errors.json\n",
@@ -18025,6 +8804,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/5/errors.json\n",
@@ -18035,6 +8815,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/5/errors.json\n",
@@ -18045,6 +8826,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/5/errors.json\n",
@@ -18055,6 +8837,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/5/errors.json\n",
@@ -18065,6 +8848,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/5/errors.json\n",
@@ -18075,6 +8859,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/5/errors.json\n",
@@ -18085,6 +8870,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/5/errors.json\n",
@@ -18095,6 +8881,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/5/errors.json\n",
@@ -18105,6 +8892,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/5/errors.json\n",
@@ -18115,6 +8903,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/5/errors.json\n",
@@ -18125,6 +8914,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/5/errors.json\n",
@@ -18135,6 +8925,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/5/errors.json\n",
@@ -18145,6 +8936,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/5/errors.json\n",
@@ -18155,6 +8947,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/5/errors.json\n",
@@ -18165,6 +8958,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/5/errors.json\n",
@@ -18175,6 +8969,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/5/errors.json\n",
@@ -18185,6 +8980,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/5/errors.json\n",
@@ -18195,6 +8991,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/5/errors.json\n",
@@ -18205,6 +9002,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/5/errors.json\n",
@@ -18215,6 +9013,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/5/errors.json\n",
@@ -18225,6 +9024,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/5/errors.json\n",
@@ -18235,6 +9035,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/5/errors.json\n",
@@ -18245,6 +9046,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/5/errors.json\n",
@@ -18255,6 +9057,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/5/errors.json\n",
@@ -18265,6 +9068,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/5/errors.json\n",
@@ -18275,6 +9079,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/5/errors.json\n",
@@ -18285,6 +9090,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/5/errors.json\n",
@@ -18295,6 +9101,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/5/errors.json\n",
@@ -18305,6 +9112,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/5/errors.json\n",
@@ -18315,5007 +9123,7 @@
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/3/errors.json\n",
-      "nb201_im\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_88/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_41/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_78/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_54/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_71/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_56/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_79/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/7/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_28/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_87/2/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/rs/config_45/2/errors.json\n",
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-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_9/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_35/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_20/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_83/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_19/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_63/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_76/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_60/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_6/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/npenas/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/5/errors.json\n",
@@ -23326,6 +9134,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/5/errors.json\n",
@@ -23336,6 +9145,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/5/errors.json\n",
@@ -23346,6 +9156,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/5/errors.json\n",
@@ -23356,6 +9167,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/5/errors.json\n",
@@ -23366,6 +9178,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/5/errors.json\n",
@@ -23376,6 +9189,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/5/errors.json\n",
@@ -23386,6 +9200,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/5/errors.json\n",
@@ -23396,6 +9211,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/5/errors.json\n",
@@ -23406,6 +9222,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/5/errors.json\n",
@@ -23416,6 +9233,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/5/errors.json\n",
@@ -23426,6 +9244,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/5/errors.json\n",
@@ -23436,6 +9255,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/5/errors.json\n",
@@ -23446,6 +9266,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/5/errors.json\n",
@@ -23456,6 +9277,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/5/errors.json\n",
@@ -23466,6 +9288,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/5/errors.json\n",
@@ -23476,6 +9299,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/5/errors.json\n",
@@ -23486,6 +9310,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/5/errors.json\n",
@@ -23496,6 +9321,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/5/errors.json\n",
@@ -23506,6 +9332,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/5/errors.json\n",
@@ -23516,6 +9343,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/5/errors.json\n",
@@ -23526,6 +9354,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/5/errors.json\n",
@@ -23536,6 +9365,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/5/errors.json\n",
@@ -23546,6 +9376,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/5/errors.json\n",
@@ -23556,6 +9387,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/5/errors.json\n",
@@ -23566,6 +9398,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/5/errors.json\n",
@@ -23576,6 +9409,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/5/errors.json\n",
@@ -23586,6 +9420,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/5/errors.json\n",
@@ -23596,6 +9431,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/5/errors.json\n",
@@ -23606,6 +9442,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/5/errors.json\n",
@@ -23616,6 +9453,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/5/errors.json\n",
@@ -23626,6 +9464,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/5/errors.json\n",
@@ -23636,6 +9475,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/5/errors.json\n",
@@ -23646,6 +9486,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/5/errors.json\n",
@@ -23656,6 +9497,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/5/errors.json\n",
@@ -23666,6 +9508,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/5/errors.json\n",
@@ -23676,6 +9519,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/5/errors.json\n",
@@ -23686,6 +9530,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/5/errors.json\n",
@@ -23696,6 +9541,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/5/errors.json\n",
@@ -23706,6 +9552,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/5/errors.json\n",
@@ -23716,6 +9563,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/5/errors.json\n",
@@ -23726,6 +9574,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/5/errors.json\n",
@@ -23736,6 +9585,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/5/errors.json\n",
@@ -23746,6 +9596,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/5/errors.json\n",
@@ -23756,6 +9607,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/5/errors.json\n",
@@ -23766,6 +9618,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/5/errors.json\n",
@@ -23776,6 +9629,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/5/errors.json\n",
@@ -23786,6 +9640,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/5/errors.json\n",
@@ -23796,6 +9651,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/5/errors.json\n",
@@ -23806,6 +9662,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/5/errors.json\n",
@@ -23816,6 +9673,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/5/errors.json\n",
@@ -23826,6 +9684,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/5/errors.json\n",
@@ -23836,6 +9695,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/5/errors.json\n",
@@ -23846,6 +9706,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/5/errors.json\n",
@@ -23856,6 +9717,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/5/errors.json\n",
@@ -23866,6 +9728,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/5/errors.json\n",
@@ -23876,6 +9739,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/5/errors.json\n",
@@ -23886,6 +9750,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/5/errors.json\n",
@@ -23896,6 +9761,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/5/errors.json\n",
@@ -23906,6 +9772,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/5/errors.json\n",
@@ -23916,6 +9783,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/5/errors.json\n",
@@ -23926,6 +9794,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/5/errors.json\n",
@@ -23936,6 +9805,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/5/errors.json\n",
@@ -23946,6 +9816,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/5/errors.json\n",
@@ -23956,6 +9827,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/5/errors.json\n",
@@ -23966,6 +9838,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/5/errors.json\n",
@@ -23976,6 +9849,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/5/errors.json\n",
@@ -23986,6 +9860,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/5/errors.json\n",
@@ -23996,6 +9871,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/5/errors.json\n",
@@ -24006,6 +9882,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/5/errors.json\n",
@@ -24016,6 +9893,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/5/errors.json\n",
@@ -24026,6 +9904,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/5/errors.json\n",
@@ -24036,6 +9915,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/5/errors.json\n",
@@ -24046,6 +9926,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/5/errors.json\n",
@@ -24056,6 +9937,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/5/errors.json\n",
@@ -24066,6 +9948,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/5/errors.json\n",
@@ -24076,6 +9959,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/5/errors.json\n",
@@ -24086,6 +9970,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/5/errors.json\n",
@@ -24096,6 +9981,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/5/errors.json\n",
@@ -24106,6 +9992,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/5/errors.json\n",
@@ -24116,6 +10003,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/5/errors.json\n",
@@ -24126,6 +10014,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/5/errors.json\n",
@@ -24136,6 +10025,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/5/errors.json\n",
@@ -24146,6 +10036,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/5/errors.json\n",
@@ -24156,6 +10047,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/5/errors.json\n",
@@ -24166,6 +10058,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/5/errors.json\n",
@@ -24176,6 +10069,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/5/errors.json\n",
@@ -24186,6 +10080,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/5/errors.json\n",
@@ -24196,6 +10091,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/5/errors.json\n",
@@ -24206,6 +10102,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/5/errors.json\n",
@@ -24216,6 +10113,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/5/errors.json\n",
@@ -24226,6 +10124,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/5/errors.json\n",
@@ -24236,6 +10135,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/5/errors.json\n",
@@ -24246,6 +10146,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/5/errors.json\n",
@@ -24256,6 +10157,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/5/errors.json\n",
@@ -24266,6 +10168,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/5/errors.json\n",
@@ -24276,6 +10179,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/5/errors.json\n",
@@ -24286,6 +10190,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/5/errors.json\n",
@@ -24296,6 +10201,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/5/errors.json\n",
@@ -24306,6 +10212,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/5/errors.json\n",
@@ -24316,6 +10223,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/5/errors.json\n",
@@ -24326,6 +10234,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/5/errors.json\n",
@@ -24336,6 +10245,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/5/errors.json\n",
@@ -24346,6 +10256,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/5/errors.json\n",
@@ -24356,6 +10267,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/5/errors.json\n",
@@ -24366,6 +10278,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/5/errors.json\n",
@@ -24376,6 +10289,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/5/errors.json\n",
@@ -24386,6 +10300,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/5/errors.json\n",
@@ -24396,6 +10311,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/5/errors.json\n",
@@ -24406,6 +10322,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/5/errors.json\n",
@@ -24416,6 +10333,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/5/errors.json\n",
@@ -24426,6 +10344,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/5/errors.json\n",
@@ -24436,6 +10355,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/5/errors.json\n",
@@ -24446,6 +10366,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/5/errors.json\n",
@@ -24456,6 +10377,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/5/errors.json\n",
@@ -24466,6 +10388,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/5/errors.json\n",
@@ -24476,6 +10399,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/5/errors.json\n",
@@ -24486,6 +10410,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/5/errors.json\n",
@@ -24496,6 +10421,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/5/errors.json\n",
@@ -24506,6 +10432,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/5/errors.json\n",
@@ -24516,6 +10443,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/5/errors.json\n",
@@ -24526,6 +10454,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/5/errors.json\n",
@@ -24536,6 +10465,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/5/errors.json\n",
@@ -24546,6 +10476,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/5/errors.json\n",
@@ -24556,6 +10487,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/5/errors.json\n",
@@ -24566,6 +10498,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/5/errors.json\n",
@@ -24576,6 +10509,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/5/errors.json\n",
@@ -24586,6 +10520,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/5/errors.json\n",
@@ -24596,6 +10531,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/5/errors.json\n",
@@ -24606,6 +10542,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/5/errors.json\n",
@@ -24616,6 +10553,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/5/errors.json\n",
@@ -24626,6 +10564,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/5/errors.json\n",
@@ -24636,6 +10575,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/5/errors.json\n",
@@ -24646,6 +10586,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/5/errors.json\n",
@@ -24656,6 +10597,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/5/errors.json\n",
@@ -24666,6 +10608,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/5/errors.json\n",
@@ -24676,6 +10619,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/5/errors.json\n",
@@ -24686,6 +10630,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/5/errors.json\n",
@@ -24696,6 +10641,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/5/errors.json\n",
@@ -24706,6 +10652,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/5/errors.json\n",
@@ -24716,6 +10663,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/5/errors.json\n",
@@ -24726,6 +10674,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/5/errors.json\n",
@@ -24736,6 +10685,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/5/errors.json\n",
@@ -24746,6 +10696,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/5/errors.json\n",
@@ -24756,6 +10707,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/5/errors.json\n",
@@ -24766,6 +10718,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/5/errors.json\n",
@@ -24776,6 +10729,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/5/errors.json\n",
@@ -24786,6 +10740,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/5/errors.json\n",
@@ -24796,6 +10751,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/5/errors.json\n",
@@ -24806,6 +10762,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/5/errors.json\n",
@@ -24816,6 +10773,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/5/errors.json\n",
@@ -24826,6 +10784,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/5/errors.json\n",
@@ -24836,6 +10795,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/5/errors.json\n",
@@ -24846,6 +10806,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/5/errors.json\n",
@@ -24856,6 +10817,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/5/errors.json\n",
@@ -24866,6 +10828,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/5/errors.json\n",
@@ -24876,6 +10839,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/5/errors.json\n",
@@ -24886,6 +10850,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/5/errors.json\n",
@@ -24896,6 +10861,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/5/errors.json\n",
@@ -24906,6 +10872,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/5/errors.json\n",
@@ -24916,6 +10883,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/5/errors.json\n",
@@ -24926,6 +10894,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/5/errors.json\n",
@@ -24936,6 +10905,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/5/errors.json\n",
@@ -24946,6 +10916,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/5/errors.json\n",
@@ -24956,6 +10927,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/5/errors.json\n",
@@ -24966,6 +10938,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/5/errors.json\n",
@@ -24976,6 +10949,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/5/errors.json\n",
@@ -24986,6 +10960,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/5/errors.json\n",
@@ -24996,6 +10971,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/5/errors.json\n",
@@ -25006,6 +10982,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/5/errors.json\n",
@@ -25016,6 +10993,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/5/errors.json\n",
@@ -25026,6 +11004,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/5/errors.json\n",
@@ -25036,6 +11015,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/5/errors.json\n",
@@ -25046,6 +11026,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/5/errors.json\n",
@@ -25056,6 +11037,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/5/errors.json\n",
@@ -25066,6 +11048,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/5/errors.json\n",
@@ -25076,6 +11059,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/5/errors.json\n",
@@ -25086,6 +11070,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/5/errors.json\n",
@@ -25096,6 +11081,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/5/errors.json\n",
@@ -25106,6 +11092,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/5/errors.json\n",
@@ -25116,6 +11103,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/5/errors.json\n",
@@ -25126,6 +11114,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/5/errors.json\n",
@@ -25136,6 +11125,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/5/errors.json\n",
@@ -25146,6 +11136,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/5/errors.json\n",
@@ -25156,6 +11147,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/5/errors.json\n",
@@ -25166,6 +11158,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/5/errors.json\n",
@@ -25176,6 +11169,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/5/errors.json\n",
@@ -25186,6 +11180,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/5/errors.json\n",
@@ -25196,6 +11191,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/5/errors.json\n",
@@ -25206,6 +11202,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/5/errors.json\n",
@@ -25216,6 +11213,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/5/errors.json\n",
@@ -25226,6 +11224,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/5/errors.json\n",
@@ -25236,6 +11235,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/5/errors.json\n",
@@ -25246,6 +11246,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/5/errors.json\n",
@@ -25256,6 +11257,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/5/errors.json\n",
@@ -25266,6 +11268,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/5/errors.json\n",
@@ -25276,6 +11279,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/5/errors.json\n",
@@ -25286,6 +11290,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/5/errors.json\n",
@@ -25296,6 +11301,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/5/errors.json\n",
@@ -25306,6 +11312,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/5/errors.json\n",
@@ -25316,6 +11323,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/5/errors.json\n",
@@ -25326,6 +11334,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/5/errors.json\n",
@@ -25336,6 +11345,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/5/errors.json\n",
@@ -25346,6 +11356,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/5/errors.json\n",
@@ -25356,6 +11367,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/5/errors.json\n",
@@ -25366,6 +11378,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/5/errors.json\n",
@@ -25376,6 +11389,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/5/errors.json\n",
@@ -25386,6 +11400,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/5/errors.json\n",
@@ -25396,6 +11411,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/5/errors.json\n",
@@ -25406,6 +11422,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/5/errors.json\n",
@@ -25416,6 +11433,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/5/errors.json\n",
@@ -25426,6 +11444,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/5/errors.json\n",
@@ -25436,6 +11455,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/5/errors.json\n",
@@ -25446,6 +11466,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/5/errors.json\n",
@@ -25456,6 +11477,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/5/errors.json\n",
@@ -25466,6 +11488,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/5/errors.json\n",
@@ -25476,6 +11499,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/5/errors.json\n",
@@ -25486,6 +11510,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/5/errors.json\n",
@@ -25496,6 +11521,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/5/errors.json\n",
@@ -25506,6 +11532,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/5/errors.json\n",
@@ -25516,6 +11543,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/5/errors.json\n",
@@ -25526,6 +11554,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/5/errors.json\n",
@@ -25536,6 +11565,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/5/errors.json\n",
@@ -25546,6 +11576,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/5/errors.json\n",
@@ -25556,6 +11587,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/5/errors.json\n",
@@ -25566,6 +11598,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/5/errors.json\n",
@@ -25576,6 +11609,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/5/errors.json\n",
@@ -25586,6 +11620,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/5/errors.json\n",
@@ -25596,6 +11631,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/5/errors.json\n",
@@ -25606,6 +11642,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/5/errors.json\n",
@@ -25616,6 +11653,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/5/errors.json\n",
@@ -25626,6 +11664,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/5/errors.json\n",
@@ -25636,6 +11675,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/5/errors.json\n",
@@ -25646,6 +11686,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/5/errors.json\n",
@@ -25656,6 +11697,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/5/errors.json\n",
@@ -25666,6 +11708,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/5/errors.json\n",
@@ -25676,6 +11719,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/5/errors.json\n",
@@ -25686,6 +11730,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/5/errors.json\n",
@@ -25696,6 +11741,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/5/errors.json\n",
@@ -25706,6 +11752,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/5/errors.json\n",
@@ -25716,6 +11763,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/5/errors.json\n",
@@ -25726,6 +11774,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/5/errors.json\n",
@@ -25736,6 +11785,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/5/errors.json\n",
@@ -25746,6 +11796,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/5/errors.json\n",
@@ -25756,6 +11807,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/5/errors.json\n",
@@ -25766,6 +11818,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/5/errors.json\n",
@@ -25776,6 +11829,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/5/errors.json\n",
@@ -25786,16 +11840,38 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/3/errors.json\n",
+      "config_65\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_6.yaml/errors.json\n",
+      "what?\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_7.yaml/errors.json\n",
+      "what?\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/9/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_4.yaml/errors.json\n",
+      "what?\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/5/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/4/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/8/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_3.yaml/errors.json\n",
+      "what?\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_1.yaml/errors.json\n",
+      "what?\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/7/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/1/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_8.yaml/errors.json\n",
+      "what?\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/0/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_2.yaml/errors.json\n",
+      "what?\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_0.yaml/errors.json\n",
+      "what?\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/3/errors.json\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_5.yaml/errors.json\n",
+      "what?\n",
+      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_9.yaml/errors.json\n",
+      "what?\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/5/errors.json\n",
@@ -25806,6 +11882,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/5/errors.json\n",
@@ -25816,6 +11893,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/5/errors.json\n",
@@ -25826,6 +11904,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/5/errors.json\n",
@@ -25836,6 +11915,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/5/errors.json\n",
@@ -25846,6 +11926,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/5/errors.json\n",
@@ -25856,6 +11937,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/5/errors.json\n",
@@ -25866,6 +11948,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/5/errors.json\n",
@@ -25876,6 +11959,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/5/errors.json\n",
@@ -25886,6 +11970,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/5/errors.json\n",
@@ -25896,6 +11981,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/5/errors.json\n",
@@ -25906,6 +11992,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/5/errors.json\n",
@@ -25916,6 +12003,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/5/errors.json\n",
@@ -25926,6 +12014,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/5/errors.json\n",
@@ -25936,6 +12025,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/5/errors.json\n",
@@ -25946,6 +12036,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/5/errors.json\n",
@@ -25956,6 +12047,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/5/errors.json\n",
@@ -25966,6 +12058,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/5/errors.json\n",
@@ -25976,6 +12069,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/5/errors.json\n",
@@ -25986,6 +12080,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/5/errors.json\n",
@@ -25996,6 +12091,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/5/errors.json\n",
@@ -26006,6 +12102,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/5/errors.json\n",
@@ -26016,6 +12113,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/5/errors.json\n",
@@ -26026,6 +12124,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/5/errors.json\n",
@@ -26036,6 +12135,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/5/errors.json\n",
@@ -26046,6 +12146,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/5/errors.json\n",
@@ -26056,6 +12157,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/5/errors.json\n",
@@ -26066,6 +12168,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/5/errors.json\n",
@@ -26076,6 +12179,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/5/errors.json\n",
@@ -26086,6 +12190,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/5/errors.json\n",
@@ -26096,6 +12201,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/5/errors.json\n",
@@ -26106,6 +12212,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/5/errors.json\n",
@@ -26116,6 +12223,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/5/errors.json\n",
@@ -26126,6 +12234,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/5/errors.json\n",
@@ -26136,6 +12245,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/5/errors.json\n",
@@ -26146,6 +12256,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/5/errors.json\n",
@@ -26156,6 +12267,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/5/errors.json\n",
@@ -26166,6 +12278,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/5/errors.json\n",
@@ -26176,6 +12289,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/5/errors.json\n",
@@ -26186,6 +12300,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/5/errors.json\n",
@@ -26196,6 +12311,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/5/errors.json\n",
@@ -26206,6 +12322,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/5/errors.json\n",
@@ -26216,6 +12333,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/5/errors.json\n",
@@ -26226,6 +12344,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/5/errors.json\n",
@@ -26236,6 +12355,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/5/errors.json\n",
@@ -26246,6 +12366,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/5/errors.json\n",
@@ -26256,6 +12377,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/5/errors.json\n",
@@ -26266,6 +12388,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/5/errors.json\n",
@@ -26276,6 +12399,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/5/errors.json\n",
@@ -26286,6 +12410,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/5/errors.json\n",
@@ -26296,6 +12421,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/5/errors.json\n",
@@ -26306,6 +12432,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/5/errors.json\n",
@@ -26316,6 +12443,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/3/errors.json\n",
+      "config_88\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/5/errors.json\n",
@@ -26326,6 +12454,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/3/errors.json\n",
+      "config_41\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/5/errors.json\n",
@@ -26336,6 +12465,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/3/errors.json\n",
+      "config_78\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/5/errors.json\n",
@@ -26346,6 +12476,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/3/errors.json\n",
+      "config_54\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/5/errors.json\n",
@@ -26356,6 +12487,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/3/errors.json\n",
+      "config_71\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/5/errors.json\n",
@@ -26366,6 +12498,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/3/errors.json\n",
+      "config_56\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/5/errors.json\n",
@@ -26376,6 +12509,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/3/errors.json\n",
+      "config_79\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/5/errors.json\n",
@@ -26386,6 +12520,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/3/errors.json\n",
+      "config_28\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/5/errors.json\n",
@@ -26396,6 +12531,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/3/errors.json\n",
+      "config_87\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/5/errors.json\n",
@@ -26406,6 +12542,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/3/errors.json\n",
+      "config_14\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/5/errors.json\n",
@@ -26416,6 +12553,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/3/errors.json\n",
+      "config_45\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/5/errors.json\n",
@@ -26426,6 +12564,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/3/errors.json\n",
+      "config_42\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/5/errors.json\n",
@@ -26436,6 +12575,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/3/errors.json\n",
+      "config_59\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/5/errors.json\n",
@@ -26446,6 +12586,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/3/errors.json\n",
+      "config_0\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/5/errors.json\n",
@@ -26456,6 +12597,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/3/errors.json\n",
+      "config_31\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/5/errors.json\n",
@@ -26466,6 +12608,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/3/errors.json\n",
+      "config_77\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/5/errors.json\n",
@@ -26476,6 +12619,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/3/errors.json\n",
+      "config_66\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/5/errors.json\n",
@@ -26486,6 +12630,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/3/errors.json\n",
+      "config_1\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/5/errors.json\n",
@@ -26496,6 +12641,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/3/errors.json\n",
+      "config_84\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/5/errors.json\n",
@@ -26506,6 +12652,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/3/errors.json\n",
+      "config_46\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/5/errors.json\n",
@@ -26516,6 +12663,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/3/errors.json\n",
+      "config_82\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/5/errors.json\n",
@@ -26526,6 +12674,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/3/errors.json\n",
+      "config_47\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/5/errors.json\n",
@@ -26536,6 +12685,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/3/errors.json\n",
+      "config_61\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/5/errors.json\n",
@@ -26546,6 +12696,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/3/errors.json\n",
+      "config_27\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/5/errors.json\n",
@@ -26556,6 +12707,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/3/errors.json\n",
+      "config_58\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/5/errors.json\n",
@@ -26566,6 +12718,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/3/errors.json\n",
+      "config_74\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/5/errors.json\n",
@@ -26576,6 +12729,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/3/errors.json\n",
+      "config_43\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/5/errors.json\n",
@@ -26586,6 +12740,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/3/errors.json\n",
+      "config_92\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/5/errors.json\n",
@@ -26596,6 +12751,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/3/errors.json\n",
+      "config_64\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/5/errors.json\n",
@@ -26606,6 +12762,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/3/errors.json\n",
+      "config_2\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/5/errors.json\n",
@@ -26616,6 +12773,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/3/errors.json\n",
+      "config_8\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/5/errors.json\n",
@@ -26626,6 +12784,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/3/errors.json\n",
+      "config_29\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/5/errors.json\n",
@@ -26636,6 +12795,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/3/errors.json\n",
+      "config_48\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/5/errors.json\n",
@@ -26646,6 +12806,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/3/errors.json\n",
+      "config_52\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/5/errors.json\n",
@@ -26656,6 +12817,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/3/errors.json\n",
+      "config_68\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/5/errors.json\n",
@@ -26666,6 +12828,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/3/errors.json\n",
+      "config_51\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/5/errors.json\n",
@@ -26676,6 +12839,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/3/errors.json\n",
+      "config_57\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/5/errors.json\n",
@@ -26686,6 +12850,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/3/errors.json\n",
+      "config_80\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/5/errors.json\n",
@@ -26696,6 +12861,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/3/errors.json\n",
+      "config_70\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/5/errors.json\n",
@@ -26706,6 +12872,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/3/errors.json\n",
+      "config_53\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/5/errors.json\n",
@@ -26716,6 +12883,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/3/errors.json\n",
+      "config_94\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/5/errors.json\n",
@@ -26726,6 +12894,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/3/errors.json\n",
+      "config_49\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/5/errors.json\n",
@@ -26736,6 +12905,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/3/errors.json\n",
+      "config_67\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/5/errors.json\n",
@@ -26746,6 +12916,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/3/errors.json\n",
+      "config_17\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/5/errors.json\n",
@@ -26756,6 +12927,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/3/errors.json\n",
+      "config_86\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/5/errors.json\n",
@@ -26766,6 +12938,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/3/errors.json\n",
+      "config_23\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/5/errors.json\n",
@@ -26776,6 +12949,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/3/errors.json\n",
+      "config_12\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/5/errors.json\n",
@@ -26786,6 +12960,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/3/errors.json\n",
+      "config_65\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/5/errors.json\n",
@@ -26796,6 +12971,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/3/errors.json\n",
+      "config_24\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/5/errors.json\n",
@@ -26806,6 +12982,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/3/errors.json\n",
+      "config_62\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/5/errors.json\n",
@@ -26816,6 +12993,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/3/errors.json\n",
+      "config_89\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/5/errors.json\n",
@@ -26826,6 +13004,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/3/errors.json\n",
+      "config_3\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/5/errors.json\n",
@@ -26836,6 +13015,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/3/errors.json\n",
+      "config_15\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/5/errors.json\n",
@@ -26846,6 +13026,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/3/errors.json\n",
+      "config_72\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/5/errors.json\n",
@@ -26856,6 +13037,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/3/errors.json\n",
+      "config_81\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/5/errors.json\n",
@@ -26866,6 +13048,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/3/errors.json\n",
+      "config_75\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/5/errors.json\n",
@@ -26876,6 +13059,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/3/errors.json\n",
+      "config_93\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/5/errors.json\n",
@@ -26886,6 +13070,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/3/errors.json\n",
+      "config_4\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/5/errors.json\n",
@@ -26896,6 +13081,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/3/errors.json\n",
+      "config_85\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/5/errors.json\n",
@@ -26906,6 +13092,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/3/errors.json\n",
+      "config_18\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/5/errors.json\n",
@@ -26916,6 +13103,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/3/errors.json\n",
+      "config_96\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/5/errors.json\n",
@@ -26926,6 +13114,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/3/errors.json\n",
+      "config_40\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/5/errors.json\n",
@@ -26936,6 +13125,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/3/errors.json\n",
+      "config_25\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/5/errors.json\n",
@@ -26946,6 +13136,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/3/errors.json\n",
+      "config_5\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/5/errors.json\n",
@@ -26956,6 +13147,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/3/errors.json\n",
+      "config_90\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/5/errors.json\n",
@@ -26966,6 +13158,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/3/errors.json\n",
+      "config_16\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/5/errors.json\n",
@@ -26976,6 +13169,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/3/errors.json\n",
+      "config_7\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/5/errors.json\n",
@@ -26986,6 +13180,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/3/errors.json\n",
+      "config_44\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/5/errors.json\n",
@@ -26996,6 +13191,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/3/errors.json\n",
+      "config_10\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/5/errors.json\n",
@@ -27006,6 +13202,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/3/errors.json\n",
+      "config_50\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/5/errors.json\n",
@@ -27016,6 +13213,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/3/errors.json\n",
+      "config_55\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/5/errors.json\n",
@@ -27026,6 +13224,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/3/errors.json\n",
+      "config_36\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/5/errors.json\n",
@@ -27036,6 +13235,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/3/errors.json\n",
+      "config_22\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/5/errors.json\n",
@@ -27046,6 +13246,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/3/errors.json\n",
+      "config_97\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/5/errors.json\n",
@@ -27056,6 +13257,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/3/errors.json\n",
+      "config_37\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/5/errors.json\n",
@@ -27066,6 +13268,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/3/errors.json\n",
+      "config_30\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/5/errors.json\n",
@@ -27076,6 +13279,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/3/errors.json\n",
+      "config_95\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/5/errors.json\n",
@@ -27086,6 +13290,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/3/errors.json\n",
+      "config_38\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/5/errors.json\n",
@@ -27096,6 +13301,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/3/errors.json\n",
+      "config_98\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/5/errors.json\n",
@@ -27106,6 +13312,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/3/errors.json\n",
+      "config_13\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/5/errors.json\n",
@@ -27116,6 +13323,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/3/errors.json\n",
+      "config_34\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/5/errors.json\n",
@@ -27126,6 +13334,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/3/errors.json\n",
+      "config_99\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/5/errors.json\n",
@@ -27136,6 +13345,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/3/errors.json\n",
+      "config_91\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/5/errors.json\n",
@@ -27146,6 +13356,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/3/errors.json\n",
+      "config_32\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/5/errors.json\n",
@@ -27156,6 +13367,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/3/errors.json\n",
+      "config_26\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/5/errors.json\n",
@@ -27166,6 +13378,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/3/errors.json\n",
+      "config_39\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/5/errors.json\n",
@@ -27176,6 +13389,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/3/errors.json\n",
+      "config_33\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/5/errors.json\n",
@@ -27186,6 +13400,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/3/errors.json\n",
+      "config_69\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/5/errors.json\n",
@@ -27196,6 +13411,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/3/errors.json\n",
+      "config_21\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/5/errors.json\n",
@@ -27206,6 +13422,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/3/errors.json\n",
+      "config_73\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/5/errors.json\n",
@@ -27216,6 +13433,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/3/errors.json\n",
+      "config_9\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/5/errors.json\n",
@@ -27226,6 +13444,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/3/errors.json\n",
+      "config_35\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/5/errors.json\n",
@@ -27236,6 +13455,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/3/errors.json\n",
+      "config_20\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/5/errors.json\n",
@@ -27246,6 +13466,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/3/errors.json\n",
+      "config_83\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/5/errors.json\n",
@@ -27256,6 +13477,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/3/errors.json\n",
+      "config_19\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/5/errors.json\n",
@@ -27266,6 +13488,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/3/errors.json\n",
+      "config_63\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/5/errors.json\n",
@@ -27276,6 +13499,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/3/errors.json\n",
+      "config_76\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/5/errors.json\n",
@@ -27286,6 +13510,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/3/errors.json\n",
+      "config_60\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/5/errors.json\n",
@@ -27296,6 +13521,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/3/errors.json\n",
+      "config_6\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/5/errors.json\n",
@@ -27306,6 +13532,7 @@
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/6/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/0/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/3/errors.json\n",
+      "config_11\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/2/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/9/errors.json\n",
       "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/5/errors.json\n",
@@ -27331,7 +13558,7 @@
     "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
     "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
     "    search_spaces = ['nb201_c10','nb201_c100','nb201_im']\n",
-    "    optimizers=('rs', 're', 'ls', 'bananas', 'npenas', 'sh', 'hb', 'bohb','dehb')\n",
+    "    optimizers=('sh', 'hb', 'bohb','dehb')\n",
     "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
     "    root = '~/plotting_data/bbo/'\n",
     "    results_dicts = {}\n",
@@ -27341,2726 +13568,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{88: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.33099999999999, 0.2147300631024888],\n",
-       "   'ls': [91.35099999999998, 0.22669142021699648],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [91.16, 0.21461593603458284],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.02000000000001, 0.237191905426807],\n",
-       "   'bohb': [91.15199998559571, 0.23745988968040901],\n",
-       "   'dehb': [91.1383999855957, 0.2642556386446044]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.24999994140623, 0.02408317293776545],\n",
-       "   'ls': [73.19999993041992, 0.11063453083944268],\n",
-       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'npenas': [72.13599996948243, 0.9459725408008554],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [71.067, 0.6550122136265849]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.156666570027674, 0.24223497679530417],\n",
-       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
-       "   'bananas': [43.10666662902832, 2.2594394686448602],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.3666666615804, 0.8423841895332618]}},\n",
-       " 41: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.322, 0.16521501142450623],\n",
-       "   'ls': [91.22, 0.28544701785094784],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.32959998901367, 0.20578201776622312],\n",
-       "   'dehb': [91.4879999892578, 0.12183923979373613]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.11399995239258, 0.2603152115986551],\n",
-       "   'ls': [73.1679999328613, 0.27600001831054616],\n",
-       "   'bananas': [70.00799994750977, 1.4137100085040557],\n",
-       "   'npenas': [69.64799996948243, 1.6819084192697746],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
-       "   'bohb': [71.965, 0.770340833657415],\n",
-       "   'dehb': [71.595, 0.6773514597312098]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.06333328247071, 0.3209187997043664],\n",
-       "   'ls': [47.00999993896484, 0.29403322438384805],\n",
-       "   'bananas': [46.683333277384435, 0.4885920719391333],\n",
-       "   'npenas': [42.3799999613444, 2.732528330244475],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.62333332824707, 0.6295148069838505]}},\n",
-       " 78: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.441, 0.19896984696179174],\n",
-       "   'ls': [91.39999999999999, 0.1684636459299134],\n",
-       "   'bananas': [89.52700000000002, 0.8955227523631091],\n",
-       "   'npenas': [90.65, 0.3594440151122292],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.35559998828124, 0.12936089274082296]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.21599994018554, 0.0884533544203067],\n",
-       "   'ls': [73.07999996826172, 0.36452704830412275],\n",
-       "   'bananas': [72.75399993652343, 0.6603060140550536],\n",
-       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.16100000000002, 0.95128807413948],\n",
-       "   'dehb': [71.66, 0.8673061743121623]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.49333329467773, 0.5932584095143788],\n",
-       "   'ls': [46.86666660054525, 0.5187591807828443],\n",
-       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.40499998575846, 1.1509669871312727]}},\n",
-       " 54: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.34100000000001, 0.18960221517692954],\n",
-       "   'ls': [91.345, 0.19210674116230142],\n",
-       "   'bananas': [89.58200000000001, 0.8309969915709677],\n",
-       "   'npenas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.01200000000001, 0.2691765220073962],\n",
-       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
-       "   'dehb': [91.3303999934082, 0.1405611621851642]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1339999572754, 0.22218010256014134],\n",
-       "   'ls': [72.90599997436523, 0.7195025715409926],\n",
-       "   'bananas': [70.0899999560547, 1.3856478538407666],\n",
-       "   'npenas': [72.65999997070313, 0.42000003691174315],\n",
-       "   'sh': [72.212, 0.8472638314008226],\n",
-       "   'hb': [71.78999997070312, 1.036387972192968],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.118, 0.7002256779067739]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.91333325907389, 0.26674998192399213],\n",
-       "   'ls': [46.92666657613118, 0.3937850572225648],\n",
-       "   'bananas': [44.04999994099934, 1.4740156691356903],\n",
-       "   'npenas': [44.3433332804362, 1.3207783721457254],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [45.231666649373366, 0.6954794557159045]}},\n",
-       " 71: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.42999999999999, 0.12393546707863573],\n",
-       "   'ls': [91.39899999999999, 0.13269890730522035],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [91.122, 0.2448182999695869],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.24199998242186, 0.22574499147440955]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.20199993530272, 0.1476346476095868],\n",
-       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
-       "   'bananas': [70.1479999621582, 1.3200969521427772],\n",
-       "   'npenas': [71.27999995239259, 1.1918724970886854],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.573, 0.49374183537553257]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93999994099935, 0.38609150929655595],\n",
-       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [46.29999998372395, 0.5418076778343078],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [44.81999998982748, 1.0729709564636138],\n",
-       "   'dehb': [44.49999998779297, 1.3177421762189991]}},\n",
-       " 56: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.47999999999999, 0.11278297743897017],\n",
-       "   'ls': [91.35300000000001, 0.1945276329984993],\n",
-       "   'bananas': [89.52700000000002, 0.8955227523631091],\n",
-       "   'npenas': [89.96700000000001, 0.6205328355534462],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [91.15199998559571, 0.23745988968040901],\n",
-       "   'dehb': [91.30119999267578, 0.19232513757428712]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23799994750978, 0.03515675951021431],\n",
-       "   'ls': [72.90599997436523, 0.7195025715409926],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [72.312, 0.4521238768302327],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [72.078, 0.7797538073007387]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.0099999399821, 0.311644368094445],\n",
-       "   'ls': [46.91999996236166, 0.27696767773433734],\n",
-       "   'bananas': [46.25999995015462, 0.3669090090843644],\n",
-       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [45.02666664733887, 1.2700656113571305]}},\n",
-       " 79: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.429, 0.11911758896149187],\n",
-       "   'ls': [91.448, 0.1297536126664655],\n",
-       "   'bananas': [90.21600000000001, 0.6384857085323041],\n",
-       "   'npenas': [89.64599999999999, 0.6072429497326389],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.46559998608397, 0.19008377035286986]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.99999995483398, 0.612731562374015],\n",
-       "   'ls': [73.03399995605469, 0.4179999701073772],\n",
-       "   'bananas': [72.43599995483399, 0.8196486837513948],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.94, 0.6287924935938713]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.91333329366048, 0.4356858871022288],\n",
-       "   'ls': [46.91999994913737, 0.4425180077250209],\n",
-       "   'bananas': [46.95666658223471, 0.35747573109543185],\n",
-       "   'npenas': [43.10666662902832, 2.2594394686448602],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.17999997151693, 0.9853876854992182],\n",
-       "   'dehb': [44.52333332519531, 0.9745141264064098]}},\n",
-       " 28: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.364, 0.2108174565827002],\n",
-       "   'ls': [91.42299999999999, 0.18841709051993846],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.41600000000001, 0.5246941966517259],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.25799999145508, 0.26821036478540417]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.22999994018554, 0.03605546975725657],\n",
-       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
-       "   'hb': [72.12600001586914, 0.7874033289220081],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [71.566, 0.7156423687848563]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93999993387858, 0.4875790172562598],\n",
-       "   'ls': [46.869999930826815, 0.3701200864094226],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [45.57666665039062, 0.6869902055743355],\n",
-       "   'dehb': [44.55499998677571, 0.70352485310024]}},\n",
-       " 87: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.273, 0.2031280384388106],\n",
-       "   'ls': [91.409, 0.1573817016047255],\n",
-       "   'bananas': [88.98700000000001, 1.2792267195458367],\n",
-       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'sh': [91.37039998999023, 0.14425755204992768],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.23799998901367, 0.11934823145143919]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.21999992675782, 0.0769415511601034],\n",
-       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [72.01799994140626, 0.8977060095869043],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [71.90799999999999, 1.0045377046183976],\n",
-       "   'dehb': [71.428, 0.699882847339465]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.05999994303385, 0.3101253790087158],\n",
-       "   'ls': [46.809999940999354, 0.46834221009228677],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [43.8699999572754, 1.6782563415101452],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [45.29666665649414, 0.8071830287075519]}},\n",
-       " 14: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.37499999999999, 0.20284230328015748],\n",
-       "   'ls': [91.308, 0.2271035006335206],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.27039999487305, 0.16564250393708543]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.96399997070313, 0.3975726094994739],\n",
-       "   'ls': [73.07999997802735, 0.22715628417767605],\n",
-       "   'bananas': [73.1199999267578, 0.32186955168325904],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.28, 1.5217621364720573],\n",
-       "   'dehb': [71.589, 0.9397068691884711]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.99666659342448, 0.2841947420244036],\n",
-       "   'ls': [46.836666599527994, 0.4822285980209421],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [46.216666606648765, 0.4955916790039053],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [44.55333331502278, 0.7721470779666786]}},\n",
-       " 45: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.366, 0.21574058496258602],\n",
-       "   'ls': [91.313, 0.22072833982069376],\n",
-       "   'bananas': [90.71, 0.30410524493997293],\n",
-       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.32799998657227, 0.24643376487943966]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.13599993530273, 0.27982853108044814],\n",
-       "   'ls': [72.93399996704102, 0.6142996147202994],\n",
-       "   'bananas': [72.68799996582031, 0.7742970958782901],\n",
-       "   'npenas': [71.44399995178223, 1.085976077010389],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.95, 0.6726217361935323]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.14666662699382, 0.28836701896157313],\n",
-       "   'ls': [46.91666663004557, 0.303406571821439],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.25833332316081, 1.0197834767756673],\n",
-       "   'dehb': [44.83333332010905, 0.610373286196737]}},\n",
-       " 42: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.375, 0.15964022049596213],\n",
-       "   'ls': [91.331, 0.19299999999999676],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [91.20599999999999, 0.18440173534975068],\n",
-       "   'bohb': [91.06719998901367, 0.3696259781464282],\n",
-       "   'dehb': [91.25119998583983, 0.17848966314169798]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.20399995971681, 0.08845335425469975],\n",
-       "   'ls': [73.22999994506836, 0.08999998168945353],\n",
-       "   'bananas': [72.71799993530274, 0.45461633795041795],\n",
-       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.612, 0.5207072113961919]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.86666662699381, 0.2569046389024295],\n",
-       "   'ls': [46.87999993591309, 0.4643274524076278],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [44.51166664428711, 0.7321600389476588]}},\n",
-       " 59: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.31899999999999, 0.21454370184183588],\n",
-       "   'ls': [91.22999999999999, 0.2846752535785277],\n",
-       "   'bananas': [90.71, 0.30410524493997293],\n",
-       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.3523999831543, 0.21592183863656533],\n",
-       "   'dehb': [91.40719999047852, 0.20210729756953177]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.12199993896486, 0.2641893425015655],\n",
-       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
-       "   'bananas': [69.93199996826172, 1.5213204578518387],\n",
-       "   'npenas': [72.20599997070313, 0.8890467001616745],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
-       "   'dehb': [71.433, 0.8784082194515275]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.019999952189124, 0.26381810903733605],\n",
-       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
-       "   'bananas': [46.66999997558594, 0.27262099588524663],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.576666621907556, 1.0037762152394802],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.86999998677572, 0.9859513109560508]}},\n",
-       " 0: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.47099999999998, 0.1223478647136882],\n",
-       "   'ls': [91.35900000000001, 0.18721378154398627],\n",
-       "   'bananas': [91.243, 0.14717676447048128],\n",
-       "   'npenas': [91.289, 0.21068697159529998],\n",
-       "   'sh': [91.395999984375, 0.12340502388445354],\n",
-       "   'hb': [90.367, 0.841166452017671],\n",
-       "   'bohb': [90.73359998535156, 0.6473310114046523],\n",
-       "   'dehb': [90.82279999096679, 0.46860490917418735]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.19999995239257, 0.08625540828832842],\n",
-       "   'ls': [73.18799995605468, 0.18508374165707397],\n",
-       "   'bananas': [73.16199996215819, 0.13189387159477736],\n",
-       "   'npenas': [73.00399994140625, 0.35177263147922017],\n",
-       "   'sh': [72.611, 0.36530672044187684],\n",
-       "   'hb': [71.78999997070312, 1.036387972192968],\n",
-       "   'bohb': [70.61499999999998, 0.7569841477864636],\n",
-       "   'dehb': [70.652, 1.4735861019974363]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.13666659851075, 0.2681003181753849],\n",
-       "   'ls': [46.81999996032715, 0.5302409812619554],\n",
-       "   'bananas': [46.939999925740565, 0.2259793435768361],\n",
-       "   'npenas': [46.836666628011066, 0.2869184938982587],\n",
-       "   'sh': [45.63499999186198, 0.6830670204276367],\n",
-       "   'hb': [44.70333327433268, 0.8579627423500099],\n",
-       "   'bohb': [44.27166665445964, 1.534819026705123],\n",
-       "   'dehb': [43.38166665344239, 2.1492175641832474]}},\n",
-       " 31: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.23799999999999, 0.19928873525615837],\n",
-       "   'ls': [91.27700000000002, 0.26630996977206617],\n",
-       "   'bananas': [89.789, 0.6473553892569365],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.00679998706055, 0.3960325269994897]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.19799994873047, 0.14790533979886478],\n",
-       "   'ls': [73.22199995117187, 0.090531741886827],\n",
-       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'npenas': [72.56399993774414, 0.4941092976334456],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.47800000000001, 0.793722873552225],\n",
-       "   'dehb': [70.75500000000001, 0.7333246211603685]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.829999943033854, 0.33778689474328394],\n",
-       "   'ls': [46.8966666208903, 0.37459604712308325],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
-       "   'dehb': [43.19333331807455, 3.4767577464827206]}},\n",
-       " 77: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.405, 0.16644819013734966],\n",
-       "   'ls': [91.223, 0.31438988533348006],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.01799998925782, 0.4770014620756056]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.12199995361327, 0.30938000223229134],\n",
-       "   'ls': [73.22999994506836, 0.08999998168945353],\n",
-       "   'bananas': [72.2059999560547, 0.8536065065116311],\n",
-       "   'npenas': [70.73399994506836, 1.2853341902554598],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.578, 0.9548800971849823]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.036666598510735, 0.2672701265824989],\n",
-       "   'ls': [46.79666662394206, 0.467487355491559],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.58333331197103, 0.7776388644728909],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.66499999084472, 0.7410371884103176]}},\n",
-       " 66: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.52299999999998, 0.09613012014971856],\n",
-       "   'ls': [91.252, 0.3223290244455159],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.27239999291992, 0.18047448686715087]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.0359999560547, 0.29241067469534],\n",
-       "   'ls': [73.24799994506836, 0.029933231363229132],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.027, 0.7369810038257442],\n",
-       "   'dehb': [71.82000000000001, 1.10583904796313]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.02666657104491, 0.28237091688634763],\n",
-       "   'ls': [46.94666660868327, 0.2717024347579306],\n",
-       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'npenas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [43.90999999491374, 1.5515261313569186]}},\n",
-       " 1: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.399, 0.2067583130130405],\n",
-       "   'ls': [91.292, 0.261335034008069],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [90.95599999999999, 0.26956260868302717],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.2127999897461, 0.18405912173470254],\n",
-       "   'dehb': [91.30279999121095, 0.15675764497530326]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.20199993896486, 0.07613147229773425],\n",
-       "   'ls': [73.15799994262694, 0.2742918336816008],\n",
-       "   'bananas': [72.98199997070313, 0.2760362339638427],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.657, 0.8339310523058859]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.97666660766602, 0.47329808911847193],\n",
-       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [45.523333323160806, 0.7968758469919937],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.93666665649414, 0.7420242570840452]}},\n",
-       " 84: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.336, 0.230659922830124],\n",
-       "   'ls': [91.313, 0.22072833982069376],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.27719999316406, 0.22078441679168903]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23799994750976, 0.03515675951021431],\n",
-       "   'ls': [72.91199996704101, 0.5166197732825225],\n",
-       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
-       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.88899999999998, 0.8187117930016641]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.126666557820634, 0.2053723199996796],\n",
-       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [45.06999995422363, 0.9393200879432245],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [42.989999991861986, 1.7495427913543904]}},\n",
-       " 46: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.362, 0.21572204337989784],\n",
-       "   'ls': [91.203, 0.28823774908918187],\n",
-       "   'bananas': [91.21, 0.135793961574143],\n",
-       "   'npenas': [90.108, 0.723696068802366],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.13359998901367, 0.28254387779751905],\n",
-       "   'dehb': [91.31399999243163, 0.18789145051014]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.22399994262693, 0.04715928916649512],\n",
-       "   'ls': [73.23999995849609, 0.03999996093749587],\n",
-       "   'bananas': [72.82999998901369, 0.4038068876513289],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [71.31700000000001, 0.6804270717718384]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.01666660054524, 0.1939358057911294],\n",
-       "   'ls': [46.90333326822916, 0.4842978752022971],\n",
-       "   'bananas': [44.04999994099934, 1.4740156691356903],\n",
-       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [43.68333332417805, 1.0264826576505077]}},\n",
-       " 82: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.435, 0.16752611736681397],\n",
-       "   'ls': [91.42699999999999, 0.11925183436743828],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [89.65, 0.7737053702799261],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.27759998999025, 0.18733669908749703],\n",
-       "   'dehb': [91.29199998583985, 0.15469195443081413]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.15799993530274, 0.2737078911053187],\n",
-       "   'ls': [72.90599997436523, 0.7195025715409926],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
-       "   'bohb': [71.965, 0.770340833657415],\n",
-       "   'dehb': [72.578, 0.45446231967018114]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.9933332631429, 0.2760032089464641],\n",
-       "   'ls': [46.869999930826815, 0.3701200864094226],\n",
-       "   'bananas': [42.3799999613444, 2.732528330244475],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [45.116666654459635, 1.0610791149790055],\n",
-       "   'dehb': [44.346666663614904, 1.3120128795746278]}},\n",
-       " 47: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46400000000001, 0.10753604047015765],\n",
-       "   'ls': [91.28199999999998, 0.23029546239559182],\n",
-       "   'bananas': [89.789, 0.6473553892569365],\n",
-       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [90.21519998999023, 0.849831604773715],\n",
-       "   'dehb': [91.37959998413086, 0.11661149323498196]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.18399994628905, 0.17036427320873365],\n",
-       "   'ls': [73.18799995605468, 0.18508374165707397],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.024, 0.7636779425909858],\n",
-       "   'dehb': [71.471, 0.3226282690651896]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.729999932861325, 0.15878003444423305],\n",
-       "   'ls': [46.82999995015462, 0.45933525204638725],\n",
-       "   'bananas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'npenas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.87499999287923, 1.3897091970654065],\n",
-       "   'dehb': [43.91833332417806, 0.88833333331234]}},\n",
-       " 61: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.348, 0.21989997726238733],\n",
-       "   'ls': [91.336, 0.2266803917413226],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [91.35999999999999, 0.19313207915827818],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
-       "   'dehb': [91.22759998901367, 0.181261803136699]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.13399995361328, 0.2671404334093051],\n",
-       "   'ls': [73.05799996459959, 0.3661092570406024],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [72.27199998168945, 0.7594840138443747],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.027, 0.7369810038257442],\n",
-       "   'dehb': [71.612, 0.5207072113961919]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.90999993082682, 0.3577863919355146],\n",
-       "   'ls': [46.83999995015462, 0.49436826421063906],\n",
-       "   'bananas': [46.66666662292481, 0.26288991417376534],\n",
-       "   'npenas': [46.00666661987304, 0.5023942676992026],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [43.98499998982747, 2.5176096328086137]}},\n",
-       " 27: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.405, 0.16644819013734966],\n",
-       "   'ls': [91.308, 0.2271035006335206],\n",
-       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'npenas': [90.32600000000001, 0.4875284607076801],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.3555999819336, 0.20602485508617766]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.05399994995116, 0.2790053888666053],\n",
-       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.965, 0.770340833657415],\n",
-       "   'dehb': [71.02199999999999, 0.7515557198238868]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.98666661478679, 0.4973708343821461],\n",
-       "   'ls': [47.00999993896484, 0.29403322438384805],\n",
-       "   'bananas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'npenas': [46.70666664835612, 0.3210399583220378],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [44.1383333211263, 0.6309186407275083]}},\n",
-       " 58: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.376, 0.16001249951175475],\n",
-       "   'ls': [91.47399999999999, 0.12034949106663831],\n",
-       "   'bananas': [90.71, 0.30410524493997293],\n",
-       "   'npenas': [90.686, 0.34115099296352724],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [90.95839998803712, 0.18921479786025383],\n",
-       "   'dehb': [91.49319998876953, 0.14836764696397006]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.05599994506835, 0.3963634373436752],\n",
-       "   'ls': [73.17799994384765, 0.17121915315817512],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.41, 1.2734284432193266],\n",
-       "   'dehb': [71.64699999999999, 0.6873870816359591]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.97666659444173, 0.2902297747714774],\n",
-       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
-       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
-       "   'npenas': [46.39999999694824, 0.5393411427589887],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.48666665547689, 1.0413879592348234],\n",
-       "   'dehb': [44.9316666615804, 0.7875930287576318]}},\n",
-       " 74: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.26599999999999, 0.1807871676862037],\n",
-       "   'ls': [91.365, 0.17246738822165503],\n",
-       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
-       "   'npenas': [90.14200000000001, 0.6853291180155694],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.26679999072266, 0.1798581636850016],\n",
-       "   'dehb': [91.21239998852539, 0.16232756978259588]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.24199993408203, 0.028913637907266736],\n",
-       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
-       "   'bananas': [71.72399996582033, 1.1192426223066643],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.64500000000001, 0.8852824408063242]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93333327128093, 0.42531035640443504],\n",
-       "   'ls': [46.75666660461426, 0.5264239721612104],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
-       "   'dehb': [43.99499998982747, 1.535018087914917]}},\n",
-       " 43: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.38699999999999, 0.22418965185752593],\n",
-       "   'ls': [91.40700000000001, 0.16601505955785859],\n",
-       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [91.075, 0.1410850807137311],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.13999991577148, 0.2705550005096594],\n",
-       "   'ls': [72.91799994628907, 0.7058300185027062],\n",
-       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
-       "   'npenas': [72.39999995483399, 0.7949842783859724],\n",
-       "   'sh': [72.535, 0.35964565894780276],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [72.017, 0.579776681145421]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.05999992675782, 0.17938164140943463],\n",
-       "   'ls': [46.86333328755697, 0.37400235099756535],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.6733333211263, 0.7913139531269915],\n",
-       "   'dehb': [44.66333332621256, 1.010962143481567]}},\n",
-       " 92: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.27799999999999, 0.22846443924602056],\n",
-       "   'ls': [91.27899999999998, 0.23649312886424345],\n",
-       "   'bananas': [90.32600000000001, 0.4875284607076801],\n",
-       "   'npenas': [90.755, 0.3000083332175982],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.1711999897461, 0.31614578936312165]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.05399994262694, 0.3590042063959192],\n",
-       "   'ls': [73.08599994628905, 0.29732810038297064],\n",
-       "   'bananas': [72.72799996948243, 0.5428222427792193],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [72.502, 0.5230831673835432]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.996666610717774, 0.2983100545599037],\n",
-       "   'ls': [46.869999930826815, 0.3701200864094226],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [46.293333305867506, 0.5860982382303974],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [44.91499999186199, 1.050567304176101],\n",
-       "   'dehb': [44.97333332621257, 0.8100480110329846]}},\n",
-       " 64: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.485, 0.1069813067783309],\n",
-       "   'ls': [91.16400000000002, 0.30137020423392685],\n",
-       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'npenas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.21959998901369, 0.17942530649368174]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.02199994873047, 0.4058028987534716],\n",
-       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
-       "   'bananas': [71.27999995239259, 1.1918724970886854],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.72799997314453, 1.1008796620930725],\n",
-       "   'bohb': [71.372, 0.8890309330951311],\n",
-       "   'dehb': [71.80799999999999, 0.8310691908619895]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.08999992167155, 0.4765734390749889],\n",
-       "   'ls': [46.829999938964846, 0.41297027213245097],\n",
-       "   'bananas': [43.87333329366048, 1.6771536434658265],\n",
-       "   'npenas': [46.39999994913737, 0.5729068277445918],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.87333331604004, 0.9071383422339475]}},\n",
-       " 2: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.47499999999998, 0.16384443841644183],\n",
-       "   'ls': [91.413, 0.10714942836991305],\n",
-       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.10199998657228, 0.2676968414510213]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.81599995971679, 0.7018147623583206],\n",
-       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
-       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
-       "   'npenas': [72.31799996337892, 0.7207468500286193],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.41, 1.2734284432193266],\n",
-       "   'dehb': [71.40600000000002, 0.6725503698608749]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.916666605631505, 0.32702359667147973],\n",
-       "   'ls': [46.92666660970052, 0.4263019350533028],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [41.469999971008306, 2.9911517777397147],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.87166664835612, 0.8069851319578044]}},\n",
-       " 8: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.32, 0.21227340860314964],\n",
-       "   'ls': [91.31, 0.22746428291052306],\n",
-       "   'bananas': [90.21600000000001, 0.6384857085323041],\n",
-       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.16959998828125, 0.1618908246319105],\n",
-       "   'dehb': [91.30079998901367, 0.181256067700162]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.04599993164064, 0.38987689361866185],\n",
-       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
-       "   'bananas': [72.27199994873048, 0.564496212204965],\n",
-       "   'npenas': [71.68799994750978, 0.8179584333220017],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.96900000000001, 0.7439952956840534]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.0299999420166, 0.27505552803844197],\n",
-       "   'ls': [46.83999992675781, 0.5272148850007627],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
-       "   'dehb': [44.926666662597654, 0.9440515432025652]}},\n",
-       " 29: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.41799999999998, 0.17639727889057452],\n",
-       "   'ls': [91.344, 0.2026425424238447],\n",
-       "   'bananas': [90.507, 0.48381918109971644],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.44279999291993, 0.15688008083343494]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.88999996337888, 0.7210131564861866],\n",
-       "   'ls': [73.00999995239258, 0.342023390263453],\n",
-       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.91999999999999, 0.9142756695876798]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.10999991251627, 0.300388639618315],\n",
-       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
-       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [45.523333323160806, 0.7968758469919937],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.51999999287923, 0.7036334231553507]}},\n",
-       " 48: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.32199999999999, 0.2232397814010728],\n",
-       "   'ls': [91.203, 0.28823774908918187],\n",
-       "   'bananas': [90.108, 0.723696068802366],\n",
-       "   'npenas': [91.09299999999999, 0.19370338148829244],\n",
-       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.14719999047851, 0.3476586789752243]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.10999994506835, 0.30675723352088197],\n",
-       "   'ls': [73.06399995605469, 0.408881393445887],\n",
-       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.86333330790202, 0.28104961436623016],\n",
-       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.58666666158041, 0.6115917295586587]}},\n",
-       " 52: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.39399999999999, 0.09551963149007406],\n",
-       "   'ls': [91.42699999999999, 0.12008746812219366],\n",
-       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.21639998754884, 0.26287305106602116]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.16199998046875, 0.1462736949288101],\n",
-       "   'ls': [73.03599996215819, 0.38613987386373666],\n",
-       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [71.12700000000001, 0.9840939995752443],\n",
-       "   'dehb': [71.531, 0.8652219368462648]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.063333261108404, 0.37281212744718006],\n",
-       "   'ls': [46.896666606648765, 0.5147706974218812],\n",
-       "   'bananas': [46.17666664835612, 0.4150100453010071],\n",
-       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.67999999796549, 0.41209492938083875]}},\n",
-       " 68: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.409, 0.15661736813009963],\n",
-       "   'ls': [91.38899999999998, 0.20349201458533675],\n",
-       "   'bananas': [90.108, 0.723696068802366],\n",
-       "   'npenas': [90.65, 0.3594440151122292],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.14639998779296, 0.5504622012880396],\n",
-       "   'dehb': [91.36119999194337, 0.2501330858688809]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.0899999597168, 0.27338618987337304],\n",
-       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.22, 0.9473119866232049],\n",
-       "   'dehb': [70.752, 0.6318829005440775]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.04999992167156, 0.3106444834264413],\n",
-       "   'ls': [46.80666657714844, 0.5197862540946532],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [44.76833332824707, 0.7160249107071831]}},\n",
-       " 51: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.40299999999999, 0.11498260738041606],\n",
-       "   'ls': [91.33500000000001, 0.19946177578673782],\n",
-       "   'bananas': [89.96700000000001, 0.6205328355534462],\n",
-       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.1275999897461, 0.2865935098400215],\n",
-       "   'dehb': [91.21239998486327, 0.13909938755285042]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.00199996215818, 0.6814953801594783],\n",
-       "   'ls': [73.12599997192383, 0.3115188412004214],\n",
-       "   'bananas': [72.48199993408204, 0.5818900266756044],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.12600001586914, 0.7874033289220081],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.91, 0.8083934685535257]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.7966666229248, 0.3689173505116282],\n",
-       "   'ls': [46.7666665822347, 0.48739672968011166],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.661666663614916, 0.9038205913677476]}},\n",
-       " 57: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.33600000000001, 0.20698792235296923],\n",
-       "   'ls': [91.30699999999999, 0.2433536521197062],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.686, 0.34115099296352724],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.053, 0.23639162421710094],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.26679998974608, 0.2231702524900759]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.01799996459961, 0.3770888512192757],\n",
-       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
-       "   'bananas': [71.8499999633789, 1.0354226419727222],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.556, 0.8598395199105476]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.99333325703939, 0.3032417354689357],\n",
-       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
-       "   'bananas': [45.586666631062826, 0.5177300850692277],\n",
-       "   'npenas': [46.12666664937338, 0.47628189133035953],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
-       "   'dehb': [44.3899999786377, 1.2362847559972756]}},\n",
-       " 80: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.42999999999999, 0.16334013591275956],\n",
-       "   'ls': [91.31, 0.22746428291052306],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [91.28299999999999, 0.18979199140110942],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.33199998852538, 0.1721952420729978]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.01799995483398, 0.5925841369709894],\n",
-       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
-       "   'bananas': [72.26199997436524, 0.6969906890763958],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
-       "   'dehb': [71.858, 0.8328601323151431]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.083333262125656, 0.33474699086362947],\n",
-       "   'ls': [46.75666660461426, 0.5264239721612104],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.79499999593098, 0.9498552510203311]}},\n",
-       " 70: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.405, 0.14298601330199853],\n",
-       "   'ls': [91.43, 0.20479257799050818],\n",
-       "   'bananas': [91.078, 0.10740577265678225],\n",
-       "   'npenas': [89.65, 0.7737053702799261],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.28599999047852, 0.18409780426224426]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.0499999633789, 0.32412962178541077],\n",
-       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.81400000000001, 0.6737981893712698]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.059999905395514, 0.30760726724524773],\n",
-       "   'ls': [46.809999940999354, 0.46834221009228677],\n",
-       "   'bananas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.11166664733887, 1.3045231193656752]}},\n",
-       " 53: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.36999999999999, 0.21042813500099883],\n",
-       "   'ls': [91.23499999999999, 0.26926752496355655],\n",
-       "   'bananas': [90.507, 0.48381918109971644],\n",
-       "   'npenas': [90.98800000000001, 0.2613350340080735],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.11839998754883, 0.27413398431540836],\n",
-       "   'dehb': [91.24239998974609, 0.12001599788544079]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.19199994140624, 0.07704545216442857],\n",
-       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
-       "   'bananas': [73.04799997314453, 0.2852648081717301],\n",
-       "   'npenas': [69.41199996093751, 2.010138300507699],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.66600000000001, 0.6460061919207903]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.973333266194665, 0.3006659156355776],\n",
-       "   'ls': [46.933333263142906, 0.500444231640458],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [46.19999994608561, 0.42972857008585347],\n",
-       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.633333329264325, 0.8220908236812496]}},\n",
-       " 94: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.40799999999999, 0.13636715146984302],\n",
-       "   'ls': [91.15899999999999, 0.3093687120573093],\n",
-       "   'bananas': [90.91, 0.23315231073270615],\n",
-       "   'npenas': [91.132, 0.20994284936620106],\n",
-       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.05039999145508, 0.32003099571870103],\n",
-       "   'dehb': [91.22599998657228, 0.23659078733409855]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.02399993530273, 0.4573663834081752],\n",
-       "   'ls': [72.93399996704102, 0.6142996147202994],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [71.27999995239259, 1.1918724970886854],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [72.01899999999999, 0.8726562897269456]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93666660257976, 0.32777021321277366],\n",
-       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.921666649373364, 0.7827675252240144]}},\n",
-       " 49: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.356, 0.21077001684299962],\n",
-       "   'ls': [91.37799999999999, 0.19051509126575586],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [91.334, 0.10499523798725151],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.32839998803712, 0.2085488937111959]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23999994384765, 0.030983843130847382],\n",
-       "   'ls': [73.03399996582031, 0.38544001214065354],\n",
-       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'npenas': [72.88799997680664, 0.501693134934719],\n",
-       "   'sh': [72.535, 0.35964565894780276],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [72.32300000000001, 0.6667840729951504]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.92333327229817, 0.28792358319467587],\n",
-       "   'ls': [46.836666599527994, 0.4822285980209421],\n",
-       "   'bananas': [46.54999991455078, 0.4763868603504092],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
-       "   'dehb': [45.069999985758464, 0.7163099974909092]}},\n",
-       " 67: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.36699999999999, 0.16589454481687857],\n",
-       "   'ls': [91.27, 0.24943937139112365],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.08679998999023, 0.21682841368657332]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.12999994873047, 0.22877934718253454],\n",
-       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
-       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
-       "   'npenas': [71.8499999633789, 1.0354226419727222],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.595, 0.6773514597312098]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.946666599528, 0.45927475259040246],\n",
-       "   'ls': [46.79666662394206, 0.467487355491559],\n",
-       "   'bananas': [46.21666658630372, 0.5506057456849347],\n",
-       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.415000000000006, 1.2946996658027377]}},\n",
-       " 17: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.42099999999999, 0.17455371666051453],\n",
-       "   'ls': [91.381, 0.15952742710894352],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [91.17699999999999, 0.14533065746771975],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.08359998754882, 0.2166098771134104]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2039999597168, 0.08845335425469975],\n",
-       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
-       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [70.58500000000001, 0.6309556244301178]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.87333326721191, 0.42838199276747696],\n",
-       "   'ls': [46.72666658732096, 0.5681940138884105],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'sh': [45.523333323160806, 0.7968758469919937],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.373333330281575, 1.6571595502251262]}},\n",
-       " 86: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.274, 0.2106751053162178],\n",
-       "   'ls': [91.252, 0.3223290244455159],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [90.65, 0.3594440151122292],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.18199993530273, 0.11608614782058224],\n",
-       "   'ls': [73.14399995605467, 0.3160759188618709],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.27799999999999, 0.5411432342735151]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.73333330383301, 0.2628899224286895],\n",
-       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [43.66000000101725, 1.394377599998569]}},\n",
-       " 23: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.16399999999999, 0.12579348154813053],\n",
-       "   'ls': [91.448, 0.1297536126664655],\n",
-       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.2563999934082, 0.24209386386542095],\n",
-       "   'dehb': [91.1803999934082, 0.26013811665669984]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.02199993652343, 0.592314086907299],\n",
-       "   'ls': [73.23999995849609, 0.03999996093749587],\n",
-       "   'bananas': [69.58599997680665, 1.7337139051941306],\n",
-       "   'npenas': [69.58599997680665, 1.7337139051941306],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [70.87499999999999, 0.7759413637640394]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.02666660563151, 0.3398692326613129],\n",
-       "   'ls': [46.933333263142906, 0.500444231640458],\n",
-       "   'bananas': [46.30999995625813, 0.5420229323362055],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.58333331197103, 0.7776388644728909],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [45.03666665649415, 0.7074523974708331]}},\n",
-       " 12: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.297, 0.24409219569662294],\n",
-       "   'ls': [91.31, 0.22746428291052306],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [90.92119999169923, 0.27704757625045284],\n",
-       "   'dehb': [91.26079998681641, 0.28508413159539336]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1319999597168, 0.23481055142091806],\n",
-       "   'ls': [72.98599995483399, 0.45695074125831625],\n",
-       "   'bananas': [69.58599997680665, 1.7337139051941306],\n",
-       "   'npenas': [71.39799995788574, 1.1144308062423007],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.43199999999999, 0.6469590404345532]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.01999995117187, 0.40993219730296676],\n",
-       "   'ls': [47.00666661478679, 0.32138586040782224],\n",
-       "   'bananas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [45.05166667378744, 1.18622768273718]}},\n",
-       " 65: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.41999999999999, 0.1687601848778277],\n",
-       "   'ls': [91.306, 0.26431799030712894],\n",
-       "   'bananas': [91.132, 0.1655777762865499],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.26239999145508, 0.27149482506865885],\n",
-       "   'dehb': [91.03244443196614, 0.2862939845812309]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23799994018555, 0.030265467781614545],\n",
-       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
-       "   'bananas': [72.57599998657227, 0.48971830220809554],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.43299999999999, 0.7531673120894177]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.87666658630372, 0.4343705612392511],\n",
-       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
-       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
-       "   'hb': [45.31333332417806, 0.5057887203043718],\n",
-       "   'bohb': [43.541666662597656, 1.713301708439184],\n",
-       "   'dehb': [44.008333328247076, 0.9754842129860548]}},\n",
-       " 24: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.453, 0.11807201192492535],\n",
-       "   'ls': [91.31, 0.22746428291052306],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.25559998999023, 0.19524303164031448]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23599993652343, 0.04543127138791328],\n",
-       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
-       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.385, 0.9288191427829203],\n",
-       "   'dehb': [71.03699999999999, 0.825748751134386]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.13999992370606, 0.2462158228979491],\n",
-       "   'ls': [46.809999940999354, 0.46834221009228677],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
-       "   'dehb': [44.948333319091795, 1.120774783881138]}},\n",
-       " 62: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.403, 0.08567963585356549],\n",
-       "   'ls': [91.413, 0.19042321287069813],\n",
-       "   'bananas': [91.225, 0.20963062753328493],\n",
-       "   'npenas': [90.65, 0.3594440151122292],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.17959999389647, 0.18826747468020216]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.18999995483398, 0.14649228503644],\n",
-       "   'ls': [73.11999994873047, 0.28227644224013915],\n",
-       "   'bananas': [70.70399995361329, 1.2985468673780534],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.027, 0.7369810038257442],\n",
-       "   'dehb': [71.604, 0.7328055676644397]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.10666657613119, 0.2520140906268146],\n",
-       "   'ls': [46.789999944051104, 0.5006773160029646],\n",
-       "   'bananas': [45.586666631062826, 0.5177300850692277],\n",
-       "   'npenas': [45.06999995422363, 0.9393200879432245],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.92499998982747, 1.1357388704929123],\n",
-       "   'dehb': [43.73833330790202, 1.1883707359173892]}},\n",
-       " 89: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.34700000000001, 0.15133076356114636],\n",
-       "   'ls': [91.31, 0.22746428291052306],\n",
-       "   'bananas': [90.21600000000001, 0.6384857085323041],\n",
-       "   'npenas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [91.053, 0.23639162421710094],\n",
-       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
-       "   'dehb': [91.21919998999024, 0.25761242538832047]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.22399993408203, 0.08845335734601689],\n",
-       "   'ls': [72.87799997192383, 0.5940673165120702],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [72.312, 0.4521238768302327],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.505, 0.672610585703199]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93999992167154, 0.32448248773548183],\n",
-       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
-       "   'bananas': [46.16666663818359, 0.3762386797796466],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.79666665649414, 0.6369981679366045]}},\n",
-       " 3: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.44399999999999, 0.1774936618586682],\n",
-       "   'ls': [91.221, 0.28532262440962985],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.16199998901367, 0.14852070453241303]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.15399994018554, 0.1627390359240187],\n",
-       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [71.756, 0.7842601609160081]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.16999996337891, 0.2040969003820301],\n",
-       "   'ls': [46.77333328552246, 0.5318311622421547],\n",
-       "   'bananas': [46.16000001525879, 0.5242560982798979],\n",
-       "   'npenas': [46.78999995625814, 0.3580967864297857],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.47166665547689, 1.0465034372858832],\n",
-       "   'dehb': [44.87499998982747, 0.7711913615281554]}},\n",
-       " 15: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.35499999999999, 0.22646191732827542],\n",
-       "   'ls': [91.207, 0.2838326972003031],\n",
-       "   'bananas': [90.71, 0.30410524493997293],\n",
-       "   'npenas': [90.686, 0.34115099296352724],\n",
-       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.10759997705078, 0.27653542565428774],\n",
-       "   'dehb': [91.1143999819336, 0.2306040718883427]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.24399993041992, 0.023323793343668523],\n",
-       "   'ls': [73.15199994750976, 0.24119700324630985],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.62, 0.8167863857827218],\n",
-       "   'dehb': [71.96199999999999, 0.7496505852728994]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.01333325500489, 0.48309187068330456],\n",
-       "   'ls': [46.79666662394206, 0.467487355491559],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [46.49333323669434, 0.563481230212473],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.576666621907556, 1.0037762152394802],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.831666659545895, 0.5728801461874484]}},\n",
-       " 72: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.45500000000001, 0.1253195914452297],\n",
-       "   'ls': [91.24299999999998, 0.3198765386832835],\n",
-       "   'bananas': [90.94699999999999, 0.1976891499298823],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.32959998901367, 0.20578201776622312],\n",
-       "   'dehb': [91.05119999267579, 0.32466500134092596]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.08599995239258, 0.3073825044437118],\n",
-       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
-       "   'bananas': [72.10399997436522, 0.6933859086894885],\n",
-       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.33300000000001, 0.5587315992495866]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.17666661885579, 0.2246231122157868],\n",
-       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [44.396666657511396, 1.0466295124789269]}},\n",
-       " 81: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.325, 0.19247077700263657],\n",
-       "   'ls': [91.15899999999999, 0.3093687120573093],\n",
-       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'npenas': [88.98700000000001, 1.2792267195458367],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.1715999868164, 0.2510741716604863]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2439999304199, 0.023323793343668527],\n",
-       "   'ls': [72.85399997436522, 0.7095378407774434],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [72.22799999023437, 0.8239999970430587],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.916666540527345, 0.2941087993010106],\n",
-       "   'ls': [46.88999991963705, 0.48236683273068753],\n",
-       "   'bananas': [46.81666661987305, 0.39306487315307015],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.98166665039063, 0.9729522596171338]}},\n",
-       " 75: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.26499999999999, 0.2260199106273591],\n",
-       "   'ls': [91.274, 0.29428557558942375],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [91.032, 0.261526289309503],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.1715999909668, 0.23684898142600977],\n",
-       "   'dehb': [91.21039998510741, 0.2072926439824124]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.14799991699218, 0.2723527273871788],\n",
-       "   'ls': [73.08599994628905, 0.29732810038297064],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [70.0899999560547, 1.3856478538407666],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.79999999999998, 0.8612316761475969]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.039999950154616, 0.3598765041126613],\n",
-       "   'ls': [46.9799999053955, 0.38244826113431935],\n",
-       "   'bananas': [46.14666664326986, 0.4529409840372371],\n",
-       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.98666664530437, 0.8390404577916358],\n",
-       "   'dehb': [44.4566666615804, 0.9982262156934935]}},\n",
-       " 93: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.338, 0.14260434775980418],\n",
-       "   'ls': [91.345, 0.19210674116230142],\n",
-       "   'bananas': [89.58200000000001, 0.8309969915709677],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
-       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.07199994384766, 0.46589268152990576],\n",
-       "   'ls': [73.03599994750975, 0.43733739631192065],\n",
-       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
-       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [72.06700000000001, 0.613156586852004]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.933333267211914, 0.4659518520237985],\n",
-       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
-       "   'bananas': [45.74666662190755, 0.49937738128039566],\n",
-       "   'npenas': [45.74666662190755, 0.49937738128039566],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
-       "   'bohb': [44.20333334147136, 1.0258275684185412],\n",
-       "   'dehb': [44.37499998474121, 1.1225034045437063]}},\n",
-       " 4: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.47999999999999, 0.14778362561528577],\n",
-       "   'ls': [91.31, 0.22445489524623813],\n",
-       "   'bananas': [91.147, 0.14893287078411946],\n",
-       "   'npenas': [90.21600000000001, 0.6384857085323041],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.15879998730468, 0.2170349333983711],\n",
-       "   'dehb': [91.28919998828125, 0.2226893839048166]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.22799994995117, 0.04118249115978971],\n",
-       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
-       "   'bananas': [71.44399994934082, 1.085976078817879],\n",
-       "   'npenas': [73.1399999206543, 0.1335664597491603],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.11, 0.8461560139832375]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.169999918619794, 0.19857828352987805],\n",
-       "   'ls': [46.94999994710287, 0.2891558226092122],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [45.13666662190755, 0.658525305689547],\n",
-       "   'bohb': [44.48666665547689, 1.0413879592348234],\n",
-       "   'dehb': [45.20833331807454, 0.8498447548008442]}},\n",
-       " 85: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.38899999999998, 0.20685502169393585],\n",
-       "   'ls': [91.41799999999999, 0.1275774274705309],\n",
-       "   'bananas': [89.789, 0.6473553892569365],\n",
-       "   'npenas': [90.686, 0.34115099296352724],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.23959998266601, 0.17976384913236232]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.20599997558595, 0.04103653276637411],\n",
-       "   'ls': [73.03599994750975, 0.43733739631192065],\n",
-       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.42199997070313, 0.5972905783047683],\n",
-       "   'bohb': [71.024, 0.7636779425909858],\n",
-       "   'dehb': [71.443, 1.1093786549235571]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.913333249918615, 0.47192274766763537],\n",
-       "   'ls': [46.86999993184408, 0.4061882014878633],\n",
-       "   'bananas': [44.3433332804362, 1.3207783721457254],\n",
-       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
-       "   'dehb': [44.5566666595459, 0.9461089888183467]}},\n",
-       " 18: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.445, 0.10385085459445893],\n",
-       "   'ls': [91.232, 0.2774094446842062],\n",
-       "   'bananas': [89.927, 0.5511633151798111],\n",
-       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'sh': [91.37159998364258, 0.14811292950094573],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.92119999169923, 0.27704757625045284],\n",
-       "   'dehb': [91.06999998535156, 0.3004702965703844]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23799994750978, 0.03515675951021431],\n",
-       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [72.09400000000001, 0.7941435638472438],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
-       "   'dehb': [71.897, 0.7753457293362738]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.90666659240723, 0.5454457672166005],\n",
-       "   'ls': [46.949999954223635, 0.30849092020267993],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [44.51499999084473, 0.9429990533019659]}},\n",
-       " 96: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.376, 0.1955095905575952],\n",
-       "   'ls': [91.41299999999998, 0.23516164653276148],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [89.789, 0.6473553892569365],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.21559998510742, 0.18816546114878685]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.02599994384765, 0.5899186579421641],\n",
-       "   'ls': [73.06399995605469, 0.408881393445887],\n",
-       "   'bananas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'npenas': [70.8579999584961, 1.2019800200639044],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.05399998413085, 0.8245265404007283],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.857, 0.7124471910254133]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.073333251953116, 0.21281185676476877],\n",
-       "   'ls': [46.91999994913737, 0.4425180077250209],\n",
-       "   'bananas': [46.249999894205736, 0.43262763292350354],\n",
-       "   'npenas': [45.586666631062826, 0.5177300850692277],\n",
-       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [45.119999990844725, 1.0011327006407615]}},\n",
-       " 40: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.357, 0.22122612865572278],\n",
-       "   'ls': [91.31899999999999, 0.2185612042426535],\n",
-       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [91.37399999511719, 0.14371360640529274],\n",
-       "   'hb': [91.006, 0.27302747114530307],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.44599998681642, 0.14224766822424434]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.84799998901369, 0.6752895406425103],\n",
-       "   'ls': [73.21599994018554, 0.09499472518250926],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [71.834, 1.091505382487874]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.16666659952799, 0.23094009798522308],\n",
-       "   'ls': [46.986666602579746, 0.27936434351666317],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [46.756666600545245, 0.26248806222081056],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [45.16499998677572, 0.6560170279957473]}},\n",
-       " 25: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.41799999999998, 0.248225703745599],\n",
-       "   'ls': [91.34399999999998, 0.242577822564223],\n",
-       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
-       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
-       "   'dehb': [91.44039998706054, 0.19235342365143787]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1299999609375, 0.2665708371717949],\n",
-       "   'ls': [73.14399995605467, 0.3160759188618709],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'sh': [72.312, 0.4521238768302327],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.675, 0.493725632310092]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.12999988708496, 0.25053279798882916],\n",
-       "   'ls': [47.05666658020019, 0.2060474406308672],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [43.8699999572754, 1.6782563415101452],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.85166664733886, 0.8143658033995257]}},\n",
-       " 5: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.42299999999999, 0.16763352886579266],\n",
-       "   'ls': [91.24299999999998, 0.3198765386832835],\n",
-       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'npenas': [91.35999999999999, 0.2077498495787624],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.23159998876953, 0.1981470151776915]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.17599994873046, 0.15919796947929366],\n",
-       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
-       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.287, 0.9833824281529533]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.92999994812011, 0.3658931134180845],\n",
-       "   'ls': [46.79666662394206, 0.467487355491559],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.551666652425126, 1.1621687212379304]}},\n",
-       " 90: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.277, 0.1921483801649101],\n",
-       "   'ls': [91.252, 0.3223290244455159],\n",
-       "   'bananas': [90.71, 0.30410524493997293],\n",
-       "   'npenas': [91.26399999999998, 0.13654303350958258],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.15279998754883, 0.19886719122692054]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.15599993530273, 0.22694491448143697],\n",
-       "   'ls': [73.06799995117187, 0.28596503931588974],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [72.29199995971679, 0.8251157543320088],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.72799997314453, 1.1008796620930725],\n",
-       "   'bohb': [70.91499999999999, 0.7585940943614059],\n",
-       "   'dehb': [71.181, 0.5073351949155495]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.00666659444173, 0.31756013995985427],\n",
-       "   'ls': [46.9699999206543, 0.4895462747637796],\n",
-       "   'bananas': [46.73333332010905, 0.45946824583320334],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
-       "   'dehb': [44.19499998677571, 0.771364233834982]}},\n",
-       " 16: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.30699999999999, 0.21508370463612164],\n",
-       "   'ls': [91.27899999999998, 0.23649312886424345],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [91.069, 0.30615192307088085],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.17519998754882, 0.2500483142877078]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23199993286133, 0.03249612582079108],\n",
-       "   'ls': [73.0759999609375, 0.36538195084049135],\n",
-       "   'bananas': [72.42799999511718, 0.6395123114637266],\n",
-       "   'npenas': [71.72399996582033, 1.1192426223066643],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.16800000000002, 1.2521565397345489],\n",
-       "   'dehb': [71.199, 1.126502996001343]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.13999992268881, 0.3241398844843741],\n",
-       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
-       "   'bananas': [45.74666662190755, 0.49937738128039566],\n",
-       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [43.399999994913735, 2.1122394738016497]}},\n",
-       " 7: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.326, 0.25542905081450473],\n",
-       "   'ls': [91.37299999999999, 0.20288173895153644],\n",
-       "   'bananas': [89.74600000000001, 0.6606693575458151],\n",
-       "   'npenas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.42879998486328, 0.16628457177193315]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.139999954834, 0.26996298058932716],\n",
-       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [72.69999998291016, 0.468444236995005],\n",
-       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [72.004, 0.8930419922937567],\n",
-       "   'dehb': [71.26800000000001, 0.8609041758523415]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.196666573079426, 0.2405780072258597],\n",
-       "   'ls': [46.80666657714844, 0.5197862540946532],\n",
-       "   'bananas': [46.279999991861985, 0.49714740034066196],\n",
-       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.76166665039062, 0.8611055500089961],\n",
-       "   'dehb': [44.90999999084473, 0.7270641024554297]}},\n",
-       " 44: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.361, 0.19856736892047192],\n",
-       "   'ls': [91.28799999999998, 0.27967123556061235],\n",
-       "   'bananas': [89.96700000000001, 0.6205328355534462],\n",
-       "   'npenas': [90.686, 0.34115099296352724],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.07300000000001, 0.14339107364128312],\n",
-       "   'bohb': [91.27759998999025, 0.18733669908749703],\n",
-       "   'dehb': [90.90519998364259, 0.27159999997878453]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.13399993286131, 0.22218011805379909],\n",
-       "   'ls': [72.85399997436522, 0.7095378407774434],\n",
-       "   'bananas': [72.87199997680663, 0.5133575844779364],\n",
-       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [70.765, 0.7578819169237366]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.08333325805664, 0.2717228864603964],\n",
-       "   'ls': [46.90333326822916, 0.4842978752022971],\n",
-       "   'bananas': [46.8299999613444, 0.32436258075569047],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.679999962361656, 0.9376329541429751],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.6733333211263, 0.7913139531269915],\n",
-       "   'dehb': [44.599999995930986, 0.7114929463416747]}},\n",
-       " 10: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.48299999999999, 0.10909170454255103],\n",
-       "   'ls': [91.361, 0.1783507779629783],\n",
-       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'npenas': [91.39699999999998, 0.17000294115102588],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.30359999072266, 0.15294914066855703]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.25799993530272, 0.006000010986325321],\n",
-       "   'ls': [73.1679999328613, 0.27600001831054616],\n",
-       "   'bananas': [71.5139999523926, 1.0188640953962913],\n",
-       "   'npenas': [70.00799994750977, 1.4137100085040557],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [71.612, 0.5207072113961919]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.95333328653971, 0.2940899169161976],\n",
-       "   'ls': [46.91999994913737, 0.4425180077250209],\n",
-       "   'bananas': [43.94999995015463, 1.173906104664524],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.81833331604004, 1.3276639885096027]}},\n",
-       " 50: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46900000000001, 0.10123734488813656],\n",
-       "   'ls': [91.274, 0.29428557558942375],\n",
-       "   'bananas': [91.244, 0.12092973166264599],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.37039998999023, 0.14425755204992768],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.18199998657227, 0.2862460478524256],\n",
-       "   'dehb': [91.3175999868164, 0.1613760878739472]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2079999621582, 0.04118247811762278],\n",
-       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
-       "   'bananas': [70.6419999584961, 1.37475668804989],\n",
-       "   'npenas': [72.06399996459962, 0.9854054985002976],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.291, 0.5339559906958629]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.02666660970052, 0.29694741735997576],\n",
-       "   'ls': [46.94999992065429, 0.2880007534723706],\n",
-       "   'bananas': [44.3433332804362, 1.3207783721457254],\n",
-       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.86333332926432, 1.035583579202776]}},\n",
-       " 55: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.40899999999998, 0.15852129194527512],\n",
-       "   'ls': [91.18299999999999, 0.3113856130266761],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.26079999096677, 0.2398919746322682],\n",
-       "   'dehb': [91.03079998828125, 0.2769320476415453]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.08399997558595, 0.31251237342691646],\n",
-       "   'ls': [73.17399994750977, 0.23255966263428118],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [68.56599994750977, 2.193318024787204],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.648, 1.0161574681121048],\n",
-       "   'dehb': [72.16900000000001, 0.7129719489573216]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.68666661987305, 0.32359096198300696],\n",
-       "   'ls': [46.833333276367185, 0.45971003488685935],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
-       "   'dehb': [44.839999996948244, 1.136520042787577]}},\n",
-       " 36: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.45599999999999, 0.1699529346613337],\n",
-       "   'ls': [91.261, 0.24772767306055807],\n",
-       "   'bananas': [90.98799999999999, 0.17971087891388332],\n",
-       "   'npenas': [90.65, 0.3594440151122292],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.29799998535157, 0.18453184219316485]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23999994384766, 0.030983843130847382],\n",
-       "   'ls': [73.24999994873046, 0.02999997070312191],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [72.004, 0.8930419922937567],\n",
-       "   'dehb': [72.03899999999999, 0.7531062342060382]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.969999937947584, 0.458124175324279],\n",
-       "   'ls': [46.9799999053955, 0.38244826113431935],\n",
-       "   'bananas': [46.24666660664876, 0.40857476400546877],\n",
-       "   'npenas': [43.10666662902832, 2.2594394686448602],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.70333327433268, 0.8579627423500099],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.52833332316081, 0.7983333363065136]}},\n",
-       " 22: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46, 0.11198214143335324],\n",
-       "   'ls': [91.27, 0.24943937139112365],\n",
-       "   'bananas': [88.98700000000001, 1.2792267195458367],\n",
-       "   'npenas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'sh': [91.37399999511719, 0.14371360640529274],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.13359998901367, 0.28254387779751905],\n",
-       "   'dehb': [91.33799999023435, 0.1773426030524605]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.15199997436522, 0.16351143368374998],\n",
-       "   'ls': [72.8579999584961, 0.6428343626872822],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [72.01000000000002, 0.8846468221838579],\n",
-       "   'dehb': [71.672, 0.5082086185809914]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.9233332631429, 0.38007309562413083],\n",
-       "   'ls': [46.81999996032715, 0.5302409812619554],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.46833333129884, 0.6893011726929609]}},\n",
-       " 97: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.345, 0.24042670400768532],\n",
-       "   'ls': [91.308, 0.2271035006335206],\n",
-       "   'bananas': [89.363, 1.132616881385759],\n",
-       "   'npenas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [90.367, 0.841166452017671],\n",
-       "   'bohb': [91.25919998535156, 0.19695928734409582],\n",
-       "   'dehb': [91.38839998388671, 0.16294735698871998]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.94399997558594, 0.4586763290747864],\n",
-       "   'ls': [73.03599996215819, 0.38613987386373666],\n",
-       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'npenas': [71.3139999621582, 1.165711812381764],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.246, 0.604337653965067]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.93999993591309, 0.4154782823909908],\n",
-       "   'ls': [46.926666622924806, 0.28039651961332607],\n",
-       "   'bananas': [46.27333329366048, 0.6051262299981502],\n",
-       "   'npenas': [47.12999995625813, 0.22776690312589737],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.97166665445964, 0.9714264997224785]}},\n",
-       " 37: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.36799999999998, 0.24326117651610174],\n",
-       "   'ls': [91.40700000000001, 0.16601505955785859],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [89.52700000000002, 0.8955227523631091],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.29439998632813, 0.2009194849603194]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1279999609375, 0.31128120323950825],\n",
-       "   'ls': [73.05799994873045, 0.38277408358260157],\n",
-       "   'bananas': [73.10599997680666, 0.1750542768368422],\n",
-       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
-       "   'sh': [72.54899999999999, 0.45654025014230665],\n",
-       "   'hb': [72.06399999633788, 0.8158823457637169],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [71.524, 0.6692264190840049]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.10999989318848, 0.2027861550933387],\n",
-       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
-       "   'bananas': [46.84333327840169, 0.45681258035956873],\n",
-       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.81999998982748, 1.0729709564636138],\n",
-       "   'dehb': [44.34999998677572, 1.0750452170628257]}},\n",
-       " 30: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.34700000000001, 0.2050390206765506],\n",
-       "   'ls': [91.361, 0.1783507779629783],\n",
-       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'npenas': [90.507, 0.48381918109971644],\n",
-       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.21879998925782, 0.17937714589744344],\n",
-       "   'dehb': [91.23319999365233, 0.190721155317251]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.00799994873049, 0.6838245016898179],\n",
-       "   'ls': [73.02999996948242, 0.41648526991204937],\n",
-       "   'bananas': [72.36599995117187, 0.5598607256095786],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.723, 0.9278043974890402],\n",
-       "   'dehb': [71.413, 0.8972073338978016]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.89999995117188, 0.26791371582685164],\n",
-       "   'ls': [46.896666606648765, 0.5147706974218812],\n",
-       "   'bananas': [46.696666621907546, 0.28341176804307383],\n",
-       "   'npenas': [45.08999995117187, 0.9072608442758965],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.77999999287924, 0.8537108583323764]}},\n",
-       " 95: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.33900000000001, 0.2031477294975227],\n",
-       "   'ls': [91.26699999999998, 0.2572955499032189],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.10479998754883, 0.3791012490814029],\n",
-       "   'dehb': [91.20239999389648, 0.1735022786346223]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1539999279785, 0.27233069669844234],\n",
-       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
-       "   'bananas': [70.8579999584961, 1.2019800200639044],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [71.024, 0.7636779425909858],\n",
-       "   'dehb': [71.868, 0.8223600184833896]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.92333324991862, 0.41124472727077055],\n",
-       "   'ls': [46.863333264160154, 0.5184270859234282],\n",
-       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
-       "   'dehb': [44.49999998779297, 1.3177421762189991]}},\n",
-       " 38: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.38399999999999, 0.1941751786403173],\n",
-       "   'ls': [91.28199999999998, 0.23029546239559182],\n",
-       "   'bananas': [90.44500000000001, 0.4959687490155007],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.27519998706056, 0.18240877118567775],\n",
-       "   'dehb': [91.37239999072266, 0.1789073493405069]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23999994384765, 0.03098384313084738],\n",
-       "   'ls': [72.97199994628906, 0.4051863996687985],\n",
-       "   'bananas': [69.98199995117189, 1.4119617528819854],\n",
-       "   'npenas': [72.86199997558593, 0.36512466901838325],\n",
-       "   'sh': [72.611, 0.36530672044187684],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.09100000000001, 0.7481236528809931]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.05333327941895, 0.28409699915032105],\n",
-       "   'ls': [46.949999954223635, 0.30849092020267993],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.65499999389648, 0.7799946632919993]}},\n",
-       " 98: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.41599999999998, 0.19698730923589636],\n",
-       "   'ls': [91.381, 0.15952742710894352],\n",
-       "   'bananas': [90.26100000000001, 0.5657287335817411],\n",
-       "   'npenas': [90.108, 0.723696068802366],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.27239998779297, 0.1608621736218432]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.01999993530274, 0.45571922036765133],\n",
-       "   'ls': [73.07399994995117, 0.3099741808725649],\n",
-       "   'bananas': [70.8579999584961, 1.2019800200639044],\n",
-       "   'npenas': [71.27999995239259, 1.1918724970886854],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.26599999999999, 0.6790905683338548]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.8666665802002, 0.2992583250500575],\n",
-       "   'ls': [46.76666659444173, 0.4777260668624399],\n",
-       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [42.84999999084473, 2.6502410795007316]}},\n",
-       " 13: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.49499999999998, 0.11689739090329977],\n",
-       "   'ls': [91.22, 0.28544701785094784],\n",
-       "   'bananas': [91.298, 0.21599999999999786],\n",
-       "   'npenas': [90.755, 0.3000083332175982],\n",
-       "   'sh': [90.98599999999999, 0.25476263462289656],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.3295999892578, 0.11911944091665896]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.18199995239257, 0.17215106194817362],\n",
-       "   'ls': [72.82799997680664, 0.7191772759739727],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [72.11399998291014, 0.8568337240411942],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.965, 0.770340833657415],\n",
-       "   'dehb': [71.819, 0.7527343488907612]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.039999933878576, 0.27153883209318436],\n",
-       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
-       "   'bananas': [44.53333327433268, 1.035159675046747],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.276666659545896, 0.5789933833108567]}},\n",
-       " 34: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.37799999999999, 0.19093454375779909],\n",
-       "   'ls': [91.35199999999999, 0.19197916553626207],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [89.96100000000001, 0.6287360336421008],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.27039998510743, 0.1907224177592602]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2419999267578, 0.022715618336443765],\n",
-       "   'ls': [73.24799994506836, 0.029933231363229132],\n",
-       "   'bananas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.41, 1.2734284432193266],\n",
-       "   'dehb': [70.934, 0.667760436084675]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.89333326619466, 0.6246421025405501],\n",
-       "   'ls': [47.01333326212565, 0.3938978746038199],\n",
-       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [45.02833333231608, 1.0689052008193356]}},\n",
-       " 99: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.32900000000001, 0.2216506259860291],\n",
-       "   'ls': [91.299, 0.2411825035113437],\n",
-       "   'bananas': [90.507, 0.48381918109971644],\n",
-       "   'npenas': [90.971, 0.1949076704493708],\n",
-       "   'sh': [91.34759998876953, 0.182694943973596],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.20439998437499, 0.2561652619701485]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.08799998779298, 0.33801772298903365],\n",
-       "   'ls': [72.83599997070311, 0.7234804416656028],\n",
-       "   'bananas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.12600001586914, 0.7874033289220081],\n",
-       "   'bohb': [71.851, 0.9484139391636973],\n",
-       "   'dehb': [71.61999999999999, 0.810234533946807]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.86666659037272, 0.26956751089620473],\n",
-       "   'ls': [47.02333325703939, 0.49081562974454335],\n",
-       "   'bananas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'npenas': [42.203333310953774, 2.9346379440313997],\n",
-       "   'sh': [45.54666665751139, 0.7687218828708362],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.749999985758464, 0.8784139794635262]}},\n",
-       " 91: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.327, 0.14360013927569598],\n",
-       "   'ls': [91.221, 0.28532262440962985],\n",
-       "   'bananas': [91.18, 0.1490637447537122],\n",
-       "   'npenas': [90.97300000000001, 0.1410000000000001],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.22999999829102, 0.1882402676274257]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.189999954834, 0.14649228503644002],\n",
-       "   'ls': [72.95599997314453, 0.5759721892784991],\n",
-       "   'bananas': [70.1479999621582, 1.3200969521427772],\n",
-       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.965, 0.770340833657415],\n",
-       "   'dehb': [71.423, 0.7924651411891871]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.056666611735025, 0.41447420927951434],\n",
-       "   'ls': [46.8966666208903, 0.37459604712308325],\n",
-       "   'bananas': [45.196666635131834, 0.7421365378565281],\n",
-       "   'npenas': [46.946666565958665, 0.3273462199712924],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [45.04999998168945, 0.9312178370031363]}},\n",
-       " 32: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.28299999999999, 0.2599634589706778],\n",
-       "   'ls': [91.413, 0.19042321287069813],\n",
-       "   'bananas': [90.755, 0.3000083332175982],\n",
-       "   'npenas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.20479998608398, 0.28419598794023915]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.21999992675781, 0.0769415511601034],\n",
-       "   'ls': [72.87799997192383, 0.5940673165120702],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.529, 1.0231368432423888],\n",
-       "   'dehb': [71.96400000000001, 0.852187772735563]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.886666620890296, 0.444522179565979],\n",
-       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
-       "   'bananas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'npenas': [44.04999994099934, 1.4740156691356903],\n",
-       "   'sh': [44.55999995524088, 0.7734195293840342],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.846666666666664, 0.7154563534659095]}},\n",
-       " 26: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46399999999998, 0.10753604047015765],\n",
-       "   'ls': [91.38899999999998, 0.20349201458533675],\n",
-       "   'bananas': [91.173, 0.2193194017865242],\n",
-       "   'npenas': [89.363, 1.132616881385759],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.3383999880371, 0.22156678124125423]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.25599993164062, 0.008000014648433761],\n",
-       "   'ls': [72.93599996459962, 0.4880819392818451],\n",
-       "   'bananas': [71.8499999633789, 1.0354226419727222],\n",
-       "   'npenas': [70.1479999621582, 1.3200969521427772],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [72.03199999511719, 0.9305568302244341],\n",
-       "   'bohb': [70.846, 0.5039087218931632],\n",
-       "   'dehb': [71.715, 0.9484329180284714]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.17333325297037, 0.2563851463888792],\n",
-       "   'ls': [46.82333329162598, 0.42818735192149715],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.73333332722982, 1.1692590257993514]}},\n",
-       " 39: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.28099999999999, 0.18880942773071246],\n",
-       "   'ls': [91.33500000000001, 0.19946177578673782],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [90.87, 0.20610676844781026],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.02639998168945, 0.29246647341578763]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.22799994140624, 0.08953209838167313],\n",
-       "   'ls': [73.04799997070313, 0.36179550849990494],\n",
-       "   'bananas': [70.73399994506836, 1.2853341902554598],\n",
-       "   'npenas': [71.44399995178223, 1.085976077010389],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.643, 0.628920503720462]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.97999996439616, 0.3297136685737497],\n",
-       "   'ls': [46.829999938964846, 0.41297027213245097],\n",
-       "   'bananas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'npenas': [43.8699999572754, 1.6782563415101452],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.58333331197103, 0.7776388644728909],\n",
-       "   'bohb': [44.97166665140788, 0.9442530779799753],\n",
-       "   'dehb': [45.04833332417806, 0.690830828018462]}},\n",
-       " 33: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.41099999999999, 0.19180458805774037],\n",
-       "   'ls': [91.27, 0.24943937139112365],\n",
-       "   'bananas': [89.363, 1.132616881385759],\n",
-       "   'npenas': [90.71, 0.30410524493997293],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [91.01200000000001, 0.2691765220073962],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.36839999121094, 0.16965329153687703]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.9399999621582, 0.5026330706498948],\n",
-       "   'ls': [73.22999994506836, 0.08999998168945353],\n",
-       "   'bananas': [73.11599994506835, 0.16292326831188456],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [72.54899999999999, 0.45654025014230665],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.62, 0.8167863857827218],\n",
-       "   'dehb': [71.35400000000001, 0.6249991999994867]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.10333327128093, 0.3291233944909905],\n",
-       "   'ls': [46.82333330790202, 0.4494564285556264],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [46.473333266194665, 0.4464177873787461],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [44.17999997151693, 0.9853876854992182],\n",
-       "   'dehb': [44.42999998270671, 0.8524735311257914]}},\n",
-       " 69: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.39699999999999, 0.16401524319403626],\n",
-       "   'ls': [91.261, 0.24772767306055807],\n",
-       "   'bananas': [90.59700000000001, 0.436487113211834],\n",
-       "   'npenas': [91.023, 0.19287560758167172],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.31559998730468, 0.1885731678640428],\n",
-       "   'dehb': [91.28959998583984, 0.15087159931181932]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23799994750976, 0.03515675951021431],\n",
-       "   'ls': [72.8579999584961, 0.6428343626872822],\n",
-       "   'bananas': [72.03199995239258, 0.8140368606327091],\n",
-       "   'npenas': [69.98199995117189, 1.4119617528819854],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [69.85, 0.6900724599634448],\n",
-       "   'dehb': [71.59700000000001, 1.0034844293759613]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.046666596476236, 0.21354155497329988],\n",
-       "   'ls': [46.82333327331544, 0.5266350010426505],\n",
-       "   'bananas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.32666663208008, 1.0456045442043505],\n",
-       "   'bohb': [44.19166666056315, 0.9899284469486298],\n",
-       "   'dehb': [43.97666664530436, 1.2357229116540107]}},\n",
-       " 21: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.303, 0.28032302795167985],\n",
-       "   'ls': [91.37299999999999, 0.20288173895153644],\n",
-       "   'bananas': [90.961, 0.19582900704441078],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [90.517199986084, 0.6501496427132121],\n",
-       "   'dehb': [91.39199999462889, 0.1714036177612395]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.1499999597168, 0.22684795826058213],\n",
-       "   'ls': [73.15799994262694, 0.2742918336816008],\n",
-       "   'bananas': [71.39799995788574, 1.1144308062423007],\n",
-       "   'npenas': [71.43799996643067, 1.090557662459411],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [70.58999999999999, 0.4154515615568193]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.086666599528, 0.35030145259925016],\n",
-       "   'ls': [46.88999994608561, 0.3022324131625011],\n",
-       "   'bananas': [46.73333325093587, 0.5131601637679889],\n",
-       "   'npenas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.3699999633789, 0.8196815063903833],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.58499998779297, 1.0919770695844846]}},\n",
-       " 73: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.52499999999998, 0.09135097153287174],\n",
-       "   'ls': [91.361, 0.20954474462510225],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [88.98700000000001, 1.2792267195458367],\n",
-       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
-       "   'hb': [91.02000000000001, 0.237191905426807],\n",
-       "   'bohb': [91.01399998828126, 0.3617656750717998],\n",
-       "   'dehb': [91.18679998999023, 0.16384432169996163]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.05599994506835, 0.3100709899342914],\n",
-       "   'ls': [73.05399995361327, 0.364971212451024],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.721, 0.610564492907999]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.84999993998209, 0.47404875829399257],\n",
-       "   'ls': [47.01666661783854, 0.24596745339994966],\n",
-       "   'bananas': [47.01333327636719, 0.5304505139615243],\n",
-       "   'npenas': [44.3433332804362, 1.3207783721457254],\n",
-       "   'sh': [45.07333330993653, 0.9779002461768335],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.69666665242513, 0.7738719364254117],\n",
-       "   'dehb': [44.616666655476884, 0.7671737510966764]}},\n",
-       " 9: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.408, 0.15810123339176987],\n",
-       "   'ls': [91.35300000000001, 0.1945276329984993],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.32159998437501, 0.1532809203844551],\n",
-       "   'hb': [91.20599999999999, 0.18440173534975068],\n",
-       "   'bohb': [91.11799999047852, 0.28588249281863337],\n",
-       "   'dehb': [91.37599998999022, 0.1469584951969324]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23399994018554, 0.033526075221060235],\n",
-       "   'ls': [73.03399996582031, 0.38544001214065354],\n",
-       "   'bananas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'npenas': [72.797999954834, 0.4221327246514823],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [72.731, 0.41795813187447345]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.156666601562506, 0.21962086459282246],\n",
-       "   'ls': [46.82999995015462, 0.45933525204638725],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [43.486666650390625, 1.7098537532460778],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.78833332824708, 1.0824368039880932],\n",
-       "   'dehb': [44.49166665649414, 1.1667202360661062]}},\n",
-       " 35: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.368, 0.219535874061618],\n",
-       "   'ls': [91.27, 0.24943937139112365],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [90.59700000000001, 0.436487113211834],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [90.91079999023438, 0.27143499984097863],\n",
-       "   'dehb': [91.19279998339843, 0.2719282237005244]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2179999194336, 0.08072176678451039],\n",
-       "   'ls': [73.22199993774413, 0.07972453768558942],\n",
-       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
-       "   'npenas': [71.86999996093752, 1.0159232512554344],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [72.221, 0.795266621957693]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.01999995117187, 0.3063041060614205],\n",
-       "   'ls': [46.833333278401696, 0.4133870958208716],\n",
-       "   'bananas': [46.786666619873046, 0.3019197574365747],\n",
-       "   'npenas': [45.72666663309733, 0.4127953260079985],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [43.64500000406901, 2.1158666430673554],\n",
-       "   'dehb': [43.83833331197103, 1.1768142151694443]}},\n",
-       " 20: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.23400000000001, 0.21827505583552106],\n",
-       "   'ls': [91.207, 0.2838326972003031],\n",
-       "   'bananas': [90.291, 0.6996063178674113],\n",
-       "   'npenas': [89.363, 1.132616881385759],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.053, 0.23639162421710094],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.37559999267577, 0.19945886851307323]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.13799993408203, 0.31463627372903924],\n",
-       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
-       "   'bananas': [72.21999996948243, 0.8076633099662458],\n",
-       "   'npenas': [71.74599995239258, 1.1033059720910443],\n",
-       "   'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "   'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [71.63199999999999, 0.7419811318355743]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.98666662394206, 0.2769676761427817],\n",
-       "   'ls': [46.8966666208903, 0.37459604712308325],\n",
-       "   'bananas': [46.09999997660319, 0.35276683099805695],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [44.24666666768392, 1.0534546359944508]}},\n",
-       " 83: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46099999999998, 0.1381629472760308],\n",
-       "   'ls': [91.207, 0.2838326972003031],\n",
-       "   'bananas': [89.789, 0.6473553892569365],\n",
-       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
-       "   'sh': [91.37039998999023, 0.14425755204992768],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.13359998901367, 0.28254387779751905],\n",
-       "   'dehb': [91.24079998950195, 0.24974739756213982]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.23199993652342, 0.047497363824885924],\n",
-       "   'ls': [73.21999994750976, 0.08988879903195432],\n",
-       "   'bananas': [71.43799996643067, 1.090557662459411],\n",
-       "   'npenas': [69.58599997680665, 1.7337139051941306],\n",
-       "   'sh': [71.99399998291014, 0.8959486712276592],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "   'dehb': [71.64, 0.6272957835024887]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.1899999399821, 0.14685590322162423],\n",
-       "   'ls': [46.77333328552246, 0.5318311622421547],\n",
-       "   'bananas': [46.55999992777507, 0.3614169606260508],\n",
-       "   'npenas': [46.55999997253418, 0.39603587690841624],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.35666666870118, 0.9129987122664861],\n",
-       "   'dehb': [44.84333331095378, 0.9943507088142549]}},\n",
-       " 19: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.46199999999999, 0.15866946776238697],\n",
-       "   'ls': [91.361, 0.20954474462510225],\n",
-       "   'bananas': [90.108, 0.723696068802366],\n",
-       "   'npenas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'sh': [91.075, 0.1410850807137311],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.17279999145508, 0.24490193947973699]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.2299999572754, 0.04219001178866624],\n",
-       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
-       "   'bananas': [68.56599994750977, 2.193318024787204],\n",
-       "   'npenas': [73.14399997924804, 0.17106721988945178],\n",
-       "   'sh': [72.15999999633787, 0.8251545388817234],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.85999999999999, 1.032085267795254]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.86666662699381, 0.33763885911754793],\n",
-       "   'ls': [46.92666657613118, 0.3937850572225648],\n",
-       "   'bananas': [46.803333261108406, 0.3177874672089124],\n",
-       "   'npenas': [45.22333328959147, 0.6909011879452681],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.65333329569499, 0.9023672603659217],\n",
-       "   'bohb': [44.06499997965494, 1.4572129057732124],\n",
-       "   'dehb': [45.049999993896485, 0.9598900372080954]}},\n",
-       " 63: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.42999999999998, 0.13319159132617717],\n",
-       "   'ls': [91.39099999999999, 0.185765981815828],\n",
-       "   'bananas': [90.78099999999999, 0.2897395382063003],\n",
-       "   'npenas': [90.755, 0.3000083332175982],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [91.053, 0.23639162421710094],\n",
-       "   'bohb': [91.15959999414062, 0.20690829209364817],\n",
-       "   'dehb': [91.15119998217773, 0.18645149713003412]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.24199994750975, 0.03155944392693056],\n",
-       "   'ls': [73.14199995239258, 0.3153981386041496],\n",
-       "   'bananas': [70.98199994750976, 1.0452731748091486],\n",
-       "   'npenas': [70.70399995361329, 1.2985468673780534],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [72.184, 0.7111568226257754],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.64199999999998, 0.6843946230063468]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.916666617838544, 0.41452779674086915],\n",
-       "   'ls': [46.72666658732096, 0.5681940138884105],\n",
-       "   'bananas': [45.06999995422363, 0.9393200879432245],\n",
-       "   'npenas': [45.43999994812012, 0.7117740100341814],\n",
-       "   'sh': [45.02999996236166, 0.7678903526915714],\n",
-       "   'hb': [44.48333330383301, 1.1406869234849981],\n",
-       "   'bohb': [44.81833332010905, 0.8326814011246708],\n",
-       "   'dehb': [45.03666665649415, 0.7074523974708331]}},\n",
-       " 76: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.337, 0.16112417571550106],\n",
-       "   'ls': [91.32399999999998, 0.20543612145871257],\n",
-       "   'bananas': [90.76299999999999, 0.2861136137970372],\n",
-       "   'npenas': [90.29800000000002, 0.5247056317593704],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.816, 0.27492544443903416],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.20079998754883, 0.23347668404484928]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [72.96999993041993, 0.36334559917312764],\n",
-       "   'ls': [72.85199994873047, 0.5403110169875509],\n",
-       "   'bananas': [72.93399995727539, 0.6165095197278333],\n",
-       "   'npenas': [72.74599995483398, 0.35066225564520687],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [71.146, 0.8489782093787815],\n",
-       "   'dehb': [71.74100000000001, 0.8927984094967917]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.04666658528646, 0.2844878423618782],\n",
-       "   'ls': [47.00666657613119, 0.2640706906448812],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [42.203333310953774, 2.9346379440313997],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.513333314005536, 0.9328808375550285],\n",
-       "   'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "   'dehb': [45.16833332214355, 0.8223087492835989]}},\n",
-       " 60: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.393, 0.18477283350102805],\n",
-       "   'ls': [91.34599999999999, 0.2226746505554651],\n",
-       "   'bananas': [90.507, 0.48381918109971644],\n",
-       "   'npenas': [91.217, 0.1533655763201092],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.891, 0.23144977856977764],\n",
-       "   'bohb': [91.32959998901367, 0.20578201776622312],\n",
-       "   'dehb': [91.20119998803712, 0.20173686188876364]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.24999994140624, 0.024083172937765445],\n",
-       "   'ls': [73.09999995483398, 0.26321091688526155],\n",
-       "   'bananas': [72.40999996704102, 0.5606603503391208],\n",
-       "   'npenas': [70.99399994628907, 1.0398865368920713],\n",
-       "   'sh': [71.942, 0.889649369133707],\n",
-       "   'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "   'bohb': [72.071, 0.7846712687488944],\n",
-       "   'dehb': [70.91600000000001, 0.43241646592145294]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [47.09333326009114, 0.2988124416726923],\n",
-       "   'ls': [46.833333278401696, 0.4133870958208716],\n",
-       "   'bananas': [45.753333280436195, 0.4888080785127855],\n",
-       "   'npenas': [45.35999994913737, 0.7762874545229375],\n",
-       "   'sh': [44.679999959309896, 0.8242437440671244],\n",
-       "   'hb': [44.596666641235345, 1.0528743945000323],\n",
-       "   'bohb': [44.35666666870118, 0.9129987122664861],\n",
-       "   'dehb': [44.606666661580405, 0.638540014553371]}},\n",
-       " 6: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.401, 0.2603247971285076],\n",
-       "   'ls': [91.26699999999998, 0.2572955499032189],\n",
-       "   'bananas': [90.65, 0.3594440151122292],\n",
-       "   'npenas': [90.755, 0.3000083332175982],\n",
-       "   'sh': [91.022, 0.23625410049351406],\n",
-       "   'hb': [91.003, 0.25060127693210343],\n",
-       "   'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "   'dehb': [91.35559998828124, 0.12936089274082296]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.17799995605468, 0.1693398539988335],\n",
-       "   'ls': [73.07999996826172, 0.36452704830412275],\n",
-       "   'bananas': [71.44399995178223, 1.085976077010389],\n",
-       "   'npenas': [71.31399996765137, 1.1657118067552972],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.9599999621582, 0.9176491960023879],\n",
-       "   'bohb': [71.59200000000001, 0.9445083377080372],\n",
-       "   'dehb': [71.542, 0.8452431602799263]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.74999998372395, 0.37006004547351595],\n",
-       "   'ls': [46.82333330790202, 0.4494564285556264],\n",
-       "   'bananas': [45.439999943033854, 0.7117740029359247],\n",
-       "   'npenas': [44.53333327433268, 1.035159675046747],\n",
-       "   'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.74666666361491, 0.9875559040350895],\n",
-       "   'dehb': [44.11333332417806, 1.4124210983303769]}},\n",
-       " 11: {'nb201_c10': {'rs': [90.87899999999999, 0.2048145502643775],\n",
-       "   're': [91.523, 0.0946625585963078],\n",
-       "   'ls': [91.32999999999998, 0.18477012745571122],\n",
-       "   'bananas': [90.686, 0.34115099296352724],\n",
-       "   'npenas': [89.363, 1.132616881385759],\n",
-       "   'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "   'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "   'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "   'dehb': [91.21919998999024, 0.25761242538832047]},\n",
-       "  'nb201_c100': {'rs': [72.22599997192384, 0.8488604076034446],\n",
-       "   're': [73.12799992065429, 0.2733788756798343],\n",
-       "   'ls': [73.11999994873047, 0.28227644224013915],\n",
-       "   'bananas': [70.70399995361329, 1.2985468673780534],\n",
-       "   'npenas': [70.6419999584961, 1.37475668804989],\n",
-       "   'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "   'hb': [71.04799996948242, 0.8528164845088334],\n",
-       "   'bohb': [71.57000000000001, 0.9677603009010038],\n",
-       "   'dehb': [71.282, 0.6934954938570262]},\n",
-       "  'nb201_im': {'rs': [46.126666607666024, 0.5634812664027173],\n",
-       "   're': [46.8299999613444, 0.3423610956362203],\n",
-       "   'ls': [46.90999992472331, 0.4568125874112534],\n",
-       "   'bananas': [44.996666610717774, 0.9182652678422419],\n",
-       "   'npenas': [45.59999996439616, 0.5004442416006849],\n",
-       "   'sh': [44.55666663004557, 1.101216496499461],\n",
-       "   'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "   'bohb': [44.37666666361491, 0.9221713453732964],\n",
-       "   'dehb': [44.62499997965495, 1.2411743974236171]}}}"
+       "{'nb201_c10': {'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "  'hb': [91.053, 0.23639162421710094],\n",
+       "  'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "  'dehb': [91.37559999267577, 0.19945886851307323]},\n",
+       " 'nb201_c100': {'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "  'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "  'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "  'dehb': [71.63199999999999, 0.7419811318355743]},\n",
+       " 'nb201_im': {'sh': [44.55666663004557, 1.101216496499461],\n",
+       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "  'dehb': [44.24666666768392, 1.0534546359944508]}}"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results"
+    "results[20]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30083,7 +13622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30094,7 +13633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30129,11 +13668,11 @@
     "        #print(search_space, means[::2])\n",
     "        if scale_type == '0-1':\n",
     "            min_acc, max_acc = np.min(means), np.max(means)\n",
-    "            print(min_acc)\n",
-    "            print(max_acc)\n",
+    "            \n",
     "            means = [(m - min_acc) / (max_acc - min_acc) for m in means]\n",
     "\n",
     "        i = 0\n",
+    "        print(means)\n",
     "        for optimizer in optimizers:\n",
     "            color = color_dict[optimizer]['color']\n",
     "            \n",
@@ -30141,7 +13680,7 @@
     "            first, second = i, i+1\n",
     "            if optimizer == 'rs' and 'mr' in search_space:\n",
     "                second = i\n",
-    "            print()\n",
+    "            \n",
     "            if width == 1:\n",
     "                ax.scatter(width-offset, means[first], label=pred_label_dict[optimizer], color=color, marker='o')\n",
     "                ax.scatter(width+offset, means[second], label=pred_label_dict[optimizer]+'+HPO', color=color, marker='x')\n",
@@ -30172,62 +13711,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "90.367\n",
-      "91.52499999999998\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "70.61499999999998\n",
-      "73.25799993530272\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "43.38166665344239\n",
-      "47.196666573079426\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "90.367\n",
-      "91.52499999999998\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n"
+      "[0.9136920570379896, 0.9136920570379896, 0.0, 0.7449831365356921, 0.32551943616346357, 0.8749777952235025, 0.4047238461303686, 1.0]\n",
+      "[0.9432892249527459, 0.9432892249527459, 0.5552929918256772, 0.8539697404079082, 0.0, 0.6880907372400793, 0.01748582230624769, 1.0]\n",
+      "[1.0, 1.0, 0.5865384398995577, 0.8572485205808359, 0.39497041376109165, 0.9741124224823862, 0.0, 0.8498520704419449]\n",
+      "[91.395999984375, 91.395999984375, 90.367, 91.20599999999999, 90.73359998535156, 91.3523999831543, 90.82279999096679, 91.49319998876953]\n",
+      "[72.611, 72.611, 71.78999997070312, 72.42199997070313, 70.61499999999998, 72.071, 70.652, 72.731]\n",
+      "[45.63499999186198, 45.63499999186198, 44.70333327433268, 45.31333332417806, 44.27166665445964, 45.57666665039062, 43.38166665344239, 45.29666665649414]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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uV0RHR9fZ292yZUuHwMDAsry8PElZWZlEoVDU2rbYo0ePkgsXLpj18JjvLOG0fpgaKC+U4t/NLgCAJz9Lw+7/euHfzS64b0oOmBqge/tON1UbrtWrVzt/8cUXrgBw7do1q2HDhgVaWFgwLy+vioMHD14CAC8vrwqlUpkg3vPKK6/UEtTFixd7rly50s3Jyanqm2++SdXzIzNLuBByWj8kEcQPAP7d7FIjiPdNycGTn6XdqwgCt+cIi4qKJP379w+MiopymTVr1k3Rhssg7a+HSZMm3Vq2bJmHtg1XQUGBXsPguXPn5s6dOzcXEOYIN23adCU4OLhRBhTLly9Pf+655/K1y2xsbNQJCQmW2r3Cf/75x/aRRx4xawccPjTmmAbaYijSRBHUhttwCcyePTv75Zdf9i4uLiYA2LVrl92ZM2fsXnzxxdzmakNLwIWQYxqIc4LaaM8ZGgBuwwUsXLgwp3v37iUKhSLU19c37L333nOPiYlJad++vVmbEnDTBU6LobfpgiiC4pyg7hyhAXuGDcFtuEwbbsPFMV1IAljbV9cSPXGYbG1f3VwiCHAbLnOF9wg5LUajbbh0V4ebsFrMaZtwGy6O6aMrelwEOQaC/yZxOJw2DxdCDofT5uFCyOFw2jxcCDltmns1TWjKsyMjIzsHBASEBgUFKeRyueLw4cPtjNWGffv22Q0YMCDAWM83F3j4DIfTjBw6dKjdgQMHOsTFxSXY2NiwrKwsWUVFRZMtrqqqqmBhYWGIJrZJeI+Qw9GhPhus+Ph4qz59+gQFBwcrFApFyIULF6wKCgokvXv3DlIoFCFBQUGKzZs3d7jbszMyMiycnJxUNjY2DADc3NxUvr6+VQBw/Phx2wceeCA4NDQ0pG/fvoFXr161AICPP/64Y1hYWEhwcLDiscce8y8qKpIAgrnq008/7d21a1f5zJkzPetqHwCUlJRIH3/88S5+fn6hI0eO9FOrDWtfZg5wIeSYDFsTtzoN2DYgvOvGrj0GbBsQvjVxa5P2+tZHfTZYTz/9tN+MGTNyEhMTE2JjY5Xe3t5Vtra26p9//jklISHh4tGjR5MWLlzoeTehGTVqVGFmZqalr69v2JQpU7x//vnn9gBQUVFBc+bM8d69e/elCxcuXHzmmWduvvbaax4AMHny5Pz4+PiLiYmJCcHBwWVr1qzpKD4vKyvL8p9//lF+/fXX6XW1DwAuXrxo83//939pKSkpF65du2Z18ODB9sb43EwZPjTmmARbE7c6fXDmA5/K6koJANwsu2n5wZkPfADgqeCnDJbkvT4brPz8fMn169ctp02bdgsAbG1tGQBWUVFB8+bN8zx16lR7iUSCnJwcy/T0dJm3t7eqruc7ODio4+PjE3755Re73377ze6ZZ57xX7JkSXrv3r1LkpOTbR599NEgQDB66NSpUxUA/P333zZLlizxKCoqkpaUlEj79etXID5vzJgx+TKZDPW1DwDCw8NL/P39qwAgNDS09NKlS412szZ3uBByTIJ159Z5iCIoUlldKVl3bp2HIYWwsXz55ZdOubm5sri4uItWVlbMw8MjvKys7K4jLZlMhuHDhxcNHz68qGvXrmWbNm1y7tWrV2lAQEDZv//+q9S9fvr06X47duxI6d27d9maNWucjx49aieea9++fYPjXCsrq5rtY1KpFCqVyqxt9+8FPjTmmAS5Zbl19mLqK79X6rPBcnR0VHfu3Lly06ZNHQCgrKyMioqKJAUFBdKOHTtWWVlZsb1799plZmbetT3nzp2ziouLsxKPz549a+Pp6VnZtWvX8ry8PJmYRKmiooLEpEmlpaUSb2/vqoqKCtqyZUud0wH1tc8Qn0lbgPcIOSaBs41z5c2ym3eIjLONc6PMSHUpLy+XuLq6dhWPZ86ceX3Dhg1XZs6c6TNnzhyJt7d3xY8//pgKAJs3b77y4osv+ixbtszdwsKCbd++/dILL7yQN3To0ICgoCBF165dS/38/MrvVl9hYaF0zpw53oWFhVKpVMp8fX0rNm7ceNXa2ppt2bLl0pw5c7yLioqk1dXVNHPmzOs9e/Ysf/PNNzMjIiJCnJycVN27dy8uLi6u07y1rvY15bNpS3DTBU6L0RjTBd05QgCwlFqq33jgjastOTTmmBbchotj0ohit+7cOo/cslxLZxvnyhndZmRwEeQYAi6EHJPhqeCn8rjwcYwBn0zlcDhtHi6EHA6nzcOFkMPhtHm4EHI4nDYPF0JOm4bbcNXPmjVrnKdNm+atXRYRERF87NgxWwDw8PAIDwoKUgQFBSkeeuihwGvXrskAYZvi6NGjfb29vcO8vLzCRo8e7Zubm6tX4vqWggshh9OMaNtwJSUlJRw5ciSpS5cuTQoKBwQbrqbg4eERfi/3HT16NCkpKSnh/vvvL12yZIkbAEyePNnHz8+v8tq1a/FpaWnxvr6+lVOmTPFpUgONDBdCjsmg1knmrntsKLgNV+Pp379/0ZUrV6zi4+Ot4uLi2n3wwQeZ4rkPP/ww8/z58+3E9rRGuBDeI0T0DhHFN1NdzxJRsR7XMSIa1xxtam4+OPOB+9sn3/YSxU/N1Hj75NteH5z5wN3QdXEbrtvs3bvXUS6XK8RXfHy8bV3X7dmzp4NCoSg7d+6ctUKhKJXJbocoy2QyKBSK0n///dda33qbm1YlhET0neaP+S2d8v6a8o513LOHiKqJaHAd52yJ6H0iSiGiciK6SUQniGiSnu2p99nNzFYAXcSD5hTh1oCaqVFUWSTdlbLLRRTDt0++7bUrZZdLUWWR1JA9w7psuE6dOtW+LpsrOzs7tVqtpnnz5nkGBQUpBgwYECTacNX3fNGG6/PPP7/aqVMn1TPPPOO/Zs0a5/Pnz1uJNlxyuVzx4YcfumVmZloAgg1Xjx49goOCghQ7d+50vnDhQo2g3M2Gy87OTg3ctuGSSqW1bLimTp3qLQpcTk6OhfhzZGRkZ/H5I0aMyFcqlQniKywsrFT7/fTr1y9ILpcrioqKJMuWLcs21P9Dc9Mad5aUA3idiNYxxm7c7UIicgMwEMAqAC8AOKhzyToADwGYCyAegCOAXgAaNPTU49nNAhFZMMbKAJS1RP2tAQlJ8G6fd9MAYFfKLpddKbtcAGBUwKicd/u8myZpwfzGpmzDtWnTpmtiuYeHR7hSqUzQ932LHD16NMnNza3Ge7Fbt27lCQkJttXV1ZBKhfWR6upqJCQk2Hbr1u2uhhQtSavqEWo4AiAVwFsNXAcAzwL4BcBnAEYSkbPO+ZEAVjDG9jHGUhljZxljXzDG/s8Az64FEcmIaBUR5Wteq4joCyL6XesaKyL6lIiua3qop4ior9Z5sec7jIj+IqJKAI9pD42J6FkAbwMI1VzLNGUiTkS0nYhKiOgyEU3Rer6v5vqJRHSUiMqI6CwRdSWiMCI6qbnvDyLy07rPi4h2E1EeEZUSkZKIJurxGRoMbTEUMYYIchuuphEWFlYRGhpaGhkZ6SaWRUZGuoWFhZWGhYVVNHd79KU1CqEawJsAZhCRf30XEREBeB7AZsbYVQCnAUzVuSwbwONE5NCYBuj5bF1egyCeL0DodUoAPK1zzQcAntI8+34AcQB+0fQ+tVkJYDEAuaZubbYC+BhAIgA3zWur1vklAHYD6KYp/5aIvHWe8a6mjvsB3ALwIwTBXwQgAoA1gDVa168FYAtgAIBQAPM09zUb4nBYu0x7zvBeEW24xNc777zjumHDhiuRkZGeQUFBivPnz9tERUVlAoLN1f/93/+5BAUFKXr27ClPS0uTvfDCC3nnzp1rFxQUpNi4caOzPjZc06ZN8/P39w8NCgpSKJVKm5UrV2aKNlxvvvmmZ3BwsCI0NFRx9OjR9gAg2nD17NlTHhgYWO/z62pfkz6ce+T7779PTU5Otvby8grz8vIKS05Otv7+++9TW6It+tKqbLiI6DsAHRljw4noCIDrjLGJRNQfQk+xE2Pspuba/gB2AnBjjFUS0fMA5jPGwrWe9wiA7wF0hiA6JwHsZozddZir57PfATCOMRamOc4CsJoxFqU5JgBKAFmMsf5E1A5APoAXGGPRmmukAJIA/MgYW6z1PscxxnZq1fUsgM8ZY+3rqlvrOgYgijG2QHMsA1AIYDpjbDMR+QK4AmAGY+xLzTXDAewFMJYxFlNPfecB7GSMvXu3z62x6GvDpT0nKA6HdY9bcnjMMR3qs+Fqzb89kQDGE1GPes6/AGAbY0yMwdoBwJ+IHhQvYIwdg7DI8CiAbQCCAPxKRF82UHeDz9ZG0+PsDOAvrbqZ9jEAfwAWAE5oXVMN4E8ACp1HxjbQvrtxXuv5KgA3ALjUdw2A65p/43TK2hGRuEK4GsBiIvqTiJbf5f/EKEhIAjtLu2pt0Xu3z7tpowJG5dhZ2lVzEeQ0lda4WAIAYIz9RUQ7IQwnl2mfI6IOAMYCsCSiF7VOSSGI2Gmt51QBOK55RRHRYgDLiGgFYyxVt97GPNuA6HbLS5rwLN3IWoY7v/CqdM7XVyYBAMbYN0R0AMAwAIMAnNR8fu80oZ2N4o0H3shUMzVE0RPFkIsgxxC09t+ihQAeBvC4TvlkCD2dbgDu03pNB/CUZhhaH+LKWH2xVI1+NmOsAMJ85ANimWZo/IDWZZcAVEJYxRavkQLordUmfamEIMzNBmMsnTG2njE2AcI85PTmrB8AdEWPiyDHULTaHiEAMMZSiGg9hPAXbf4DYAdjrFYsHRElAfgQwoLEt5oV2x8hDDVzIQxB34cwd3exnmr1enYd960G8IbmugQAL0FYyMjSvJcSIvoCwEoiuglhrm4+AFcIixGNIRWADxF1B3ANQBFjzGgrckS0GsD/IMxn2kP4Ymp0qAWH01oxha/UpQBq4pQ0f/z3Q5i3q4VmTm8PhCEsAByAsNp7AIL4rYUwRB6imZ+rRSOfrctHADYB2ADglKbsJwhxkSKREFZyNwD4F0BXAI8zxrLqeWZ97ASwH8BvEHqvegWINwEJhFXlBAjxlNcBPGPkOjmcZqNVrRqbG0R0FsAfjLH/tnRbWiONSd7E4RgCU1w1NimIyIeIphNRsCY4eTWEHt/Glm4bp36kUmkPuVyuEI0KDh48WGsOeOnSpS5WVlbdtW2k9u3bZ0dEPX744Yea+NQBAwYE7Nu3r2bHR1ZWlkwmk3X/4IMPOmk/z8PDI/yxxx6riY/dsGGD49ixY321rxk0aJB/t27d5Npl586ds4qIiAiWy+WKLl26hE6aNKlVu7mYGlwIDYcawDQIITN/QgiqHsoYa0ooDMfIWFlZqZVKZUJiYmLCsmXLMhYuXOipfX7Hjh1OYWFhJbquMq6urlUrV67UDYSvITo62rFbt24l27dvv2MnSHx8vO3ff/9dpwHBzZs3pfHx8e2KioqkCQkJNbtUXn75Ze85c+ZcVyqVCZcvX74wf/78nEa/WU69cCE0EIyxNMZYX8aYA2PMjjH2IGPs15ZuF0d/CgoKpA4ODjXz0RcuXLAqLS2VLl26NGPbtm21BC0kJKTUzs6u+qeffrKv61nbt293+uijj9KuX79ucenSJQvtc7Nmzbr+7rvv1imimzdv7jBo0KBbo0ePzouOjq6pMycnx8LHx6fGtzAiIqLN7j03BlwIOSZD3o9bnJIffiT8YoiiR/LDj4Tn/Vj3vtvGUFFRIZHL5Qo/P7/QuXPn+rz99ts1C1fR0dGOo0ePznv88ceLr1y5Yq27ZW3RokVZ77///h2ClpKSYnHjxg2LAQMGlI4cOTJfW9AAYNq0aXnx8fG28fHxd/jzbdu2zWnKlCl5zzzzTF5MTEzNfS+//PL1YcOGBT3yyCOB7777rsvNmzdbteOzqcGFkGMS5P24xSknKspHdeOGJRiD6sYNy5yoKJ+miqE4NL5y5cqFn376Kfm5556rMS6NiYlxnjZtWp5UKsWwYcPyN23a5Kh979ChQ4sB4MCBA7ViUqOjo51GjhyZDwBTp07N27lzZ602ymQyzJkzJ3vp0qWdtcvT0tJkV69etR4yZEhx165dK2QyGTtz5ow1AMydOzc3Li7uwpgxY/KOHTtm98ADD8jLysqoKe+dc5tWHUd4r3Ts2JH5+vq2dDNaJX///fdNxlinhq9sXeSuXevBKipqfXGzigpJ7tq1Hk6TJhok6fugQYNK8vPzZVlZWbKMjAyLq1evWj3++ONBAFBVVUWenp6VCxcurGUNt2DBgqzly5e7yWSymvCLnTt3Ot24ccNC7NHl5ORYxMXFWYWHh9fEes6cOTNv1apVbqGhoTVD3OjoaKfCwkKpl5dXOAAUFxdLo6OjnR944IEMAPD19a2aN29e7rx583IDAwNDY2NjbR5++OFa/oCce6NZhJCIvgUwHECOrlGA5jxBCEgeBqAUwLOMsX80556B4MQCAMsZYw2uwvr6+iI2tp41CsYAovqPjQRTq0ESSb3Hxqn0zvdKEslV41ZqHFQ3b9Zpb1Vf+b1w9uxZa7VaDVdXV9WKFStcX3311cwVK1bUmI16eHiEJyUl1apvzJgxhe+88477jRs3LADg/PnzViUlJdKcnJya/dzz589337hxo9NHH31UM+y2srJiM2fOvL569erOffr0KQKEhZmffvopedCgQSUAoFQqLQcPHhz02WefZezYscN+xIgRRVZWVuzatWuyW7duSbXnDDlNo7mGxt/hzm1y2gwFEKh5TQfwBQAQkRME770HIdhDvU1EjvU9pEGOrAB+WSAIBCD8+8sCodyIrP1pEj7Y8SSYZsjF1Gp8sONJrP3JiHHQ9bxXT3syuLV9cyDr2LHOP/r6yvVFnCOUy+WKiRMndvniiy9SZTIZdu3a5TRhwoRb2tcOHTo0f+PGjXcMxSMjI7Oys7MtAWDjxo1Ow4YNy9c+P3HixHzt+T6RuXPn3qyuriYASExMtMzIyLB89NFHa/aZy+XySjs7u+rDhw+3++WXX+yDg4NDg4ODFYMHDw5699130729vVW6z+TcG83SI2SMHdNYQNXHkwCiNY4tp4iog8ajrz+Ag4yxPAAgooMQBPXHe2gEUF4AnP5COH5cIxSnvwAenGm0niFTq1FUVYzNZanAjifxxrjd+GDHk9hcloop8DVOz1DzXtmpL0BAzXtlp76AlJp3j7KhcJ41KyMnKspHe3hMVlZq51mzMpry3Orq6r/rKk9PT4/TLfv666/TxZ+HDx9eJP48efLkgsmTJ/+tWy7y4IMPll2+fPkCAGRkZNQ818bGhmn3HLV/FklISLgIABqBTNc9zzEMrWWO0AOAtvtwuqasvvLGQwQ8vgIMDHT6ixpBZA/OAD2+wnAieH4b8NtSoCAdcPAEDVyCN8btBjTit3lTNwDAFBtfvDFut2FEUKdODFyCv1TTUdEuGH1PzQOd/gKMAX+0+xQFqsg7thaaAuI8YO7atR6qmzctZR07VjrPmpVhqPlBTtumtQhhkyGi6dA4onh76xoyC1y+sgZVXdoh6DRAELymkrq0g8WVNejSRdfX4R44vw0/H3odq+1tke3oic6qasw99DqeAPDamJ+w+fvb+b5fG/OT4URw7xygSjPnXpAGtmcOKpy+wfnLPoDt8+hr9y3+KHoe56/7QEISk+wRAoIYcuHjGAO9hJCIpHWZFBiQDADaNuyemrIMCMNj7fLf63oAY2w9gPUA0LNnzzs2UDPG8HfWcThVnAW6tEPQ5RIkdWmH9IyNyLO6H35+c0BN7BX+fHwpDmTaYvEWwLmwGrn2wI5HbFH26WmUShKBhxggIUDN8P2MFfDqHIgBy5uY+uO3pbdFUAOpynAekaj0fBbn00fgfOkIAECl5z8otMg1yR4hh2NM9O2SZBHRaiLqaaR27AEwjQR6ASjQOLIcADCEiBw1iyRDNGX6cX4bsCoMeKcDsCoMl1OycbRIhnRPGxx+pCPSPW1wtEiGyxnltxcVmsAfl6rx3C9Ap0Lhg+1UCDz7P6Cg3AZFsofw+omn8O/ks3j9xFMokj2EtOxkVKuaON9dcOe0EQPgcbIc1crabmHVym/hwSR3pETlcNo6+grh4wCqAewlootEtJCIvBq6SYSIfoSw/zaYiNKJ6D9ENIOIZmgu2Q/gMoAUAF8BmAUAmkWSZQDOaF5LxYWTBhGHjAVpABioMB1vXL2Idrdqh9C1qwjEGx37GGSYOvQPwFpH12yqgYDknbBTnUCR7CGsm30MRbKHhOOhFyCVNXF2wsHzzjIGPFjCEHhrTK3iwFtjYFnJeMgFh6ODXn+Fmpi+f4joNQi9sikA4onoHwgefFsZY/XayzPG7honolktfrmec9+ibiPUu1PHkBFqFUZZ59RaehvVLQIIWtDox9dFx8K6yzsVAX3XLcC62cdqyiavW9B0EQSAgUtQsPpV5Jy1hqpUCpltNTrdX46UQauRHmcLz/TDCEzZieSAsUj3fBS5kq+bkgaAwzFLGtUNYoypITg7KyEYgnpAsLZPI6KG0l02LzpDRnFhJL1TNY4WyTAvTRgWp2dsRFLychjCl1Hl0qHO8qpODji0YGutskMLtkLcytUUCq7aIOuMI1SlMgAEVakM2WccwcpkqFRkIzBlJwhAYMpOVCqyoWZqPkeoRVuw4fLw8AjPysqq+dbdt2+f3YABAwIAYM2aNc6Ojo7d5HK5wt/fP/Tjjz+umTrZtGlTh6CgIEWXLl1Cg4KCFGLOZHNELyHUzNG9RER/APgHggBOY4wFMcYGAngMtfPgtjx1DBn/srTC0SIZ2nV+DuenxaFd5+dwtEiGv67fEb51T/i8vhBqq1pGI6i2lCEj5BmkFLkhwC4LM9f2R4BdFlKK3HAw8scmi2HOqk+R1aEbTvRaisP9PseJXkuR1aEb2sUsQvVfyyEu/xCA6r+Wo8T6lsmuGhsDc7LheuWVV9zXrFnjrMfbrsWIESPylUplwrFjxxKXL1/ukZaWJvvzzz9tFi1a5Llnz56Uy5cvX9izZ0/KokWLPE+fPm3T2OebAvr2CNMBjIAgdu6MsZmMsZPiScbYGQhJxVsPA5cAFrf/zwhA0Q1CO2kfvBERCSLCGxGRaOfyLIrsBjR5xRgAHEaMQNXMKJx86D0c7vc5Tj70HlSzVsLG1QkBdlkYvHISJBIJBq+chAC7LFhayyBp4txkusoDyuCnUWHtDBChwtoZyuCnUSBT4IlYBsepUyG/mADHqVPxRCyDS/ldE1u1apjOl4bucVNp6zZcHh4eKm9v74qUlBTLlStXdn7llVey5HJ5JSDscpk/f372ihUrOjf0HFNE30mqLoyx63e7gDH2bNObY0C6TsDF+Ms4/r/fUVQphZ1lNWxvBsI+yxNsGAMRgTEGj70SWNmqhTx1TSTpdDZOJ9hCZSF82ZdbdMDpBAkGTO6OgAdcakRPFMOmiiAAXAp4EmppbTcntdQKl/2fhOKxELguXAAigutCYR60+p2365nJbN1cXxHlXl1UJHVbviyNJBIwtRpZi9/yktrZVbsueDPzXp8rbrGrqKigmzdvWuzfvz9JPKdtw/Xiiy9ap6Wlyby8vGqEctGiRVlLlizxGD16dK3PtC4brnfffbfm72fatGl533zzTaf6bLiWLFmS5e7uXjVu3Dj/qKiobOC2Ddf9999fMnDgwIKXX345t2PHjnpPc/Tr1y9I/H0rLS2V+Pv7l+tek5CQYJmWlmalUCgqkpKSrCMjI7O1z/fq1avkq6++MjnDDn3Q9y/xOSLSTk0JIoogojeM0CaDcPH4Efz6vzMoqhTmzooqZci9eRXXLx3HD299CLVajR/e+hDXLx1HRVmJQebr/tx9CarK2s9RVarx5+5Ld4ieIUQQAMotO9RdbuVYI4IAasQwW6W6Z9FoKZhajeqiImlBTIxL1uK3vEQRLIiJcakuKpI2pWdo6jZcf/31l424Vzo6OrrTihUr3MXj7OzsmmmQo0ePJimVygSlUpmwdu3aWsYbe/fudRT3Wn/66adXXV1d29w8sr49wrkQsphpkwBgF4QE7K2O41uioarUyXDJ1JBIrXA95ThWTToOAHANeBhPL3vdIMJUnFd3Rs36yg1BsWU+7CrvtOQrtsy/Y7hviOF/S0ASCdyWL0sDgIKYGJeCmBgXAHAYMyZH7CEaAlO14VIqlQmAMEfo6+tbMWfOnNzGvO8RI0bkR0dHX9MuCwwMLD99+rRt7969a9p3+vRp26CgoDt6kuaAvr9BlgCqdMoqAdQ54dsaKMqtOzmaWlX7/3HS0tcM1jtr73THSOeu5YYgMfA4qiS1QwOrJJVIDDxutDpbAm0xFDGkCAK1bbiio6OdXn311cyMjIy4jIyMuJycnPPXr1+3qMuGq6CgQKpUKm2A2jZc4r2zZ8/O1nWtEW241q1b5yqWiTZc4n2nT59O2LVrl6PmnH1FRQUBQHPYcEVGRmavWrXKLTEx0RIQ3HE++eQTtzfeeCO7oXtNEX1/i/6GJshZixkQVpBbJTKHO9cEArPzEJKVX8ua6viT45GzRreze2/0ftIfMsvaH6nMUoLeT/rXc0fTGffEEJwM2IkiyzwwMBRZ5uFkwE6Me2KI0epsCcThsHaZOExuCtyGq2769OlTtnTp0vQRI0YE+Pn5hY4YMSJg2bJl6X369DHLXCl65TUmolAIib2zAFwC4A+gM4DBjLEEo7bwHujZsydTTOqI0FgJZGqNMDGGkMxc+N0swPWAEDy8ezuOPzkerikXa46l0qZHliSdzsafuy+hOK8C7Z2s0PtJfwQ9aNyFtp8v/4zV/6xGdkk2OrfrjLnd5+KJLk/UeS0R/c0YM9ZWyUahb15j7TlBcTise2x0k1uOWVBfXmN9d5ZcIKIgCC7TXgBiAOxjjBUbtJUG5N+OmbgVboMeiY5oVy5FiU01TgX7oMIqB/KUi0gKDYMrgOsBIbjx4ACDiCAABD3Y2ejCp8sTXZ6oV/jMAZJIILWzq9YWPXGYLLWzq+YiyGkqeu/x0ojeFiO2xaB0btcZVzyycMVDO6VDBk4N6YzVb98uMVRPkGNcXBe8maltYiuKIRdBjiHQd2eJjIjmENFOIjpKRMfEl7EbeK/M7T4X1tLaaznWEiss/ce3VtmNqJUG2V7XELp1NEed5oau6HER5BgKfXuEqwA8CsHv7z0AiwDMRCvuIT7R5QkUJ0hx7VAZbMrtUWZVgL7Jm+Fw+Q84Tp0K14ULcP39FcjftAll587Bd+sWo4WXXL68GipVIQIDF9cEcicnL4dMZm8YQ1gOh9Mk9P1KHQNgKGNsNQCV5t9RAAYYq2FNJel0Nm79ag3bcgcQCLYVHXBL7YMSGxcwCL0x8V9jwhiDSlWItPTvkKwxd0hOXo609O+gUhXyniGH0wrQt0doi9u5Q8qIyJYxpiSi++92U0tS1y6Py74jkNVlMNptehW3Nm0GAHSYOgWdFy40Wm+QiBAYKGQjTUv/Dmnp3wEAvDyfrekhcjiclkXfHuFFAOIWu1gA7xDRYghW+q2S+nZzlKlrzxsaUwRFiAgBAYtqlQUELOIi2Aogoh4vvvhijePMkiVLXF955RV3QNip4eLi0lUulysCAwNDv//+ewfdcvF18+ZNaVPsuT799FPnoKAgRVBQkCIwMDBU1+2GY1z0FcK5uL2z5BUA3SG40UzXtyIiepyIEokohYjerOP8KiL6V/NKIqJbWueqtc7t0ae++nZz2EhrC+T1FSsMOjy9ePwI1r/8HD6eOALrX34OF48fwYntm3FkT+3cJEf2TMSJ7ZsNVi/n3rC0tGT79+931Pbr02bGjBnXlUplwtatWy/Nnj3bt7q6ula5+BINEO7FnuvSpUsWH3/8sduff/6ZmJSUlBAbG3uxZ8+epfU9g2N4GhRCIpICCIewtxiMsWTG2CDG2IOMMb32cWme8X8QErkrAEwiIoX2NYyx+Yyx+xhj90HY1xyjdbpMPMcYG6lPnd28CyCprr0DSVJdCb+4H+A4TWNNNW0q8qM3GUwMLx4/gl/Xf46imzcAxlB08wYOfLkGBZU/APZ/A4U9MKB/MlDYA7D/G+UWewxi9sC5d6RSKZs2bdqN999/3/Vu13Xv3r1cKpUiOzv7rtNJ92LPlZWVZdGuXTu1g4NDNQA4ODioRfsrTvPQoBBqstd9whhrinNABIAUxthlxlglhNXmJ+9y/STcSxJ3Ldpt+xjyxO9hVZ4LMAar8lzIE7+HW8lFuC7QWFMtWIAOU6dCamdvkGFqXUYP1VVVKMkvAwp74N8fS7Bq0kj8+2MJUNgDvl37GGyfc1sg7mi604bIP8L/b8bhHhsi/wiPO5p+p9vEPfD666/nxMTEOGm7UOty+PDhdhKJhLm5uakAYN26da7isPjBBx8M0r520aJFWe+///4dvcK67LkAoFevXqUdO3as8vLyCh83bpyv9tCa0zzo+1e4l4hGNKEevRO1E5EPAD8Ah7WKrYkolohOEdEofSpUZWWhc04sHjq1BI8enY2HTi2Bt5MbLHV2YFiHPwVLeVPe2m3qM3q49kd7DBi5BdDyix4wcgv8u8wzSL31YU6xi3FH051ObE/xKS2otASA0oJKyxPbU3wMIYZOTk7q8ePH50ZFRbnonhMF7/XXX/eMjo6+LH5xaQ+NT58+naR9T2PtuWQyGY4dO5b8ww8/XAoMDCx/8803vcR5Sk7zoO+qsTWAHUT0JwRBq/mLYoxNM3CbJgLYoZNH2YcxlkFEXQAcJqI4xtgl7Zt0E7zL/AOgyqxtvUcWNrD0H4SCfZfhMLwLCvZdRsnJTLR/yB2MsSb3Cu2cOwrDYh3aOznjaPTXtcqORn+N/s+8aJCe6K6zGfjwQCIyb5XBvYMNXn8sGFdulqCwvApLhitqYheX7kuA1K6jSf6Bxe5P9ahWqWt9cVer1JLY/ake4f08m5z0fcGCBde7d++umDhxYq1vsxkzZlxfunTpXU2J63leo+y5JBIJBgwYUDpgwIDSoUOHFr7wwgu+n3zyicl5R5oq+vYI4wG8D+AIhJSbl7Re+lBfAve6mAidYTFjLEPz72UICd7vCNthjK1njPVkjPXs1KkTXObPA1nXXiGuTN4DC7cKFJ/IRMaCP1B8QhBBh+FdDCJID0+cBpll7UUaqYUlOnp645//7UH3oSPxypa96D50JP753x78vvGrJvfSdp3NwIKYOGTcKgMDkHGrDG/uPI+z1/Kx4UQqlu5LqBHBDSdSAZKY5H5CsSeob3ljcXV1rR4xYkT+Dz/8YJC8z42x50pNTbX4448/bMV7Y2NjbT08PPgcYTOir+nCu02s5wyAQCLygyCAEwE8rXsREckBOELIgSyWOQIoZYxVEFFHAA9BDzNYhxHCcDdn1adQZWVB5uYGl/nzYD98IDIW/HH7OgOJIACEPCzElx/fEo2i3Juwc+6IhydOQ352JpzcPGt6gP2feRFggFW7dk2u+8MDiSirqm0oXK5SIyWnGM895IsNJ1IFAQTw3EO+eGdlTlodj2n12DpYVtYlerYOlgYTjEWLFmVv3LhRLyv6devWuW7btq0mUdLu3btTdK+JjIzMmjJlSgBQvz3XpEmTukyfPj33tdde87x+/bqFlZUVc3Jyqvrqq6+u6T6PYzz0teF6tL5zjLHD9Z3TecYwAJ8CkAL4ljH2HhEtBRDLGNujueYdANaMsTe17usD4EsAagg92E8ZY9/cra6ePXuy2NjYutqKgn2XUXzi9ojDkD3C+ig4eBXqMhU6jOhSM0y9tfcyJDYyOAzWOytjnfi9+XOd+2MIwOUVw+C3YH9N2ZUVwyCRSEzOhgu4PUeoPTyWyiTqh8YHXDXE0JjTNmiSDRcAXeHpBMG1Oh1AF30ewBjbD2C/TtkSneN36rjvJITwnSahLYKi+GmLorHEkDEGVq5CyclMEMHgc5PuHWyQcetOr0w3B2ss3VfbKlL32JQQxS52f6pHaUGlpa2DZWXPYb4ZXAQ5hkDfobGf9rEmLnAxgCJjNMoYEBHIWlarB+gwXNBwspYZdYudWE/xicwa4TVUT/T1x4KxICau1vDYWiZBgEt7bDiRiuce8sWS4YqaOUKpvYvXXR7Xqgnv55nHhY9jDPT2I9SGMVZNRO9B6BF+YtgmGQ+HwT61emCiSDXHFjuH4V1qDckNVe+o+4UopLpWjf1d2tesGi8ZLsSvL1urbnMZyjichrgnIdQwGMK8nUnREpndxGG5NmIIj6HEUBRE3Xq1RX/JcAXeKbrJQzI4HB30EkIiqhU7CMGNxhp3JnTi6NBSc5NAy4g+h2OK6NsjnKJzXAIgiTFWaOD2mB0tNTfJ4XD0R9+A6lMATjLGjmpesRB8CY2XsNeMcBjsA/sn/GoNU+2f8Gty6Ayn6Zi6Dde+ffvsBgwYEKBdNnbsWN8NGzY4AkBERESwr69vWHBwsKJ79+7yc+fOWQFAeXk5Pf/8817e3t5hPj4+YQMHDvQXTSDaIvoK4UEAPXTKegA4YNjmmD67zmbgoajD8HvzZzwUdRi7zmZg1cEkLPv5Ys0uEsYYlv18EasOJjXwNI6xMSUbLg8Pj3sKI4uOjr6cmJiY8PTTT9+cP3++FwDMmTPHo7i4WHL58uX4q1evxo8cOfLWqFGjAtqqG5K+QhgO4LRO2V8Auhm2OaZNY7e7FZZXGdUIwZxMFwDjvJ+2ZMM1cODA4qtXr1oVFRVJtm3b1nHdunVpMpnwdubOnZtraWmp3rt3r10DjzFL9BXCAgC6vyiuEOYKORr02e7mt2B/rfg+Q8wR1mUGe3L79/j9u/W1eqG/f7ceDjbWJmm68Mf2ZPfDm5Re2u/n8Cal1x/bk5v8fkzdhis2Nra99jD90KFDHeq6LiYmxkEul5clJCRYubm5VTo5OdXq/t13332lcXFxNo2p21zQVwh3AviBiMKIyJaIwgFEA9hmvKaZHpl17PAAgKyC8po4PhFDiuCdZrCfQbU9BmXfbKgRw9+/W4+ybzagA1BnT6U1wxhDRZlKqjyZ5SKK4eFNSi/lySyXijKVtKk9w9ZswxUZGdlZFLicnBwL8eepU6d6i8/t2bNnsfYwfdCgQbe06502bVoXuVyu+PPPP9uvXr3aJPeaGxt9V40XAfgYwnDYCkA5gA0AFhqpXSZJY7e7GUIM6zSDraxARV4p/G4W4Mo3G/DJ//YgJDMXfjcLUFVVbXK9eCLCo1PlaQCgPJnlojyZ5QIA8j5uOY9OlacZ4gultdpwrVy5MnvlypXZgDBHqFQqG71PMjo6+vIjjzxSM+fo4uJSnZWVZZmfny9xdHSs6RWeO3fOduTIkbca+3xzQK8eIWOsnDH2MoB2ADoDaM8Ym80YKzdq60yM1x8Lho1F7dFVzXa3P67guYd8cWXFMGGY/MeVmjnDplCnGSwRzju3g+PUqfC7WYBh5y/D72YBHKdORaaqyiR7BNpiKGIoEQTalg2Xvb29ety4cTdnzpzppVKpAACff/65c3l5uWTEiBEms23WkOglhEQ0jYi6MoEbjDFGRN2IaKqxG2hKjLrfAyvGhMOjgw0IgEcHG0SN7Yrx8b9gde7veOuJEBAR3noiBKtzf0f3wzsMYgZbF+2dO+Kiu3OtMt1jU0IcDmuXac8ZGoJFixZl37p1S69RkvYcoVwuVyQmJt5hERYZGZmVnZ1tCdRvwxUTE+NUWVlJr732mqefn1+oXC5X7Nixw/Hzzz836hfWZ599lmFlZaX28/ML8/HxCYuJiXHctWtXSltNHaGvDddVAPcxxvK1ypwAnGWMtbpguPpsuFoCxhiur1iB/OhNcJw2Fa4LFtxx3BQxFOcItYfHUgtLeIWEwuZ/B+F3s6Cm/EpHB0w5ez4nr6T0riukzYW+Nlzac4LicFj3mAemc/ShqTZc9gB0d5EUAOjQtGaZP2KSKADIj96E/OhNAGAQEQTqMYN9airKN2yEtWY47LpwAa6/vwLYtAkOatauae+o+SEiWNnIqrVFTxwmW9nIqrkIcpqKvkKYAGAsaq8Sj4aQ+J3TAKIYiiIIwCAiKBLy8IAaQRS5cS4BqoBAuC7UZOxbuAAMapS/865JbovsOz4wU9dEgvcEOYZC3wmBSABfE9FOIvqAiGIgmLW+qm9FeiR4f5aIbmglcn9B69wzRJSseT2jb52tBXF4rI2hE8vrciMkFzd65NeKI7zRIx/WLjYmGzDLTSQ4xkLfVeM/AIRByD3SDkIYTRhj7IQ+9+uT4F3DVq1E7l9r7nUC8DaAByHkR35bk8fEJNCdIzRGYnld1Go1qquLcMNyFy4cfANqtRoXDr6BG5a7IJGQSSZv4nCMid5+hIyxawCiAICIJACGEtHHjLEJetxek+Bdc7+Y4F2fmKjHABxkjOVp7j0I4HE0MQF8c0FEkNrZ15oTFOcMDZVYXheJRILQwR8AB4Eci5+Q8/tPgAXgUjUal9M/Nl2/fg7HSDTKmJWIugF4BkIGOhsAm+5+Rw11JXh/sI7rxhLRIwCSAMxnjKXVc2+dyeFbK53+O/sOk1RDzhHWhSiGOb//VFMWOvgDCHHxHA5HmwaHxkTkQkSvENE5ALEQjBbaAejGGJttwLbsBeDLGOsKwe1mY2NuJqLpRBRLRLE3btyZZL2lae75LXE4rI3uMUfYwhYQEBAaFBSkkMvlisOHD7fYqvr9998vN8RzbG1ta+X9XrNmjfO0adO867te32vmzZvnvmvXLpOdY74bd+0REtHPECz5z0MQpi2MsUwiygJwh03QXWgwwTtjLFfr8Gvczl2cAaC/zr2/61bAGFsPYD0gxBHW1xDdrHFNzSLXGhFFMMfiJ7hUjUbo4A9qjrt42tc1N9smOXToULsDBw50iIuLS7CxsWFZWVmyioqKFvtlOHv2rLKl6taHTz/91GzTPDTUI+wHIX7wfwD2M8bu9YOoSfBORJYQErzv0b6AiLTdOkbidmjOAQBDiMhRs0gyBHr6IOp6A760KbbWtjbREsuQvoAlZ3OQFfUX0t88jqyov1ByNsdgz9YXiUQCqdSuRgTFYbJL1Wio1Ywnb9KQkZFh4eTkpLKxsWEA4ObmpvL19a0ChH29okfhsWPHbCMiIoIBoKCgQDJu3Dhf0UT1u+++6wAAO3bssFcoFCHBwcGK3r17BwFAYWGhZPz48b7h4eEhISEhCtFsNTY21jo8PDxELpcrgoKCFHFxcVbA7Z7c8OHDu2zZsqXGgUY0WlWpVHjppZc8w8LCQoKCghQffvhho7cD/vDDDw5du3aVh4SEKPr06ROUlpZWqzOUm5srdXd3Dxd9FwsLCyWdO3fuWlFRQdqGrx4eHuHz5893VygUIUFBQYqzZ89aA0BmZqasT58+gQEBAaFPPfWUj7u7e3h9Xo+tiYaE0BXAKwD6ALhARP8Q0asALIA684rXCWNMBWA2BAG7CGAbY+wCES0lopGay+YQ0QXNEHwOgGc19+YBWAZBTM8AWCounNyNurwBD13MMaovYMnZHNyKSUb1LWGXR/WtCtyKSW4RMVQMertGBIHbc4apmUWJzd4YA/Hvwf1O616aGv7xU8N7rHtpavi/B/c7NXxX/YwaNaowMzPT0tfXN2zKlCneP//8c/uG7nnzzTfd7O3tq5OSkhKSkpISnnjiiaLMzEzZ7NmzfWNiYi4lJiYm7Nq16xIALFy40G3AgAGFcXFxF48fP564ePFiz8LCQslnn33WadasWdeVSmXC+fPnL/r5+dXaVzxhwoS8bdu2OQKCk/SJEyfsx48ff+vTTz/t6ODgUB0fH3/x3LlzFzdu3NhJqVTesbWvoqJCor39b8WKFTVWZYMHDy7+999/lRcvXkwYN25c3tKlSztr3+vs7FwdEhJSun//fjsA2Lp1q0O/fv0KrKys7vgD6dixoyohIeHi888/fyMqKspV8/m49+vXryglJeXC+PHj87Oysu5oX2vkrkrNGCuBYLcVTUQ+AKYCmA7ACcAmIlqjSdzeIA0leGeMLQCwoJ57vwXwrT71iNTlDVitZmhnKcWGE6nYcCIVAAzqC1h4IBWsqrbDL6tSo/BAKtrdf4fDk9HR3TdqyvtI/z243+n3jV/5VFdVSQCg5Fa+5e8bv/IBgPsGD7unXMcODg7q+Pj4hF9++cXut99+s3vmmWf8lyxZkj5nzpzc+u45duyY/ZYtW2pSEnbq1Kn6hx9+cIiIiCgSzVRdXV2rAeD333+3P3DgQIc1a9Z0BoCKigpKSUmx7N27d8lHH33klp6ebjlx4sT88PDwWvZB48aNK4iMjPQqKyujnTt3OkRERBS1b9+eHTp0yF6pVNru2bPHEQCKioqkCQkJ1romrlZWVmptl5o1a9Y4x8bGtgOAK1euWI4aNcrzxo0bFpWVlRIvL6/a1kUAxo8fn//jjz86jhgxomjbtm1Os2bNqnPS/emnn84HgIiIiFKxTX/99Vf7Xbt2pWjeR6G9vb1JjED0/stgjF1ljC1njAUDeAjAVei/atzs1OcNWFJZ+//FUCIIoKYnqG85R39O7fjRQxRBkeqqKsmpHT82KYJAJpNh+PDhRatWrcr88MMPr+3atcsREJyrRdv6srKye/oGYYxhx44dKaJPYFZWVlz37t3LZ8yYkbd79+4UGxsb9fDhwwP37NlTawHC1taW9erVqygmJsZ+69atjk899VSe5nn08ccfXxOfl5GRETdmzJhG7RSaPXu296xZs3KSkpISPv/886sVFRV3vLdJkybdOnr0qMP169el8fHxtiNGjKizDmtrawYAMpmMqVQqk55ov9f/4D8ZYy8BaLVux+4d6jbabWdZO57YEFZYItIOdeeyqq+coz8lt/LrHGLVV64P586dsxLn5wDg7NmzNp6enpUA4OnpWXnixAlbABCHqQDQr1+/wlWrVtV072/cuCHt379/yV9//WUnDlOvX78uBYABAwYUfvzxx66ioJ44ccIGABISEixDQkIqFi9enPPYY4/d+vfff+/4ZX3qqafyv/vuu45nzpyxGzt2bCEADB48uOCLL77oJC7onD9/3qqwsLBRf8NFRUVSb2/vKgD47rvv6rQjcnBwUHft2rXkpZde8h44cGCBaOevDw888EDxpk2bnAAgJibGvrCw0CQC+Js0VmKMtdquzuuPBeNhj7+x8uG38fXgOVj58Nvo7RaLksrq2r6AWnOGTcX+MV+QRe2PlCwksH/Mt8nPbuu06+BYpz9ffeX6UFhYKJ02bZqfv79/aFBQkEKpVNqsXLkyEwCWLFmS+cYbb3iHhYWFSKXSml+OFStWZN26dUsaGBgYGhwcrNi/f7+du7u7as2aNamjR48OCA4OVowePboLAERFRWWqVCqSy+WKgICA0MWLF3sAwObNm52CgoJC5XK54uLFizYvvfTSHUPx0aNHF/711192ffv2LRR7XvPnz78pl8vLw8PDQwIDA0NffPFFn6qqqkb1xBYtWpQ5adIk/9DQ0BBnZ2dVfddNmDAhf/fu3U6TJk1q1LRDVFRU5uHDh+0DAwNDt23b5tixY8eqDh06tPrhsV42XKZGz5492d59b+FCwgIQbmu1Sm2JlMrZmDV0FoioZsHE3toC8wcH3eWJ+lNyNgeFB1JRfasC0g5WsH/Mt0XmB+uDiP5mjPVs6XYA+ttwAXfOEQKA1MJC3f+ZF6/e6xwhx/CUlZWRTCZjFhYWOHToULvZs2f73IurtrFoqg2XyXH50ke1RBAAZJJKdHPYAqKXAQhBzYacIwSAdve7tCrhMxdEsTu140ePklv5lu06OFb2Gjcpg4tg6yIlJcVywoQJ/mq1GhYWFuzLL79Mbek26YPZCmF5RVad5RU65eYWTG3O3Dd4WB4XvtZNeHh4xcWLF1tND1Bf6hVCIjoOPWIFGWOPGLRFBsLayg3lFXfGf1tb1Zt7m8PhtFHu1iP8WutnfwDPQ9hmdxWANwTzhUbF9jUnXfxfg1K5CGr17TAaicQGXfxfa8FWGYfWPi/J4bR26hVCxliN6QERnQLwGGPsglbZDxCE8G2jtvAecev8JABhrrC8IgvWVm7o4v9aTbm5IO5mEQO5xd0sALgYcjh6ou8cYQiASzplVwAYxC3DWLh1ftLshE+X1rabhcMxRfSNIzwK4DsiCiQiGyIKgmDVf9x4TWs6BXv3IvnRgbgYokDyowNRsHdvSzfJ4PDdLE2D23Dpf42xbbi0TR1ExPeSmJhoaW1t3V0ulyv8/f1Dn376aW/RGCI2Nta6V69eQb6+vmE+Pj5hr7/+upsYxK4v+grhs5p/LwAoARAHgAA816jampGCvXuR9dYSqDIzAcagysxE1ltLzE4M+W6We0fbhispKSnhyJEjSV26dDFaYvWGMAUbrlGjRjU5Afy+ffvsxo4d69vY+7y8vCo02wsvJCUl2WzevLlDcXExjR49OuCNN97ITk1NjY+Pj084ffp0+5UrV3ZqzLP1zVmSxxibCMAagBsAG8bYJMaYXsGwLUHOqk/BystrlbHycuSs+rRlGmQk2tJuFt3g/6ZuBuA2XKZpw2VhYYGIiIji5ORkq6+++sq5Z8+exeKeazs7O/UXX3xxbfXq1Y0KD9F7ix0RyQEsAvAWY0xNRMFE1LVxb6H5UGXVHUdYX7mp0u5+F3QYE1jTA5R2sEKHMYFmNz/4+8av3H9dt9pL20vy13WrvX7f+NU973fnNlytz4Zr8eLFntptr+uaoqIiybFjx+y7du1aduHCBevu3bvXMokODQ2tKC0tleTl5emtb3opNRGNB7AWwE4I+UpmA7CDkMxpkL6VNScyNzdhWFxHublh7rtZGGOoKC2Rxv9+yAUAhsyYm/brutVe8b8fcgnrPyjnXl3GuQ1X89pwde3aVV5ZWSkpLS2VFBQUyEShe++999JFY4nly5enP/fcc/niPdrznWlpaVZyuVxBRBg6dOitCRMmFP7666/29f1fNQZ9u6xLAQxijJ0joqc0Zecg5C/RCyJ6HMBqAFIAXzPGonTOvwLgBQAqADcAPM8Yu6o5Vw1hXhIArjHGRqIBXObPQ9ZbS2oNj8naGi7z5+nbZE4rgYgwZMbcNACI//2QiyiIYf0H5QyZMbdJSd5FG67hw4cXde3atWzTpk3Oc+bMyTWkDVe3bt1qiU337t3LH3744ZKffvrJYfjw4YGfffbZ1ZEjR9bMvenacE2cOLGWDZcoGvfC7NmzvefOnZs9efLkgn379tktXbr0jh71pEmTbi1btszD0DZc58+fVwLCHOGGDRucd+7cmdqYtotzhNplCoWi/Pjx47V68gkJCZa2trZqJycnvVdM9P0PdoGQtwS4vduEQU+Xaj3zGp8F0FOTvGkHbucsAYAyrXzHDYogADiMGAG3ZUshc3cHiCBzd4fbsqVwGDFCn9s5rQxtMRRpqghyGy7Tt+GaPn167pkzZ+zE1ezi4mJ6+eWXvf/73/9mN+Y5+n6If0Nwp9ZmIoRE7/pQk9eYMVYJQMxrXANj7AhjTBzrn4KQpKlJOIwYgcDDvyHkYgICD//GRdCEEecEtcu05wzvBW7DZfo2XO3bt2cxMTEp77//vruvr2+YQqEI7d69e8mCBQsalR9DLxsuzULJrxCCqHtByCIXBGAIYyxZj/vHAXicMfaC5ngqgAfrSwdKRJ8DyGaMLdccqwD8C2HYHMUY23W3+nr27MliY2MbfF/mQsHevchZ9SlUWVmQubnBZf68ekXfFG24RBEU5wR15wib2jPkGA6ztuFijCk1YjgcwD4ICdf3McaKDdpKAEQ0BUBPCBn0RHwYYxlE1AXAYSKKY4xd0rlvOoR8KvD2vmtcqFkhxkuKc6FivCQAs+kBExGsbNtVa4ueOEy2sm1XzUWw9WCqNlzNYsxKRL0BvMMYe0xzvAAAGGMrdK4bBOAzAP0YY3V2bYnoOwgivKO++tpSjzD50YF1r467uyPw8G93lJtij1CkLeSk5hiXRvcIDWzDVZPXGELC9okQwnC067sfwJcQhtA5WuWOAEoZYxVE1BFC4ijthZQ2TVuJlwTu9I7kIsgxFPracDUJxpiKiMS8xlIA34p5jQHEMsb2APgQQHsA2zW/4GKYTAiAL4lIDWFxJ4ox1mrmHFqathQvyeEYC71suAyBHnmN6wzMZoydBBBuyLaYEzxeksNpOnoHCBGRK4QwmI4QDBcA1CRf57QQ4oKIvqvGHA7nTvSKIySiURD8CJdCmMf7r+Zf3dhCTgvA4yXvnXuxrIqIiAg+duyY7b3WuW/fPrsBAwYE3Ov9d0PbLIKjP/oGVC8H8Bxj7H4AJZp/p0MItOZwOHpSVVXV0k3g1IG+QujNGNuuU7YRwDQDt4fDaRXk5+dLPDw8wsXtbHl5ebWON2zY4CyXyxWBgYGhR44csQXqt91as2aN86OPPhrQq1evoD59+gQDwla3/v37B/j6+oZpm4xOnjzZOywsLCQgICB0/vz5NfuA67O9ys7Olj700EM1tlfmmKe8OdBXCHM0c4QAkKqJC/SHsALM4TQLxacynTLfOx2e/ubxHpnvnQ4vPpXp1NRn1mdZ5ejoqO7du3fRtm3bHADg22+/dRo2bFi+aEdVVlYmUSqVCWvWrLk6ffp0P6B+2y0AuHDhgu3u3bsvnTlzJhEA4uLi2q1du/ZaSkpKfGpqqlV0dLQjAHzyyScZ8fHxF5VK5YUTJ07YnT59umYfcn22V7179y5OSUm5MHr06FuNtb3iCOgrhF8B6Kv5eRWAIxDcZ9Yao1Ecji7FpzKdbu274qMuqrQEAHVRpeWtfVd8miqGomWV+FqwYEFNLNL06dNviMYEmzdv7jh9+vSa4O+nn346DwCGDh1aXFxcLLl586b0999/t1+1apWbXC5X9O3bN1i03QKAhx9+uFC05wKA8PDwEoVCUSmTyTBhwoQ80UFl48aNTgqFIkShUCiSk5Otz507Z61VZ43tVVpamhUAnDp1yu7555/PBYCJEycWaNtecfRH3y12K7V+jiai3wG0Y4xdNFbDOBxtCn9L84BKXfuLW6WWFP6W5tG+l7tRkr4PGTKk5L///a/Vvn377Kqrq+mBBx6oiVGqK7i7PtutP/74o52tra1a93rdY6VSafn555+7/v333xc7depUPXbsWN/y8vKa99xY2yuO/ui7anwfEdU4fzDGrgEoJiK9/Qg5nKYg9gT1LTcUEydOzH3++ef9pkyZUmsr4I8//ugIAAcOHGhvZ2dX7ezsXF2f7VZdxMXFtVMqlZbV1dXYsWOH08MPP1yUn58vtbGxUTs5OVWnpaXJfv/9d4f67hfp1atXkdhr3bZtm9Ftr8wVfZfZNwPQ9QG0BLAJQKu16+eYDxI7y8q6RE9iZ2nUZEv/+c9/cleuXOnxn//8p1av09ramoWEhChUKhWtX7/+CiBYUE2fPt1bLpcr1Go1eXl5VRw5ciSlrueGhYWVzJgxwzs1NdW6T58+hVOnTr0llUoRFhZW6u/vH+bm5lbZo0ePBk1NoqKiMseOHdslICAgtGfPnsVubm4tlnzKlNHXhquQMXaHJXZ95S1NWzJdaCymarogzhHWGh7LJOoOw/2uGmtoDAAbNmxw3L17d4ddu3ZdMVYdnOajSTZcANKJqDtj7B+xgIi6A7hzkyuHYwREsSv8Lc1DXVRpKbGzrLQf6JVhTBF85plnvI4cOeKwb9++Bj03OaaNvkK4CsBuIvoAwg4TfwCvAXjPWA3jcHRp38s9z5jCp8vGjRvTIHhvcswcfVeNvyKiWwD+A8ALwi/Hq3fzBORwOBxTQe89iZqdJbq7SzgcDsfkuWv4DBH1IKIwreNORPQ9EZ0jonVE1GBCbA6Hw2ntNBRH+CmAzlrHX0NI2rQeQBi4UzSHwzEDGhLCEADHAYCIOkDISzyZMfZ/ACYB0NvviYgeJ6JEIkohojfrOG9FRFs1508Tka/WuQWa8kQiekzfOluCrOzdOHHiYfx2OAAnTjyMrOzdLd0kzl1oyzZcY8eO9d2wYYOjdpn4eSQmJlpaW1t3l8vlCn9//1BtY4jY2FjrXr16Bfn6+ob5+PiEvf76625iELmp0pAQygCIAZq9IKTYTAIAxlgagA76VKJngvf/AMhnjAVAWKVeqblXASHHSSiAxwGs1TyvQUrO5iAr6i+kv3kcWVF/oeRso1KdNpqs7N1QXohEeUUmAIbyikwoL0QaXQy5+JoOLWHDtW/fPruxY8f6NvY+Ly+vCs0e7AtJSUk2mzdv7lBcXEyjR48OeOONN7JTU1Pj4+PjE06fPt1+5cqVnYzQ9GajISG8AGC85ueJAA6JJ4jIA0CBnvU0mOBdcyymB9gBYCAJGzKfBLCFMVbBGLsCIEXzvLtScjYHt2KSUX1L2PZZfasCt2KSjSqGKfHLoKbav+hqqkJK/DKj1ZmVvRtK5aLa4qtcZJZiqBv8b0zLKW7DdRsLCwtEREQUJycnW3311VfOPXv2LB4zZkwhANjZ2am/+OKLa6tXrzbpJDkNCWEkhMRJeQCegKaXpuEpACf0rMcDteOx0jVldV7DGFNBEFlnPe+9g8IDqWBVtbvrrEqNwgOpeja58VRSfqPKDcHlSx9BrS6rVaZWl+HypY+MVmdLcGvfZff8ncle4h86Ywz5O5O9bu277N7ArXelrdtwLV682FP7/dd1TVFRkeTYsWP2Xbt2Lbtw4YJ19+7dS7XPh4aGVpSWlkry8vL0dbNqddy14YyxPwB4AxgMoAtjLFHr9M8A5huxbY2CiKYTUSwRxd64caOmJ6hLfeWGQFpPqG995YagvKLutJ31lZsijDGoy1XS0tjrLqIY5u9M9iqNve6iLldJm9ILMmcbrq5du8rlcrli1qxZPocOHeogit3OnTtrtsUuX748Xfv9a382aWlpVnK5XBERESEfMmRIwYQJEwrv+YNu5TQ4qcoYK0Idlvw6otgQGRACsUU8NWV1XZNORDIADgBy9bwXjLH1EFaz0bNnTybtYFWn6Ek7WDWi2Y3D4VhH5A+7CaZVBVUI5TUTDAbG2spNMyy+s9xcICI4jg1MA4DS2OsupbHXXQDAtqdrjuPYwDRj5Tc2dRuu8+fPKwFhjnDDhg3OO3fuTNX7zeP2HKF2mUKhKBdFWyQhIcHS1tZW7eTkZLIrJs3Vla1J8E5ElhDmG/foXLMHwDOan8cBOMyEr/o9ACZqVpX9AAQC+KuhCu0f8wVZ1H57ZCGB/WO+TXojd6PLkMVw2G4NaS4ABkhzAYft1ugyZLHx6vR/DRJJbbcnicQGXfxfM1qdLYG2GIoYUwRFuA1XbaZPn5575swZu127dtkBQHFxMb388sve//3vf7ONWa+xaZZsV3omeP8GwCYiSgGQB0EsobluG4AEACoALzPGGnThbXe/CwBhrrD6VgWkHaxg/5hvTbkxcBgxAgFo3tSabp2FNafLlz5CeUUWrK3c0MX/tZpyc0EcDmuX5e9M9jK2GHIbrtq0b9+excTEpMyePdt73rx5Fmq1GuPHj89dsGCBcUMyjIxeNlymBrfhqh9TtOHSnhMUh8O6x8YSQ27DZV401YbLpPj7779vEtHVek53BKCXB54BaU11+jRzO5oMEUFiLavWFj1xmCyxllUbSwS5DVfbwSx7hHeDiGKbu0fUVupsLI0xZgWEnqG26OkeczgNUV+P0GTjfjhtj7pWWjkcQ8CFkMPhtHnaohCu53VyOBxt2pwQagKveZ0cDqeGNieEHI42Uqm0h1wuVwQHBysUCkXIwYMH24nnDhw40D48PDzEz88v1M/PL/Sjjz7qKJ575ZVX3JcsWeKq/SxtC6y7PZfT+jDL8BkOR1/EvcYAsHPnTvuFCxd6Dh48OPHatWuyZ5991m/79u2X+vbtW5qVlSUbNGhQoKenZ9XEiRMbdF2q77nGfj+ce8Nse4REJCOiIUQ0n4iWaP4dotnHbFYQ0Zp6yj9t5qaYNAUFBVIHBwcVAHz88ccuTz31VG7fvn1LAcDNzU31/vvvp3/44Yed7/6Uuz+X0zoxO1EAACLqBmA3AAJwHoKllz2AuQAYET3JGDtvhHqnA3gWgolsewDFEDwdNzDGvjJ0fVo8C2BOHeVTAcwzYr3NSt6PW5xy1671UN28aSnr2LHSedasDKdJE5vk7SPacFVUVNDNmzct9u/fnwQAFy9etJk2bVqu9rV9+/YtTUlJqdk/vG7dOtdt27Y5i8c5OTkWDT2X0zoxSyGEkFvlY8bYZ7onNHuevwVg0GBjIoqCkLrgYwDncFt87wPwChF1YYwtMHCdz2t+lGn9LNIFzb+bxWjk/bjFKScqyodVVEgAQHXjhmVOVJQPADRFDLWHsIcOHWr33HPP+SUlJV3Q594ZM2ZcX7p06XXx2MPDI7yh50okZjsIM2nM9X9FAWBdPefWQ8jFYmj+A2AQY+xbxtjfjLEUxtg/jLFvAQwB8KIR6pyqeVlq/TwVwBQA/rjt5mPy5K5d6yGKoAirqJDkrl3boEmvvgwaNKgkPz9flpWVJZPL5WWxsbG18pKcOHHCNiAgoKy++/V5rqHayjEs5vofcxHATAB1zZ29pDlvaBra5mDwbRCMsQEAQETLGWPG8/pqBahu3qzTebm+8nvh7Nmz1mq1Gq6urqpXX331Rq9eveQTJkzI79OnT1l2drZ04cKFngsXLrzT/LERzzVUWzmGxVyF8AUAu4joddSeI+wKoBrAKCPU+Q2Aw0SkOzTuBuAVAEabI2SMLSYiZwDDAHRmjH1IRO4AJIyxdGPV25zIOnasVN24cYfoyTp2bJLtlDiXBwh7l7/44otUmUwGHx+fqm+//fbK9OnTfUtKSiSMMZo5c+b1p59+Wq88PfU9l9M6MVvTBSKyANAfdy5c/M4YM0oqMSJ6CcC0OuqMZox9aYw6NfX2A7ATQCyAhxhjdpqy1xhjxjNDbCKNMV3QnSMEALKyUru8+ebVpi6YcNoObcqGCwA0YndQ82quOr8EYDTBuwufAniKMfYbUU2mqNPQI9ufqSCKnaFXjTkcwIyFsD40cYTrGWO6q6yGevajEHqEdgCKAMQDOKLJzGcsfBljv2l+Frv4lTCz/1+nSRPzuPBxjIG5rhrfDSmMsJqqiV1MgTAX+CiAIAADNMfJRNTV0HVqkUBEj+mUDQIQZ8Q6ORyzwax6DCJEdPgup42V3KbZYxe1eBXAPiL6GYANEX0JIabRvBKXcDhGwiyFEMCDAFYAqCu5rwWAvkaos6HYxZVGqBMAwBg7pemRToYguGkAIsxlxZjDMTbmKoT/AlAyxnboniAiKwBrjVBnS8Qu1sAYywDwgTHr4HDMFXOdI/wUQkrQuqgC8JwR6nwBwla6NCL6mYh+IKJ9RHQNQhzhfwxdIRF928DrG0PXaW60RhuuiIiI4GPHjtXsaklMTLQMDAwMBYRk7XZ2dvfJ5XJFly5dQl999VU3fdrLuTtm2SNkjG2/yzk1gI1GqPNfIgrEnbGLq2G82MWMesptISwIOcIIAmxOtKQN15o1a5xTU1OtPvnkk0btVunZs2fxkSNHUgoLCyXh4eGK0aNHF3h7e1c2pb1tHbMUwrtBRC4AXmeMvW7oZzd37CJj7C3tY034zgwACwH8A8Cstt0xtRqkZVqge9xU9LHhWrp0qXtjhcVYNlz29vbq8PDwUqVSabV9+/YOhmpvW8QshZCE9GbPQ3B+SQbwBYB2AN6GYH5wtJnbY7TYRc3zJRCsuN4CkA5gEmOsWd+jsbm+Isq9uqhI6rZ8WRpJJGBqNbIWv+UltbOrdl3wZqP3/4q0VhuuadOmdbG2tlYDQFVVFdXlWpOdnS09e/Zsu3feeSdz27ZtTg21l1M/ZimEAD4C8BSAEwDGQlhFfhDAKQC9GGPxzdweMXbRGEHcEwEshbC3eRZj7H+GrqOlYWo1qouKpAUxMS4A4LZ8WVrW4re8CmJiXBzGjMlpSs+wuW24cnJypP379w8GhJ5iVVWVZP/+/R0AIDo6+kpERESZ5ufLjzzySCkgzBEOHz48UHx2bGxs+5CQEIVEImFz587N7tmzZ/k9vXlODeYqhBMAPMIYu0xEcgAJACbUtYpsKFoidpGIzgFwh7BaHAPBdLaL9jWMscvGqLs5IYkEbsuXpQFAQUyMiyiIDmPG5Ig9RENQlw3XlClTbonnDWHD5eHhoRIFsqlzhNplhmxvW8RcV40dRAFgjCkBlBpTBDU8COAwgO/reP1opDrDAThDiFFMgrCzRfuVbKR6mx1tMRQxpAgCd9pwbd261fnkyZM2gDAMXbhwoeerr76a3ZTnGqyxOhiyvW0Rc+0REhH54bYHoErn2Bg9pX/RzLGLjDFz/SK7A3FOULssa/FbXk0VQ3Ox4Wpqe9s6ZmnDRURqCOYD9ZmhMsaYQYerRDQeQC5j7I4hsmYxYypjzOBhOzr1eAHwYIydMmY9hkJfGy5RBMU5Qd05QkP3DDnmS5uy4WqJnlJLxC6KEJE3hOH3fRC+ANoT0TgAjzPGXjBWvc0FSSSQ2tlVa4ueOEyW2tlVcxHkNBWzFMK7QUShAJ5hjL3RjHUaLXZRw5cAfgbwMAAxhOIghERSZoHrgjcztVeHRTHkIsgxBG3it4iIOhLRHCL6G4J1v8IIdRAR/YeIPtPUZUFEHYhoFYDLxqhTiwgAUZqeJwMAxlgBAAcj1tns6IoeF0GOoTDbHqHGqn8EhPi9xyE4srgDeIAx9o8RqmzJ2MXrAAIgrBwDAIhIAeCaEevkcMwGsxRCIvo/CKJUBWA7gH4aq6osCDsvjEGzxy5q8REEP8IVEHIcT4KwzS6qGermcEwesxRCCPtt8wC8A2CLZphobGrFLhJRc8QuQlPft0SUC8HuKw1CL/gtxtiu5qifwzF1zHWSxR/AZwBeB5BNRDuJaCyM+36JiPyIqItmd4dK+1h3x4ehYYztZowNY4yFMsYe5yKoH6JdVkBAQGhwcLDi7bffdq2urgZQ2/JKfO3atcsOAGxtbe/Xfs6aNWucp02b5g0IFl0uLi5d5XK5ws/PL3Ty5Mne4jM5rROz7BEyxlIh7L9dSkQPQ+ghfQMhz/B7RLSKMZZg4GrbQdjNoR27eEm7WTDeVrv69jBXQJgKOMUYqzBG3aaO9p7gjIwM2fjx47sUFhZKV61alQnUvZ1NH8R9yNXV1YiIiAjev3+/3YgRI4oM3X6OYTBLISSizoyxbABgjB0HcFyTN2Q0BFH8F8AdycKbQgvv8pgGoDeERZN0AJ4AXCHkOfYFACJ6kjEW21INNAU8PDxUX3/9dWqfPn0UH3/88T072mhTUVFBFRUVEmdnZ2NmMeQ0EbMUQgirp/biARHFMMbGQAg6/pGI3JuzMc0Qu3gBQAxjrCZNgEb45RDysyyCMFXQ20j1Nwvp6d87XUn93KOy8oalpWWnSj/f2RmenpMNmt5ToVBUVldXIyMjQwYITi/iVjkA2Llz56XQ0NAK7S10gOAkM3jw4Jq5aNGiKzMz07Jfv34Fffr04eYHrRhznSPU3VrXX/uAMWaQb/u7NqAZYhe1eBrA5zplXwCYzIQ9lB8auX6jk57+vVNyyns+lZU5lgBDZWWOZXLKez7p6d87GbPenj17FiuVygTxFRoaWgHcHlKLrwULFtT6nZoxY8Z1pVKZcOPGjXOlpaWS9evXOxqznZymYa5C2CIbqDVB1GOIaDcEG/05AEIgxC4ON2LV1yHETGrzBIAczc/WEEKJTJYrqZ97qNUVtX5f1eoKyZXUzz0MWU9CQoKlVCqFh4eHQYayVlZWbMiQIYXHjh2zM8TzOMbBXIfGMiIagNs9Q91j1GWO0BRaKHZRZA6A7UQUDyF8xgtAGIDxmvMPQhgamyyVlTfqnNOtr/xeyMzMlL344os+zz33XE5djtD3glqtxsmTJ9vfd999pQZ5IMcomKsQ5kDI7yuSq3PMABg6nKUlYhcBAIyxXzXhOcMg7J7ZD+BnxliueB7Ar83VHmNgadmpUhgW31nelOeKc30qlYqkUil76qmnct9+++0a12ndOcLIyMis5557Lr+h54pzhCqVikJCQkpff/31nIbu4bQcZmnD1RIQkS+E1dtpADwgiNEPEHwIwxlj/A9BB31tuIDbc4Taw2OJxEodGLDoqqEXTDjmS302XOY6R9jsMMZSGWNLGWMBAIYAyIcQu9gJQuyi0RYriEimWZjZSURHieiY+DJWnc2Np+fkvMCARVctLV0qAYKlpUslF0GOoTDLoTERHUcDCyaMsUcMXGezxy5qsQrAowDWA3gPQrjMTABbjFRfi+DpOTmPCx/HGJilEAL4ugXqbMnYxTEAejPGrhHRu4yx1UR0AIJP4TtGrJfDMQvMUgiNbYlfDy0Zu2gLYbUYAMqIyFZj/HD/3W7icDgCZjlHSETDiejLes6tI6KhRqi2JVedLgJ4QPNzLIB3iGgxhFhGDofTAGbZIwTwKoAl9ZzbDMGQwdCJ0Js9dlGLuQBEe5NXIOwqsQMw3Uj1cThmhVn2CAEoNAsWdXECQKgR6hRjF7/RvHJ1jo02b8kYOyO6bjPGkhljgxhjD97lM+BoaG02XPv27bMbMGBAgHbZ2LFjfTds2OAIABEREcG+vr5hwcHBiu7du8vPnTtnBQDl5eX0/PPPe3l7e4f5+PiEDRw40P/SpUsWTf182grm2iO0ISI7xlhdtkftAdgYukLGmK+hn9kYiMgHQDcI768GxtgPLdMi06Albbg8PDzCMzIy4hr77Ojo6MuPPPJI6UcffdRx/vz5XocPH06ZM2eOR3FxseTy5cvxMpkMq1evdh41alTAuXPnLhpql4w5Y66f0FkA4+o5NwZCKIvZQEQLIMwTLoEQNiO+ZrRkuwyNkJuq/uOmItpwbdiwwUWtNsyzjWnDNXDgwOKrV69aFRUVSbZt29Zx3bp1aWIS+blz5+ZaWlqq9+7dy/c464G59gjfB7CNiBwB7ASQBcANQlKltyDsCTYoLRG7qMWrAHoawWy21ZCU/J67SlUoDZGvSCOSgDE1LioXeMlk9tVBgYsMtiLfGmy4dOvMysqyHD58+B1bNmNiYhzkcnlZQkKClZubW6WTk1Mt9b7vvvtK4+LibJ588kluCNsAZimEjLEDRPQfCHl9P9Q6lQbgBc3eW0PTErGLIrkAUluwfqPCmBoqVaE0K2uHCwCEyFekXVQu8MrK2uHi5jYuhzE1iIwzuKlvaKw9pAaEOcLY2Nh24rE4NK6oqKBhw4Z1Wb9+veP06dPzIyMjO+/evdsJAHJycixEwXvggQeKN23adK2uOseOHeurXfe0adO6WFtbqz09PSvWrVt3LTc31yz/jpsTs/0ANYmTdhBRMABnALmMsUQj1tcSsYsi8wCsJ6JPcdt6CwDAGDP5lJ5EEoTIV6QBQFbWDhdREN3cxuWIPURDoW3Dde7cuSY/T9uGa/r06fkrV67MXrlyZTYgzBFqi6m+iHOE4rGLi0t1VlaWZX5+vsTR0bGmV3ju3DnbkSNH3mrym2gDmOscYQ2MsUTG2EljiiDQYrGLIpYQ9jf/BaFnKL6uGLHOZkVbDEUMLYLGtOHy9/c3Ws4Ye3t79bhx427OnDnTS6USpiI///xz5/LycgnPk6IfZi+EzcirEGIU62IzhIx6xmIthDzG9gAstF7G2tvc7IhzgtplF5ULvJq6YCLO9QUEBIQOGDAgaODAgYUfffRRzZyjOF8nvsQwloZYt26dq1wuVwQFBYWq1WoY24brs88+y7CyslL7+fmF+fj4hMXExDju2rUrha8Y6we34TIQRHSdMeZazzkCkF3feUPUDcCdMWZSOSP1teESRVCcE9SdIzR0z5BjvtRnw2W2c4QtQLPHLmrxEYA3ieh9ZobfbEQSyGT21dqiJw6TZTL7ai6CnKbSJoWQiLyNsIggxi5uqOOcsWMX5wDoDGAhEeVqn2CMeRux3mYjKHBRpvbqsCiGXAQ5hqDNCSERWUFYRDB0svVmj13UYooRn91q0BU9LoIcQ2GWQkhEdwtctjJGnS0UuyjWfdRYzzYyarVaTRKJxOyG85zWh1qtJgB1rq6ZpRAC+B1Cj8ywe7AaoLljF4noeT3a9G1D17Qg8Tdu3FB06tSpgIshx5io1Wq6ceOGA4D4us6bqxBehZDc/KTuCSKyBlBizMqNHbOoxdQGzjPUzt7XqlCpVC9kZ2d/nZ2dHQYeysUxLmoA8SqV6oW6Tppl+AwRbQdwnDG2po5zlgASGWN+zd8yDofTGjFXIbQAAMZYVUu3hcPhtH7MUgg5HA6nMZjtvAwRDSGiH4joHBFd1vz7AxENbqH2mEU8H4djjphlj5CI5gN4A8BXAM4BKICwD7cbgBcBrGSMrW7G9lgBKGWMGTp2kcPhGABzFcJMAI8yxpR1nJMDOMIYczNwnQ3FLv7S3EJopB00HI7ZYa7hM+0A1OdanA0hD7Ch+R0tELtYH0bcQcPhmB3mKoQ7AewloqUAzqP20HgxgB1GqLPZYxdbYgcNh2OOmKsQzgDwLoBoCPt9xfF/FoBNAN42Qp2xAHoCuEMIIfQSjTFE/R2tqBfK4ZgqZjlHqA0RdYBgg1XMGLtlxHqaPXaRiK6ggV4oX6DhcBrGbMNnRBhjtxhj6cYUQU09VS0QwC32QuvCWL1QDsfsMMseIRG1g+AA8xCACwDeYoxd0jofxxgLN0K9QwA8CyAUgB2AIk39GxhjB41QH99Bw+EYAHPtEa4C4AvgHQhZ3f4iov5a530NXaEmdnEjgBQASwFMhzBPmQxgIxHNNXSdLdQL5XDMDnPtEWYCCGGMFWiOBwP4HsCzjLH9RFTEGLMzQp3NGruoeXaz9kI5HHPEXFeNbQDUpE9kjB0kopEAdhHRf3F7FdmQNHvsos4Omp2oHSa0kYiadQcNh2OqmGuP8DiAZbqu0ER0P4D/AejIGDPolwARfQvAH8KwuK7YxcuMsf8YuM4W6YVyOOaGuc4RrgDgpFvIGDsLYBCE+EJDMwNCDGE0gOsAyjT/bgRwCsBMI9TZEjtoOByzwyx7hC1NM8YuNnsvlMMxR9qMEBorZKYl0bhtvwtgGurZQcMYq2yh5nE4JkNbEsJCxpi9EZ/fIrGLWs/vgGbohXI45oi5zhHWBRn5+c0eu6hNc+2g4XDMkbYkhEON/PzhACYxxn5ijM0DMBFCwvdhxqqQiNoR0Vot921/nfNxxqqbwzEn2pIQ/kVE3TVDSGNwR+wigJEAviWi8TBO7GKL9kI5HHPBLOcIicgegjgoAPwJYD2APwD4ASgFMIoxdsjAdbZE7GKz76DhcMwRc+0RfgEgBMBuAH0A/AZgNYTFhDcBvGeEOlsidrEleqEcjtlhrj3CHABdGGPFROQIYdhozRirJiIJgDzGWIcWbaQBaIleKIdjjphrj9CaMVYMAIyxfAghJdWaYzWa6X03w2JFS/RCORyzw1x7hEUAuuJ2yMw/AO7XOj7XHHNnxo5d5HA4hsFchVANYX6svthB1hwW9i2xWGGOO2g4HGNjlvNHjLHWMuQ3duxiXfi0QJ0cjknTWgTDXDF27GJdGHsHDYdjdphlj5CIjuDuoSOMMTbQwHXeNXaRiAweu1gPLdEL5XBMGnOdI6zPesoDwBwAtowxg3r1EdH3EFZw9wAYBcALwDoAXwN4HsBUxtiDhqyznnZYAgiDYMF1y9j1cTjmgFkKoS5E5AxgAYAXAWwFsJQxlm7gOpo9drEldtBwOOaIWc8REpE9ES2DkFnOFUB3xth0Q4ughpaIXWyJHTQcjtlhlj1CIrIBMA/AqwB+h2BQesHIdTZ77GJb2UHD4RgbcxXC6xB6YB8CiK3rGsbYYQPX2eyxi7oB20SUzxhzrO88h8OpG7NcNYaQOImh/oRJDEAXQ1bYQrGLRER+uC2+dxy3QJs4HJPDLHuEbYXWsoOGwzF1uBAaiJaIXeRwOIbBXIfGLcHmesprYhebsS0cDqcR8B6hkWim2EXeC+VwDADvERoYTZDz6wBmA9gHIXbx0t3vumd4L5TDMQC8R2ggWiJ2sY42GL0XyuGYI7xHaDhSIcQufgAhdtGViFy1LzB07KJIM/dCORyzg/cIDQQRpaLh+TqDxi62hl4oh2MOcCE0YVpiBw2HY45wITRhWqIXyuGYI1wIORxOm8esbbg4HA5HH7gQcjicNg8XQg6H0+bhQsjhcNo8XAg5HE6bhwshh8Np8/w/7IsUcuTvESUAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 90.72x144 with 1 Axes>"
       ]
@@ -30239,9 +13740,9 @@
     },
     {
      "data": {
-      "image/png": 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fOkHoAwvy8OSnGSBtt9/dVQ78ySefOH722WcuAJCeni6cNGmSn5mZGfXw8FBwiT49PDwU2gk716xZ08CQvPnmm+5bt251dXBwqP3qq6/S9PzKeiRsBGDsaBsBjnZ2fqD7yoFXrlxZyLUtKCio8sSJEylyuVzGdX594HwAly5dShkyZEi1g4ODytLSUiWTyRr4E/73v/9ZBQYGVjdVT0+AGQBjhxv2a6PtE2gnTA6sZvny5Tkvv/yyZ0VFBQGAw4cP21y9etXmxRdfLOysNnQFzAAYM9pz/gcW5OGt4r/wwIK8Bj4BA8DkwMD69evzBg0adE8qlQZ6e3sHvfvuu24xMTGp1tbWPVouy+TAXUCr5MA/r3NDdRm/ftjPGQWL3nWYsKXTsgMzOXD3hsmBuysTtmSBqlA/5+d8Au30AbQWJgfumTAD0B3Q7eyd3PkBJgfuqTAfAINhwjADwGCYMMwAMBgmDDMADIYJwwyAidJWMU976g4PD+/r6+sbKBaLpRKJRHr27NleHdWG48eP24wZM8a3o+rvKbCnAIxO4fTp071OnjxpFx8fL7O0tKTZ2dkChULRbqltbW0tzMzMDNFEk4SNALoBB5IOOIyJHhM8YM+AwWOixwQfSDpg0OzAHE3JcRMSEoTDhw8X+/v7S6VSaUBiYqKwtLSUN2zYMLFUKg0Qi8XSffv22TVXd2ZmppmDg4PS0tKSAoCrq6vS29u7FgB+++03qyFDhvgHBgYGjBgxwu/OnTtmALBt2zanoKCgAH9/f+njjz/ev7y8nAeog3rMnz/fc8CAAZKlS5e6N9Y+ALh37x5/woQJ/Xx8fAKnTp3qo1IZZuVkT4IZACPnQNIBh/evvu9VUFVgTkFRUFVg/v7V9706wgg0JcedP3++z5IlS/KSkpJksbGxck9Pz1orKyvVTz/9lCqTyW6cP38+ef369e7NdbBp06aVZWVlmXt7ewctWLDA86effrIGAIVCQVasWOF55MiRm4mJiTeeeeaZgldffVUEAE8//XRxQkLCjaSkJJm/v39VZGSkE1dfdna2+f/+9z/5l19+ebex9gHAjRs3LP/zn/9kpKamJqanpwtPnTplbejvrLvDpgBGzq64XaKaupoGhrqmroa3K26X6Cn/pwyWLKQpOW5xcTEvNzfXPCwsrAQArKysKACqUCjIqlWr3C9fvmzN4/GQl5dnfvfuXYGnp6eysfptbW1VCQkJsp9//tnmzJkzNs8880z/DRs23B02bNi9lJQUy0cffVQMqAVIffr0qQWAv/76y3LDhg2i8vJy/r179/ijRo0q5eqbMWNGsUAgQFPtA4Dg4OB7/fv3rwWAwMDAyps3b7YroWpPhBkAI6ewqrDRH21T5Z3F559/7lBYWCiIj4+/IRQKqUgkCq6qqmp2RCkQCDB58uTyyZMnlw8YMKBq7969jkOHDq309fWt+vvvv+W6xy9evNjn4MGDqcOGDauKjIx0PH/+vA23z9rausXxvFAorBe68Pl8KJXKHh3eqy2wKYCR42jp2GgW4KbK23ydJuS49vb2qr59+9bs3bvXDgCqqqpIeXk5r7S0lO/k5FQrFArpsWPHbLKyspo1SHFxccL4+Hght33t2jVLd3f3mgEDBlQXFRUJuOCcCoWCcME4KysreZ6enrUKhYLs37+/0SlPU+0zxHdiCrAvyshZMnBJpjnfvMHdzpxvrloycEm7sgNXV1fzXFxcBnCvt99+26UpOe6+fftu/+c//3EWi8XSkJAQSUZGhuCFF14oiouL6yUWi6V79uxx9PHxaTZwRllZGT8sLMynf//+gWKxWCqXyy23bt2aZWFhQffv339z7dq17v7+/tLAwEDp+fPnrQFg7dq1WaGhoQEhISESPz+/JutvrH3t+W5MCSYH7gJamx34QNIBh11xu0SFVYXmjpaONUsGLsk05Pyf0fNhcuBuzFP+TxWxDs/oCNgUgMEwYZgBYDBMGGYAGAwThhkABsOEYQaAwTBhmAEwUZgcuGkiIyMdw8LCPLXLQkND/S9cuGAFACKRKFgsFkvFYrH04Ycf9ktPTxcA6uXU06dP9/b09Azy8PAImj59undhYWGH5jdsL8wAdANUOvH/dbe7A9py4OTkZNm5c+eS+/Xr1+7VjLW1eqcpbBSRSBTclvPOnz+fnJycLHvwwQcrN2zY4AoATz/9tJePj09Nenp6QkZGRoK3t3fNggULvNrVwA6GGQAj5/2r77u9dektD67Tq6gKb116y+P9q+8bLDswB5MDt57Ro0eX3759W5iQkCCMj4/v9f7779fnavjggw+yrl+/3otrjzHCDIARo6IqlNeU8w+nHnbmjMBbl97yOJx62Lm8ppxv6JEAkwP/w7Fjx+y1MwwnJCRYNXbc0aNH7aRSaVVcXJyFVCqtFAj+WVsnEAgglUor//77b6PNMMxWAhoxPMLDO8PfyQCAw6mHnQ+nHnYGgGm+0/LeGf5OBs+A+QFMSQ68cOFCz6tXr1oDQF5enhmX2uzJJ58s2rp1aw4ATJkypTgqKiqdu15oaKi/9ucZNWqUmMfjISAgoPKjjz7KPHnyZLeMNcAMgJHDGQGu8wOAoTt/W+jOcuC9e/fWd2yRSBSsnUpcX86fP5/s6upab+wGDhxYLZPJrOrq6sDnq/1+dXV1kMlkVgMHDjTaDMNsCmDkcMN+7TJtn4ChYHLg9hEUFKQIDAysDA8Pd+XKwsPDXYOCgiqDgoIUnd0efWEGwIjRnvNP852WFxcW99c032l52j6BtsLkwIbn22+/TUtJSbHw8PAI8vDwCEpJSbH49ttv07qiLfrC5MBdQGvkwO9ffd+tvKaczw37OaNgY25T9/qQ1zstOzCje8PkwN2U14e8nqWiKnBzfs4n0NU+AEbPgP2KugG6nZ11foahYL8kBsOEYQaAwTBhmAFgMEwYZgAYDBOGGQAThc/nD5ZIJFJOQHPq1KkG0tyNGzc6C4XCQdpy1uPHj9sQQgZ/9913tlzZmDFjfI8fP16/Qi87O1sgEAgGvf/++3206xOJRMGPP/54f2579+7d9jNnzvTWPmbcuHH9Bw4cKNEui4uLE4aGhvpLJBJpv379AufNm2fU6rruBjMAJopQKFTJ5XJZUlKSbNOmTZnr1693195/8OBBh6CgoHu6Kj8XF5farVu3uqIJoqKi7AcOHHjvhx9+uG/lXkJCgtVff/3VqDCmoKCAn5CQ0Ku8vJwvk8nqVxW+/PLLnitWrMiVy+WyW7duJa5evTqv1R+W0STMAHQDir7f75Ay8pHgGwHSwSkjHwku+r7xZbFtpbS0lG9ra1u/rj0xMVFYWVnJ37hxY2Z0dHSDawUEBFTa2NjU/fjjj70bq+uHH35w+PDDDzNyc3PNbt682SBv97Jly3LfeeedRo3Hvn377MaNG1cyffr0oqioqPpr5uXlmXl5edXHDQgNDa1q6+dk3A8zAEZO0ff7HfIiIryU+fnmoBTK/HzzvIgIr/YaAYVCwZNIJFIfH5/AlStXer311lvZ3L6oqCj76dOnF02YMKHi9u3bFrpLa994443s9957776OnJqaapafn282ZsyYyqlTpxZrd2QACAsLK0pISLBKSEi4Tx8fHR3tsGDBgqJnnnmmKCYmpv68l19+OXfSpEniRx55xO+dd95xLigoMOoIO90NZgCMnMKdO0VUoWjwf6IKBa9w505Re+rlpgC3b99O/PHHH1MWLVpUHzAjJibGMSwsrIjP52PSpEnFe/futdc+d+LEiRUAoCuBjYqKcpg6dWoxACxcuLDo0KFDDQyAQCDAihUrcjZu3NhXuzwjI0Nw584di8cee6xiwIABCoFAQK9evWoBACtXriyMj49PnDFjRtGFCxdshgwZIqmqqmJJPg0EMwBGjrKgoFGVXVPlbWHcuHH3iouLBdnZ2YI///zT8s6dO8IJEyaIRSJR8NGjRx0OHjzoqHvOunXrsjdv3txgFHDo0CGHAwcOOIpEouAZM2b4JiUlWWorAAFg6dKlRVeuXLFJT0+vnx5ERUU5lJWV8T08PIJFIlFwZmamMCoqqv6a3t7etatWrSo8c+bMTYFAgNjYWEtDfXZThxkAI0fg5NRo3LymytvCtWvXLFQqFVxcXJRRUVEOr7zySlZmZmZ8ZmZmfF5e3vXc3Fyz5OTkBgZnxowZZaWlpXy5XG4JANevXxfeu3ePn5eXd507d/ny5Tl79uxpMAoQCoV06dKlubt27XLhyg4ePOjw448/pnDnXblyRXb48GF7zb7eCoWCAEB6erqgpKSEr+0TYLQPZgCMHMdlyzKJUNhA90uEQpXjsmXtyg7M+QAkEol07ty5/T777LM0gUCAw4cPO8yZM6dE+9iJEycW63ZkAAgPD8/OyckxB4A9e/Y4TJo0qVh7/9y5c4u15/McK1euLKirqyMAkJSUZJ6ZmWn+6KOP3uP2SySSGhsbm7qzZ8/2+vnnn3v7+/sH+vv7S8ePHy9+55137jYVdYjRepgcuAtobXbgou/3OxTu3ClSFhSYC5ycahyXLct0mDeXJQtl6A2TA3djHObNLWIdntERsCkAg2HCMAPAYJgwzAAwGCYMMwAMhgnDDACDYcIwA2CimIIcWCQSBWdnZ9c/6dLOGBwZGelob28/UCKRSPv37x+4bdu2+rRje/futROLxdJ+/foFisViKZdzoCfCDEA3gOrk3NPdbgs9SQ68Zs0at8jIyPuWK7fElClTiuVyuezChQtJmzdvFmVkZAj++OMPyzfeeMP96NGjqbdu3Uo8evRo6htvvOF+5cqVHrn8mBkAIyd3S4Rb9pv/9uA6PVWpkP3mvz1yt0QYLDuwqcuBRSKR0tPTU5Gammq+devWvmvWrMmWSCQ1gHpV4urVq3O2bNnSt6V6uiPMABgxVKVCXXk5vzQmxpkzAtlv/tujNCbGua68nN+ekYCpyIFHjRol5pY8L1u2rNHpg0wmM8/IyBBKpVJFcnKyxUMPPVSpvX/o0KH3kpOTjTbDb3tgBsCIITweXDdvyrCdMSOvNCbGWS4NHFwaE+NsO2NGnuvmTRmE1/Z/X3eXA//555+WXMeOiorqs2XLFjduOycnp95InD9/Plkul8vkcrls586dd7Svy6UAnzt3br+PP/74jouLS12bv9BuClsKbORwRqA0JqY+O3B7O78u2nLgzMxMM04ODAC1tbXE3d29Zv369fna53ByYIFAUC8mOXTokEN+fr4ZdwfPy8szi4+PFwYHB9cnx1y6dGnR9u3bXQMDA+uH8tpyYACoqKjgR0VFOQ4ZMiQT+EcOvGrVqkI/P7/A2NhYy5EjR1ZyWX3XrFnj5u3trVixYkVhaz63bgpwAPDz86u+cuWK1bBhw+rbd+XKFSuxWGy0GX7bAxsBGDncsF+7TNsnYAiYHPgfwsPDc7Zv3+6alJRkDqjVih999JHr66+/ntNR1+xKmAEwYrTn/LYzZuRJZIl/cdOB9hoBJgdunOHDh1dt3Ljx7pQpU3x9fHwCp0yZ4rtp06a7w4cP75GxCJkcuAtojRw4d0uEW115OZ8b9nNGgW9jU+eybi3LDszQCyYH7qa4rFubRVUqcHN+zidgSB8Aw3Rhv6JugG5nZ52fYSjYL4nBMGGYAWAwTBhmABgME4YZAAbDhGEGwEQhhAx+8cUX6xWAGzZscFmzZo0boF5Z5+zsPEAikUj9/PwCv/32W1vdcu5VUFDAb49M+OOPP3YUi8VSsVgs9fPzC9RVHzI6FmYATBRzc3N64sQJe229vDZLlizJlcvlsgMHDtxcvny5d11dXYNy7uXk5FQHtE0mfPPmTbNt27a5/vHHH0nJycmy2NjYGyEhIZVN1cEwPMwAdAPiz9912B3+e/B/lpwdvDv89+D483fbnR2Yz+fTsLCw/Pfee8+lueMGDRpUzefzkZOT0+yakbbIhLOzs8169eqlsrW1rQMAW1tbFSfDZXQOzAAYOfHn7zpc/CHVq7K0xhwAKktrzC/+kOplCCPw2muv5cXExDhoR/3R5ezZs714PB51dXVVAsCuXbtcuOH/Qw89JNY+trUy4aFDh1Y6OTnVenh4BM+aNctbewrB6ByYATByYk+kieqUqgb/pzqlihd7Iq1d2YEBwMHBQTV79uzCiIgIZ919XEd/7bXX3KOiom7xNIuPtKcAV65cSdY+p7UyYYFAgAsXLqR89913N/38/KrXrl3rwfkhGJ0DWwps5HB3fn3LW8u6detyBw0aJJ07d24DbcKSJUtyN27cmNuG+lolE+bxeBgzZkzlmDFjKidOnFj2wgsveH/00UdM49BJsBGAkWNla97onLip8tbi4uJSN2XKlOLvvvvOqeWjW6Y1MuG0tDSz33//3Yo7NzY21kokEjEfQCfCDICREzLJO5Mv4DXQ/fIFPFXIJO92ZQfW5o033sgpKSnRazSo7QOQSCRSTjevjb4y4ZqaGvLqq6+6+/j4BEokEunBgwftd+zYkWGYT8XQByYH7gJamx04/vxdh9gTaaLK0hpzK1vzmpBJ3pnBo9xZslCG3jA5cDcmeJR7EevwjI6ATQEYDBOGGQAGw4RhBoDBMGGYAWAwTBhmABgME4YZABOlu8uBtTP9csycOdN79+7d9gAQGhrq7+3tHeTv7y8dNGiQJC4uTggA1dXV5LnnnvPw9PQM8vLyCho7dmx/3RyGpgQzAN0A3bUahli70Z3kwCKRKLgtnzEqKupWUlKSbP78+QWrV6/2AIAVK1aIKioqeLdu3Uq4c+dOwtSpU0umTZvmqzJgopXuBDMARs7vP6S4nd0r9+A6PaUUZ/fKPX7/IaVdohlTkgOPHTu24s6dO8Ly8nJedHS0065duzIEAvXHWblyZaG5ubnq2LFjNi1U0yNhBsCIoZRCUaXkyy9lO3NG4OxeuYf8UrazokrJb+9IoLvLgWNjY621pyOnT5+2a+y4mJgYW4lEUiWTyYSurq41Dg4ODW73DzzwQGV8fLxla67dU2ArAY0YQggeXSjJAAD5pWxn+aVsZwCQDHfNe3ShJIMQ0q76teXAlpaWDTrFrl27XKKjox179epVpysHbkolOHHixIoNGza0KAd+/vnnvd95551cTg58/vx5q19++aX32rVrPWJjY3t99NFHWeHh4X2PHDlSrx6USCRSABgyZEjF3r170wEgJCSk4ty5c6ncdWbOnOmtfd2wsLB+FhYWKnd3d8WuXbvSCwsL2e9dB/aFGDmcEeA6PwAYovNzGKsceOvWrTlbt27NAdQ+AC4TcGuIioq69cgjj9T7FJydneuys7PNi4uLefb29vUGLy4uzmrq1Kklra2/J8CmAEYON+zXLtP2CbQXU5ID9+7dWzVr1qyCpUuXeiiV6vyiO3bscKyuruZNmTKlvKOua8wwA2DEaM/5JcNd85Z9NuYvyXDXPG2fgCEwJTnwp59+mikUClU+Pj5BXl5eQTExMfaHDx9O5ZloujWTlQMTQt4GMItSGtQJ13oWwA5KqTXQtBw4NjZ2sLe39y0nJ6f6DvP7Dyluiiolnxv2c0ZBaCmoGzHbj0XOYehFU3Jgg5o9Qsg3hBBKCPm3TvloTfl9w0xCyFFCSB0hZHwj+6wIIe8RQlIJIdWEkAJCyEVCyDw929Nk3Z3MAQD9uI3y8nK7+Pj4QH1OHDHbL0t7zs/5BFjnZxiCjhj3VAN4jRDSp6UDCSGuAMYC2A7ghUYO2QXgKQCrAEgAjAewD0CLEXH1qLtTIISYUUqrKKV57aij2W0Go610xFOAcwDcAfwbwIoWjn0WwM8APgUgJ4Q4UkoLtfZPBfAqpfS4ZjsNwDU929FS3Q0ghAgAfKA5DwC+AWABIIBSOlpzjBDAVgDzANgC+FvTvt81+0dD/fmfAPA2gAcAzNCMfHZQSq0JIc/+3//9n62FhQViY2MHA4CHh0eai4tLIQAolUp+cnJyv/LycluBQKB0dXXNdHZ2LgKA6upq84SEhGAvL69b+fn5zlVVVb2EQmG1t7f3bUIIvXPnjndVVZWlpaVlpY+Pz21LS8sazXlmd+7c8aysrLRRqVTEzMysxtXVNatPnz4N5uYM06MjRgAqAGsBLCGE9G/qIKK+jT0HYB+l9A6AKwAW6hyWA2ACIaRVC0T0rFuXV6Hu/C8AGAr1dzNf55j3oR6RPAfgQQDxAH7WjDa02QrgTahHLVd09h2wsrIqMzc3rx4wYEDcgAED4pycnOqj/eTk5LjZ2dmVSKVSmZ2dXVFGRoZ3dXV1A0dbdna2qG/fvjkBAQEyPp+vvH37dr/09HRPNze3TIlEckOlUvHS09M9uePv3LnjpVKpeH5+fklSqTTR3d09g8/n17XwfTBMgA5xfVJKTwC4CODdZg4bBfVQ/ifNdhSA53WOWQzgIQAFhJD/EUJ26Dmf16duXVYC2EopPUQpTYJ62pHD7SSE9AKwFEA4pfQnSukNAEsA5AJ4Waeutymlv1BKb1FK87V3UEqreDweJYTA3NxcaW5uruTz+fWeWHt7+0JnZ+ciS0tLhYeHRyYAWlZW1mBhjbOzc46Dg0OplZVVtYuLS65CobDo06dPnp2dXXmvXr2q+/Tpk3fv3r36pa01NTXm1tbWFdbW1lWWlpY1Dg4OZQ4ODmUtfYmMnk9HPvsIBzCbEDK4if0vAIimlHLPfQ8C6E8IeYg7gFJ6AWrn2aMAogGIAfxCCPm8hWu3WLc2mhFGXwB/al2bam8D6A/ADGrDxh1TB+APAFKdKmNbaF+TWFlZVXF/83g8CAQCpVKpNGvqGDMzs9rGylQqFa+uro4HAM7Oznl5eXmuiYmJkvT0dLfy8nIrMBjoQANAKf0TwCGoh80NIITYAZgJYDEhREkIUQIoAmAJHYcdpbSWUvobpTSCUvoY1L6FxYQQ78au25q6DYjus9R7ba2IEHLfc1ndR7WNHcPj8Zo8z8XFpSAoKCje0dGxQKFQWCQnJ0tWrVol9vX1DRSLxVKJRCI9e/Zsr7a2ub08+OCDEkPUY2Vl9aD2dmRkpGNYWJhnU8fre8yqVavcDh8+3CPFQh29FHg9ABmACTrlTwPIBzBJp3wYgG2EkFWU0qY6Ebck1LqJ/a2um1JaSgjJATAEwFmg3o8wBP9MA24CqAHwsOZvEEL4mnq/a6ItTdHpiy+EQmFt3759C/r27Vtw8OBBz7NnzzrFx8dfs7S0pNnZ2QKFQtFljxauXbsm76pr68PHH3/cYx+5dujyJ0ppKoAvoJ5fa/M8gIOU0gTtF4A9UDsRnwIAQsivhJCXCCGDCSHehJBJAN4DIAdwo4nL6lV3I3wC4HVCyHRCiD+AbQBcoemsGqPxGYCthJBJhJAAzbYLgJ2t+V74fL6ypqbGvLy83Kq2tlagUqma7Xy3rly02vXSwuD/PDMr+PLn23HjwplGJbdNcfv2bY+ioqLeVVVV5hUVFZYZGRnWdnZ2dZaWlhQAXF1dld7e3rWAet09FyPgwoULVqGhof4AUFpayps1a5Y3F7zjm2++sQOAgwcP9pZKpQH+/v7SYcOGiQGgrKyMN3v2bO/g4OCAgIAAKRfkIzY21iI4ODhAIpFIxWKxND4+Xgj8c+eePHlyv/3799c7fLkAH0qlEi+99JJ7UFBQgFgsln7wwQetXrb83Xff2Q4YMEASEBAgHT58uDgjI6PBza+wsJDv5uYWzMU9KCsr4/Xt23eAQqEg2oFGRCJR8OrVq92kUmmAWCyWXrt2zQIAsrKyBMOHD/fz9fUNfOqpp7zc3NyCm4q1YEx0xvrHjQCU3AYhZBDUHvSDugdq5uxH8c9Q/STU3vuTUHf6nQB+A/CYZv7dgFbWrcuHAPYC2A3gsqbsR6jXNXCEQ72oZzfUjwAHAJhAKc1uos5GsbS0rLSxsSlNSUkRx8XFDczPz29yXUN2wjXeXzHf298rKTYHAEVZKS59v8ft71MnWpUd+O7du54ymSwoJSVFPGbMmOq8vDylt7d30IIFCzx/+umnpkZT9axdu9a1d+/edcnJybLk5GTZE088UZ6VlSVYvny5d0xMzM2kpCTZ4cOHbwLA+vXrXceMGVMWHx9/47fffkt688033cvKyniffvppn2XLluXK5XLZ9evXb/j4+DRY9z9nzpyi6Ohoe0AduefixYu9Z8+eXfLxxx872dra1iUkJNyIi4u7sWfPnj5yufy+JcgKhYKnvUx5y5Yt9TETxo8fX/H333/Lb9y4IZs1a1bRxo0b+2qf6+joWBcQEFB54sQJGwA4cOCA7ahRo0qFQuF9ozUnJyelTCa78dxzz+VHRES4aL4ft1GjRpWnpqYmzp49uzg7O9sguRs7GoNaKErps42U5QHQnj8VAGjyjkcpDdP6ewuALa24/v9aUffbUD+r57aVUHv+V3FlhJBrAH7XOkahe4xO/b82dn1K6TdQryvg6qVisfiW7nEhISF/6ZalXTijqqutbfB/qqutJZcPfi96YPykIhsbm0rd8zRe/voyHx+fDAAN1tgnJCTg559/tjlz5ozNM88803/Dhg13V6xY0eQ6iQsXLvTev39/fZv79OlT991339mGhoaWc0E8XFxc6gDg119/7X3y5Em7yMjIvgCgUChIamqq+bBhw+59+OGHrnfv3jWfO3ducXBwsEL7GrNmzSoNDw/3qKqqIocOHbINDQ0tt7a2pqdPn+4tl8utjh49ag8A5eXlfJlMZqEbPEQoFKq0VYORkZGOsbGxvQDg9u3b5tOmTXPPz883q6mp4Xl4eDS4NgDMnj27+Pvvv7efMmVKeXR0tMOyZcvydY8BgPnz5xcDQGhoaCXXpj///NP68OHDqZrPUda7d+9u8ZjVNBUQjUAI8SKELCaE+BNCggghn0B9h9/Tle3i7vz6luuLQCDA5MmTy7dv3571wQcfpB8+fNgeUEcK4sJjVVVVten3QSnFwYMHU7mwYdnZ2fGDBg2qXrJkSdGRI0dSLS0tVZMnT/Y7evRoA8ealZUVHTp0aHlMTEzvAwcO2D/11FNFmvrItm3b0rn6MjMz42fMmNGqx5jLly/3XLZsWV5ycrJsx44ddxQKxX2fbd68eSXnz5+3zc3N5SckJFhNmTKl0WtYWFhQABAIBFSpVHbrZZnMAPyDCkAY1I/+/oB6MdBESmmbH+kZgl529o3KY5sq14e4uDghN/8GgGvXrlm6u7vXAIC7u3vNxYsXrQCAG44DwKhRo8q2b99eH5MgPz+fP3r06Ht//vmnDTccz83N5QPAmDFjyrZt2+bCGZKLFy9aAoBMJjMPCAhQvPnmm3mPP/54yd9//31fFJ6nnnqq+JtvvnG6evWqzcyZM8sAYPz48aWfffZZH85Ref36dWFZWVmrfrvl5eV8T0/PWgD45ptvHBs7xtbWVjVgwIB7L730kufYsWNLubBh+qAJVOIAADExMb3LysqajLJkTDADoIFSmkEpHUEptaWU2lBKH6KU/tLV7Ro6a14m38ysYXZgMzPV0Fnz2pwduKysjB8WFubTv3//QLFYLJXL5ZZbt27NAoANGzZkvf76655BQUEB2guUtmzZkl1SUsL38/ML9Pf3l544ccLGzc1NGRkZmTZ9+nRff39/6fTp0/sBQERERJZSqSQSiUTq6+sb+Oabb4oAYN++fQ5isThQIpFIb9y4YfnSSy/dN+WYPn162Z9//mkzYsSIMu5Ou3r16gKJRFIdHBwc4OfnF/jiiy961dbWturO+8Ybb2TNmzevf2BgYICjo6OyqePmzJlTfOTIEYd58+a1KhdjRERE1tmzZ3v7+fkFRkdH2zs5OdXa2dkZ/TTAZOXAXUlrswP/feqEw+WD34vulRSb97Kzrxk6a17mA+MnsWShRkRVVRURCATUzMwMp0+f7rV8+XKvtkQx6ihYduBuzAPjJxWxDm/cpKamms+ZM6e/SqWCmZkZ/fzzz9O6uk360CMNgJOTE/X29u7qZjTJu+++q2rNCIBh/AQHBytu3LhhNHd8fekUA0AI+RrAZAB5jUXg0ay6+wTq1XuVAJ7VPNIDIeQZqJV1ALCZUtqiV97b2xuxsV3qu2sIpYCWhv/06dNNzkEZjM6ks5yA3+D+5cDaTATgp3kthnqFHQghDgDegloRGArgLUKIfVOVGCXntgA/r1MbAQCgFISqeuTIi9H96BQDoFH1NTeHfRJAFFVzGYCdRmP/OIBTlNIiSmkxgFNo3pAYF5QC1aWgVz77xwj8vA4EKvb0hWEUGMudSISGK9XuasqaKu8eEIJb/n2h5A+D36XPQK58BgqAjhjDHr0wjIIecyfSrOKLJYTE5uc3uoKz06GUQqksR4YgBSn9eoECmnfa5avHwsPD+zI5sHHIgbXFRhzcZ0lKSjK3sLAYJJFIpP379w+cP3++JydYio2NtRg6dKjY29s7yMvLK+i1115zbW2SU70MgEb22pFkAtBOfuGuKWuq/D4opV9QSkMopSF9rAXA9iDgbTv1+/Xojmp3sxBCcLrCHrlVDshwt8TZR5yQ4W6JZuQKjWLo7MCnT5/udfLkSbv4+HhZcnKy7Ny5c8n9+vXrsIQcLdEd5MDTpk1rd+KQ48eP2+imL9MHDw8PhWYZdGJycrLlvn377CoqKsj06dN9X3/99Zy0tLSEhIQE2ZUrV6y3bt3aYjBebfQdAWQTQj4hhIS0tvF6chRAGFEzFECpRmF3EsBjhBB7jfPvMU1Z85SkA6UZAKj6/diKLjECVKVCeepJbCmoalDemuVhv+75r9svuz5pkB34l12fePy6579tzg6cmZlp5uDgoGRy4H/oDnJgMzMzhIaGVqSkpAj/+9//OoaEhFRwmggbGxvVZ599lv7JJ580maK9MfQ1ABOg/t0eI4TcIISsJ4R4tHQSByHke6jX1/sTQu4SQp4nhCwhhCzRHHICwC0AqQD+C2AZAFBKiwBsAnBV89qoKWseqjMMqq0CzmzUt7kGg/B4eM1pKNY5NVzyru9wilIKReU9fsKvp505I/DLrk88En497ayovNfm7MDTpk0ry8rKMmdy4H/oajnwm2++6a7d9saOKS8v5124cKH3gAEDqhITEy0GDRpUqb0/MDBQUVlZySsqKtJ7aq+XhdI8k/8fIeRVqO/CCwAkEEL+B7WG/kAzEXxAKW02kYcm/p5uYE1u39cAvtannc1SerfdVbQWSilS3VVwuVuE8+UC/Fhihul2tXikl34dlxCCx5aszACAhF9POyf8etoZAIJGj8t7bMnKNicItbW1VSUkJMiYHLghHSUHHjBggKSmpoZXWVnJKy0tFXAd/N13373LCZ42b958d9GiRfVh2rX9GRkZGUKJRCIlhGDixIklc+bMKfvll19aFRSmKVo1RKGUqgghN6AOzjEUao/80wA+JISspJTuNUSjOgRb906/JCEEfIENcs2C8GPJTQAEP5aYYWSv+2P6NVfHY0tWZnCdHwDa0/k5ODnw5MmTywcMGFC1d+9exxUrVhQaUg48cODABp1s0KBB1SNHjrz3448/2k6ePNnv008/vTN16tT6ubWuHHju3LkN5MBcZ2kLy5cv91y5cmXO008/XXr8+HGbjRs33jeFmjdvXsmmTZtEhpYDX79+XQ6ofQC7d+92PHToUFpr2s75ALTLpFJp9W+//dZg5CaTycytrKxUDg4OensC9XUC2mtCc/0O4H9Qd/wwSqmYUjoW6uf1kfpetMMhOh/LzBIYu6HTm0EpxcECBbbcvIkFAQtxPew6FgQshKoVXkBu2K9dpu0TaAtMDtz95cCLFy8uvHr1qg33dKKiooK8/PLLnv/6179yWjpXG32/xLsApkDdyd0opUsppZe4nZTSqwCOtObCHYqdJ2DrAYCo36dEAgPmdHozCCGwjvPBotJ1eC3kNRBC8FrIa+DR+yP4Nob2nD9o9Li8NfuP/RU0elyetk+gLTA5cPeXA1tbW9OYmJjU9957z83b2ztIKpUGDho06N66detalYJOLzkwIcSFUprb5tZ2MkESf7pi7HCUFxbAxtEJI+eGIWDkmE5vB6UU+9/5FLm38jHoiQUYOUeM36KTobBPx/jHxt8X/qsxft3zXzdF5T0+N+znjILQqlfd6Gde7LHRarsbPV0OvIgQckZzpwcAEEJCAYymlN4X97+rKcvPQ3mB2n9TXpCPX77YAQBdYgRcvC2QdeMa/vcTcP3saCirfkXgghF6nz/6mRezKKX1CUE5nwBLEGpc9HQ58Eqok2xqIwNwGI0k/uhqdEc1yhoFftsf1ekGgBCCMc+8CFCKaz8fQ53imqb8kVYt12LZgY2fni4HNgdQq1NWA3X23G5BeWHXyO/zd+yA7V/p/0iCKQVo1y8FZjAA/Z2Af0GzOEeLJVA/EegWCGw7f6m7SqVCxl/psLp8HAFZhQClCMgqBGEGgGEk6DsCWA3gFCFkIdRpsfpDnUxTn0y9nQ7Vybyl5KkQJy7p9HYQQpDs2Qe2qbbwKSiFT0EpgCbEDAxGF6DXCIBSmgh1Zt4PoF6S+wEAf0qpUc55KiyVqLBQgoKiwkKJi8GF+Nup8x3mhBB4BYtg8fyihjt4vNZJthiMDkLvxRSU0gpK6X5K6Qea94qObFh7UJircPDRTOyZlI6Dj2bitqgSfXv1bfnEDmDYrPmQZjX0PxAjiMTcFulsaGio/4ULF9qcWvz48eM2Y8aM8W3r+c2hLWJi6I9eXxghRAC1D2AUACdorWSjlD7SMU1rOzydlYAWfAusHKSbn7TjoZQi7am5qL5+HfYLF8Jl/TrkvrcFWZT2mDgM+lJbq+tDZhgD+v4QtwN4CcAFAIMBHALgDE0qbWOjL1+EsGsb8dIfHyPs2ka8arcJT/R7okvbxPkldP0T+lBxOcsh690rwXfX/jY4690rwRWXs1qVGLQ1FBcX80QiUTC37LaoqKjB9u7dux0lEonUz88v8Ny5c1ZA0/LfyMhIx0cffdR36NCh4uHDh/sD6iW5o0eP9vX29g7SDm7x9NNPewYFBQX4+voGrl69un6dflPy25ycHP7DDz9cL79l+S3ahr4GYAbUabI+AaDUvE8D0Pkra/RAWUZgVW0LAvV7yS8WSL7SqiXSBoEQAu8D+2G3cAFK9u6DPECKkr37AEL09gFUXM5yKDl+20tVXmMOAKryGvOS47e92msEmpLO2tvbq4YNG1YeHR1tCwBff/21w6RJk4o5WWxVVRVPLpfLIiMj7yxevNgHaFr+CwCJiYlWR44cuXn16tUkAIiPj++1c+fO9NTU1IS0tDRhVFSUPQB89NFHmQkJCTfkcnnixYsXba5cuVKvE2hKfjts2LCK1NTUxOnTp5d0l2y8xoa+BsAK/8TmqyKEWFFK5VCn4jY6dMMBKGtU+OPIzS5pCyEEfdevb1BGeTy9w4KXnckQQakTRFSp4pWdyWhXbEROOsu91q1bV+8lXbx4cT4nmNm3b5/T4sWL650Y8+fPLwKAiRMnVlRUVPAKCgr4v/76a+/t27e7SiQS6YgRI/w5+S8AjBw5soyTCQNAcHDwPalUWiMQCDBnzpwiTtG2Z88eB6lUGiCVSqUpKSkWcXFxFlrXrJffZmRkCAHg8uXLNs8991whAMydO7e0u2TjNTb0dZrcADAE6sSZsQDeJoSUoRs90aoouk/+3SlQSpG7pWGGc6LSPyw4d+fXt9wQPPbYY/f+9a9/CY8fP25TV1dHhgwZUs3ta2xVYlPy399//72XlZWVSvd43W25XG6+Y8cOl7/++utGnz596mbOnOldXV1db/R6UjZeY0PfEcBK/LMScA2AQVCrAxfreyFCyARCSBIhJJUQsraR/dsJIX9rXsmEkBKtfXVa+47qe01trB2ELR9kYLjOXxy1F/ZhCyG5IYN92EKgFU5Ano15o7H6mio3FHPnzi187rnnfBYsWNDgEcb3339vDwAnT560trGxqXN0dKxrSv7bGPHx8b3kcrl5XV0dDh486DBy5Mjy4uJivqWlpcrBwaEuIyND8Ouvv9o2dT7H0KFDy7lRSnR0dLfJxmtstHgn0gQEDQbwLQBQSlMAjGvNRTR1/AfqhUN3AVwlhBzVXkdAKV2tdfy/0HB6UUUpfUDv6+k42nh1NRjoWd3E0R0HIQR8m96wD1sIl3XrQAiBy7p1SDx1Sm8fQO+xHpklx297NZgGCHiq3mM9OnT09fzzzxdu3bpV9PzzzzeQxVpYWNCAgACpUqkkX3zxxW1ALYVdvHixp0QikapUKuLh4aE4d+5camP1BgUF3VuyZIlnWlqaxfDhw8sWLlxYwufzERQUVNm/f/8gV1fXmsGDB7f4iDkiIiJr5syZ/Xx9fQNDQkIqXF1duyyoaXdGXzlwCaXUrs0XIWQYgLcppY9rttcBAKV0SxPHXwLwFqX0lGa7glLaYtw6Dn8bR/razF1QCB0gVBSh/62jcBdkwe/smbZ+hHahUqnA4/3Tf0+fPl0zbty4eH3Pr7ic5VB2JkOkKq8x59mY1/Qe65FpPdStQ5OF7t692/7IkSN2hw8fvt2R12F0Du2VAx8jhEyhlB5r4/UbS/DxUGMHEkK8APig4SNGC0JILAAlgAhK6eFGzlsMzZTEVSDAw5cbRgBSdpGCrvTUHdBqJWwn96ufLxPaulBs1kPdijq6w2vzzDPPeJw7d872+PHjKZ11TUbXoO8P0QLAQULIH1B35PphA6U0zMBtmgvgIKVU26vrRSnNJIT0A3CWEBJPKW3g1qeUfgHgCwAIsrC8b1gjcG1VtGSDQCkFrVai4qLawW47uR9Kj98CLHRjlhkXe/bsyUBDg83ooehrABI0r7aid4IPqA1AgwjBlNJMzfstQsivUPsHmn6ux9PxNFtYwHn1qlY2uf0QQmA7uR8AoOJiVr0hwFjdB5UMRtegb1jwd9p5nasA/AghPlB3/LkA5useRAiRALCHOocAV2YPoJJSqiCEOAF4GC0EITETiSBwc4MyOxsCV1c4r14F2ylT2vkR2gZnBOo7PwBKwNKDM4wCfbUAjza1j1La4nJgSqmSELIc6qw+fABfU0oTCSEbAcRSSrlHe3MB7KcNPZMBAD4n6tVzPKh9AM2qEPm2tl3m8NOFUqoe9mvRWh8Ag9FR6PtD/Epnuw/UUYLuAuinTwWU0hNQZwDSLtugs/12I+ddgvoxZLeD6/wVF7Ng/bDbPz4ApBm1D4BhOugbD8BH+wXAFsC7AHZ0aOu6OYQQEAtBfef/xyfQ9T4AU5YDd2U2XmOjrZlf6qA2AK8btjk9D9vxXvWdH9AsnW2lD8DQ2YG7gq6QAxtjNl5joz1D0fEAurf56yTaE8W35Pgtt+JDKQ2yAxcfSvEoOX6rzdmBm4PJgf+hI7LxGhv6pgbLIISka70KAPwA4L41/QzDQSmFqlrJr4zNdeaMQPGhFI/K2FxnVbWyzdmBASYH7qpsvMaGvk7ABTrb9wAkU0rbnKyR0TKEENjP9MsAgMrYXOfK2FxnALAKccmzn+nXruQgzWXSXbx4cf7WrVv7Lly4sGTfvn1O//3vf9O445qSAzeWDRhoWg4MoF4OvGjRouI9e/Y4fPPNN05KpZLk5+ebxcXFWTz00ENVmmvel4338uXLNjExMamAWg780ksvdYtsvMaGvgbgMgAVpbR+IkcIMSOECCmlXaOzNRE4I8B1fgBob+dvie4uBzbmbLzGhr5Dl1NQhwLTZjDUz/UZHQg37Ncu0/YJdBRMDtwQQ2XjNTb0HQEEA7iiU/YngIGGbQ5DG+05Pzfs57aBjh0JMDlwQ7hsvMuXL/dctWqVmUqlwuzZswtbm43X2NBXDpwGYCilNEerzBXAVUqpe8c1r22EhITQ2NjYrm5Gk7RGDlxy/JabqlrJ5zo7ZxR4FoI6u8n9OizZAZMD9yzaKwc+BOA7QsgKALegzgz0EYBog7WQ0Sh2k/vdlx24o30ATA5sOuhrAN4AsA3qYb8QQDWA3QDWN3cSwzB0dnZgJgc2HfRVA1YDeFkj6HECUEC743I0BoPRAH0XAoURQgZQNfmUUkoIGahJFspgMLop+j4G3IT7h4QZADYbtjkMBqMz0dcA9Aagu+qvFICdQVvDYDA6FX0NgAzATJ2y6VAnDGF0Q/h8/mCJRCL19/eXSqXSgFOnTvXi9p08edI6ODg4wMfHJ9DHxyfwww8/dOL2rVmzxm3Dhg0u2nVpS3Gbq5dhfOj7FCAcwAlCyFNQx+LzBTAWwCR9L0QImQDgE6gjAn1JKY3Q2f8sgA/wT6zAHZTSLzX7ngHwpqZ8M6V0j77XZTSOthbg0KFDvdevX+8+fvz4pPT0dMGzzz7r88MPP9wcMWJEZXZ2tmDcuHF+7u7utXPnzi1ta70d/XkYbUPfgCC/AwiCOrZfL6gfBwZRSi/qc75WYpCJAKQA5hFCGlNgHaCUPqB5cZ3fAcBbUIcRDwXwliZOoMlQ9P1+h5SRjwTfCJAOThn5SHDR9/sNmh24tLSUb2trqwSAbdu2OT/11FOFI0aMqAQAV1dX5XvvvXf3gw8+6NueehnGid6x6Sil6QAiAIAQwgMwkRCyjVI6R4/TQwGkUkpvac7fD+BJqKcWLfE4gFOU0iLNuacATADwvb5t784Ufb/fIS8iwosqFDwAUObnm+dFRHgBgMO8uW3OFcDJgRUKBSkoKDA7ceJEMgDcuHHDMiwsrFD72BEjRlSmpqbWr+/ftWuXS3R0tCO3nZeXZ9ZSvQzjpFU6Zs2jv48AZAH4DoC+66AbSwzSWHbbmYSQ64SQg4QQTgCj17mEkMWEkFhCSGx+fr6ezTJ+CnfuFHGdn4MqFLzCnTsNkh349u3biT/++GPKokWLfPQNb7VkyZJc7czCzs7O9SrR9tTL6HxaNACEEGdCyBpCSBzUmYEHQj0NGEgpXW7AthwD4E0pHQC1+rBV83xK6ReU0hBKaUifPt06SlMDlAUFjQa6aKq8LYwbN+5ecXGxIDs7WyCRSKpiY2MbxP27ePGila+vb1V76jVUWxmGpVkDQAj5Ceo77nyoO6QXpXQsgAoAlc2dq0OLiUEopYVasQW+xD/y49YkFTFK2rNoUuDk1KjKranytnDt2jULlUoFFxcX5SuvvJJ/4MABx0uXLlkC6tBb69evd3/llVdaLXvVrtdQbWUYlpYs8yion///H4ATlNK2qs9aTAxCCHGllGZrNqfin0eMJwG8p+X4ewzAuuYuVlJZi4cjziKrpApudpZ47XF/THuwXSPmNrP9VDLKqmuxYbK0PniGqhV5ARyXLcvU9gEAABEKVY7LlrXLCHJzdUBtoD777LM0gUAALy+v2q+//vr24sWLve/du8ejlJKlS5fmzp8/v8UnAM3VyzBOWvrPuED9/P8ZAOs104BvAZgB0Pu2pmdikBWEkKlQJwAtAvCs5twiQsgmqI0IAGzkHIJNkVlSBWVJVf3f62LUytvONgKUUpRV12L3xTQAwIbJUmw8LsNDFvr7XjhHX+HOnSJlQYG5wMmpxnHZssz2OAABoK6u7q+m9k2cOLFi4sSJja7x+Oijj+67CWRmZtZLm5url2F86BUPAKjP2rtQ8/ID8AuASE3CD6NC6OpHXZ/5uEGZyM4SF9c2meCow6CUYuNxWb0RAIAvxgpVj40fd63TG8MwWZqKB6D3nYhSeodSuplS6g91fr47APYarokdS1ZJq31YBoEQgg2TGy554LHcgAwjoU2TM0rpHwD+0AQIMTp6Ce5h68i34GhRjMJqe8SkTMbd6ke6pC3cCECb1vgAGIyOpF0/RGONCOxoWQQnS/W6FSfLYjwTuB8qG+9Ob4f28H/Rw971PgAVsrttHHlGz6JH/hB5pKFfQ8ivhX2dbn7TjocQgt4WZvWdn5sO8FhGJYaRYDJD0WpFdssHdQCrx4uhG9OP+QAYxkKPHAE0hoWw61K4dXQMv7ZgjHJg3ezDSUlJ5n5+foGAOsmHjY3NAxKJRNqvX7/AV155pf4f2lx7Gc3T5AiAEPIb9HjWTyntGu9aszS0azyeJfr1f7WL2tJ+qEoFwuM1ud0WulIOHBkZ6ZiWliZsbE1Bc4SEhFScO3cutaysjBccHCydPn16qaenZ0172mvqNPcr+hLAV5rXrwD6AfgNwD4AFwD4ADjXwe1rExYWIlgI3QAQWAjdIJG8C9e+T3Z1s9pE7pYIt+w3/+1BNYIaqlIh+81/e+RuiTBYduDuJgfu3bu3Kjg4uFIulwsN2V5TpMkRgHbQDULIZQCPU0oTtcq+A/A11Fp9o8LMzA4PP/xbVzej3VCVCnXl5fzSmBhnAHDdvCkj+81/e5TGxDjbzpiR156RgLHKgcPCwvpZWFioAKC2tpbwGvl8OTk5/GvXrvV6++23s6Kjox1aai+jafR1AgZAHQlIm9sAJIZtjmGoKy1FyqNjoczOhsDVFc6rV8F2ypSublarITweXDdvygCA0pgYZ84Q2M6Ykee6eVNGe6YB2kP106dP91q0aJFPcnJyYkvnAWo58MaNG3O5bZFIFNxSvXl5efzRo0f7A+qRQW1tLe/EiRN2ABAVFXU7NDS0SvP3rUceeaQSUPsAJk+e7MfVHRsbax0QECDl8Xh05cqVOSEhIfVJSxltQ18DcB7AN4SQf0OtDvQA8DbUUwKjozYzE0q++qMps7KQ/e8NANCtjQDX+QH1SKC9PgBtGpMDL1iwoITbbwg5sEgkUnKGob0+AO0yQ7bXFNH3V/Ss5j0RwD0A8QAIgEUd0Kb2o2rou6TV1cjb/nHXtKWdcHN+7TJtn4Ah6M5yYEO21xTRNzNQEYC5mlBgfQDkU0q71WIWZXbXrANoD1zn5+b82j4AoH0jgZ4iB25ve02d1qgBJQBmA3ChlC4nhPgDEFJKr3dkA9tCkIUl/cHbu0GZwM0NfmfPdE2DdGhNduDcLRFudeXlfK6zc0aBb2NT57JubYdlB2b0LNqVHZgQMhvATqizBM8HsByADdRBQscZrpkGgqeTTNPCAs6rV3VNW9qJy7q1Wdrefs4nYEgfAMN00fdXtBHAOErpEgB1mrI4qOMDGh1mIhEEbm4AIRC4ucF108Zu6QDk0O3srPMzDIW+kzNnANxQn2q96x0VSI/EIGsAvAB1RKB8AM9RSu9o9tVB7XgEgHRK6dTmrsW3tTWa4T6DYczoeyv5C+pIQNrMhTpBSIvomRjkGoAQTVTggwDe19pXpZUwpNnOz2Aw9EffEcAKAL8QQp4H0IsQchKAGOoAnfrQYmIQSqn2suLLABboWTeDwWgj+j4GlGueAkwGcBzqRB3HKaUVel6nseQeDzVz/PNQRyLmsCCExEI9PYiglB7WPYEQshjAYgDw9PTUs1kMhmnTmpiAlZTSaErpB5TS/a3o/K2CELIAQAjUiUI5vCilIVA/gfiYENK/kfb1yMQgHQUn2/X19Q309/eXvvXWWy51dWr/rrb0lnsdPnzYBgCsrKwe1K4nMjLSMSwszBNQS4WdnZ0HSCQSqY+PT+DTTz/tydXJME46Sw6sV3IPQsg4AG8AGKUdboxSmql5v0UI+RXAg7hfm8BoBdpr9jMzMwWzZ8/uV1ZWxt++fXsW0PiyW33gdAJ1dXUIDQ31P3HihM2UKVPKDd1+hmFobgrwpQGvo09ikAcBfA5gAqU0T6vcHkAlpVRBCHGCOiKxtoOwx3P37rcOt9N2iGpq8s3NzfvU+Hgvz3R3f7pdeQG0EYlEyi+//DJt+PDh0m3bthlkcZFCoSAKhYLn6OjIoh8ZMXrJgduLnolBPgBgDeAHTQQd7nFfAIDPCSEqqKcsEZRSfbIK9wju3v3WISX1XS+VSp0ZqKYmzzwl9V0vADCkEZBKpTV1dXXIzMwUAGrlHbekFwAOHTp0MzAwUKG91BdQK/vGjx9fv+yWkwpnZWWZjxo1qnT48OFMlGPE6L1ImxDiArU33wlqIRAAgFL6tT7naxKInNAp26D1d6MrCimllwAEN7bPFLidtkPEdX4OlUrBu522Q2RIA6BLU1MA7akDoPYBxMbG1of94qYACoWCTJo0qd8XX3xhv3jx4uKOaiejfejlBCSETIN6zr0R6mH6vzTvumsDGAampia/0SzATZW3FZlMZs7n8yESiQwyZBcKhfSxxx4ru3Dhgo0h6mN0DPo+BdgMYBGl9EEA9zTvi6FeIMToQMzN+zSaBbip8raQlZUlePHFF70WLVqU11gEnragUqlw6dIl6/79+xtl7giGGn3/256U0h90yvYACDNwexg6+Hgvz+TxhA2k1zyeUOXjvdwg2YF9fX0Dx4wZIx47dmzZhx9+WO8A5HwA3Gv37t32zdXHsWvXLheJRCIVi8WBKpUKr732Wl7LZzG6Cr3kwISQVAAPU0pzCSHXACwDUADgMqXUsfmzO5+QkBAaGxvb1c1oktbIgYGOfwrA6Pm0Sw4M4L8ARkAtB94OdTRgFYBthmogo2nc3Z8uYh2e0RHouxR4q9bfUZrFOL0opY3mkGcwGN0DfZ8CPEAIqV/JRylNB1BBCDHKeAAMBkM/9HUC7gNgplNmDmCvYZvDYDA6k9Y8BbilXUApvQnA2+AtYjAYnYa+BuAuIWSQdoFmmwWlZDC6MfoagO0AjhBC/kUImUQI+ReAHwF81HFNY3QkxiYHPn78uM2YMWN8tctmzpzpza0/CA0N9ff29g7y9/eXDho0SBIXFycEgOrqavLcc895eHp6Bnl5eQWNHTu2/82bN3Wnq4wm0MsAUEr/C2ANgCegFu08AeAVSukXHdg2hgbdFAyGSMnArelPTU1NPHv2bPKpU6dsX3311fqEoyEhIRVyuVzGvaZNm6aXpHfJkiW5XL1yudzyxIkT9y0F1k4l1hqioqJuJSUlyebPn1+wevVqDwBYsWKFqKKignfr1q2EO3fuJEydOrVk2rRpvioDJk7pybQmIMgPlNIJlNJAzfvBjmwYQ01yyrtuN+TrPLhOT6kKN+TrPJJT3jVYdmBODrx7925nQ3WcjpQDjx07tuLOnTvC8vJyXnR0tNOuXbsyuOQjK1euLDQ3N1cdO3aMaRD0oNl1AISQwQAUlNIEzXYfAB8DCALwB4BXOyoyEEPd2ZXKMn529kFnAAiQbMm4IV/nkZ190NnVdVYepSqokzW1H2OQA+teMzs723zy5Mn3ZfiJiYmxlUgkVTKZTOjq6lrj4ODQwGo98MADlfHx8ZZPPvkkC0TSAi0tBPoYwDsAEjTbXwJwA/AFgHlQB+ZY1lGNM3UI4SFAsiUDALKzDzpzhsDVdVZegGRLhqE6f2MYWg4cHh7e98iRIw6AOp0419GHDBlSsXfv3vTGrjlz5kxv7WtzqcPd3d0Vu3btSi8sLOy4nGMmQku/oABoMgATQuygDuv9NKX0P1AbAL2zbRBCJhBCkgghqYSQtY3sFxJCDmj2XyGEeGvtW6cpTyKEPN7StVSVSmRH/Im7a39DdsSfuHet++pRtI0AR0d0/o6WA2/dujWH8yc4OzvXcn9znV8foqKibsnlctnp06dv+vr61gYEBCiys7PNi4uLG3wZcXFxVsHBwSwQiR609CsSAOBkp0MB5FBKkwGAUpoBwE6fi+iZF+B5AMWUUl+onzps1ZwrhTqEWCCACQB2auprkrqSatSVKDR/K1ASk9JtjQA359cu0/YJGILuKgfu3bu3atasWQVLly71UCrVdmvHjh2O1dXVPBaHUD9a+m8nQp0QFFB3wtPcDkKICIC+GVjr8wJQSmsAcHkBtHkSaokxoE4MMpaoY4M9CWA/pVRBKb0NIFVTX5PoChxprQplJ9P0bKrxwHV+bs7/6JiUv1xdZ+VlZx90bq8R6Cly4E8//TRTKBSqfHx8gry8vIJiYmLsDx8+nGooQ9bTaVYOTAgZAeAY1NGB6wCMoJQmafatAfAQpfSpFi9CyCyog32+oNleqDl3udYxCZpj7mq2b0KdO+BtqGXH+zTlXwH4P92nENp5AUS9XQZfXqobvgBwjxjZUlM7hdbIgZNT3nVTKsv43LCfMwoCQe86sd8bbCEWQy/aJAemlP5OCPGEOgtQMqVUe1j1E9R3cqNAsybhCwAY4Cq5z6rx7YSd3iZDIPZ7I0vb28/5BDrSAcgwHVr0omo6/X2hv7iRgJ7okxeAO+YuIUQAwBZAoZ7nNoAQnW0zHno/7t2K5hoXup2ddX6GoeisX1J9XgBCiDnU/oSjOsccBfCM5u9ZAM5S9fzkKIC5mqcEPgD80EJSUr6dRf0dn28nhN0MP/R60Nlwn6adUEqhUqlIy0cyGO1H81tr1GHUKc9R9cwL8BWAvZrwY0VQGwlojouGOpGoEsDLlNJmF5jzrARwXdusn7BLSUtLq8rPz7ft06dPKY/H0zvFOoPRWlQqFcnPz7fFP2t5GqBXTMDuBiEkH8AdA1bpBHUMRIPg4ODgffbs2UtQr6hk43lGR6ICkKBUKl8YPHjwfU9keqQBMDSEkFhNclIGo0fB7j4MhgnDDACDYcIwA6AfLO4Bo0fCfAAMhgnDRgAMhgnDDACDYcIwA8BgmDDMADAYJgwzAAyGCfP/hW+URd8sUe4AAAAASUVORK5CYII=",
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",
       "text/plain": [
-       "<Figure size 50.4x144 with 1 Axes>"
+       "<Figure size 90.72x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -30252,19 +13753,611 @@
    ],
    "source": [
     "plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
+    "plot_default_hpo(results, ss_group_full, optimizers, scale_type=None, one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# run the transabilty model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_65\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/3/errors.json\n",
+      "config_85\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/3/errors.json\n",
+      "config_9\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/3/errors.json\n",
+      "config_28\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/3/errors.json\n",
+      "config_59\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/3/errors.json\n",
+      "config_60\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/3/errors.json\n",
+      "config_87\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/2/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/9/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/5/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/3/errors.json\n",
+      "config_58\n",
+      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/2/errors.json\n",
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+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/4/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/8/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/7/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/1/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/6/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/0/errors.json\n",
+      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/3/errors.json\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{65: {'nb201_c10': {'hb': [91.01200000000001, 0.2691765220073962]}, 'nb201_c100': {'hb': [72.42199997070313, 0.5972905783047683]}, 'nb201_im': {'hb': [45.31333332417806, 0.5057887203043718]}}, 85: {'nb201_c10': {'hb': [91.01200000000001, 0.2691765220073962]}, 'nb201_c100': {'hb': [72.42199997070313, 0.5972905783047683]}, 'nb201_im': {'hb': [45.31333332417806, 0.5057887203043718]}}, 9: {'nb201_c10': {'hb': [91.20599999999999, 0.18440173534975068], 'dehb': [91.23519999096679, 0.1777620888920531]}, 'nb201_c100': {'hb': [72.31199997070311, 0.7659869445931258], 'dehb': [72.731, 0.41795813187447345]}, 'nb201_im': {'hb': [45.13666662190755, 0.658525305689547], 'dehb': [45.169999989827474, 0.9864864827496567]}}, 28: {'nb201_c10': {'bohb': [91.27759998999025, 0.18733669908749703]}, 'nb201_c100': {'bohb': [71.776, 0.820538847343621]}, 'nb201_im': {'bohb': [45.57666665039062, 0.6869902055743355]}}, 59: {'nb201_c10': {'bohb': [91.3523999831543, 0.21592183863656533]}, 'nb201_c100': {'bohb': [72.064, 1.0305357829789332]}, 'nb201_im': {'bohb': [44.17999997151693, 0.9853876854992182]}}, 60: {'nb201_c10': {'bohb': [91.12359999145508, 0.19334900890647694]}, 'nb201_c100': {'bohb': [72.071, 0.7846712687488944]}, 'nb201_im': {'bohb': [44.72333331095378, 0.9683376387499879]}}, 87: {'nb201_c10': {'dehb': [91.29879998388671, 0.1593893441106283]}, 'nb201_c100': {'dehb': [71.3, 0.6805145112339641]}, 'nb201_im': {'dehb': [45.29666665649414, 0.8071830287075519]}}, 58: {'nb201_c10': {'dehb': [91.49319998876953, 0.14836764696397006]}, 'nb201_c100': {'dehb': [72.427, 0.6027611467239742]}, 'nb201_im': {'dehb': [45.00333332519531, 1.4558273842695673]}}}\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['nb201_c10', 'nb201_c100', 'nb201_im']\n",
+      "[1, 1]\n",
+      "['nb201_c10', 'nb201_c100', 'nb201_im']\n",
+      "[1, 1]\n",
+      "\t Avg. rank, default\t Num. #1's, default\t Avg. rank, HPO\t Num. #1's, HPO\n",
+      "sh \t 1.0\t\t\t 3\t\t\t 2.0\t\t\t 1\t\t\t\n",
+      "hb \t 4.0\t\t\t 0\t\t\t 4.0\t\t\t 0\t\t\t\n",
+      "bohb \t 3.0\t\t\t 0\t\t\t 3.0\t\t\t 0\t\t\t\n",
+      "dehb 2.0\t\t\t 0\t\t\t 1.0\t\t\t 2\t\t\t\n",
+      "Succesive Halving & 1.0 & 3 & 2.0 & 1 &\n",
+      "Hyberband & 4.0 & 0 & 4.0 & 0 &\n",
+      "BOHB & 3.0 & 0 & 3.0 & 0 &\n",
+      "DEHB & 2.0 & 0 & 1.0 & 2 &\n"
+     ]
+    }
+   ],
+   "source": [
+    "#full_ranks_no_hpo, full_ranks_hpo = compute_ranks(results, optimizers)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 3., 2., 0.]])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#full_ranks_hpo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.0] [6466, 6466, 6466]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "All inputs to `kendalltau` must be of the same size, found x-size 1 and y-size 3",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_2954/2188243408.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0malg_rank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mss\u001b[0m \u001b[0;32min\u001b[0m \u001b[0malg_rankings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkendalltau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkendalltau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0;31m# print in readable format:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslibenv/lib/python3.7/site-packages/scipy-1.7.1-py3.7-linux-x86_64.egg/scipy/stats/stats.py\u001b[0m in \u001b[0;36mkendalltau\u001b[0;34m(x, y, initial_lexsort, nan_policy, method, variant)\u001b[0m\n\u001b[1;32m   4719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4720\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4721\u001b[0;31m         raise ValueError(\"All inputs to `kendalltau` must be of the same \"\n\u001b[0m\u001b[1;32m   4722\u001b[0m                          f\"size, found x-size {x.size} and y-size {y.size}\")\n\u001b[1;32m   4723\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: All inputs to `kendalltau` must be of the same size, found x-size 1 and y-size 3"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "plot a matrix where entry [i,j] is the regret of a predictor \n",
+    "tuned on search space i and evaluated on search space j.\n",
     "\n",
-    "ss_group = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp', \\\n",
-    "            'mr_3ddet', 'mr_video', 'mr_cls', 'mr_seg', \\\n",
-    "            'transmicro_scene', 'transmicro_segment', 'transmacro_scene', 'transmacro_segment']\n",
-    "ss_group = ['nb201_c10']\n",
+    "Some of the matrix code is different types of scaling so that we can see the trends better.\n",
+    "\"\"\"\n",
+    "def replace_if_nan(num, replacement=0):\n",
+    "    if np.isnan(num) or np.isinf(num):\n",
+    "        return replacement\n",
+    "    return num\n",
     "\n",
-    "plot_default_hpo(results, ss_group, optimizers, scale_type='0-1', label=False) #, save='optimizers.pdf')"
+    "def compute_matrix(results, ss_group, optimizers, optimizer):\n",
+    "\n",
+    "    # first, compute a list of all seeds that have full info\n",
+    "    hpo_seeds = []\n",
+    "    for hpo_seed in results.keys():\n",
+    "        valid = True\n",
+    "        for search_space in ss_group:\n",
+    "            for opt in optimizers:\n",
+    "                if not (search_space in results[hpo_seed] and \\\n",
+    "                        opt in results[hpo_seed][search_space]):\n",
+    "                    valid = False\n",
+    "        if valid:\n",
+    "            hpo_seeds.append(hpo_seed)\n",
+    "        else:\n",
+    "            pass\n",
+    "    print('num valid', len(hpo_seeds))\n",
+    "\n",
+    "    # now compute the raw results and best seed for each search space\n",
+    "    best_seeds = []\n",
+    "    raw_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
+    "    scaled_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
+    "    normed_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
+    "    for i, search_space in enumerate(ss_group):\n",
+    "        raw_seed_results[i] = [results[hpo_seed][search_space][optimizer][0] for hpo_seed in hpo_seeds]\n",
+    "        best_seeds.append(np.argmax(raw_seed_results[i]))\n",
+    "        # 0-1 scaling:\n",
+    "        min_acc, max_acc = np.min(raw_seed_results[i]), np.max(raw_seed_results[i])\n",
+    "        scaled_seed_results[i] = [(m - min_acc) / (max_acc - min_acc) for m in raw_seed_results[i]]\n",
+    "        for j in range(len(scaled_seed_results[i])):\n",
+    "            scaled_seed_results[i][j] = replace_if_nan(scaled_seed_results[i][j], 0)\n",
+    "\n",
+    "        # normalization:\n",
+    "        if optimizer != 'bananas':\n",
+    "            mean = np.mean(raw_seed_results[i])\n",
+    "            std = np.std(raw_seed_results[i])\n",
+    "            normed_seed_results[i] = [(m - mean) / (std) for m in raw_seed_results[i]]\n",
+    "        else:\n",
+    "            # todo: fix NaN errors when bananas is normalized\n",
+    "            normed_seed_results[i] = copy.deepcopy(raw_seed_results[i])\n",
+    "\n",
+    "    # compute the matrices\n",
+    "    div_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "    kt_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "    scaled_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "    normed_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "\n",
+    "    for i, ss_1 in enumerate(ss_group):\n",
+    "        for j, ss_2 in enumerate(ss_group):\n",
+    "            div_matrix[i][j] = scaled_seed_results[i][best_seeds[j]] / scaled_seed_results[i][best_seeds[i]]\n",
+    "            scaled_matrix[i][j] = scaled_seed_results[i][best_seeds[i]] - scaled_seed_results[i][best_seeds[j]]\n",
+    "            normed_matrix[i][j] = normed_seed_results[i][best_seeds[i]] - normed_seed_results[i][best_seeds[j]]\n",
+    "            kt_matrix[i][j] = replace_if_nan(kendalltau(scaled_seed_results[i], scaled_seed_results[j])[0], 0)\n",
+    "\n",
+    "    return div_matrix, scaled_matrix, normed_matrix, kt_matrix, normed_seed_results\n",
+    "\n",
+    "def scale_matrix(matrix, mean_div=2.5):\n",
+    "    # further scaling of the results\n",
+    "    # needs to happen in its own method so that an avg_matrix is input\n",
+    "    scaled_matrix = np.zeros((len(matrix), len(matrix)))\n",
+    "    maximum = np.max(matrix)\n",
+    "    mean = np.mean(matrix)\n",
+    "    for i in range(len(matrix)):\n",
+    "        for j in range(len(matrix)):\n",
+    "            if mean_div > 0:\n",
+    "                scaled_matrix[i][j] = np.minimum(matrix[i][j], mean * mean_div)\n",
+    "            else:\n",
+    "                scaled_matrix[i][j] = np.maximum(matrix[i][j], mean / mean_div * -1)\n",
+    "\n",
+    "    # 0-1 scale\n",
+    "    min_acc, max_acc = np.min(scaled_matrix), np.max(scaled_matrix)\n",
+    "    for i in range(len(scaled_matrix)):\n",
+    "        for j in range(len(scaled_matrix)):\n",
+    "            scaled_matrix[i][j] = (scaled_matrix[i][j] - min_acc) / (max_acc - min_acc) \n",
+    "\n",
+    "    return scaled_matrix\n",
+    "\n",
+    "def plot_matrix(matrix, ss_group, save=None, cmap='viridis', hparam=False):\n",
+    "    coords = matrix\n",
+    "    cellsize = 5\n",
+    "    fig, ax = plt.subplots(1,1,figsize=(cellsize*1.2, cellsize*coords.shape[0]/coords.shape[1]))\n",
+    "    im = ax.pcolormesh(coords, edgecolor='k', linewidth=0.5, cmap=cmap) \n",
+    "    if hparam:\n",
+    "        plt.title('HParam. Corr. for Black-Box Algorithms')\n",
+    "    else:\n",
+    "        plt.title('Generalizability for Black-Box Algorithms')\n",
+    "    plt.gca().invert_yaxis()\n",
+    "    ax.set_xticks([])\n",
+    "    ax.set_xticklabels('')\n",
+    "    ax.set_xticks([c+0.5 for c in range(coords.shape[0]-0)], minor=True)\n",
+    "    ax.set_xticklabels([ss_dict_mat[ss] for ss in ss_group], minor=True, rotation=90)\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_yticklabels('')    \n",
+    "    ax.set_yticks([c+0.5 for c in range(coords.shape[1]-0)], minor=True)\n",
+    "    ax.set_yticklabels([ss_dict_mat[ss] for ss in ss_group], minor=True)\n",
+    "    fig.colorbar(im, ax=ax)\n",
+    "    \n",
+    "    if save:\n",
+    "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "        \n",
+    "    return matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "num valid 100\n",
+      "num valid 100\n",
+      "num valid 100\n",
+      "num valid 100\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "norm_matrices = []\n",
+    "kt_matrices = []\n",
+    "for i, optimizer in enumerate(optimizers):\n",
+    "    _, _, norm, kt_mat, _ = compute_matrix(results, search_spaces, optimizers, optimizer=optimizer)\n",
+    "    norm_matrices.append(norm)\n",
+    "    kt_matrices.append(kt_mat)\n",
+    "\n",
+    "avg_norm_matrix = np.mean(np.array(norm_matrices), axis=0)\n",
+    "avg_kt_matrix = np.mean(np.array(kt_matrices), axis=0)\n",
+    "\n",
+    "scaled_matrix = scale_matrix(avg_norm_matrix, mean_div=2.5)\n",
+    "scaled_kt_matrix = scale_matrix(avg_kt_matrix, mean_div=-1000000)\n",
+    "\n",
+    "avg_scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis') #, save='bbo_matrix_avg.pdf')\n",
+    "_ = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r', hparam=True) #, save='bbo_corr_matrix.pdf')"
    ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "32a71298b708eebe2f0561b11965f79c2979d900d0e7d249b32c2891c916ed1a"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.7.11 ('naslibenv')",
    "language": "python",
    "name": "python3"
   },
diff --git a/naslib/analysis/analyze_results.py b/naslib/analysis/analyze_results.py
new file mode 100644
index 000000000..dce0e5c55
--- /dev/null
+++ b/naslib/analysis/analyze_results.py
@@ -0,0 +1,95 @@
+import os
+import json
+import numpy as np
+
+base_path = '/home/till/plotting_data/bbo/nasbench201/'
+benchmark = 'ImageNet16-120/' #ImageNet16-120
+base_path += benchmark
+
+optimizers = {'dehb'}
+
+optimizer_paths = []
+for filename in os.listdir(base_path):
+    optimizer_path = os.path.join(base_path, filename)
+    if filename not in optimizers:
+        continue
+    if not os.path.isdir(optimizer_path):
+        continue
+    optimizer_paths.append(optimizer_path)
+
+config_paths = []
+for path in optimizer_paths:
+    for filename in os.listdir(path):
+        config_path = os.path.join(path, filename)
+        if not os.path.isdir(config_path):
+            continue
+        config_paths.append(config_path)
+
+result_files = []
+for path in config_paths:
+    for filename in os.listdir(path):
+        result_file = os.path.join(path, filename, 'errors.json')
+        if not os.path.isfile(result_file):
+            continue
+        result_files.append(result_file)
+
+results = []
+for result_file in result_files:
+    f = open(result_file)
+    results.append(json.load(f))
+
+metric = 'valid_acc'
+# metric = 'test_acc'
+
+config_ids = 100
+avgs = []
+for id in range(config_ids):
+    arch_seeds = [d for d in results if d[0]['config_id'] == id]
+    avg_acc_seed = [max(d[1][metric]) for d in arch_seeds]
+    avgs.append(np.mean(avg_acc_seed))
+best_arch_avg_index = avgs.index(max(avgs))
+best_arch_avg = max(avgs)
+
+
+print("best archiecteure in average is config:{}, with: {} acc".format(best_arch_avg_index, best_arch_avg))
+print("for the {} benchmark and the {} optimizer".format(benchmark, optimizers))
+
+best_arch = max(results, key=lambda x:(x[1][metric]))
+best_config_id = best_arch[0]['config_id']
+best_arch_seeds = [d for d in results if d[0]['config_id'] == best_config_id]
+
+
+worst_arch = min(results, key=lambda x:x[1][metric][-1])
+default_archs = [d for d in results if d[0]['config_id'] == 0]
+print(len(default_archs))
+best_default_seed = max(default_archs, key=lambda x:x[1][metric][-1])
+worst_default_seed = min(default_archs, key=lambda x:x[1][metric][-1])
+
+
+print("-- Best Architecture --")
+print("config_id: {}".format(best_arch[0]['config_id']))
+print("Valid accuracy: {}".format(max(best_arch[1][metric])))
+# [1][metric][-1]
+print("Worst seed within architecture: {}".format(min(best_arch_seeds, key=lambda x:x[1]['valid_acc'][-1])[1][metric][-1]))
+test = [d[1][metric][-1] for d in best_arch_seeds]
+print("mean:{}".format(np.mean(test)))
+
+print()
+print("Default Architecture (without HPO)")
+print("config_id: {}".format(best_default_seed[0]['config_id']))
+print("Best Seed: Valid accuracy: {}".format(best_default_seed[1][metric][-1]))
+print("Worst Seed: Valid accuracy: {}".format(worst_default_seed[1][metric][-1]))
+test = [d[1][metric][-1] for d in default_archs]
+print("mean:{}".format(np.mean(test)))
+
+print()
+print("-- Worst Architecture --")
+print("config_id: {}".format(worst_arch[0]['config_id']))
+print("Valid accuracy: {}".format(worst_arch[1][metric][-1]))
+
+
+#results:
+#sh for all try the config_0
+#hb cifar 100: 85 cifar 10:  9   image_net: 65 
+#bohb image net: 28   cifar 10: 59   cifar100: 60
+#dehb: cifar100: 9    cifar10: 58      image_net: 87 

From 31fb1158e87ce2f9a086b50eb7759adfb5c0def2 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Fri, 13 May 2022 18:53:31 +0200
Subject: [PATCH 109/130] add to our experiments

---
 examples/optimizers_HPO.ipynb | 627 ++++++++--------------------------
 1 file changed, 144 insertions(+), 483 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 3f477c39a..e3bb3aa14 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,18 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -29,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -13561,45 +13552,23 @@
     "    optimizers=('sh', 'hb', 'bohb','dehb')\n",
     "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
     "    root = '~/plotting_data/bbo/'\n",
-    "    results_dicts = {}\n",
+    "\n",
     "\n",
     "    results = get_hpo_results(optimizers, search_spaces, root=root)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'nb201_c10': {'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "  'hb': [91.053, 0.23639162421710094],\n",
-       "  'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "  'dehb': [91.37559999267577, 0.19945886851307323]},\n",
-       " 'nb201_c100': {'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "  'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "  'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "  'dehb': [71.63199999999999, 0.7419811318355743]},\n",
-       " 'nb201_im': {'sh': [44.55666663004557, 1.101216496499461],\n",
-       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "  'dehb': [44.24666666768392, 1.0534546359944508]}}"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "results[20]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13622,7 +13591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13633,7 +13602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13711,7 +13680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -13728,7 +13697,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 90.72x144 with 1 Axes>"
       ]
@@ -13740,7 +13709,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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Rx+rojI+rIvIxVprzDcBc4H1V3SMiOcBxP9TPMCrts88+q7NixYoGGzduzKxdu7bm5OREFBQUBGxuz/r167MCVbY3XnjhhT2BroOdKmrJ9cOaH/d/wHJVDekfhhEga9+I49nEVB5r0JNnE1NZ+0ZcVW63e/fuWnFxcUW1a9dWgObNmxclJCScAmjZsmVqTk5OBMCqVati0tLSOgPk5uaGXXPNNQmJiYnJiYmJyW+99VYDgIULF9ZLTk7u0rlz5+QLLrggESAvLy9s+PDhCampqV26dOmSPG/evAYA69ati05NTe2SlJSUnJiYmLxx48Yo+KMFNnjw4Pbvv/9+fVc9hw0bljBnzpyGRUVFjBs3rlXXrl27JCYmJj/zzDONKvs9v/vuu/W7deuW1KVLl+Q+ffok7ty5s1QD5uDBg+EtWrRILXauqc7Lywtr1qxZt4KCAnHVw/Xzuffee1skJyd3SUxMTF6/fn00wJ49eyL69OnTqWPHjikjR45s26JFi5KfY3VXUZBrCvwN6ANsFpH/iMh9QC1K7/lgGGdn7RtxrJjUlvx9kaCQvy+SFZPaViXQDRkyJG/Pnj2RCQkJXa+//vo2H3/8cd2KPvPggw82r1evXvG2bdsyt23blvlf//VfR/fs2RMxYcKEhMWLF/+0devWzCVLlvwE8NBDDzXv379/3saNG7d8/fXXWydPntwqLy8v7MUXX2x8++2378vKysrcsGHDlnbt2pWaQT5ixIhDCxYsaAhw8uRJWb16db3hw4cfeeGFFxrVr1+/eNOmTVsyMjK2zJ07t3FWVlakZx0LCgrCkpKSkl2vp556qoXrvUGDBuX/+OOPWVu2bMm85pprDk2ZMqWZ+2fj4+OLu3Tpcnz58uWxAPPnz6/fr1+/3KioqNN+jxs1alSUmZm55eabbz4wderUps6fT4t+/fodzc7O3jx8+PDDOTk5p9WvujpjkFPVY6r6tqoOxNpEejGQDsQB74hIjZknZ9jkq2ktKSoo/e+wqCCMr6a1PNtb1q9f37Fp06bMl1566dfGjRsX3XjjjR1mzpwZf6bPrFq1qt69995bMpDWuHHj4i+//LJOWlra0aSkpEKApk2bFgN8+eWX9aZPn948KSkpuW/fvp0LCgokOzs78oILLjj23HPPNX/44Yebbd++PbJu3bqlAsg111yT++9//zv2xIkTsnDhwvppaWlH69atq5999lm9BQsWxCclJSWfe+65XQ4fPhyRmZkZ7VnHqKgoR1ZWVqbrNWnSpJInq19++SXywgsv7JSYmJg8c+bMZllZWbU9Pz98+PDD7733XkOABQsWxI0aNepwWT+La6+99jBAWlra8Z07d0YBrFmzpu6NN954yPl95NWrVy9o0ux4Pbqqqr+q6uOq2hn4E/ArNWDdqmGz/P1ltwjKO++liIgIBg8efHT69Ol7nnnmmd+WLFnSECA8PFwdDmsnzRMnTpztvsMsXLgw2xVscnJyNvbo0ePk+PHjDy1dujS7du3ajsGDB3f68MMPS3Xmx8TEaO/evY8uXry43vz58xuOHDnykPN+8txzz/3mut/u3bs3Xn311ZXaJGrChAltbr/99v3btm3LfOmll34tKCg47XsbPXr0ka+++qr+vn37wjdt2hRzxRVXlFlGdHS0AkRERGhRUVHQr1M827/kf6vqOKBFhRcbxpnUbVJYqfNeyMjIiHL1hwGsX7++dqtWrQoBWrVqVbh69eoYANejI0C/fv3ypk+f3sR1fODAgfCLL7742Jo1a2Jdj4779u0LB+jfv3/ec88919QVLFevXl0bIDMzM7JLly4FkydP3v/nP//5yI8//nhaa2rkyJGH33rrrUZr166NHTZsWB7AoEGDcmfNmtXYNTiyYcOGqLy8vEr9bh49ejS8TZs2pwDeeuutMlut9evXd3Tr1u3YuHHj2gwcODA3IsL7LrXzzjsv/5133okDWLx4cb28vLygyQpbpXWFNWanLsM+/SbuJiKq9CblEVEO+k3cfba3zMvLCx8zZky7Dh06pCQmJiZnZWXVnjZt2h6ARx55ZM8DDzzQpmvXrl3Cw8NLHiefeuqpnCNHjoR36tQppXPnzsnLly+PbdGiRdHMmTN3DB06tGPnzp2Thw4d2h5g6tSpe4qKiiQpKSm5Y8eOKZMnT24JMG/evLjExMSUpKSk5C1bttQeN27cQc+6DR06NG/NmjWxffv2zXO1mO69997fk5KSTqampnbp1KlTyq233tr21KlTlWpBPfzww3tGjx7dISUlpUt8fHxRedeNGDHi8NKlS+NGjx59qDL3nzp16p4vvviiXqdOnVIWLFjQsFGjRqcaNGgQFI+sohpc4we9evXSdevWBboa1Y6I/KCqvQJdD4CMjIwd3bt3/93rD6x9I46vprUkf38kdZsU0m/ibs67pVK/hIa9Tpw4IREREVqrVi0+++yzOhMmTGiblZWVGeh6uWRkZDTq3r17QlnvBcUQsBHizrvlkAlq1Vt2dnbkiBEjOjgcDmrVqqWvvPLKjkDXyVsmyBmGUaHU1NSCLVu2VJuWW2WUG+RE5Gu8mAunqhd5U5CINABcOcYUuBlrM5z5QAKwAxihqmUOaxuGYZyNMw08vA684Xx9iTVP7mtgHrAKaAesrERZM4BPVDUJa/3rFuBB4HNV7QR87jw2DMPwmXJbcqo61/W1iHwH/FlVN7udexd4E3i0okJEpD5wEXCT896FQKGIXAVc7LxsLlYwnVjJ78EwDKNc3k4h6YKVEdjdL4C32RXaAQeAOSKyXkReF5E6QFNVzXFesxdrGdlpRCRdRNaJyLoDBw54WaRhGIb3Qe4r4C0R6SQitUUkEesx9msvPx8B9ABmqeq5wDE8Hk3VmstSZh+gqr6qqr1UtVfjxo29LNKoyUyqJe+vsTvVknsCABfX97J169bI6OjoHklJSckdOnRIufbaa9u4kgisW7cuunfv3okJCQld27Zt2/X+++9v7pqAXRneBrmbnH9uxgpQGwEBxnr5+V3ALlX93nm8ECvo7ROR5gDOP00SzppIHWc+riT3VEvbtm3LXLly5bb27duf9QqKqgqGVEtDhgw5WtX7LFu2LHbYsGEJlf1c69atC5xL2jZv27at9rx58xrk5+fL0KFDOz7wwAN7d+zYsWnTpk2Z33//fd1p06ZVupXj7R4Ph1R1FBANNAdqq+poVfVqwqeq7gV2ikhn56mBWDuAfQjc6Dx3I7C0MpU3QsAnk1qw9M7WJYFNHbD0ztZ8MumslwyaVEvBmWqpVq1apKWl5W/fvj3qtddei+/Vq1e+aw1vbGysY9asWb/NmDGjeWXv6/WyLhFJAh4G/q6qDhHpLCLdKlHWncD/iMgGrI2qn8TaM2KQiGwHLnEeGzWFOuBkXjg/zmtSEuiW3tmaH+c14WRe+Nm26EyqpeqXamny5Mmt3Ote1jVHjx4NW7VqVb1u3bqd2Lx5c3SPHj1KJeZNSUkpOH78eNihQ4cqtRzVq0gsIsOBl4FFwLXABCAWKyhd4s09VPVHoKxlRwO9+bwRgiQMrnpxJwA/zmvCj/OsBfLnXL+fq17ciZzd0mpXqqVPPvkk9vPPP4+98cYbOzzyyCO77rrrrtPWkrqsWrWq3vvvv/+z67hx48bF7777bv3yUi2tWLGiwcyZM5sBuKdaevbZZ5vv2rUrctSoUYdTU1NLre2+5pprcidOnNj6xIkTsmjRolKplrKysmI+/PDDhmAtts/MzIx2leviSrXkOp45c2b8unXr6oCVamnIkCGtDhw4UKuwsDCsdevWp60rd6VauuKKK44uWLAg7vbbby9zFM891ZKrTmvWrKm7ZMmSbOf3USrVUrdu3ZIKCwvDjh8/HpabmxvhCmJPPPHELlcSgscff3zX2LFjS+bAuvcv7ty5MyopKSlZRLjsssuOjBgxIu9f//pXvfL+rirL2+bmFOASVc1w7r0KkIE1380wzp4r0LkCHFCVAOfiSrU0ePDgo926dTvxzjvvxN91110HfZlqqXv37qUCSY8ePU5eeOGFx/73f/+3/uDBgzu9+OKLv1555ZUlfV2eqZZGjRpVKtWSKyCcjQkTJrS5++6791533XW5y5Yti50yZcppj/ujR48+8s9//rOlr1MtbdiwIQusPrk5c+bEL1q0aEdl6u7qk3M/l5ycfPLrr78u1QLPzMyMjImJccTFxVWqie/tX3ITrH0e4I8R0HJHQw3Da65HVHfufXRnwaRaCv5US+np6QfXrl0b6xr1zc/PlzvuuKPNnXfeubey9/L2B/kDcIPHuVFYm0wbxtlx74M75/r9PHr4B865fn+pPrqzYFItBX+qpbp16+rixYuzn3zyyRYJCQldk5OTU3r06HFs0qRJlZ6B4VWqJeegw7+wJgD3xlqZkAhcqqrbK1toVZhUS2UL2lRLn0xqwcm88JJHVFfgi65XzF+eMhsnVRMhn2pJVbOcgW4wsAzYCSxT1Xyf1dKomf7y1B7UQUkfnKuProp9coZv1YhUS6p6HFhgY12MmsozoJkAV+2YVEuGYRjV1Jlacq/7rRaGYRg28SrVkmEYRrDyuk9ORJoCaUAjrMX5AKjqmzbUyzAMwye86uEVkSFY+eSmAK9grUN9hdPnzhlGtXA2aYnS0tI6r1q1KuZsy1y2bFls//79O57t58/EPbGAUTneDmM9Dox15YJz/pmONUnYMGq8U6dOBboKRjm8DXJtVPUDj3NzgTE+ro9RA83fOj+u/4L+qd3mduvZf0H/1Plb58fZVdbhw4fDWrZsmepaQnXo0KFSx3PmzIlPSkpK7tSpU8rKlStjoPzUSjNnzowfMGBAx969eyf26dOnM1jLqy6++OKOCQkJXd0TQF533XVtunbt2qVjx44p9957b8m60vJSG+3duzf8T3/6U0lqo2DbH7k68TbI7Xf2yQHsEJELgA6ArevXjNA3f+v8uKfXPt329xO/RyrK7yd+j3x67dNtqxroyktL1LBhQ8cFF1xwdMGCBfUB3nzzzbjLL7/8sCvl0IkTJ8KysrIyZ86c+Wt6eno7KD+1EsDmzZtjli5d+tPatWu3AmzcuLHOyy+//Ft2dvamHTt2RL399tsNAZ5//vndmzZt2pKVlbV59erVsd9//33JutbyUhtdcMEF+dnZ2ZuHDh16pLKpjYw/eBvkXgP6Or+ejrVLVwZW+iXDOGuzM2a3LCwuLPXvsLC4MGx2xuyWVbmvKy2R6zVp0qSSJWLp6ekHXIvY582b1yg9Pb1kCdq11157COCyyy7Lz8/PD/v999/Dv/zyy3rTp09vnpSUlNy3b9/OrtRKABdeeGGeKwUTQGpq6rHk5OTCiIgIRowYcciVSWPu3LlxycnJXZKTk5O3b98enZGREe1WZklqo507d0YBfPfdd7E333zzQYBRo0bluqc2MirH22Vd09y+fltEvgTqqOoWuypm1AwHTxwss4VS3nlfuPTSS4/deeedUcuWLYstLi6W884776TrPZHS6+JFpNzUSt98802dmJgYh+f1nsdZWVmRL730UtMffvhhS+PGjYuHDRuWcPLkyZLAXtnURkbleDu6eo6IlKTDUdXfgHwRMfnkjCqJrx1f5t4L5Z33lVGjRh28+eab211//fWlEgm89957DQFWrFhRNzY2tjg+Pr64vNRKZdm4cWOdrKysyOLiYhYuXBh34YUXHj18+HB47dq1HXFxccU7d+6M+PLLL+uX93mX3r17H3W1NhcsWGB7aqNQ5u3j6jyglse5SOAd31bHqGnGdx+/OzI8slRrKDI80jG++/jddpZ7yy23HMzLy4u45ZZbSqUcio6O1i5duiRPmDChrWsRenmplcrStWvXY+PHj2/ToUOHrm3atCm44YYbjlxwwQUnunbterxDhw5dR4wY0b5nz54VJraYOnXqntWrV9ft2LFjyuLFixs2b948YBvxBDtvUy3lqepp6YjLO28nk2qpbEGbaglr8GF2xuyWB08cjIyvHV84vvv43SM7j6xUvrPKmjNnTsOlS5c2WLJkyS92lmP4R5VTLQG7RKSHqv7HdUJEegAm31cAqMOBhIWVOg5mIzuPPGR3UHN34403tl65cmX9ZcuW+TUXohEY3ga56cBSEXkaa+VDB+D/AU/YVTED2LAAPp8CubugfisY+AgH5i6iKC+fZq98hISFoQ4He8ddQceYiM4V39AAmDt37k6snIhGDeDt6OprInIEuAVojfUP5D5VXWhj3Wq2DQvgo7vg1AnrOHcnuvROPjs8lpj9EaSNu4Jmr3zE3nFXsCbnXE5FrTvr5UiGEcoqkzTzA8Bz1YNhl8+n8HGkMKNpC/ZGhNOsqJg7Dx1hb4taREb2h13QKTmF7R2HsavVAPLD3zhtCzrDMCoIciLSEyhQ1U3O48bAC0BX4N/A/zMp0O3xcdEhVuxrwOQPID6vmIP1YOFFDRjU5A2ymySxiwHsajUAgMLkHA6+sjcos7Yaht0qmkLyAuC+E/frWBvYvIoV6J62p1rGN/saMPYTaJxn/SU1zoOxn8C3++ozauO7pa71PDYM4w8VBbkuwNcAItIAuAy4TlX/GxgNXGFr7Wqwy1YpR+J6sbr3FL7o9xKre0/hSFwvhn0Ka3JKZRFiTc65JNaplRygqlZLNTnV0rBhwxLmzJnT0P2c6+exdevWyOjo6B5JSUnJHTp0SHFPIrBu3bro3r17JyYkJHRt27Zt1/vvv7+5I8hH7qHiIBcBuCYh9gb2quo2AFXdCTSwr2o1W1F0L7I6X0tBdDyIUBAdz5bO17Kt8+3sajWAwuQcbnv5YgqTc9jVagAno+KjKr5r9eTw2F/V8zgYBCLV0rJly2KHDRuWUNnPuXasz8rK2rxt27ba8+bNa5Cfny9Dhw7t+MADD+zdsWPHpk2bNmV+//33dadNm9bYhqr7VUVBbjMw3Pn1KOAz1xsi0hLItaleNd7PHYfgCC8dtzQ8itwG7SlMzuHuCaMJCwvj7gmjKUzOIV8cxwNU1Sp5eu3TLR799tHWrsDmUAePfvto66fXPt2igo+eFZNq6Q+1atUiLS0tf/v27VGvvfZafK9evfKvvvrqPIDY2FjHrFmzfpsxY0ZznxfsZxUFuYnAKyJyCPgvYJrbeyOB1XZVrKYriGxQ5nlHeHRJgANKAt2hg/u3+rF6PuFQB0cLj4YvyV7SxBXoHv320dZLspc0OVp4NLwqLbqanmpp8uTJrdy//7KuOXr0aNiqVavqdevW7cTmzZuje/ToUeo/ypSUlILjx4+HHTp0KKj3iDzjM76qfiMibbAGG7ap6lG3tz8G3rezcjVZ3bho8g+dPiukblx0SYBz8TwOFmESxj/6/GMnwJLsJU2WZC9pAjCk45D9/+jzj51hVdh/1ZVqyXU8c+bM+HXr1tUBK9XStGnTmt1www1H5s2b1+i1117b4bquvFRLK1asaDBz5sxmAN6kWgJKUi2NHTv28Ny5c+PeeuutRkVFRXLgwIFaGRkZ0eeff/4JZ5klqZY+/PDDhmClWlq8eHE2WKmWxo0bV1JGt27dkgoLC8OOHz8elpubG+EKYk888cSuYcOG5QE8/vjju8aOHXvY9Rn3PsqdO3dGJSUlJYsIl1122ZERI0bk/etf//Lr8kx/qrAj0xnYTktzrqpB13IIJhdc1YGV/5NFUeEfrZmIyDAuuKpDAGvle65A5wpwAFUNcBUJ9lRLGzZsyAKrT27OnDnxixYt2uH1N88ffXLu55KTk0+6ct+5ZGZmRsbExDji4uKCr5PUTXA2AWqAxPOb0f+6JOrGWf1ydeOi6H9dEonnN6vgk8HF9Yjqfs69j84uJtVSaenp6QfXrl0bu2TJkliA/Px8ueOOO9rceeede+0s1x/M7j/VWOL5zUIuqLlz74NzPaK6jsHeFt0tt9xycNq0aS3LS7VUVFQkr7766i9gpT1KT09vk5SUlOxwOKR169YFK1euzC7rvq5USzt27Iju06dP3g033HAkPDwcV6ql5s2bF3qbamnYsGHtO3bsmNKrV698u1Mt1a1bVxcvXpw9YcKENvfcc08th8PB8OHDD06aNGm/neX6g1eplqoTETkA/FrGW40Ar9P7+Eh1KrOtqlaL4f7KpFp6eu3TLY4WHg13BTRX4IuNjC1+4LwHbMtyY1IthRZfpFqqNsr7RRaRdf7Op1ZTyrTTA+c9sMehDlwtNlcfnZ19cibVUs0SdEHOCD2eAc3OAAcm1VJNYwYeDMMIaaEU5F41ZRqG4Slkgpyq+v2Xv6aUaRjBLGSCnGEYRllMkDNCUnh4eM+kpKTkzp07JycnJ3f59NNP67jeW7FiRd3U1NQu7dq1S2nXrl3Ks88+28j13t/+9rcWjzzySFP3e7mnOTrTfY3qyYyuGiHJfe3qokWL6j300EOtBg0atPW3336LuOmmm9p98MEHP/Xt2/d4Tk5OxCWXXNKpVatWp0aNGlVhVp3y7mv392OcvaBryYlIhIhcKiL3isgjzj8vFZGQDNgiMrOc8y/4uSq2OfTe+3HbL7wodUuX5J7bL7wo9dB778f58v65ubnh9evXLwJ47rnnmowcOfJg3759jwM0b9686Mknn9z1zDPPVHppift9jeorqAKDiHQHlgICbMDKZ1cPuBtQEblKVTfYUG46cBOQAtQF8rFy7c1R1dd8XZ6Hm4C7yjh/A3CPzWXb7tB778ftnzq1rRYUhAEUHTgQuX/q1LYAcaNHnfVerK5USwUFBfL777/XWr58+TaALVu21B4zZsxB92v79u17PDs7u2Q96uzZs5suWLAg3nW8f//+WhXd16i+girIYe0x8Zyqvuj5hohMAN4EfLoaQESmYqV5fw7I4I/Aeg7wNxFpr6qTfFmms9ybnV9GuH3t0h7/LyezxcGXX27pCnAuWlAQdvDll1tWJci5P1Z+9tlndcaOHdtu27Ztm7357Pjx4/dNmTJln+u4ZcuWqRXdN1jTXdUEwRbkkoHZ5bz3KqWTevrKLUA3Vc3xOP8fEfkEq0Xp8yCH1VIDiHT7GkCBfcCNNpTpd0W//15mMsjyzp+NSy655Njhw4cjcnJyIpKSkk6sW7cu5vrrrz/ien/16tUxHTt2PFGV+7Zs2dI8tlZTwfbfzxbgtnLeG+d839cqyu9VYf6vs6Gq/VW1PzDV9bXzNUBVR6vqd3aU628RjRqVmV2jvPNnY/369dEOh4OmTZsW3XfffQfmz58f/+2339YGK834Qw891Oq+++6rdEoh9/v6qq6G7wVbS+6vwBIRuZ/SfXLdgGJgiA1lvgF8ISKej6vdgb8BtvbJqepkEYkHLgeaqeozItICCFPVXXaW7Q/xt9++271PDkCiohzxt9++uyr3dfWdAagqs2bN2hEREUHbtm1Pvfnmm7+kp6cnHDt2LExV5bbbbtt37bXXerVfSXn3NaqvYEy1VAu4mNMHAb5UVVu2TBKRccCYMsp8W1VfsaNMt7L7AYuAdcCfVDXWee7/qWq13BKyMqmWwBp8OPjyyy2Lfv89MqJRo8L422/fXZX+OKPmOVOqpaALcjWNiKzHCmifi8hhVW0oItHAr6ratKLPB0Jlg5xhVFVI5ZMrj3Oe3Kuq6jkS6at7D8BqycUCR4FNwEpVtbs/JkFVP3d+7fofqZAQ+rszDDsF28DDmYRjw4ijc25eNlbf2wCsncv6O4+3i0g3X5fpIVNE/uxx7hJgo83lGkZICKrWgIh8cYa37drow+9z8zzcBywTkY+B2iLyCta8vatsLNMwQkZQBTngfOApwHPOGkAtoK8NZQZibl4JVf3O2Zq8Diug7gTSQmFk1TD8IdiC3I9Alqou9HxDRKKAl20o0zU3r6w1pHbNzStFVXcDT9tdjmGEomDrk3sBKG9qwSlgrA1l/hVr+dZOEflYRN4VkWUi8hvWPLlbbCgTEXmzgtcbdpQbKqpjqqW0tLTOq1atinEdb926NbJTp04pYG0UHRsbe05SUlJy+/btU+67777m3tTXqFhQteRU9YMzvOcA5tpQ5o8i0onT5+bNwMa5eUB5k2FjsAZYGmJTgPU3dTgQt7WfnsdnI5CplmbOnBm/Y8eOqOeff75SWyr26tUrf+XKldl5eXlhqampyUOHDs1t06ZNYVXqawRfS65cItJERJ6x496qekpVP1XVF1T1ceefn9oY4FDVv7u/gH9grVkdDfwHuMCusv1p31NTW+RM/ntrde5Orw4HOZP/3nrfU1Nb+KqMYEu1VK9ePUdqaurxrKysKF/Wt6YKqpaciAhwM1YGkO3ALKAO8ChwK/CVn+tj29w8tzLCsNIt/R3YBYxWVb9+n3ZRh4Pio0fDcxcvbgLQ/PF/7syZ/PfWuYsXN6l/9dX7q9Kiq66plsaMGdM+OjraAXDq1CkpK3vJ3r17w9evX1/nscce27NgwYK4iuprnFlQBTngWWAksBoYhjXaej7wHdBbVTf5uT6uuXm2BDkRGQVMwVove7uq/p8d5QSKhIXR/PF/7gTIXby4iSvY1b/66v3NH//nzqo8svo71dL+/fvDL7744s5gtfBOnToVtnz58gYAb7/99i9paWknnF//fNFFFx0Hq09u8ODBnVz3XrduXd0uXbokh4WF6d133723V69eJ8/6B2CUCLYgNwK4SFV/FpEkIBMYUdZoq68EaG4eIpIBtMAaVV2MlRS0vfs1qvqzXeX7iyvQuQIcWC26qvbJufNXqiVX8Ktqn5z7OV/Wt6YKtj65+q5fbFXNAo7bGeCczge+AP6njNd7NpabCsRjzcPbhrXqwv213cay/cbVB+d+zr2PzheCOdWSL+tbUwVbS05EpB1/5HAr8ji2o3XzI/6fm4eqBtt/QJXmCnCuPjj3PjmoWosuVFItVbW+RpBlIRERB9Yi9fISVaqq+vQRUkSGAwdV9bTHVuegwA2q6vOpK2WU1RpoGQzJMiuThWTfU1NbFB89Gu4KaK7AFx4bW9x00oOVetwzai6TaimIiUgbrMfic7CCeF0RuQb4i6r+NaCVK0dlUy3ZMU/OqFnOFORC5l+SiKSIiF+XPtk5N8/NK8DHWCmeXPPyPgUG2Vyu33gGNBPgDF8K6n9NItJIRO4SkR+w0qEn21CGiMgtIvKis6xaItJARKYDP9tRpoc0rH0eXI/qqGouUN/mcg0jJATbwIMr/fkVWPPT/oKVlaMFcJ6q/seGIgM9N28f0BFrhBUAEUkGfrO5XMMICUEV5ETkv7ECzingA6CfMxVRDtZqADv4fW6eh2ex8sk9hbUH62jgIWCqn8o3jKAWVEEOGI+VheQx4H3nY5vdSs3NExF/zM0roapvishBrLROO7FasH9X1SX+qoNhBLNg65PrALwI3A/sFZFFIjIMe78PEZF2ItLeueKgyP3YcxWCHVR1qaperqopqvoXE+Aq5kqJ1LFjx5TOnTsnP/roo02Li4uB0mmNXK8lS5bEAsTExJzrfp+ZM2fGjxkzpg1YaZiaNGnSLSkpKbldu3Yp1113XRvXPY3qK6hacqq6A2st5xQRuRCrVfMG1j6oT4jIdFXN9HGxdbBWGLjPzfvJvVrYu7yrvHWxBViP6N+paoFd5Qcr9zWmu3fvjhg+fHj7vLy88OnTp++BspdQecO1rrW4uJi0tLTOy5cvj73iiiuO+rr+hu8EVZATkWaquhdAVb8GvnbuszAUK+D9CET6ssxqsPJgDFZapX1YQa0V0BRrH9YEABG5SlXXBaqCVbXxq11x65bvaHk8tzAypn5kYa/LE3an9mvls31XW7ZsWfT666/v6NOnT/Jzzz3nkwnGBQUFUlBQEBYfH2/3bm1GFQVVkMMaYaznOhCRxap6NdZk2fecO8v7jYikADeq6gM2FrMZWKyqJenXnYE9CWtPi4exHuGDMr/cxq92xa3+ILttcZEjDOB4bmHk6g+y2wL4MtAlJycXFhcXs3v37giwMn64lmcBLFq06KeUlJQC92VbYGUUGTRoUEnfrysN0549eyL79euX26dPH7NQvpoLdCulsjyXc13sfqCqti8D8sfcPA/XAi95nJsFXKfWcpVn/FAH26xbvqOlK8C5FBc5wtYt39HSznJ79eqVn5WVlel6paSkFMAfj7mu16RJk0r9mxo/fvy+rKyszAMHDmQcP3487NVXX21oZz2Nqgu2IBeQNWjOCcBXi8hSrLTkdwFdsObmDba5+H1Y8wLd/Rew3/l1NH+shAg6x3MLy+xeKO/82crMzIwMDw+nZcuWPnm8jIqK0ksvvTRv1apVsb64n2GfYHtcjRCR/vzRovM8pqyF9FURoLl57u4CPhCRTVhTSFoDXYHhzvfPx3pcDUox9SMLywpoMfUjC31Vxp49eyJuvfXWtmPHjt1fVibes+FwOPj222/rnnPOOcd9ckPDNsEW5PZj7T3qctDjWAFfT+kIxNy8Eqr6L+c0lcuxVnYsBz5W1YOu94F/+bNOvtTr8oTd7n1yAOERYY5elyeUt5GPV1x9a0VFRRIeHq4jR448+Oijj5Zk+/Xsk5s4cWLO2LFjD1d0X1efXFFRkXTp0uX4/fffv7+izxiBZbKQVEBEErBGOMcALbGCzLtYeeRSVdX8I/dQ2Swkdo+uGqHvTFlIgq0l53cBmptXwrlZzu1AP6ARpR/NL7KrXH9K7dfqkAlqhl2CKsiJyNdUMPjg61/8QMzN8zAdGAC8CjyBNWXkNuB9G8s0jJARVEEOeD0AZQZ6bt7VwAWq+puI/ENVZ4jICqw8c4/ZXLZhBL2gCnL+SDNehkDPzYvBGlUFOCEiMc5EAeee6UOGYViCap6ciAwWkVfKeW+2iFxmQ7GBHpnZApzn/Hod8JiITMaar2cYRgWCqiUH3Ac8Us5787AGCHy9AbPf5+Z5uBtwpbr4G9Zqh1gg3cYyDSNkBFVLDkh2dv6XZTWQYkOZrrl5bzhfBz2Obe0nVNW1rozHqrpdVS9R1fPP8HMwqH6plpYtWxbbv3//ju7nhg0bljBnzpyGAGlpaZ0TEhK6du7cOblHjx5JGRkZUQAnT56Um2++uXWbNm26tm3btuvAgQM7/PTTT7Wq+vOpSYKtJVdbRGJVtazUNnWB2r4uUFUTfH3PyhKRtkB3rO+xhKq+G5ga+ZaqIiLlHp+NQKZaatmyZeru3bs3Vvbeb7/99s8XXXTR8WeffbbRvffe2/qLL77Ivuuuu1rm5+eH/fzzz5siIiKYMWNG/JAhQzpmZGRs8dXqjVAXbD+l9cA15bx3NdZ0jpAiIpOw+uUewZo64nqND2S9fOWbD7a3+OKdrNauSemqyhfvZLX+5oPtPhu1dqVamjNnThOHw+GTe9qZamngwIH5v/76a9TRo0fDFixY0Gj27Nk7XRtY33333QcjIyMdH330kVkz66Vga8k9CSwQkYbAIiAHaI61wczfsdaY+lQg5uZ5uA/oZeeE40BRVQpOFIVnfZvTBGDADUk7v3gnq3XWtzlNkvo03++LFp1LdUi15FlmTk5O5ODBg09bJrh48eL6SUlJJzIzM6OaN29eGBcXVyoyn3POOcc3btxY+6qrrjLJOr0QVEFOVVeIyC3Ac1gphlx2An91ruP0tUDMzXN3ENgR4DrYQkQYcEPSToCsb3OauIJdUp/m+wfckLTTVwGuLOU9rro/5oLVJ7du3bo6rmPX42pBQYFcfvnl7V999dWG6enphydOnNhs6dKlcQD79++v5Qpm5513Xv4777zzW1llDhs2LMG97DFjxrSPjo52tGrVqmD27Nm/HTx4MKh+P6uroPshOjeRWSginYF44KCqbrWxvEDMzXN3D/CqiLzAH+mVAFDVoN+W0BXoXAEOrBadrwOce6qljIyMKt/PPdVSenr64WnTpu2dNm3aXrD65NwDpbdcfXKu4yZNmhTn5OREHj58OKxhw4YlrbmMjIyYK6+88kiVv4kaItj65Eqo6lZV/dbOAAcBm5vnLhK4FFiD1aJzvX6xuVy/cPXBuZ9z76PzBTtTLXXo0MG2/TXq1avnuOaaa36/7bbbWhcVWV1/L730UvzJkyfDzL4S3gvaIOdH92HNwSvLPKydw+z0MtY+q/WAWm4vO9fL+oUrwLn64G6f1f+HpD7N92d9m9OkqoHO1bfWsWPHlP79+ycOHDgw79lnny1ZneLqH3O9XFM5KjJ79uymSUlJyYmJiSkOhwO7Uy29+OKLu6Oiohzt2rXr2rZt266LFy9uuGTJkmwzsuo9k2qpAiKyT1WblvOeAHvLe99X5QMtVDVo9r6rTKqlbz7Y3qLgRFG46xHVFfiiakcU9x3eyfZ09kZoMKmWqsbvc/M8PAs8KCJPagj+j9R3eKc97qOorj46OwcdjJolpNq8ItLGhtsGem7eXVjZRvJF5Df3l83l+o1nQDMBzvClkGnJiUgUVme8rzd69vvcPA/X23x/OzgcDoeEhYWFXMvTqH4cDocA5c7yDqogJyJnmnQbZUeZAZqb517+V3be3yabDhw4kNy4ceNcE+gMOzkcDjlw4EB9YFN51wTVwIOIOLBaUuVF7Raq6uuWnHv5fpmb5yzr5oquUdU3K7omEH744YcmERERr2PtKhZSXSJGteMANhUVFf21Z8+eZY50B1uQ+wVrU+Vvy3gvGjhmZ5DzJxFZWcElqqoD/FIZwwhiQfW4ipU0shdwWpDDiugh0xmvqv0DXQfDCAXB1pKrBaCqQbtjvGEY/hVUQc4wDKOygq5TWEQuFZF3RSRDRH52/vmuiAwKUH3smJtnGIaPBFVLTkTuBR4AXgMygFysNZ3dgVuBaao6w4/1iQKOh8pgh2GEomALcnuAAaqaVcZ7ScBKVW3u4zIrmpv3SSCCnIi0CYVUS4Zht2AbXa0DlLdoey/WHqW+9iVnnpvndzau7jCMkBNsQW4R8JGITAE2UPpxdTKw0IYyf6WCuXk2lBmQ1R2GEYqCLciNB/4BvI21ftT1rJ0DvAM8akOZgZqb9yXVrAVpGMEoqPrk3IlIA6xUR/mqesTGcgIyN68mre4wDDsF3RQSF1U9oqq77AxwznJOBWjysasFWZaQWt1hGHYKqpaciNTBygTyJ2Az8HdV/cnt/Y2qmmpDuZcCNwEpQCxw1Fn+HFX91NflOcs0qzsMwweCrSU3HUjASiK5H1gjIhe7vZ/g6wKdc/PmAtnAFCAdq19wOzBXRO72dZkQ0BakYYSUYGvJ7QG6qGqu83gQ8D/ATaq6XESOqqpPdxYPxNw8t/v7vQVpGKEm2EZXawMlW8Cp6qciciWwRETupIKd7s9SIObmea7uWETp6TJzRcSvqzsMI1gFW0vua+Cfntl4ReRc4P+ARqrq08AtIm8CHbAeVcuam/ezqt7iyzKd5QasBWkYoSTY+uSeAuI8T6rqeuASrPlzvjYea47c28A+4ITzz7nAd8BtNpQJAWpBGkaoCaqWXKD5a26es6yAtCANI9QEfZCza9pIoIlIJNYo7hjKWd2hqoUBqp5hBI1QCHJ5qlrPxvsHZG6eRx0a4KcWpGGEmmDrkyuL3TsR+31unid/re4wjFAUCkHuMpvvPxgYrar/q6r3AKOwNpu+3M5CRaSOiLzslvm4g8f7G+0s3zBCRSgEuTUi0sP5SGeH0+bmAVcCb4rIcOyZmwfVoAVpGKEgqPrkRKQe1i99MvBv4FXgG6AdcBwYoqqf+bhMv8/Nc97f76s7DCMUBVtLbhbQBVgK9AE+B2Zgdco/CDxhQ5mBmJsHgWtBGkZICbaW3H6gvarmi0hDrMe4aFUtFpEw4JCqNghoJX0kUC1Iwwg1wdaSi1bVfABVPYw1paLYeezAT9+Pnzr9A9WCNIyQEmwtuaNAN/6YNvIf4Fy34wx/9FPZPTfPMAzfCbYg58Dqiypvbpz6IyV4oDr9Q3V1h2HYKaj6dFS1ujxe2z03rzxtA1SuYQSt6hI0go3dc/PKY/fqDsMIOUHVkhORlZx56oSq6kAfl3nGuXki4vO5eWcQqBakYQStYOuTKy+1UEvgLiBGVX2aZ01E/gdrlPNDYAjQGpgNvA7cDNygquf7sswz1CUS6IqVZumIP8o0jGAXVEHOk4jEA5OAW4H5wBRV3eXjMgIyNy8QqzsMIxQFZZ+ciNQTkX9i7aDVFOihqum+DnBOgZqbF4jVHYYRcoKqJScitYF7gPuAL7ESR262ucyAzM2rSas7DMNOwRbk9mG1nJ7B2mH+NKr6hY/LDMjcPM8JxyJyWFUblve+YRhlC6rRVaxNZJTyN49RoL0vCwzg3DwRkXb8EVxPOw5MtQwjuARVS64mqS6rOwwj2JkgV4FAzM0zDMN3gu1xNRDmlXO+ZG6eH+tiGEYlmZZcJfljbp6zHNOCNAwfMC05Lzkn594PTACWYc3N++nMn6oS04I0DB8wLbkKBGJuXjn18EsL0jBCjWnJVWwH1ty8p7Hm5jUVkabuF/h6bp67ALQgDSOkmJZcBURkBxX3jfl0bp6z3GrRgjSMYGeCXDUViNUdhhGKTJCrpgLVgjSMUGOCnGEYIS0oUy0ZhmF4ywQ5wzBCmglyhmGENBPkDMMIaSbIGYYR0v4/2BItvq048ZcAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 90.72x144 with 1 Axes>"
       ]
@@ -13767,416 +13736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "config_65\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_65/3/errors.json\n",
-      "config_85\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_85/3/errors.json\n",
-      "config_9\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/hb/config_9/3/errors.json\n",
-      "config_28\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_28/3/errors.json\n",
-      "config_59\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_59/3/errors.json\n",
-      "config_60\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/bohb/config_60/3/errors.json\n",
-      "config_87\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_87/3/errors.json\n",
-      "config_58\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/cifar10/dehb/config_58/1/errors.json\n",
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-      "config_87\n",
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-      "config_58\n",
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-      "config_9\n",
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-      "config_65\n",
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-      "config_85\n",
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-      "config_28\n",
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-      "config_60\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/bohb/config_60/3/errors.json\n",
-      "config_87\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/5/errors.json\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_87/3/errors.json\n",
-      "config_58\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_58/9/errors.json\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_58/7/errors.json\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_58/3/errors.json\n",
-      "config_9\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/5/errors.json\n",
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-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/transfer/nasbench201/ImageNet16-120/dehb/config_9/3/errors.json\n"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{65: {'nb201_c10': {'hb': [91.01200000000001, 0.2691765220073962]}, 'nb201_c100': {'hb': [72.42199997070313, 0.5972905783047683]}, 'nb201_im': {'hb': [45.31333332417806, 0.5057887203043718]}}, 85: {'nb201_c10': {'hb': [91.01200000000001, 0.2691765220073962]}, 'nb201_c100': {'hb': [72.42199997070313, 0.5972905783047683]}, 'nb201_im': {'hb': [45.31333332417806, 0.5057887203043718]}}, 9: {'nb201_c10': {'hb': [91.20599999999999, 0.18440173534975068], 'dehb': [91.23519999096679, 0.1777620888920531]}, 'nb201_c100': {'hb': [72.31199997070311, 0.7659869445931258], 'dehb': [72.731, 0.41795813187447345]}, 'nb201_im': {'hb': [45.13666662190755, 0.658525305689547], 'dehb': [45.169999989827474, 0.9864864827496567]}}, 28: {'nb201_c10': {'bohb': [91.27759998999025, 0.18733669908749703]}, 'nb201_c100': {'bohb': [71.776, 0.820538847343621]}, 'nb201_im': {'bohb': [45.57666665039062, 0.6869902055743355]}}, 59: {'nb201_c10': {'bohb': [91.3523999831543, 0.21592183863656533]}, 'nb201_c100': {'bohb': [72.064, 1.0305357829789332]}, 'nb201_im': {'bohb': [44.17999997151693, 0.9853876854992182]}}, 60: {'nb201_c10': {'bohb': [91.12359999145508, 0.19334900890647694]}, 'nb201_c100': {'bohb': [72.071, 0.7846712687488944]}, 'nb201_im': {'bohb': [44.72333331095378, 0.9683376387499879]}}, 87: {'nb201_c10': {'dehb': [91.29879998388671, 0.1593893441106283]}, 'nb201_c100': {'dehb': [71.3, 0.6805145112339641]}, 'nb201_im': {'dehb': [45.29666665649414, 0.8071830287075519]}}, 58: {'nb201_c10': {'dehb': [91.49319998876953, 0.14836764696397006]}, 'nb201_c100': {'dehb': [72.427, 0.6027611467239742]}, 'nb201_im': {'dehb': [45.00333332519531, 1.4558273842695673]}}}\n"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['nb201_c10', 'nb201_c100', 'nb201_im']\n",
-      "[1, 1]\n",
-      "['nb201_c10', 'nb201_c100', 'nb201_im']\n",
-      "[1, 1]\n",
-      "\t Avg. rank, default\t Num. #1's, default\t Avg. rank, HPO\t Num. #1's, HPO\n",
-      "sh \t 1.0\t\t\t 3\t\t\t 2.0\t\t\t 1\t\t\t\n",
-      "hb \t 4.0\t\t\t 0\t\t\t 4.0\t\t\t 0\t\t\t\n",
-      "bohb \t 3.0\t\t\t 0\t\t\t 3.0\t\t\t 0\t\t\t\n",
-      "dehb 2.0\t\t\t 0\t\t\t 1.0\t\t\t 2\t\t\t\n",
-      "Succesive Halving & 1.0 & 3 & 2.0 & 1 &\n",
-      "Hyberband & 4.0 & 0 & 4.0 & 0 &\n",
-      "BOHB & 3.0 & 0 & 3.0 & 0 &\n",
-      "DEHB & 2.0 & 0 & 1.0 & 2 &\n"
-     ]
-    }
-   ],
-   "source": [
-    "#full_ranks_no_hpo, full_ranks_hpo = compute_ranks(results, optimizers)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1., 3., 2., 0.]])"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#full_ranks_hpo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.0] [6466, 6466, 6466]\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "All inputs to `kendalltau` must be of the same size, found x-size 1 and y-size 3",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_2954/2188243408.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0malg_rank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mss\u001b[0m \u001b[0;32min\u001b[0m \u001b[0malg_rankings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkendalltau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkendalltau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg_rank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_rank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0;31m# print in readable format:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/naslibenv/lib/python3.7/site-packages/scipy-1.7.1-py3.7-linux-x86_64.egg/scipy/stats/stats.py\u001b[0m in \u001b[0;36mkendalltau\u001b[0;34m(x, y, initial_lexsort, nan_policy, method, variant)\u001b[0m\n\u001b[1;32m   4719\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4720\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4721\u001b[0;31m         raise ValueError(\"All inputs to `kendalltau` must be of the same \"\n\u001b[0m\u001b[1;32m   4722\u001b[0m                          f\"size, found x-size {x.size} and y-size {y.size}\")\n\u001b[1;32m   4723\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: All inputs to `kendalltau` must be of the same size, found x-size 1 and y-size 3"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14191,7 +13751,7 @@
     "        return replacement\n",
     "    return num\n",
     "\n",
-    "def compute_matrix(results, ss_group, optimizers, optimizer):\n",
+    "def compute_matrix_stand(results, ss_group, optimizers, optimizer):\n",
     "\n",
     "    # first, compute a list of all seeds that have full info\n",
     "    hpo_seeds = []\n",
@@ -14212,7 +13772,6 @@
     "    best_seeds = []\n",
     "    raw_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
     "    scaled_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
-    "    normed_seed_results = np.zeros((len(ss_group), len(hpo_seeds)))\n",
     "    for i, search_space in enumerate(ss_group):\n",
     "        raw_seed_results[i] = [results[hpo_seed][search_space][optimizer][0] for hpo_seed in hpo_seeds]\n",
     "        best_seeds.append(np.argmax(raw_seed_results[i]))\n",
@@ -14222,29 +13781,16 @@
     "        for j in range(len(scaled_seed_results[i])):\n",
     "            scaled_seed_results[i][j] = replace_if_nan(scaled_seed_results[i][j], 0)\n",
     "\n",
-    "        # normalization:\n",
-    "        if optimizer != 'bananas':\n",
-    "            mean = np.mean(raw_seed_results[i])\n",
-    "            std = np.std(raw_seed_results[i])\n",
-    "            normed_seed_results[i] = [(m - mean) / (std) for m in raw_seed_results[i]]\n",
-    "        else:\n",
-    "            # todo: fix NaN errors when bananas is normalized\n",
-    "            normed_seed_results[i] = copy.deepcopy(raw_seed_results[i])\n",
-    "\n",
+    "        \n",
     "    # compute the matrices\n",
-    "    div_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "  \n",
     "    kt_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
-    "    scaled_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
-    "    normed_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
-    "\n",
+    "    \n",
     "    for i, ss_1 in enumerate(ss_group):\n",
     "        for j, ss_2 in enumerate(ss_group):\n",
-    "            div_matrix[i][j] = scaled_seed_results[i][best_seeds[j]] / scaled_seed_results[i][best_seeds[i]]\n",
-    "            scaled_matrix[i][j] = scaled_seed_results[i][best_seeds[i]] - scaled_seed_results[i][best_seeds[j]]\n",
-    "            normed_matrix[i][j] = normed_seed_results[i][best_seeds[i]] - normed_seed_results[i][best_seeds[j]]\n",
     "            kt_matrix[i][j] = replace_if_nan(kendalltau(scaled_seed_results[i], scaled_seed_results[j])[0], 0)\n",
     "\n",
-    "    return div_matrix, scaled_matrix, normed_matrix, kt_matrix, normed_seed_results\n",
+    "    return  kt_matrix\n",
     "\n",
     "def scale_matrix(matrix, mean_div=2.5):\n",
     "    # further scaling of the results\n",
@@ -14295,7 +13841,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'nb201_c10': {'sh': [90.87, 0.20610676844781026],\n",
+       "  'hb': [90.98599999999999, 0.25476263462289656],\n",
+       "  'bohb': [91.21319999121093, 0.2601295113691563],\n",
+       "  'dehb': [91.30359999072266, 0.15294914066855703]},\n",
+       " 'nb201_c100': {'sh': [71.84599996582031, 0.9117916772568413],\n",
+       "  'hb': [71.89999996093749, 0.9765654354455887],\n",
+       "  'bohb': [70.846, 0.5039087218931632],\n",
+       "  'dehb': [71.612, 0.5207072113961919]},\n",
+       " 'nb201_im': {'sh': [44.68999999186198, 0.9841578443027897],\n",
+       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "  'dehb': [44.81833331604004, 1.3276639885096027]}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "plot a matrix where entry [i,j] is the regret of a predictor \n",
+    "tuned on search space i and evaluated on search space j.\n",
+    "\n",
+    "Some of the matrix code is different types of scaling so that we can see the trends better.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "def compute_matrix(results, best_configs, optimizer, search_spaces):\n",
+    "    \"\"\"\n",
+    "    best_configs: denodes the best config for every search space\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    matrix= np.zeros((len(search_spaces), len(search_spaces)))\n",
+    "    for i, ss_1 in enumerate(best_configs, search_spaces):\n",
+    "        for j, ss_2 in enumerate(best_configs, search_spaces): \n",
+    "            matrix[i][j] =  results[i][ss_1][optimizer][0] - results[j][ss_2][optimizer][0]\n",
+    "    return matrix \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('sh', 'hb', 'bohb', 'dehb')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "optimizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['nb201_c10', 'nb201_c100', 'nb201_im']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_spaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -14308,9 +13952,16 @@
       "num valid 100\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/till/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/ipykernel_launcher.py:76: RuntimeWarning: invalid value encountered in double_scalars\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -14322,7 +13973,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -14334,10 +13985,20 @@
     }
    ],
    "source": [
+    "#have to load different results\n",
     "norm_matrices = []\n",
     "kt_matrices = []\n",
+    "results_stand = results\n",
+    "root = '~/plotting_data/trans/'\n",
+    "results = get_hpo_results(optimizers, search_spaces, root=root)\n",
+    "\n",
+    "best_configs = [[0,0,0],\n",
+    "                [9, 85, 65],\n",
+    "                [59,60,28],\n",
+    "                [58,9,87]]\n",
     "for i, optimizer in enumerate(optimizers):\n",
-    "    _, _, norm, kt_mat, _ = compute_matrix(results, search_spaces, optimizers, optimizer=optimizer)\n",
+    "    kt_mat = compute_matrix_stand(results_stand, search_spaces, optimizers, optimizer=optimizer)\n",
+    "    norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\n",
     "    norm_matrices.append(norm)\n",
     "    kt_matrices.append(kt_mat)\n",
     "\n",
@@ -14354,10 +14015,10 @@
  ],
  "metadata": {
   "interpreter": {
-   "hash": "32a71298b708eebe2f0561b11965f79c2979d900d0e7d249b32c2891c916ed1a"
+   "hash": "a100edf3d46bc2e6c79c61e7e0e4c85fe6b13f4c7883d33d138476068eadc537"
   },
   "kernelspec": {
-   "display_name": "Python 3.7.11 ('naslibenv')",
+   "display_name": "Python 3.7.13 ('naslib_exercises')",
    "language": "python",
    "name": "python3"
   },
@@ -14371,7 +14032,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.7.13"
   }
  },
  "nbformat": 4,

From aed5c8af9165e99c88c49e1c04b0397e4eca577b Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 21 May 2022 13:09:50 +0200
Subject: [PATCH 110/130] fix lab mixed up

---
 examples/mf_demo.py                           |    14 +-
 examples/mf_demo.yaml                         |     2 +-
 examples/optimizers_HPO.ipynb                 | 12323 +---------------
 .../nas_predictors/discrete_config_HB.yaml    |     6 +-
 .../nas_predictors/discrete_config_SH.yaml    |    54 +-
 naslib/defaults/trainer_multifidelity.py      |    34 +-
 naslib/optimizers/discrete/sh/optimizer.py    |    16 +-
 naslib/search_spaces/nasbenchasr/graph.py     |    25 +-
 8 files changed, 345 insertions(+), 12129 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index 1aa26fb35..bebca6553 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -4,17 +4,17 @@
 from naslib import search_spaces
 from naslib.optimizers.discrete.sh import optimizer
 from naslib.search_spaces import NasBench201SearchSpace as NB201
-
+from naslib.search_spaces import NasBenchASRSearchSpace as NBASR
 import logging
-from naslib.utils import utils, setup_logger, get_dataset_api
-
+#from naslib.utils import utils, setup_logger, get_dataset_api
+from naslib.utils import utils, utils_asr ,setup_logger, get_dataset_api
 from naslib.optimizers import RandomSearch as RS
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers import HB
 from naslib.optimizers import BOHB 
 from naslib.optimizers import DEHB
-
+#from naslib.search_spaces.nasbenchasr.conversions as h
 from naslib.defaults.trainer_multifidelity import Trainer
 #from naslib.defaults.trainer import Trainer
 # TODO: rethinking logging is rigth now, completly ingnored 
@@ -36,7 +36,7 @@
 config_optimizer = demo_config['optimizer']
 config_path_optimizer = os.path.join(
     utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config"
-) + "_" + config_optimizer + ".yaml"
+) + "_" + "SH" + ".yaml"
 default_config_path = os.path.join(
     utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config.yaml"
 ) 
@@ -44,7 +44,9 @@
 copyfile(config_path_optimizer, default_config_path)
 
 # init search space
-search_space = NB201()
+#search_space = NB201()
+#TODO: fix overflow error 
+search_space = NBASR()
 
 # read config
 config = utils.get_config_from_args(config_type="nas_predictor")
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
index 2a773b180..ec48d5c3b 100644
--- a/examples/mf_demo.yaml
+++ b/examples/mf_demo.yaml
@@ -1,3 +1,3 @@
-optimizer: SH
+optimizer: DEHB
 # Activates/deactivates plotting
 plot: FAlSE
\ No newline at end of file
diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index e3bb3aa14..8b1a26b76 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,9 +9,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -20,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +88,7 @@
     "                    values = []\n",
     "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
     "                        result_path =   optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
-    "                        print(result_path)\n",
+    "                        #print(result_path)\n",
     "                        if not os.path.isfile(result_path):\n",
     "                            print(\"what?\")\n",
     "                            continue\n",
@@ -111,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 34,
    "metadata": {
     "scrolled": true
    },
@@ -316,13224 +325,1215 @@
      "output_type": "stream",
      "text": [
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/sh/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/hb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/bohb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar10/dehb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/sh/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/hb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/bohb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/cifar100/dehb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/sh/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/hb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_6.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_7.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_4.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_3.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_1.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_8.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_2.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_0.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/3/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_5.yaml/errors.json\n",
       "what?\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_65/seed_9.yaml/errors.json\n",
       "what?\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_6/3/errors.json\n",
       "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/bohb/config_11/3/errors.json\n",
       "config_88\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_88/3/errors.json\n",
       "config_41\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_41/3/errors.json\n",
       "config_78\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_78/3/errors.json\n",
       "config_54\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_54/3/errors.json\n",
       "config_71\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_71/3/errors.json\n",
       "config_56\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_56/3/errors.json\n",
       "config_79\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_79/3/errors.json\n",
       "config_28\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_28/3/errors.json\n",
       "config_87\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_87/3/errors.json\n",
       "config_14\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_14/3/errors.json\n",
       "config_45\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_45/3/errors.json\n",
       "config_42\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_42/3/errors.json\n",
       "config_59\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_59/3/errors.json\n",
       "config_0\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_0/3/errors.json\n",
       "config_31\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_31/3/errors.json\n",
       "config_77\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_77/3/errors.json\n",
       "config_66\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_66/3/errors.json\n",
       "config_1\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_1/3/errors.json\n",
       "config_84\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_84/3/errors.json\n",
       "config_46\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_46/3/errors.json\n",
       "config_82\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_82/3/errors.json\n",
       "config_47\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_47/3/errors.json\n",
       "config_61\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_61/3/errors.json\n",
       "config_27\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_27/3/errors.json\n",
       "config_58\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_58/3/errors.json\n",
       "config_74\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_74/3/errors.json\n",
       "config_43\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_43/3/errors.json\n",
       "config_92\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_92/3/errors.json\n",
       "config_64\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_64/3/errors.json\n",
       "config_2\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_2/3/errors.json\n",
       "config_8\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_8/3/errors.json\n",
       "config_29\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_29/3/errors.json\n",
       "config_48\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_48/3/errors.json\n",
       "config_52\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_52/3/errors.json\n",
       "config_68\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_68/3/errors.json\n",
       "config_51\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_51/3/errors.json\n",
       "config_57\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_57/3/errors.json\n",
       "config_80\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_80/3/errors.json\n",
       "config_70\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_70/3/errors.json\n",
       "config_53\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_53/3/errors.json\n",
       "config_94\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_94/3/errors.json\n",
       "config_49\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_49/3/errors.json\n",
       "config_67\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_67/3/errors.json\n",
       "config_17\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_17/3/errors.json\n",
       "config_86\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_86/3/errors.json\n",
       "config_23\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_23/3/errors.json\n",
       "config_12\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_12/3/errors.json\n",
       "config_65\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_65/3/errors.json\n",
       "config_24\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_24/3/errors.json\n",
       "config_62\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_62/3/errors.json\n",
       "config_89\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_89/3/errors.json\n",
       "config_3\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_3/3/errors.json\n",
       "config_15\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_15/3/errors.json\n",
       "config_72\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_72/3/errors.json\n",
       "config_81\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_81/3/errors.json\n",
       "config_75\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_75/3/errors.json\n",
       "config_93\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_93/3/errors.json\n",
       "config_4\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_4/3/errors.json\n",
       "config_85\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_85/3/errors.json\n",
       "config_18\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_18/3/errors.json\n",
       "config_96\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_96/3/errors.json\n",
       "config_40\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_40/3/errors.json\n",
       "config_25\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_25/3/errors.json\n",
       "config_5\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_5/3/errors.json\n",
       "config_90\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_90/3/errors.json\n",
       "config_16\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_16/3/errors.json\n",
       "config_7\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_7/3/errors.json\n",
       "config_44\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_44/3/errors.json\n",
       "config_10\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_10/3/errors.json\n",
       "config_50\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_50/3/errors.json\n",
       "config_55\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_55/3/errors.json\n",
       "config_36\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_36/3/errors.json\n",
       "config_22\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_22/3/errors.json\n",
       "config_97\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_97/3/errors.json\n",
       "config_37\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_37/3/errors.json\n",
       "config_30\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_30/3/errors.json\n",
       "config_95\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_95/3/errors.json\n",
       "config_38\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_38/3/errors.json\n",
       "config_98\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_98/3/errors.json\n",
       "config_13\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_13/3/errors.json\n",
       "config_34\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_34/3/errors.json\n",
       "config_99\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_99/3/errors.json\n",
       "config_91\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_91/3/errors.json\n",
       "config_32\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_32/3/errors.json\n",
       "config_26\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_26/3/errors.json\n",
       "config_39\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_39/3/errors.json\n",
       "config_33\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_33/3/errors.json\n",
       "config_69\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_69/3/errors.json\n",
       "config_21\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_21/3/errors.json\n",
       "config_73\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_73/3/errors.json\n",
       "config_9\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_9/3/errors.json\n",
       "config_35\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_35/3/errors.json\n",
       "config_20\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_20/3/errors.json\n",
       "config_83\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_83/3/errors.json\n",
       "config_19\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_19/3/errors.json\n",
       "config_63\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_63/3/errors.json\n",
       "config_76\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_76/3/errors.json\n",
       "config_60\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_60/3/errors.json\n",
       "config_6\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_6/3/errors.json\n",
-      "config_11\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/2/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/9/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/5/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/4/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/8/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/7/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/1/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/6/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/0/errors.json\n",
-      "/home/till/plotting_data/bbo/nasbench201/ImageNet16-120/dehb/config_11/3/errors.json\n"
+      "config_11\n"
      ]
     }
    ],
@@ -13559,16 +1559,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'nb201_c10': {'sh': [90.87899999999999, 0.2048145502643775],\n",
+       "  'hb': [91.053, 0.23639162421710094],\n",
+       "  'bohb': [91.17559998706055, 0.19551327291051013],\n",
+       "  'dehb': [91.37559999267577, 0.19945886851307323]},\n",
+       " 'nb201_c100': {'sh': [71.89999996093749, 0.9765654354455887],\n",
+       "  'hb': [72.31199997070311, 0.7659869445931258],\n",
+       "  'bohb': [71.58099999999999, 0.9223063482379389],\n",
+       "  'dehb': [71.63199999999999, 0.7419811318355743]},\n",
+       " 'nb201_im': {'sh': [44.55666663004557, 1.101216496499461],\n",
+       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
+       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
+       "  'dehb': [44.24666666768392, 1.0534546359944508]}}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results[20]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13591,7 +1613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13602,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13680,7 +1702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -13736,7 +1758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13780,7 +1802,7 @@
     "        scaled_seed_results[i] = [(m - min_acc) / (max_acc - min_acc) for m in raw_seed_results[i]]\n",
     "        for j in range(len(scaled_seed_results[i])):\n",
     "            scaled_seed_results[i][j] = replace_if_nan(scaled_seed_results[i][j], 0)\n",
-    "\n",
+    "        #normed_seed_results[i] = copy.deepcopy(raw_seed_results[i])\n",
     "        \n",
     "    # compute the matrices\n",
     "  \n",
@@ -13813,15 +1835,15 @@
     "\n",
     "    return scaled_matrix\n",
     "\n",
-    "def plot_matrix(matrix, ss_group, save=None, cmap='viridis', hparam=False):\n",
+    "def plot_matrix(matrix, ss_group, save=None, cmap='viridis', title=\"No title specifics\"):\n",
     "    coords = matrix\n",
     "    cellsize = 5\n",
     "    fig, ax = plt.subplots(1,1,figsize=(cellsize*1.2, cellsize*coords.shape[0]/coords.shape[1]))\n",
     "    im = ax.pcolormesh(coords, edgecolor='k', linewidth=0.5, cmap=cmap) \n",
-    "    if hparam:\n",
-    "        plt.title('HParam. Corr. for Black-Box Algorithms')\n",
-    "    else:\n",
-    "        plt.title('Generalizability for Black-Box Algorithms')\n",
+    "   \n",
+    "    plt.title(title)\n",
+    "\n",
+    "       \n",
     "    plt.gca().invert_yaxis()\n",
     "    ax.set_xticks([])\n",
     "    ax.set_xticklabels('')\n",
@@ -13841,38 +1863,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'nb201_c10': {'sh': [90.87, 0.20610676844781026],\n",
-       "  'hb': [90.98599999999999, 0.25476263462289656],\n",
-       "  'bohb': [91.21319999121093, 0.2601295113691563],\n",
-       "  'dehb': [91.30359999072266, 0.15294914066855703]},\n",
-       " 'nb201_c100': {'sh': [71.84599996582031, 0.9117916772568413],\n",
-       "  'hb': [71.89999996093749, 0.9765654354455887],\n",
-       "  'bohb': [70.846, 0.5039087218931632],\n",
-       "  'dehb': [71.612, 0.5207072113961919]},\n",
-       " 'nb201_im': {'sh': [44.68999999186198, 0.9841578443027897],\n",
-       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "  'dehb': [44.81833331604004, 1.3276639885096027]}}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "results[10]"
+    "ss_dict_mat = {'nb101':'NB-101', \n",
+    "                 'nb201_c10':'NB-201', \n",
+    "                 'nb201_c100':'NB-201', \n",
+    "                 'nb201_im':'NB-201', \n",
+    "                 'darts':'DARTS',\n",
+    "                 'nlp':'NB-NLP',\n",
+    "                 'asr':'NB-ASR',\n",
+    "                 'mr_3ddet':'NB-MR',\n",
+    "                 'mr_cls':'NB-MR',\n",
+    "                 'mr_seg':'NB-MR',\n",
+    "                 'mr_video':'NB-MR',\n",
+    "                 'transmicro_obj':'TNB-Micro', \n",
+    "                 'transmicro_scene':'TNB-Micro', \n",
+    "                 'transmicro_jigsaw':'TNB-Micro', \n",
+    "                 'transmicro_room':'TNB-Micro',\n",
+    "                 'transmicro_segment':'TNB-Micro', \n",
+    "                 'transmicro_normal':'TNB-Micro', \n",
+    "                 'transmicro_auto':'TNB-Micro', \n",
+    "                 'transmacro_obj':'TNB-Macro', \n",
+    "                 'transmacro_scene':'TNB-Macro', \n",
+    "                 'transmacro_jigsaw':'TNB-Macro', \n",
+    "                 'transmacro_room':'TNB-Macro',\n",
+    "                 'transmacro_segment':'TNB-Macro', \n",
+    "                 'transmacro_normal':'TNB-Macro', \n",
+    "                 'transmacro_auto':'TNB-Macro', \n",
+    "          }"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13890,56 +1915,97 @@
     "    \n",
     "    \"\"\"\n",
     "    matrix= np.zeros((len(search_spaces), len(search_spaces)))\n",
-    "    for i, ss_1 in enumerate(best_configs, search_spaces):\n",
-    "        for j, ss_2 in enumerate(best_configs, search_spaces): \n",
-    "            matrix[i][j] =  results[i][ss_1][optimizer][0] - results[j][ss_2][optimizer][0]\n",
+    "    for i, ss_1 in enumerate(best_configs):\n",
+    "        for j, ss_2 in enumerate(best_configs): \n",
+    "            #print(\"i:{},j:{},ss_1:{},ss_2:{}\".format(i,j,ss_1, ss_2))\n",
+    "            #print(\"i:{},j:{},ss_1:{},ss_2:{}\".format(i,j,ss_1, ss_2))\n",
+    "            print(results[ss_2][search_spaces[j]][optimizer][0])\n",
+    "            matrix[i][j] =  results[ss_2][search_spaces[j]][optimizer][0] - results[ss_1][search_spaces[i]][optimizer][0]\n",
     "    return matrix \n",
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('sh', 'hb', 'bohb', 'dehb')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "optimizers"
+    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im'] "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['nb201_c10', 'nb201_c100', 'nb201_im']"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_0\n",
+      "config_1\n",
+      "config_2\n",
+      "config_65\n",
+      "config_85\n",
+      "config_9\n",
+      "config_28\n",
+      "config_59\n",
+      "config_60\n",
+      "config_87\n",
+      "config_58\n",
+      "config_9\n",
+      "config_0\n",
+      "config_1\n",
+      "config_2\n",
+      "config_65\n",
+      "config_85\n",
+      "config_9\n",
+      "config_28\n",
+      "config_59\n",
+      "config_60\n",
+      "config_87\n",
+      "config_58\n",
+      "config_9\n",
+      "config_0\n",
+      "config_1\n",
+      "config_2\n",
+      "config_65\n",
+      "config_85\n",
+      "config_9\n",
+      "config_28\n",
+      "config_59\n",
+      "config_60\n",
+      "config_87\n",
+      "config_58\n",
+      "config_9\n"
+     ]
     }
    ],
    "source": [
-    "search_spaces"
+    "#have to load different results\n",
+    "norm_matrices = []\n",
+    "kt_matrices = []\n",
+    "optimizer_matrix = []\n",
+    "results_stand = results\n",
+    "root = '~/plotting_data/transfer/'\n",
+    "optimizer_dict = {}\n",
+    "results_transfer = get_hpo_results(optimizers, search_spaces, root=root)\n",
+    "\n",
+    "best_configs = [[0,0,0],\n",
+    "                [9, 85, 65],\n",
+    "                [59,60,28],\n",
+    "                [58,9,87]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -13953,15 +2019,43 @@
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/till/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/ipykernel_launcher.py:76: RuntimeWarning: invalid value encountered in double_scalars\n"
+     "ename": "TypeError",
+     "evalue": "plot_matrix() got an unexpected keyword argument 'hparam'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_9423/2558175469.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mavg_scaled_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, save='bbo_matrix_avg.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_kt_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis_r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, save='bbo_corr_matrix.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: plot_matrix() got an unexpected keyword argument 'hparam'"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -13973,7 +2067,19 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -13998,9 +2104,11 @@
     "                [58,9,87]]\n",
     "for i, optimizer in enumerate(optimizers):\n",
     "    kt_mat = compute_matrix_stand(results_stand, search_spaces, optimizers, optimizer=optimizer)\n",
-    "    norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\n",
+    "    #norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\n",
     "    norm_matrices.append(norm)\n",
     "    kt_matrices.append(kt_mat)\n",
+    "    kt_matrix = scale_matrix(kt_mat, mean_div=2.5)\n",
+    "    avg_scaled_matrix = plot_matrix(kt_matrix, search_spaces, cmap='viridis', title=optimizer) #, save='bbo_matrix_avg.pdf')\n",
     "\n",
     "avg_norm_matrix = np.mean(np.array(norm_matrices), axis=0)\n",
     "avg_kt_matrix = np.mean(np.array(kt_matrices), axis=0)\n",
@@ -14011,6 +2119,37 @@
     "avg_scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis') #, save='bbo_matrix_avg.pdf')\n",
     "_ = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r', hparam=True) #, save='bbo_corr_matrix.pdf')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[ 1.        ,  0.12386693,  0.10568158],\n",
+       "        [ 0.12386693,  1.        , -0.0165503 ],\n",
+       "        [ 0.10568158, -0.0165503 ,  1.        ]]),\n",
+       " array([[ 1.        ,  0.12252777,  0.0331289 ],\n",
+       "        [ 0.12252777,  1.        , -0.00830929],\n",
+       "        [ 0.0331289 , -0.00830929,  1.        ]]),\n",
+       " array([[1.        , 0.11976836, 0.02144988],\n",
+       "        [0.11976836, 1.        , 0.03865534],\n",
+       "        [0.02144988, 0.03865534, 1.        ]]),\n",
+       " array([[1.        , 0.06899342, 0.02952478],\n",
+       "        [0.06899342, 1.        , 0.07582652],\n",
+       "        [0.02952478, 0.07582652, 1.        ]])]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kt_matrices"
+   ]
   }
  ],
  "metadata": {
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
index d4bc754e3..2373d7ca5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
@@ -1,5 +1,5 @@
 seed: 0
-search_space: nasbench201
+search_space: asr
 dataset: cifar10
 out_dir: run
 
@@ -7,10 +7,10 @@ search:
   seed: 0
   checkpoint_freq: 10 # 1000
   fidelity: 200
-  budgets: 4000
+  budgets: 400
   eta: 3
   n_process: 100
-  max_budget: 512
+  max_budget: 39
   min_budget: 12
   epsilon: 1e-6
   predictor_type: var_sparse_gp
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
index 7b5f5cc24..750fcbff5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
@@ -1,17 +1,41 @@
-seed: 0
-optimizer: bananas
-search_space: nasbench201
-dataset: cifar10
+config_id: 0
+dataset: None
+optimizer: sh
 out_dir: run
-
 search:
-  seed: 0
-  checkpoint_freq: 10 # 1000
-  fidelity: 200 #max fidelity of the benchmark fix 
-  budgets: 400 #budget inseconds the hole algorithm have
-  eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
-  max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
-  min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)
-  n_process: 1000
-  epsilon: 1e-6 #small param that is added to prevend that floor(x.9999999999999999) is x not x+1 fix 
-  predictor_type: var_sparse_gp
+  acq_fn_optimization: mutation
+  acq_fn_type: its
+  budget_max: 128
+  budget_type: epoch
+  budgets: 16000
+  checkpoint_freq: 5000
+  crossover_prob: 0.5
+  debug_predictor: false
+  enc_dim: 6
+  encoding_type: path
+  epochs: 200
+  epsilon: 1.0e-06
+  eta: 2
+  fidelity: 39
+  k: 10
+  max_budget: 39
+  max_mutations: 1
+  method: random
+  min_bandwith: 0.001
+  min_budget: 1
+  min_fidelity: 1
+  min_points_in_model: 7
+  mutate_prob: 0.5
+  n_process: 1000000
+  num_arches_to_mutate: 1
+  num_candidates: 50
+  num_ensemble: 3
+  num_init: 10
+  number_archs: 128
+  predictor_type: test
+  population_size: 50
+  predictor: var_sparse_gp
+  sample_size: 10
+  top_n_percent: 0.1
+search_space: asr
+seed: 0
diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index 175daf531..f8e5f60a6 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -147,6 +147,7 @@ def search(self, resume_from=""):
                         else:
                             end_time = time.time()
                             train_acc, valid_acc, test_acc, train_time = self.optimizer.train_statistics()
+                            
                             train_loss, valid_loss, test_loss = -1, -1, -1
 
                             self.errors_dict.train_acc.append(train_acc)
@@ -156,12 +157,21 @@ def search(self, resume_from=""):
                             self.errors_dict.test_acc.append(test_acc)
                             self.errors_dict.test_loss.append(test_loss)
                             self.errors_dict.runtime.append(end_time - start_time)
-                            self.errors_dict.train_time.append(train_time)
+                           
                             self.train_top1.avg = train_acc
                             self.val_top1.avg = valid_acc
-                            self.search_time += end_time - start_time
-                            self.search_time += train_time
-
+                            if train_time > 0:
+                                self.search_time += end_time - start_time
+                                self.search_time += train_time
+                            else:
+                                #this if we have no time only epochs
+                                #self.fidelities[self.round_number]
+                                #if work proberly has to be check 
+                                #ofcourse now we doesn't factor in runetime of optimizer, but this not hat large comapre to training time normaly
+                                _, latest_arch_epoch  = self.optimizer.get_latest_architecture()
+                                self.search_time += 1 * latest_arch_epoch
+                                train_time = 1 * latest_arch_epoch
+                            self.errors_dict.train_time.append(train_time)
                         self.periodic_checkpointer.step(e)
 
                         anytime_results = self.optimizer.test_statistics()
@@ -223,11 +233,21 @@ def search(self, resume_from=""):
                     self.errors_dict.test_acc.append(test_acc)
                     self.errors_dict.test_loss.append(test_loss)
                     self.errors_dict.runtime.append(end_time - start_time)
-                    self.errors_dict.train_time.append(train_time)
+                    
                     self.train_top1.avg = train_acc
                     self.val_top1.avg = valid_acc
-                    self.search_time += end_time - start_time
-                    self.search_time += train_time
+                    if train_time > 0:
+                        self.search_time += end_time - start_time
+                        self.search_time += train_time
+                    else:
+                        #this if we have no time only epochs        
+                        #self.fidelities[self.round_number]
+                        #if work proberly has to be check         
+                        #ofcourse now we doesn't factor in runetime of optimizer, but this not hat large comapre to training time normaly
+                        _, latest_arch_epoch  = self.optimizer.get_latest_architecture()
+                        self.search_time += 1 * latest_arch_epoch
+                        train_time = 1
+                    self.errors_dict.train_time.append(train_time)
 
                 self.periodic_checkpointer.step(e)
 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 3d01b9d7b..9373b1f28 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,6 +1,7 @@
 import collections
 import logging
 import math
+from re import I
 import torch
 import copy
 import numpy as np
@@ -41,7 +42,7 @@ class  SuccessiveHalving(MetaOptimizer):
     # training the models is not implemented
     using_step_function = False
     
-    def __init__(self, config):
+    def __init__(self, config, hash_function = convert_naslib_to_str): 
         super().__init__()
         # Hyperband related stuff
         self.config = config
@@ -51,7 +52,7 @@ def __init__(self, config):
         self.min_budget = self.config.search.min_budget
         self.eta = self.config.search.eta 
         self._epsilon = float(self.config.search.epsilon) 
-        
+        self.hash_function = hash_function
         times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta)  + self._epsilon )
         # set up round sizes, fidelities, and list of arches
         
@@ -167,17 +168,24 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch) and x.epoch < self.history[-1].epoch]
+        #this for for checking if this hash function really works 
+        #for i in range(len(self.history)):
+        #    print(self.history[i].arch.get_hash())
+        models = [x for x in self.history if x.arch.get_hash() == latest_arch.get_hash() and x.epoch < self.history[-1].epoch] 
+        ##for more nasbench to make this possible
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
         train_time_scaled = train_time * latest_arch_epoch
         if len(models) > 1:
             train_time_scaled = train_time_scaled - train_time * models[-2].epoch
+
+        
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+           best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             train_time_scaled, 
         )
+        
     
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()
diff --git a/naslib/search_spaces/nasbenchasr/graph.py b/naslib/search_spaces/nasbenchasr/graph.py
index 108e4cc2e..47560409c 100644
--- a/naslib/search_spaces/nasbenchasr/graph.py
+++ b/naslib/search_spaces/nasbenchasr/graph.py
@@ -100,6 +100,7 @@ def sample_random_architecture(self, dataset_api):
         self.set_compact(compact)
         return compact
 
+
     def mutate(self, parent, mutation_rate=1, dataset_api=None):
         """
         This will mutate the cell in one of two ways:
@@ -113,7 +114,7 @@ def mutate(self, parent, mutation_rate=1, dataset_api=None):
 
         for _ in range(int(mutation_rate)):
             mutation_type = np.random.choice([2])
-
+           
             if mutation_type == 1:
                 # change an edge
                 # first pick up a node
@@ -137,7 +138,29 @@ def mutate(self, parent, mutation_rate=1, dataset_api=None):
                 compact[node_id][0] = new_op_id
 
         self.set_compact(compact)
+    def crossover_bin(self, parent, mutant, dim, prob, dataset_api=None):
+        '''Performs the binomial crossover of DE
+           this write now only for the things that mutate 
+        '''
+        parent_compact = parent.get_compact()
+        compact = copy.deepcopy(parent_compact)
+        mutant_compact= mutant.get_compact()
+        cross_node = np.random.rand(3) < prob
+        cross_type = np.random.rand(2) < prob
+        for i,node in enumerate(cross_node):
+            if not node:
+                continue
+            if cross_type[0]:
+                cross_edges = np.random.rand(len(compact[i][1:])) 
+                for j,egde in enumerate(cross_edges):
+                    if not egde:
+                        continue
+                    compact[i][j+1] =  mutant_compact[i][j+1]
+            if cross_type[1]:
+                compact[i][0] = mutant_compact[i][0]
+        self.set_compact(compact)
 
+      
 
     def get_nbhd(self, dataset_api=None):
         """

From 8d9be5ad43e11e643b64426df5117c98cf5c163a Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sat, 21 May 2022 14:51:17 +0200
Subject: [PATCH 111/130] make multifidelity consistend

---
 .../nas_predictors/discrete_config.yaml       | 54 +++++++++++++------
 .../nas_predictors/nas_predictor_config.yaml  |  4 +-
 .../predictors/predictor_config.yaml          |  4 +-
 3 files changed, 43 insertions(+), 19 deletions(-)

diff --git a/naslib/benchmarks/nas_predictors/discrete_config.yaml b/naslib/benchmarks/nas_predictors/discrete_config.yaml
index d29c562e5..750fcbff5 100644
--- a/naslib/benchmarks/nas_predictors/discrete_config.yaml
+++ b/naslib/benchmarks/nas_predictors/discrete_config.yaml
@@ -1,17 +1,41 @@
-seed: 0
-optimizer: bananas
-search_space: nasbench201
-dataset: cifar10
+config_id: 0
+dataset: None
+optimizer: sh
 out_dir: run
-
 search:
-  seed: 0
-  checkpoint_freq: 10 # 1000
-  fidelity: 200 #max fidelity of the benchmark fix 
-  budgets: 400000 #budget inseconds the hole algorithm have
-  eta: 2    #times the number of archiekteurs is decreased and the fiedleity is increased (train 1 to 5)
-  max_budget: 200    #maximal budget that we want to reach (train 100 to 200)
-  min_budget: 1      #minimum budget for the last fidelity (train 1 to 10)
-  n_process: 1000
-  epsilon: 1e-6 #small param that is added to prevend that floor(x.9999999999999999) is x not x+1 fix 
-  predictor_type: var_sparse_gp
+  acq_fn_optimization: mutation
+  acq_fn_type: its
+  budget_max: 128
+  budget_type: epoch
+  budgets: 16000
+  checkpoint_freq: 5000
+  crossover_prob: 0.5
+  debug_predictor: false
+  enc_dim: 6
+  encoding_type: path
+  epochs: 200
+  epsilon: 1.0e-06
+  eta: 2
+  fidelity: 39
+  k: 10
+  max_budget: 39
+  max_mutations: 1
+  method: random
+  min_bandwith: 0.001
+  min_budget: 1
+  min_fidelity: 1
+  min_points_in_model: 7
+  mutate_prob: 0.5
+  n_process: 1000000
+  num_arches_to_mutate: 1
+  num_candidates: 50
+  num_ensemble: 3
+  num_init: 10
+  number_archs: 128
+  predictor_type: test
+  population_size: 50
+  predictor: var_sparse_gp
+  sample_size: 10
+  top_n_percent: 0.1
+search_space: asr
+seed: 0
diff --git a/naslib/benchmarks/nas_predictors/nas_predictor_config.yaml b/naslib/benchmarks/nas_predictors/nas_predictor_config.yaml
index 0c7fb5afd..4023f47ec 100644
--- a/naslib/benchmarks/nas_predictors/nas_predictor_config.yaml
+++ b/naslib/benchmarks/nas_predictors/nas_predictor_config.yaml
@@ -1,7 +1,7 @@
 seed: 0
 optimizer: oneshot
-search_space: darts
-dataset: cifar10
+search_space: asr
+dataset: None
 out_dir: run
 
 experiment_type: single
diff --git a/naslib/benchmarks/predictors/predictor_config.yaml b/naslib/benchmarks/predictors/predictor_config.yaml
index 5c88a81bb..c00753e12 100644
--- a/naslib/benchmarks/predictors/predictor_config.yaml
+++ b/naslib/benchmarks/predictors/predictor_config.yaml
@@ -7,10 +7,10 @@
 experiment_type: single
 
 # nasbench101, nasbench201, darts, or nlp
-search_space: nasbench201
+search_space: asr
 
 # cifar10, cifar100, or ImageNet16-120 (only important for nasbench201)
-dataset: cifar10
+dataset: None
 
 # one of the 31 predictors in benchmarks/predictors/runner.py
 predictor: xgb

From 14840fab715079f70a346318e6accd271ca18817 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Sun, 22 May 2022 11:29:54 +0200
Subject: [PATCH 112/130] debug

---
 examples/mf_demo.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
index bebca6553..57e3fc47d 100644
--- a/examples/mf_demo.py
+++ b/examples/mf_demo.py
@@ -9,14 +9,15 @@
 #from naslib.utils import utils, setup_logger, get_dataset_api
 from naslib.utils import utils, utils_asr ,setup_logger, get_dataset_api
 from naslib.optimizers import RandomSearch as RS
+from naslib.optimizers import Npenas as npenas
 from naslib.optimizers import RegularizedEvolution as RE
 from naslib.optimizers import SuccessiveHalving as SH
 from naslib.optimizers import HB
 from naslib.optimizers import BOHB 
 from naslib.optimizers import DEHB
 #from naslib.search_spaces.nasbenchasr.conversions as h
-from naslib.defaults.trainer_multifidelity import Trainer
-#from naslib.defaults.trainer import Trainer
+#from naslib.defaults.trainer_multifidelity import Trainer
+from naslib.defaults.trainer import Trainer
 # TODO: rethinking logging is rigth now, completly ingnored 
 import yaml
 from pathlib import Path
@@ -72,6 +73,12 @@
     optimizer = BOHB(config)
     plot_func = mf_plot.plot_hb
     predictor = "tpe"
+elif config_optimizer == "NPENAS":
+    #i thin kby save dir is soometihng strange
+    
+    predictor = "var_sparse_gp"
+    optimizer = npenas(config)
+    plot_func = mf_plot.plot_hb
 elif config_optimizer == "DEHB":
     #i thin kby save dir is soometihng strange
     

From bf1c03ff2f43ee382739dd0cc9ad1abe57bc52f2 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 11 Jun 2022 17:37:40 +0200
Subject: [PATCH 113/130] work on optimizer plot & improve mf-optimizers

---
 .gitignore                                   |   4 +
 examples/optimizers.ipynb                    | 386 ++++++++++++-------
 naslib/optimizers/discrete/bohb/optimizer.py |   2 +-
 naslib/optimizers/discrete/dehb/optimizer.py |   2 +-
 naslib/optimizers/discrete/hb/optimizer.py   |   2 +-
 naslib/optimizers/discrete/sh/optimizer.py   |   6 +-
 naslib/search_spaces/nasbench201/graph.py    |   3 +-
 7 files changed, 257 insertions(+), 148 deletions(-)

diff --git a/.gitignore b/.gitignore
index 3362dbf3a..1baa34e73 100644
--- a/.gitignore
+++ b/.gitignore
@@ -151,3 +151,7 @@ Session.vim
 *~
 # Auto-generated tag files
 tags
+
+# ignore png files in nb_models
+nasbench301/nb_models
+naslib/checkpoints
\ No newline at end of file
diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index 49176c169..4cccb4de0 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -22,11 +22,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
     "import os\n",
     "import numpy as np\n",
     "import json\n",
@@ -36,20 +35,21 @@
     "import pickle\n",
     "import pandas as pd\n",
     "\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
+    "from collections import defaultdict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "# this defines all of the default colors and markers for the plots\n",
     "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
-    "predictors = ['rs', 'sh']\n",
-    "\n",
+    "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
+    "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
+    "benchmark_datasets = ['cifar10', 'cifar100', 'ImageNet16-120', ]\n",
+    "# benchmark_datasets = ['ImageNet16-120', ]\n",
     "# TODO: What of this am I really needing?\n",
     "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
     "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
@@ -75,17 +75,19 @@
     "    'ls':{'color':defaults[0], 'fmt':fmts[0]},\n",
     "    # 'ls_lce':{'color':defaults[0], 'fmt':fmts[1]},\n",
     "    # 'ls_svr':{'color':defaults[0], 'fmt':fmts[2]},\n",
-    "    # 're':{'color':defaults[1], 'fmt':fmts[0]},\n",
+    "    're':{'color':defaults[1], 'fmt':fmts[2]},\n",
     "    # 'rea_lce':{'color':defaults[1], 'fmt':fmts[1]},\n",
     "    # 'rea_svr':{'color':defaults[1], 'fmt':fmts[2]},\n",
     "    'bananas/bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    'bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
     "    # 'bananas_lce/bananas':{'color':defaults[2], 'fmt':fmts[1]},\n",
     "    # 'bananas_svr/bananas':{'color':defaults[2], 'fmt':fmts[2]},    \n",
     "    'rs':{'color':defaults[3], 'fmt':fmts[0]},\n",
     "    'sh': {'color': defaults[1], 'fmt':fmts[0]},\n",
     "    'hb':{'color':defaults[4], 'fmt':fmts[0]},\n",
-    "    # 'bohb':{'color':defaults[5], 'fmt':fmts[0]},\n",
-    "    # 'dehb':{'color':defaults[6], 'fmt':fmts[0]},\n",
+    "    'bohb':{'color':defaults[5], 'fmt':fmts[0]},\n",
+    "    'dehb':{'color':defaults[6], 'fmt':fmts[2]},\n",
+    "    'npenas':{'color':defaults[0], 'fmt':fmts[1]},\n",
     "}\n",
     "\n",
     "# define colors, formats, and markers\n",
@@ -98,10 +100,10 @@
     "\n",
     "# some of them are not used\n",
     "pred_label_dict={\n",
-    "    'bananas/gcn':'BANANAS-GCN', 'bananas/bananas':'BANANAS', 'bananas/xgb':'BANANAS-XGB', \n",
+    "    'bananas/gcn':'BANANAS-GCN', 'bananas':'BANANAS', 'bananas/xgb':'BANANAS-XGB', \n",
     "    'npenas/gcn':'NPENAS-GCN', 'npenas/bananas':'NPENAS-BANANAS', 'npenas/xgb':'NPENAS-XGB', \n",
     "    'rs':'RS',\n",
-    "    're': 'REA',\n",
+    "    're': 'RE',\n",
     "    'ls': 'LS',\n",
     "    'hb': 'HB',\n",
     "    'sh': 'SH',\n",
@@ -116,11 +118,12 @@
     "    'ls_svr_hp': 'LS-SVR-HP',\n",
     "    'rea_svr_hp': 'REA-SVR-HP',\n",
     "    'bananas_svr_hp/bananas': 'BANANAS-SVR-HP',\n",
+    "    'npenas': 'NPENAS',\n",
     "}\n",
     "\n",
     "val_gts = {'cifar10': 91.61, \n",
     "           'cifar100': 73.49, \n",
-    "           'ImageNet16-120': 46.77, \n",
+    "           'ImageNet16-120': 50.0, \n",
     "           'nasbench311': 95.8, \n",
     "           'nasbench111':95.6, \n",
     "           'nasbenchnlp':100.7}\n",
@@ -136,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,108 +180,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
     "def get_results(predictor, path, metric='valid_acc', key_choice=None):\n",
-    "    output = []\n",
-    "    time = []\n",
+    "    output = defaultdict(list)\n",
+    "    time = defaultdict(list)\n",
     "    nan_count = 0\n",
     "    missing_results_count = 0\n",
     "    file_count = 0\n",
-    "    for folder in os.listdir(path):\n",
-    "        if not(folder == datasets[key_choice] and os.path.isdir(os.path.join(path, folder))):\n",
+    "    dataset_paths = []\n",
+    "    for dataset_folder in os.listdir(path):\n",
+    "        dataset_path = os.path.join(path, dataset_folder)\n",
+    "        if not(dataset_folder == datasets[key_choice] and os.path.isdir(dataset_path)):\n",
     "            continue\n",
-    "        # TODO: This needs to be done better !!\n",
-    "        # Because config_0 is just one config, with multiple seed.\n",
-    "        # But more than one config with multiple seed should be possible. \n",
-    "        algo_path = os.path.join(path, folder, predictor, 'config_0')\n",
-    "        if not os.path.isdir(algo_path):\n",
+    "        dataset_paths.append(dataset_path)\n",
+    "    \n",
+    "    optimizer_paths = []\n",
+    "    for dataset_path in dataset_paths:\n",
+    "        optimizer_path = os.path.join(dataset_path, predictor)\n",
+    "        if not os.path.isdir(optimizer_path):\n",
     "            continue\n",
-    "        seed_dirs = list(os.listdir(algo_path))\n",
-    "        \n",
-    "        print(seed_dirs)\n",
-    "        # seed_dirs = sorted(seed_dirs, key=lambda x: int(x))\n",
-    "        seed_dirs = sorted(seed_dirs)\n",
-    "\n",
-    "        for seed_dir in seed_dirs:\n",
-    "            '''if not os.path.isdir(os.path.join(algo_path, seed_dir)):\n",
+    "        optimizer_paths.append(optimizer_path)\n",
+    "        # for optimizer_folder in os.listdir(dataset_path):\n",
+    "        #     optimizer_path = os.path.join(dataset_path, optimizer_folder)\n",
+    "        #     if not os.path.isdir(optimizer_path):\n",
+    "        #         continue\n",
+    "        #     optimizer_paths.append(optimizer_path)\n",
+    "\n",
+    "    config_paths = []\n",
+    "    for optimizer_path in optimizer_paths:\n",
+    "        for config_folder in os.listdir(optimizer_path):\n",
+    "            if config_folder[:7] != \"config_\":\n",
     "                continue\n",
-    "            result_file = os.path.join(algo_path, seed_dir, 'errors.json')\n",
-    "\n",
-    "            if not (os.path.isfile(result_file) and file_count < 100):\n",
+    "            config_path = os.path.join(optimizer_path, config_folder)\n",
+    "            if not os.path.isdir(config_path):\n",
+    "                continue\n",
+    "            config_paths.append(config_path)\n",
+    "    \n",
+    "    result_paths = []\n",
+    "    for config_path in config_paths:\n",
+    "        for seed_folder in os.listdir(config_path):\n",
+    "            seed_dir = os.path.join(config_path, seed_folder)\n",
+    "            if not os.path.isdir(seed_dir):\n",
+    "                continue\n",
+    "            result_file = os.path.join(config_path, seed_folder, 'errors.json')\n",
+    "            if not os.path.isfile(result_file):\n",
     "                continue\n",
-    "            file_count += 1'''\n",
-    "            result_file = os.path.join(algo_path, seed_dir)\n",
-    "            result = json.load(open(result_file))                        \n",
-    "            # config = result[0]\n",
-    "            epochs = len(result[1]['train_time'])\n",
-    "\n",
-    "            val_acc = result[1]['valid_acc'][:epochs]\n",
-    "\n",
-    "            if key_choice == 'nasbench111':\n",
-    "                val_incumbent = [(val_gts[key_choice] - val_acc[0] *  100.) / 100.]\n",
-    "                for  ind in range(1, len(val_acc)):\n",
-    "                    val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
-    "            elif key_choice == 'nasbenchnlp':\n",
-    "                val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
-    "            else:\n",
-    "                val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
-    "                for ind in range(1, len(val_acc)):\n",
-    "                    val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])) / 100)\n",
-    "\n",
-    "\n",
-    "            runtime = result[1]['runtime']\n",
-    "            train_time = result[1]['train_time']\n",
-    "            runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
-    "            train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
-    "            total_time = [i+j for i,j in zip(runtime, train_time)]\n",
-    "            if predictor == 'sh':\n",
-    "                total_time[:] = [x / 200.0 for x in total_time]\n",
-    "            # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
-    "            if metric == 'valid_acc':\n",
-    "                incumbent = val_incumbent\n",
-    "                #incumbent = val_acc\n",
-    "            elif metric == 'test_acc':\n",
-    "                test_err = [100 - x for x in result[1]['test_acc']]\n",
-    "                inc_idx, best, best_idx = [], np.inf, 0\n",
-    "                for i, err in enumerate(val_err):\n",
-    "                    if err < best:\n",
-    "                        best, best_idx = err, i\n",
-    "                    inc_idx.append(best_idx)\n",
-    "                incumbent = [test_err[idx] for idx in inc_idx]\n",
-    "\n",
-    "            if len(incumbent) == epochs:\n",
-    "                output.append(incumbent)\n",
-    "                time.append(total_time)\n",
-    "            else:\n",
-    "                nan_count += 1\n",
-    "\n",
-    "    output = np.array(output)\n",
-    "    time = np.array(time)\n",
+    "            result_paths.append(result_file)\n",
     "    \n",
-    "    output, time = get_trajectories(output, time)\n",
+    "    for result_file in result_paths:\n",
+    "        try:\n",
+    "            result = json.load(open(result_file))\n",
+    "        except:\n",
+    "            print(\"An exception occurred\")\n",
+    "            continue\n",
+    "        # config = result[0]\n",
+    "        epochs = len(result[1]['train_time'])\n",
+    "\n",
+    "        val_acc = result[1]['valid_acc'][:epochs]\n",
+    "\n",
+    "        if key_choice == 'nasbench111':\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0] *  100.) / 100.]\n",
+    "            for  ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
+    "        elif key_choice == 'nasbenchnlp':\n",
+    "            val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
+    "        else:\n",
+    "            # TODO: is this scaling really necessary?\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
+    "            for ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])) / 100)\n",
+    "\n",
+    "\n",
+    "        runtime = result[1]['runtime']\n",
+    "        train_time = result[1]['train_time']\n",
+    "        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
+    "        train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
+    "        total_time = [i+j for i,j in zip(runtime, train_time)]\n",
+    "        if predictor in {'rs', 'ls', 're', 'bananas', 'npenas'}:\n",
+    "            # continue\n",
+    "            total_time[:] = [x * 200.0 for x in total_time]\n",
+    "        # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
+    "        if metric == 'valid_acc':\n",
+    "            incumbent = val_incumbent\n",
+    "            #incumbent = val_acc\n",
+    "        elif metric == 'test_acc':\n",
+    "            test_err = [100 - x for x in result[1]['test_acc']]\n",
+    "            inc_idx, best, best_idx = [], np.inf, 0\n",
+    "            for i, err in enumerate(val_err):\n",
+    "                if err < best:\n",
+    "                    best, best_idx = err, i\n",
+    "                inc_idx.append(best_idx)\n",
+    "            incumbent = [test_err[idx] for idx in inc_idx]\n",
+    "\n",
+    "        if len(incumbent) == epochs:\n",
+    "            output[result[0]['config_id']].append(incumbent)\n",
+    "            time[result[0]['config_id']].append(total_time)\n",
+    "\n",
+    "            # output.append(incumbent)\n",
+    "            # time.append(total_time)\n",
+    "        else:\n",
+    "            nan_count += 1\n",
+    "\n",
+    "    output = {key: np.array(item) for key,item in output.items()}\n",
+    "    time = {key: np.array(item) for key,item in time.items()}\n",
+    "    # output = np.array(output)\n",
+    "    # time = np.array(time)\n",
     "    \n",
-    "    print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
-    "    mean = np.mean(output, axis=0)\n",
-    "    std = np.std(output, axis=0)\n",
-    "    std_error = np.sqrt(np.var(output, axis=0, ddof=1)/np.asarray(output).shape[0])\n",
+    "    # output, time = get_trajectories(output, time)\n",
+    "\n",
+    "    for config_id in output.keys():\n",
+    "        output[config_id], time[config_id] = get_trajectories(output[config_id], time[config_id])\n",
+    "    # output, time = get_trajectories(output, time)\n",
+    "    print(f\"predictor: {predictor}\")\n",
+    "    means = defaultdict(list)\n",
+    "    means = {key: np.mean(item, axis=0) for key,item in output.items()}\n",
+    "    incumbent_settings = min(means.items(), key=lambda item: item[1][-1])\n",
+    "    # incumbent_settings = [0, means[0]]\n",
+    "    mean = incumbent_settings[1]\n",
+    "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
+    "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
+    "    time = time[incumbent_settings[0]]\n",
+    "    # print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
+    "    # print(f\"predictor: {predictor}, best config: {incumbent_settings[0]}\")\n",
     "    print('first mean', mean[0], 'last mean', mean[-1])\n",
     "    print('first std', std[0], 'last std', std[-1])\n",
+    "    print('time ', time[-1])\n",
     "    return mean, std, std_error, time"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
     "def save_results_to_dict(folder, predictors, key_choice):\n",
     "    results_dict = {}\n",
     "    print('\\n saving', key_choice)\n",
-    "    for i, predictor in enumerate(predictors):\n",
+    "    for _, predictor in enumerate(predictors):\n",
     "        mean, std, std_error, runtime = get_results(predictor, folder, metric='valid_acc', key_choice=key_choice)\n",
     "        key = predictor\n",
     "        results_dict[key] = {'label':pred_label_dict[predictor], \n",
@@ -289,18 +331,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def save_all_results():\n",
-    "    \n",
-    "    all_results = {}\n",
-    "     \n",
+    "def save_all_results():     \n",
     "    # that was/is very very shity code!\n",
-    "    key_choice = 'nasbench311'\n",
+    "    # key_choice = 'nasbench311'\n",
     "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
-    "    all_results[key_choice] = save_results_to_dict(folder, predictors, key_choice)       \n",
+    "    all_results = {dataset:save_results_to_dict(folder, predictors, dataset) for dataset in benchmark_datasets}\n",
     "    return all_results"
    ]
   },
@@ -313,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -321,22 +360,84 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " saving nasbench311\n",
-      "['errors.json']\n",
-      "rs output shape (1, 200) nans 0 missing files 0\n",
-      "first mean 0.08989999999999995 last mean 0.0536999999999999\n",
-      "first std 0.0 last std 0.0\n",
-      "['errors.json']\n",
-      "sh output shape (1, 2953) nans 0 missing files 0\n",
-      "first mean 0.3997999999267578 last mean 0.04564000014648428\n",
-      "first std 0.0 last std 0.0\n"
+      " saving ImageNet16-120\n",
+      "predictor: ls\n",
+      "first mean 0.1281666671702067 last mean 0.027066667521158848\n",
+      "first std 0.042031271577528125 last std 0.0006110104655917387\n",
+      "time  5713274.493876268\n",
+      "predictor: bananas\n",
+      "first mean 0.1281666671702067 last mean 0.03093333388264974\n",
+      "first std 0.042031271577528125 last std 0.00320693704693154\n",
+      "time  5093832.737680004\n",
+      "predictor: re\n",
+      "first mean 0.1281666671702067 last mean 0.026666667277018226\n",
+      "first std 0.042031271577528125 last std 6.938893903907228e-18\n",
+      "time  6223506.154484801\n",
+      "predictor: rs\n",
+      "first mean 0.1281666671702067 last mean 0.035033333760579426\n",
+      "first std 0.042031271577528125 last std 0.004188476714839849\n",
+      "time  4552233.556291293\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3789807649.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  output = {key: np.array(item) for key,item in output.items()}\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3789807649.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  time = {key: np.array(item) for key,item in time.items()}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predictor: bohb\n",
+      "first mean 0.40754999999380115 last mean 0.03556666660563151\n",
+      "first std 0.042707405685386175 last std 0.00217332313519604\n",
+      "time  6510118.864055392\n",
+      "predictor: dehb\n",
+      "first mean 0.3650333333396912 last mean 0.03314999985758464\n",
+      "first std 0.03514476407331037 last std 0.000616666684812266\n",
+      "time  6511342.426820518\n",
+      "predictor: hb\n",
+      "first mean 0.38350000001207984 last mean 0.03566666705322265\n",
+      "first std 0.040044558503294796 last std 0.003467307816906263\n",
+      "time  6513290.2482745955\n",
+      "predictor: sh\n",
+      "first mean 0.38350000001207984 last mean 0.03340000009155271\n",
+      "first std 0.040044558503294796 last std 0.0010598741974143196\n",
+      "time  6504810.671534024\n",
+      "predictor: npenas\n",
+      "first mean 0.1281666671702067 last mean 0.028466667419433583\n",
+      "first std 0.042031271577528125 last std 0.0025612496835876377\n",
+      "time  3225795.3340755766\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function print>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "base_path = '/Users/lars/Projects/NASLib-mf/naslib/benchmarks/mf/run/'\n",
+    "base_path = '/Users/lars/Projects/results/4_experiments'\n",
     "\n",
-    "results = save_all_results()"
+    "results = save_all_results()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'wb')\n",
+    "# pickle.dump(results, result_file)\n",
+    "# result_file.close()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
+    "# results = pickle.load(result_file)\n",
+    "print"
    ]
   },
   {
@@ -348,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,12 +460,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3772773774.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
+      "Invalid limit will be ignored.\n",
+      "  ax.set_ylim([-0.2, 1e-1])\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -373,15 +483,6 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -432,40 +533,43 @@
     "    else:\n",
     "        ax.set_ylabel('Valid. regret', fontsize=14) #nas201\n",
     "\n",
-    "    ax.grid(True, which=\"both\",ls=\"-\", alpha=.5)\n",
+    "    # ax.grid(True, which=\"both\",ls=\"-\", alpha=.5)\n",
     "\n",
     "    if key_choice == 'cifar10':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([.001, .19])\n",
-    "        ax.set_xlim([1e4, 4*10e4])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_ylim([2e-4, 1e-2])\n",
+    "        ax.set_xlim([2.0e5, 7.6*1e5])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
     "    elif key_choice == 'cifar100':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([0.002, 0.3])\n",
-    "        ax.set_xlim([1e4, 8.1e5])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_ylim([0.0020, 0.3])\n",
+    "        ax.set_xlim([1e4, 1e6])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
     "    elif key_choice == 'ImageNet16-120':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([.002, .4])\n",
-    "        ax.set_xlim([1e4, 1.0e6])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_ylim([-0.2, 1e-1])\n",
+    "        # ax.set_ylim([0.0025, 0.3])\n",
+    "        ax.set_xlim([1.0e5, 5e6])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
     "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
     "    elif key_choice == 'nasbench311':    \n",
-    "        # ax.set_xscale('log')\n",
-    "        # ax.set_yscale('log')\n",
-    "        ax.set_ylim([.04, .06])\n",
-    "        ax.set_xlim([500, 2000])\n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([.035, .066])\n",
+    "        ax.set_xlim([400, 2100])\n",
+    "        # ax.set_ylim([2e-3, .028])\n",
+    "        # ax.set_xlim([5e4, 4*10e5])\n",
     "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
     "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas311_c10.pdf')\n",
@@ -486,7 +590,7 @@
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
     "        ax.set_ylim([.5, 4.2e1])\n",
-    "        ax.set_xlim([5e4, 1.0*10e5])\n",
+    "        ax.set_xlim([5e4, 1.1*10e6])\n",
     "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
     "        ax.set_title('NAS-Bench-NLP11 PTB', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nasn11_ptb.pdf')\n",
@@ -503,12 +607,12 @@
  ],
  "metadata": {
   "interpreter": {
-   "hash": "37e59fccee3cee055d0c30896f8ae2ee545f4691b0b28f0762ae05c70393af3f"
+   "hash": "e28e65abac2798f30acc960ffff31a2f506562b074c32d55b97f302318a49627"
   },
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit ('jupyter-venv': venv)",
+   "display_name": "jupyter-venv",
    "language": "python",
-   "name": "python3"
+   "name": "jupyter-venv"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 4c8bd21d4..c5d438765 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -266,7 +266,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index be7301075..f1d0f6a04 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -195,7 +195,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1),
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index fe783acb1..49bfb3372 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -152,7 +152,7 @@ def train_statistics(self):
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch) * latest_arch_epoch, # TODO: Maybe we have to solve this directly in benchmark API 
+            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API 
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 3d01b9d7b..ab55c0d48 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -61,6 +61,7 @@ def __init__(self, config):
            
             # n= 2*3 ** 1/2  for i in range(s + 1):
             n = math.floor(n / self.eta)
+            # TODO: maybe this can be replaced by search space get_max_epoch()
             r = min(math.floor(r * self.eta), config.search.fidelity)
             self.round_sizes.append(n)
             self.fidelities.append(r)
@@ -167,11 +168,12 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch) and x.epoch < self.history[-1].epoch]
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
+        models = [x for x in self.history if x.arch.get_hash() == latest_arch.get_hash() and x.epoch < self.history[-1].epoch]
+        # models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch) and x.epoch < self.history[-1].epoch]
         train_time_scaled = train_time * latest_arch_epoch
         if len(models) > 1:
-            train_time_scaled = train_time_scaled - train_time * models[-2].epoch
+            train_time_scaled = train_time_scaled - train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
diff --git a/naslib/search_spaces/nasbench201/graph.py b/naslib/search_spaces/nasbench201/graph.py
index 52f6cc35d..3713bbe00 100644
--- a/naslib/search_spaces/nasbench201/graph.py
+++ b/naslib/search_spaces/nasbench201/graph.py
@@ -188,8 +188,7 @@ def query(
             # return hyperparameter info
             return query_results[dataset]["cost_info"]
         elif metric == Metric.TRAIN_TIME:
-            return query_results[dataset]["cost_info"]["train_time"]
-
+            return query_results[dataset]['cost_info']['train_time'] * epoch
         if full_lc and epoch == -1:
             return query_results[dataset][metric_to_nb201[metric]]
         elif full_lc and epoch != -1:

From 9faed3bcdc9871fc6e7111e1c6c9d421dfee0aab Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 13 Jun 2022 11:53:21 +0200
Subject: [PATCH 114/130] adapt train time

---
 examples/optimizers.ipynb                    | 175 +++--
 naslib/benchmarks/mf/create_configs.py       | 690 ++++++-------------
 naslib/optimizers/discrete/bohb/optimizer.py |   5 +-
 naslib/optimizers/discrete/dehb/optimizer.py |  11 +-
 naslib/optimizers/discrete/hb/optimizer.py   |   5 +-
 naslib/optimizers/discrete/sh/optimizer.py   |  25 +-
 6 files changed, 353 insertions(+), 558 deletions(-)

diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index 4cccb4de0..be501784d 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -22,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -360,32 +351,32 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " saving ImageNet16-120\n",
+      " saving cifar10\n",
       "predictor: ls\n",
-      "first mean 0.1281666671702067 last mean 0.027066667521158848\n",
-      "first std 0.042031271577528125 last std 0.0006110104655917387\n",
-      "time  5713274.493876268\n",
+      "first mean 0.04324999999999997 last mean 0.000870000000000047\n",
+      "first std 0.021849313490359377 last std 0.000946625585963078\n",
+      "time  891204.295082887\n",
       "predictor: bananas\n",
-      "first mean 0.1281666671702067 last mean 0.03093333388264974\n",
-      "first std 0.042031271577528125 last std 0.00320693704693154\n",
-      "time  5093832.737680004\n",
+      "first mean 0.04324999999999997 last mean 0.003050000000000012\n",
+      "first std 0.021849313490359377 last std 0.0017024981644630083\n",
+      "time  876740.4151002569\n",
       "predictor: re\n",
-      "first mean 0.1281666671702067 last mean 0.026666667277018226\n",
-      "first std 0.042031271577528125 last std 6.938893903907228e-18\n",
-      "time  6223506.154484801\n",
+      "first mean 0.04324999999999997 last mean 0.0004000000000000625\n",
+      "first std 0.021849313490359377 last std 0.0\n",
+      "time  968633.8430881497\n",
       "predictor: rs\n",
-      "first mean 0.1281666671702067 last mean 0.035033333760579426\n",
-      "first std 0.042031271577528125 last std 0.004188476714839849\n",
-      "time  4552233.556291293\n"
+      "first mean 0.04324999999999997 last mean 0.005819999999999979\n",
+      "first std 0.021849313490359377 last std 0.0023714130808444043\n",
+      "time  761624.9727487558\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3789807649.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/3789807649.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
       "  output = {key: np.array(item) for key,item in output.items()}\n",
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3789807649.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/3789807649.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
       "  time = {key: np.array(item) for key,item in time.items()}\n"
      ]
     },
@@ -393,6 +384,82 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "predictor: bohb\n",
+      "first mean 0.24727200008300776 last mean 0.0013320001391601579\n",
+      "first std 0.11420437960697061 last std 0.0007157206395425516\n",
+      "time  1102489.6275617096\n",
+      "predictor: dehb\n",
+      "first mean 0.3576000000567627 last mean 0.00031600016113279145\n",
+      "first std 0.14031822252285087 last std 0.0007581451321007654\n",
+      "time  1102614.4726861517\n",
+      "predictor: hb\n",
+      "first mean 0.43363200006103514 last mean 0.003290000000000006\n",
+      "first std 0.08783890716983626 last std 0.0018619613314996453\n",
+      "time  1101775.6664992971\n",
+      "predictor: sh\n",
+      "first mean 0.43363200006103514 last mean 0.0010760001538085648\n",
+      "first std 0.08783890716983626 last std 0.0005408733542267523\n",
+      "time  1100574.386521101\n",
+      "predictor: npenas\n",
+      "first mean 0.04324999999999997 last mean 0.0005900000000000603\n",
+      "first std 0.021849313490359377 last std 0.0005699999999999931\n",
+      "time  946390.188622475\n",
+      "\n",
+      " saving cifar100\n",
+      "predictor: ls\n",
+      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
+      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
+      "time  1870579.6545982368\n",
+      "predictor: bananas\n",
+      "first mean 0.07454000036010736 last mean 0.004780000341796821\n",
+      "first std 0.028682370761335663 last std 0.002698444125281455\n",
+      "time  1858928.1182050698\n",
+      "predictor: re\n",
+      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
+      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
+      "time  2024720.5091238003\n",
+      "predictor: rs\n",
+      "first mean 0.07454000036010736 last mean 0.010660000280761651\n",
+      "first std 0.028682370761335663 last std 0.00712168501714301\n",
+      "time  1509874.2697715748\n",
+      "predictor: bohb\n",
+      "first mean 0.5971599999999999 last mean 0.004929999999999936\n",
+      "first std 0.05310638756307945 last std 0.003036461756716197\n",
+      "time  2003797.8242489465\n",
+      "predictor: dehb\n",
+      "first mean 0.43855999999999995 last mean 0.005079999999999941\n",
+      "first std 0.10679414965249734 last std 0.001905150912657582\n",
+      "time  2003974.1317677882\n",
+      "predictor: hb\n",
+      "first mean 0.5971599999999999 last mean 0.004280000427246052\n",
+      "first std 0.05310638756307945 last std 0.0030925073913141074\n",
+      "time  2001546.2954940004\n",
+      "predictor: sh\n",
+      "first mean 0.50251 last mean 0.0046399999999999445\n",
+      "first std 0.08918686506431316 last std 0.0022415173432298262\n",
+      "time  2002205.9176952029\n",
+      "predictor: npenas\n",
+      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
+      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
+      "time  2072520.2831745134\n",
+      "\n",
+      " saving ImageNet16-120\n",
+      "predictor: ls\n",
+      "first mean 0.1281666671702067 last mean 0.027066667521158848\n",
+      "first std 0.042031271577528125 last std 0.0006110104655917387\n",
+      "time  5713274.493876268\n",
+      "predictor: bananas\n",
+      "first mean 0.1281666671702067 last mean 0.03093333388264974\n",
+      "first std 0.042031271577528125 last std 0.00320693704693154\n",
+      "time  5093832.737680004\n",
+      "predictor: re\n",
+      "first mean 0.1281666671702067 last mean 0.026666667277018226\n",
+      "first std 0.042031271577528125 last std 6.938893903907228e-18\n",
+      "time  6223506.154484801\n",
+      "predictor: rs\n",
+      "first mean 0.1281666671702067 last mean 0.035033333760579426\n",
+      "first std 0.042031271577528125 last std 0.004188476714839849\n",
+      "time  4552233.556291293\n",
       "predictor: bohb\n",
       "first mean 0.40754999999380115 last mean 0.03556666660563151\n",
       "first std 0.042707405685386175 last std 0.00217332313519604\n",
@@ -421,7 +488,7 @@
        "<function print>"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -449,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,21 +527,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_14318/3772773774.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/1044284299.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
       "Invalid limit will be ignored.\n",
       "  ax.set_ylim([-0.2, 1e-1])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
+      "text/plain": [
+       "<Figure size 324x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 324x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -540,7 +631,7 @@
     "        ax.set_yscale('log')\n",
     "        ax.set_ylim([2e-4, 1e-2])\n",
     "        ax.set_xlim([2.0e5, 7.6*1e5])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
@@ -549,7 +640,7 @@
     "        ax.set_yscale('log')\n",
     "        ax.set_ylim([0.0020, 0.3])\n",
     "        ax.set_xlim([1e4, 1e6])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
@@ -559,7 +650,7 @@
     "        ax.set_ylim([-0.2, 1e-1])\n",
     "        # ax.set_ylim([0.0025, 0.3])\n",
     "        ax.set_xlim([1.0e5, 5e6])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
     "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index a2d7a35ed..d2cd9fec1 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -3,37 +3,30 @@
 import random
 from venv import create
 import yaml
-
+from glob import glob
 import numpy as np
+from addict import Dict
+import itertools
+
+# pycodestyle --max-line-length=120 naslib/benchmarks/mf/create_configs.py
 
+# TODO: add max_budget
+# TODO: add fidelity
+# TODO: add budget
 
 def create_configs(
-    config_type: str,
     start_seed: int = 0,
     trials: int = 100,
     optimizer: str = "rs",
-    predictor_type: str = "full",
     predictor: str = "xgb",
-    test_size: int = 30,
-    uniform_random: int = 1, 
-    train_size_single:int = 10,
-    fidelity_single: int = 5,
     fidelity: int = 200,
     acq_fn_optimization: str = "mutation",
     dataset: str = "cifar10",
     out_dir: str = "run",
     checkpoint_freq: int = 5000,
     epochs: int = 150,
+    budget: int = 1000,
     search_space: str = "nasbench201",
-    experiment_type: str = "single",
-    run_acc_stats: int = 1,
-    max_set_size: int = 10000,
-    run_nbhd_size: int = 1,
-    max_nbhd_trials: int = 1000,
-    run_autocorr: int = 1, 
-    max_autocorr_trials: int = 10,
-    autocorr_size: int = 36,
-    walks: int = 1000,
     HPO: bool = False, 
     num_config: int = 100,
     **kwargs
@@ -44,27 +37,12 @@ def create_configs(
         start_seed: starting seed.
         trials: Number of trials.
         optimizer: which optimizer.
-        predictor_type: which predictor.
         predictor: which predictor.
-        test_size: Test set size for predictor.
-        uniform_random: Train/test set generation type (bool).
-        train_size_single: Train size if exp type is single.
-        fidelity_single: Fidelity if exp type is single.
         fidelity: Fidelity.
         acq_fn_optimization: acq_fn.
         dataset: Which dataset.
         epochs: How many search epochs.
-        config_type: nas or predictor?.
         search_space: nasbench201 or darts?.
-        experiment_type: type of experiment.
-        run_acc_stats: run accuracy statistics.
-        max_set_size: size of val_acc stat computation.
-        run_nbhd_size: run experiment to compute nbhd size.
-        max_nbhd_trials: size of nbhd size computation. 
-        run_autocorr: run experiment to compute autocorrelation.
-        max_autocorr_trials: number of autocorrelation trials.
-        autocorr_size: size of autocorrelation to test.
-        walks: number of random walks.
         HPO: Hyperparameter Optimisation enabled/disabled.
         num_config: Number of configs explored by HPO (Random Search).
 
@@ -72,21 +50,109 @@ def create_configs(
         No return.
 
     """
-    if config_type == 'bbo-bs':
-        start_seed = int(start_seed)
-        trials = int(trials)
+    start_seed = int(start_seed)
+    trials = int(trials)
+    
+    # first generate the default config at config 0
+    config_id = 0
+    # TODO: add config out dir to config
+    folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+    filename = f"./configs/{search_space}_{dataset}_{optimizer}_config_{config_id}"
+    # folder = f"naslib/benchmarks/bbo/configs_cpu/{search_space}/{dataset}/{optimizer}/config_{config_id}"
+    os.makedirs(folder, exist_ok=True)       
         
-        # first generate the default config at config 0
-        config_id = 0
-        # TODO: add config out dir to config
+    for seed in range(start_seed, start_seed + trials):
+        # np.random.seed(seed)
+        # random.seed(seed)
+
+        config = {
+            "seed": seed,
+            "search_space": search_space,
+            "dataset": dataset,
+            "optimizer": optimizer,
+            "out_dir": out_dir,
+            "config_id": config_id,
+            "search": {
+                "sample_size": 10,
+                "population_size": 50,
+                "num_init": 10,
+                "k":10,
+                "num_ensemble": 3,
+                "acq_fn_type": "its",
+                "num_arches_to_mutate": 1,
+                "max_mutations": 1,
+                "num_candidates": 50,
+                "checkpoint_freq": checkpoint_freq,
+                "epochs": epochs,
+                "fidelity": fidelity,
+                "min_fidelity": 1,
+                "number_archs": 128,
+                "budget_type": "epoch",
+                "budget_max": 128,
+                "method": "random",
+                "eta": 2,
+                "max_budget": 200,
+                "min_budget": 1,
+                "n_process": 1000,
+                "epsilon": 1e-6,
+                "num_ensemble": 3,
+                "acq_fn_type": "its",
+                "acq_fn_optimization": acq_fn_optimization,
+                "encoding_type": "path",
+                "predictor": predictor,
+                "debug_predictor": False,
+                # config secton for successive halving/ hyperband,
+                "min_budget": 1,
+                "max_budget": 200,
+                "fidelity": 200,
+                "n_process": 1_000_000,
+                "budgets": 360_000, 
+                # config section for bohb
+                "min_bandwith": 0.001,
+                "top_n_percent": 0.1,
+                "min_points_in_model": 7,
+                # config section for dehb
+                "enc_dim": 6,
+                "max_mutations": 1,
+                "crossover_prob": 0.5,
+                "mutate_prob": 0.5,
+            },
+        }
+        # path = os.path.join(filename, f"seed_{seed}.yaml")
+        path = filename + f"_{seed}.yaml"
+
+        with open(path, "w") as fh:
+            yaml.dump(config, fh)
+    num_config = num_config if HPO else 1
+    for config_id in range(1, num_config):
         folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-        # folder = f"naslib/benchmarks/bbo/configs_cpu/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-        os.makedirs(folder, exist_ok=True)       
-            
+        filename = f"./configs/{search_space}_{dataset}_{optimizer}_config_{config_id}"
+
+        os.makedirs(folder, exist_ok=True)
+
+        # SH/HB
+        max_fidelity = 200 # int(np.random.choice(range(100, 200)))
+        min_budget = int(np.random.choice(range(1, 50)))
+        eta = int(np.random.choice(range(2, 5)))
+        # BOHB
+        min_bandwith = float(np.random.choice(np.arange(0.0, 0.011, 0.001)))
+        top_n_percent = float(np.random.choice(np.arange(0.05, 0.31, 0.01)))
+        # DEHB
+        max_mutations = int(np.random.choice(range(1, 5)))
+        crossover_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
+        mutate_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
+
         for seed in range(start_seed, start_seed + trials):
             # np.random.seed(seed)
             # random.seed(seed)
-
+            # TODO: max_fidelity should be dependent on eta and min_fidelity
+            # fidelity_range = [2**i for i in range(0, 9)]
+            # max_fidelity = int(np.random.choice(fidelity_range))
+            # min fidelity has to be lower/equal to max_fidelity
+            # min_fidelity = int(np.random.choice(
+            #     list(
+            #         filter(
+            #             lambda n: n <= max_fidelity, fidelity_range))))
             config = {
                 "seed": seed,
                 "search_space": search_space,
@@ -95,478 +161,112 @@ def create_configs(
                 "out_dir": out_dir,
                 "config_id": config_id,
                 "search": {
-                    "sample_size": 10,
-                    "population_size": 50,
-                    "num_init": 10,
-                    "k":10,
-                    "num_ensemble": 3,
-                    "acq_fn_type": "its",
-                    "num_arches_to_mutate": 1,
-                    "max_mutations": 1,
-                    "num_candidates": 50,
                     "checkpoint_freq": checkpoint_freq,
                     "epochs": epochs,
                     "fidelity": fidelity,
-                    "min_fidelity": 1,
-                    "number_archs": 128,
-                    "budget_type": "epoch",
-                    "budget_max": 128,
-                    "method": "random",
-                    "eta": 2,
-                    "max_budget": 200,
-                    "min_budget": 1,
-                    "n_process": 1000,
-                    "epsilon": 1e-6,
+                    "sample_size": int(np.random.choice(range(5, 100))),
+                    "population_size": int(np.random.choice(range(5, 100))),
+                    "num_init": int(np.random.choice(range(5, 100))),
+                    "k":int(np.random.choice(range(10, 50))),
                     "num_ensemble": 3,
                     "acq_fn_type": "its",
                     "acq_fn_optimization": acq_fn_optimization,
                     "encoding_type": "path",
+                    "num_arches_to_mutate": int(np.random.choice(range(1, 20))),
+                    "max_mutations": int(np.random.choice(range(1, 20))),
+                    "num_candidates": int(np.random.choice(range(5, 50))),
                     "predictor": predictor,
                     "debug_predictor": False,
-                    # config secton for successive halving/ hyperband,
-                    "min_budget": 1,
-                    "max_budget": 200,
+                    # config section for successive halving,
+                    # config secton for Hyperband,
+                    "min_budget": min_budget,
+                    "max_budget": max_fidelity,
                     "fidelity": 200,
                     "n_process": 1_000_000,
                     "budgets": 360_000, 
-                    # config section for bohb
-                    "min_bandwith": 0.001,
-                    "top_n_percent": 0.1,
+                    "eta": eta,
+                    "epsilon": 1e-6,
+                    # config section for BOHB
+                    "min_bandwith": min_bandwith,
+                    "top_n_percent": top_n_percent,
                     "min_points_in_model": 7,
+                    # config section for DEHB
                     # config section for dehb
                     "enc_dim": 6,
-                    "max_mutations": 1,
-                    "crossover_prob": 0.5,
-                    "mutate_prob": 0.5,
+                    "max_mutations": max_mutations,
+                    "crossover_prob": crossover_prob,
+                    "mutate_prob": mutate_prob,
                 },
             }
-            path = os.path.join(folder, f"seed_{seed}.yaml")
+            print(f"folder: {folder}")
+            # path = os.path.join(filename, f"seed_{seed}.yaml")
+            path = filename + f"_{seed}.yaml"
+            print(f"path: {path}")
             with open(path, "w") as fh:
                 yaml.dump(config, fh)
-        num_config = num_config if HPO else 1
-        for config_id in range(1, num_config):
-            folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-            os.makedirs(folder, exist_ok=True)
-
-            # SH/HB
-            max_fidelity = 200 # int(np.random.choice(range(100, 200)))
-            min_budget = int(np.random.choice(range(1, 50)))
-            eta = int(np.random.choice(range(2, 5)))
-            # BOHB
-            min_bandwith = float(np.random.choice(np.arange(0.0, 0.011, 0.001)))
-            top_n_percent = float(np.random.choice(np.arange(0.05, 0.31, 0.01)))
-            # DEHB
-            max_mutations = int(np.random.choice(range(1, 5)))
-            crossover_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
-            mutate_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
-
-            for seed in range(start_seed, start_seed + trials):
-                # np.random.seed(seed)
-                # random.seed(seed)
-                # TODO: max_fidelity should be dependent on eta and min_fidelity
-                # fidelity_range = [2**i for i in range(0, 9)]
-                # max_fidelity = int(np.random.choice(fidelity_range))
-                # min fidelity has to be lower/equal to max_fidelity
-                # min_fidelity = int(np.random.choice(
-                #     list(
-                #         filter(
-                #             lambda n: n <= max_fidelity, fidelity_range))))
-                config = {
-                    "seed": seed,
-                    "search_space": search_space,
-                    "dataset": dataset,
-                    "optimizer": optimizer,
-                    "out_dir": out_dir,
-                    "config_id": config_id,
-                    "search": {
-                        "checkpoint_freq": checkpoint_freq,
-                        "epochs": epochs,
-                        "fidelity": fidelity,
-                        "sample_size": int(np.random.choice(range(5, 100))),
-                        "population_size": int(np.random.choice(range(5, 100))),
-                        "num_init": int(np.random.choice(range(5, 100))),
-                        "k":int(np.random.choice(range(10, 50))),
-                        "num_ensemble": 3,
-                        "acq_fn_type": "its",
-                        "acq_fn_optimization": acq_fn_optimization,
-                        "encoding_type": "path",
-                        "num_arches_to_mutate": int(np.random.choice(range(1, 20))),
-                        "max_mutations": int(np.random.choice(range(1, 20))),
-                        "num_candidates": int(np.random.choice(range(5, 50))),
-                        "predictor": predictor,
-                        "debug_predictor": False,
-                        # config section for successive halving,
-                        # config secton for Hyperband,
-                        "min_budget": min_budget,
-                        "max_budget": max_fidelity,
-                        "fidelity": 200,
-                        "n_process": 1_000_000,
-                        "budgets": 360_000, 
-                        "eta": eta,
-                        "epsilon": 1e-6,
-                        # config section for BOHB
-                        "min_bandwith": min_bandwith,
-                        "top_n_percent": top_n_percent,
-                        "min_points_in_model": 7,
-                        # config section for DEHB
-                        # config section for dehb
-                        "enc_dim": 6,
-                        "max_mutations": max_mutations,
-                        "crossover_prob": crossover_prob,
-                        "mutate_prob": mutate_prob,
-                    },
-                }
-                print(f"folder: {folder}")
-                path = os.path.join(folder, f"seed_{seed}.yaml")
-                print(f"path: {path}")
-                with open(path, "w") as fh:
-                    yaml.dump(config, fh)
-    
-    elif config_type == "predictor-bs":
-        folder = f"naslib/benchmarks/predictors-bs/configs_{search_space}/{dataset}"
-        os.makedirs(folder, exist_ok=True)
-        start_seed = int(start_seed)
-        trials = int(trials)
-
-        if search_space == "nasbench101":
-            total_epochs = 108 - 1
-            max_train_size = 1000
-        elif search_space == "nasbench201":
-            total_epochs = 200 - 1
-            max_train_size = 1000
-        elif search_space == "darts":
-            total_epochs = 96 - 1
-            max_train_size = 500
-        elif search_space == "nlp":
-            total_epochs = 50 - 1
-            max_train_size = 1000
-
-        train_size_list = [
-            int(j)
-            for j in np.logspace(
-                start=np.log(5.1) / np.log(2),
-                stop=np.log(max_train_size) / np.log(2),
-                num=11,
-                endpoint=True,
-                base=2.0,
-            )
-        ]
-        # train_size_list = [i for i in train_size_list if i < 230]
-        fidelity_list = [
-            int(j)
-            for j in np.logspace(
-                start=0.9,
-                stop=np.log(total_epochs) / np.log(2),
-                num=15,
-                endpoint=True,
-                base=2.0,
-            )
-        ]
-
-        if search_space == "nlp":
-            fidelity_list.pop(2)
-            fidelity_list.insert(5, 6)
-
-        if "svr" in predictor:
-            train_size_list.pop(0)
-            fidelity_list.pop(0)
-            fidelity_list.pop(0)
-
-        for i in range(start_seed, start_seed + trials):
-            config = {
-                "seed": i,
-                "search_space": search_space,
-                "dataset": dataset,
-                "out_dir": out_dir,
-                "predictor": predictor,
-                "test_size": test_size,
-                "uniform_random": uniform_random,
-                "experiment_type": experiment_type,
-                "train_size_list": train_size_list,
-                "train_size_single": train_size_single,
-                "fidelity_single": fidelity_single,
-                "fidelity_list": fidelity_list,
-                "max_hpo_time": 900,
-            }
-
-            with open(folder + f"/config_{predictor}_{i}.yaml", "w") as fh:
-                yaml.dump(config, fh)
-
-    elif config_type == "nas":
-        folder = f"{out_dir}/{dataset}/configs/nas"
-        os.makedirs(folder, exist_ok=True)
-        start_seed = int(start_seed)
-        trials = int(trials)
-
-        for i in range(start_seed, start_seed + trials):
-            config = {
-                "seed": i,
-                "search_space": search_space,
-                "dataset": dataset,
-                "optimizer": optimizer,
-                "out_dir": out_dir,
-                "search": {
-                    "checkpoint_freq": checkpoint_freq,
-                    "epochs": epochs,
-                    "fidelity": 200,
-                    "sample_size": 10,
-                    "population_size": 30,
-                    "num_init": 10,
-                    "k": 25,
-                    "num_ensemble": 3,
-                    "acq_fn_type": "its",
-                    "acq_fn_optimization": "mutation",
-                    "encoding_type": "path",
-                    "num_arches_to_mutate": 2,
-                    "max_mutations": 1,
-                    "num_candidates": 100,
-                    "predictor_type": "feedforward",
-                    "debug_predictor": False,
-                },
-            }
-
-            if optimizer == "lcsvr" and experiment_type == "vary_fidelity":
-                path = folder + f"/config_{optimizer}_train_{i}.yaml"
-            if optimizer == "lcsvr" and experiment_type == "vary_train_size":
-                path = folder + f"/config_{optimizer}_fidelity_{i}.yaml"
-            else:
-                path = folder + f"/config_{optimizer}_{i}.yaml"
-
-            with open(path, "w") as fh:
-                yaml.dump(config, fh)
-
-    elif config_type == "predictor":
-        folder = f"{out_dir}/{dataset}/configs/predictors"
-        os.makedirs(folder, exist_ok=True)
-        start_seed = int(start_seed)
-        trials = int(trials)
-
-        if search_space == "nasbench101":
-            total_epochs = 108 - 1
-            max_train_size = 1000
-        elif search_space == "nasbench201":
-            total_epochs = 200 - 1
-            max_train_size = 1000
-        elif search_space == "darts":
-            total_epochs = 96 - 1
-            max_train_size = 500
-        elif search_space == "nlp":
-            total_epochs = 50 - 1
-            max_train_size = 1000
-
-        train_size_list = [
-            int(j)
-            for j in np.logspace(
-                start=np.log(5.1) / np.log(2),
-                stop=np.log(max_train_size) / np.log(2),
-                num=11,
-                endpoint=True,
-                base=2.0,
-            )
-        ]
-        # train_size_list = [i for i in train_size_list if i < 230]
-        fidelity_list = [
-            int(j)
-            for j in np.logspace(
-                start=0.9,
-                stop=np.log(total_epochs) / np.log(2),
-                num=15,
-                endpoint=True,
-                base=2.0,
-            )
-        ]
-
-        if search_space == "nlp":
-            fidelity_list.pop(2)
-            fidelity_list.insert(5, 6)
-
-        if "svr" in predictor:
-            train_size_list.pop(0)
-            fidelity_list.pop(0)
-            fidelity_list.pop(0)
-
-        elif "omni" in predictor and search_space != "darts":
-            train_size_list.pop(0)
-            train_size_list.pop(-1)
-            fidelity_list.pop(1)
-
-        elif "omni" in predictor and search_space == "darts":
-            train_size_list.pop(0)
-            train_size_list.pop(-1)
-            fidelity_list.pop(1)
-            fidelity_list.pop(1)
-
-        for i in range(start_seed, start_seed + trials):
-            config = {
-                "seed": i,
-                "search_space": search_space,
-                "dataset": dataset,
-                "out_dir": out_dir,
-                "predictor": predictor,
-                "test_size": test_size,
-                "uniform_random": uniform_random,
-                "experiment_type": experiment_type,
-                "train_size_list": train_size_list,
-                "train_size_single": train_size_single,
-                "fidelity_single": fidelity_single,
-                "fidelity_list": fidelity_list,
-                "max_hpo_time": 900,
-            }
-
-            with open(folder + f"/config_{predictor}_{i}.yaml", "w") as fh:
-                yaml.dump(config, fh)
-
-    elif config_type == "nas_predictor":
-        folder = f"{out_dir}/{dataset}/configs/nas_predictors"
-        os.makedirs(folder, exist_ok=True)
-        start_seed = int(start_seed)
-        trials = int(trials)
-
-        for i in range(start_seed, start_seed + trials):
-            config = {
-                "seed": i,
-                "search_space": search_space,
-                "dataset": dataset,
-                "optimizer": optimizer,
-                "out_dir": out_dir,
-                "search": {
-                    "predictor_type": predictor,
-                    "epochs": epochs,
-                    "checkpoint_freq": checkpoint_freq,
-                    "fidelity": 200,
-                    "sample_size": 10,
-                    "population_size": 30,
-                    "num_init": 20,
-                    "k": 20,
-                    "num_ensemble": 3,
-                    "acq_fn_type": "its",
-                    "acq_fn_optimization": "random_sampling",
-                    "encoding_type": "adjacency_one_hot",
-                    "num_arches_to_mutate": 5,
-                    "max_mutations": 1,
-                    "num_candidates": 200,
-                    "batch_size": 256,
-                    "data_size": 25000,
-                    "cutout": False,
-                    "cutout_length": 16,
-                    "cutout_prob": 1.0,
-                    "train_portion": 0.7,
-                },
-            }
-
-            path = folder + f"/config_{optimizer}_{predictor}_{i}.yaml"
-            with open(path, "w") as fh:
-                yaml.dump(config, fh)
-
-    elif config_type == "statistics":
-        folder = f"{out_dir}/{search_space}/{dataset}/configs/statistics"
-        os.makedirs(folder, exist_ok=True)
-        start_seed = int(start_seed)
-        trials = int(trials)
-
-        for i in range(start_seed, start_seed + trials):
-            config = {
-                "seed": i,
-                "search_space": search_space,
-                "dataset": dataset,
-                "out_dir": out_dir,
-                "run_acc_stats": run_acc_stats,
-                "max_set_size": max_set_size,
-                "run_nbhd_size": run_nbhd_size,
-                "max_nbhd_trials": max_nbhd_trials,
-                "run_autocorr": run_autocorr,
-                "max_autocorr_trials": max_autocorr_trials,
-                "autocorr_size": autocorr_size,
-                "walks": walks,
-            }
-
-            with open(folder + f"/config_{i}.yaml", "w") as fh:
-                yaml.dump(config, fh)
-
-    else:
-        print("invalid config type in create_configs.py")
 
 if __name__ == "__main__":
-    """This is executed when run from the command line"""
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--start_seed", type=int, default=0, help="starting seed")
-    parser.add_argument("--trials", type=int, default=100, help="Number of trials")
-    parser.add_argument("--optimizer", type=str, default="rs", help="which optimizer")
-    parser.add_argument("--predictor_type", type=str, default="full", help="which predictor")
-    parser.add_argument("--predictor", type=str, default="xgb", help="which predictor")
-    parser.add_argument(
-        "--test_size", type=int, default=30, help="Test set size for predictor"
-    )
-    parser.add_argument(
-        "--uniform_random",
-        type=int,
-        default=1,
-        help="Train/test set generation type (bool)",
-    )
-    parser.add_argument(
-        "--train_size_single",
-        type=int,
-        default=10,
-        help="Train size if exp type is single",
-    )
-    parser.add_argument(
-        "--fidelity_single", type=int, default=5, help="Fidelity if exp type is single"
-    )
-    parser.add_argument(
-        "--fidelity", type=int, default=200, help="Fidelity"
-    )
-    parser.add_argument(
-        "--acq_fn_optimization", type=str, default="mutation", help="acq_fn"
-    )
-    parser.add_argument("--dataset", type=str, default="cifar10", help="Which dataset")
-    parser.add_argument("--out_dir", type=str, default="run", help="Output directory")
-    parser.add_argument(
-        "--checkpoint_freq", type=int, default=5000, help="How often to checkpoint"
-    )
-    parser.add_argument(
-        "--epochs", type=int, default=150, help="How many search epochs"
-    )
-    parser.add_argument(
-        "--config_type", type=str, default="nas", help="nas or predictor?"
-    )
-    parser.add_argument(
-        "--search_space", type=str, default="nasbench201", help="nasbench201 or darts?"
-    )
-    parser.add_argument(
-        "--experiment_type", type=str, default="single", help="type of experiment"
-    )
-    parser.add_argument(
-        "--run_acc_stats", type=int, default=1, help="run accuracy statistics"
-    )
-    parser.add_argument(
-        "--max_set_size", type=int, default=10000, help="size of val_acc stat computation"
-    )    
-    parser.add_argument(
-        "--run_nbhd_size", type=int, default=1, help="run experiment to compute nbhd size"
-    )    
-    parser.add_argument(
-        "--max_nbhd_trials", type=int, default=1000, help="size of nbhd size computation"
-    )
-    parser.add_argument(
-        "--run_autocorr", type=int, default=1, help="run experiment to compute autocorrelation"
-    )
-    parser.add_argument(
-        "--max_autocorr_trials", type=int, default=10, help="number of autocorrelation trials"
-    )
-    parser.add_argument(
-        "--autocorr_size", type=int, default=36, help="size of autocorrelation to test"
-    )
-    parser.add_argument(
-        "--walks", type=int, default=1000, help="number of random walks"
-    )
-    parser.add_argument(
-        "--HPO", type=bool, default=False, help="Optimisation enabled/disabled"
-    )
-    parser.add_argument(
-        "--num_config", type=int, default=100, help="Number of configs explored by HPO (Random Search)"
-    )
-    
-    args = parser.parse_args()
-    arguments = vars(args)
-    create_configs(**arguments)
\ No newline at end of file
+    main()
+
+def check_config(search_space, dataset, optimizer, trials, HPO, num_config):
+    folder = os.path.join(".", "configs", search_space, dataset, optimizer)
+    test = os.path.join(folder, "**", "*.yaml")  
+    files = glob(test, recursive=True)
+    print(f"{len(files)} files")
+    num_config = num_config if HPO else 1
+    if len(files) == trials * num_config:
+        print("Created config file(s) successfully")
+        return
+    print("Config file(s) not successfully created")
+    exit(1)
+
+def main():
+    with open("config.yaml", 'r') as stream:
+        config = yaml.safe_load(stream)
+    # convert such that config elements are accessiable via attributes
+    config = Dict(config)
+    start_seed = config.start_seed if config.start_seed else 0
+    trials = config.trials
+    end_seed = start_seed + trials - 1
+
+    optimizers = config.optimizers
+    # TODO: Implement check for optimizers
+    # See lines 13 - 18 in make_configs_nb201.sh
+
+    out_dir = config.out_dir
+
+    config_type = config.config_type
+    if config_type not in {'bbo-bs', 'predictor-bs'}:
+        print('Invalid config')
+        print('config_type either bbo-bs or predictor-bs')
+        exit(1)
+
+    search_space = config.search_space
+
+    datasets = config.datasets
+    fidelity = config.fidelity
+    epochs = config.epochs
+    budget = config.budget
+    predictor = config.predictor
+
+    HPO = config.HPO
+    num_config = config.num_config
+
+    for dataset, optimizer in itertools.product(datasets, optimizers):
+        print(f"Creating config for dataset: {dataset}: & optimizer: {optimizer}")
+        create_configs(
+            start_seed=start_seed,
+            trials=trials,
+            out_dir=out_dir,
+            dataset=dataset,
+            config_type=config_type,
+            search_space=search_space,
+            optimizer=optimizer,
+            predictor=predictor,
+            fidelity=fidelity,
+            epochs=epochs,
+            budget=budget,
+            HPO=HPO,
+            num_config=num_config
+        )
+        check_config(search_space, dataset, optimizer, trials, HPO, num_config)
\ No newline at end of file
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index c5d438765..146cd0cc9 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -262,11 +262,14 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API
+            train_time, 
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index f1d0f6a04..7fe6a2068 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -191,11 +191,14 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = train_time - previous_train_time
         return (
-            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1),
-            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            train_time, 
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 49bfb3372..d205ec008 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -148,11 +148,14 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
             best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch), # DONE: Maybe we have to solve this directly in benchmark API 
+            train_time, 
         )
     
     def test_statistics(self):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index cfd2d9e01..181420e30 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,7 +1,6 @@
 import collections
 import logging
 import math
-from re import I
 import torch
 import copy
 import numpy as np
@@ -42,7 +41,7 @@ class  SuccessiveHalving(MetaOptimizer):
     # training the models is not implemented
     using_step_function = False
     
-    def __init__(self, config, hash_function = convert_naslib_to_str): 
+    def __init__(self, config):
         super().__init__()
         # Hyperband related stuff
         self.config = config
@@ -52,20 +51,20 @@ def __init__(self, config, hash_function = convert_naslib_to_str):
         self.min_budget = self.config.search.min_budget
         self.eta = self.config.search.eta 
         self._epsilon = float(self.config.search.epsilon) 
-        self.hash_function = hash_function
+        
         times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta)  + self._epsilon )
         # set up round sizes, fidelities, and list of arches
         
         n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (times_of_split + 1) + self._epsilon) # initial number of configurations
-        r = self.max_budget / self.eta**times_of_split # initial number of iterations to run configurations for
-        for i in range(times_of_split):
+        r = int(self.max_budget / self.eta**times_of_split) # initial number of iterations to run configurations for
+        for i in range(times_of_split + 1):
            
+            self.round_sizes.append(n)
+            self.fidelities.append(r)
             # n= 2*3 ** 1/2  for i in range(s + 1):
             n = math.floor(n / self.eta)
             # TODO: maybe this can be replaced by search space get_max_epoch()
             r = min(math.floor(r * self.eta), config.search.fidelity)
-            self.round_sizes.append(n)
-            self.fidelities.append(r)
         
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
@@ -170,18 +169,14 @@ def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        models = [x for x in self.history if x.arch.get_hash() == latest_arch.get_hash() and x.epoch < self.history[-1].epoch]
-        # models = [x for x in self.history if convert_naslib_to_str(x.arch) == convert_naslib_to_str(latest_arch) and x.epoch < self.history[-1].epoch]
-        train_time_scaled = train_time * latest_arch_epoch
-        if len(models) > 1:
-            train_time_scaled = train_time_scaled - train_time
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
             best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-           best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            train_time_scaled, 
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
+            train_time, 
         )
-        
     
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()

From bceddf79fe61ff3b6f7fe7c1c31a27ee731f6c1e Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 13 Jun 2022 14:41:56 +0200
Subject: [PATCH 115/130] adapt min budget

---
 naslib/optimizers/discrete/bohb/optimizer.py | 1 +
 naslib/optimizers/discrete/dehb/optimizer.py | 1 +
 naslib/optimizers/discrete/sh/optimizer.py   | 2 +-
 3 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 146cd0cc9..16f907e79 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -37,6 +37,7 @@ def __init__(self, config):
         self.eta = self.config.search.eta
         self.min_points_in_model = self.config.search.min_points_in_model
         self.top_n_percent = self.config.search.top_n_percent
+        self.min_budget = min(self.min_budget, self.max_budget)
         s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
         # set up round sizes, fidelities, and list of arches
         for s in reversed(range(s_max + 1)):
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 7fe6a2068..b02c2b5fb 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -39,6 +39,7 @@ def __init__(self, config):
         self.de = dict()
         self.pop_size = {}
         self.counter = 0
+        self.min_budget = min(self.min_budget, self.max_budget)
         s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
         # set up round sizes, fidelities, and list of arches
         for s in reversed(range(s_max + 1)):
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 181420e30..97004bd2b 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -51,7 +51,7 @@ def __init__(self, config):
         self.min_budget = self.config.search.min_budget
         self.eta = self.config.search.eta 
         self._epsilon = float(self.config.search.epsilon) 
-        
+        self.min_budget = min(self.min_budget, self.max_budget)
         times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta)  + self._epsilon )
         # set up round sizes, fidelities, and list of arches
         

From ed96cb5dc267c1e187390d682c7d88099809b4bd Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 13 Jun 2022 15:40:26 +0200
Subject: [PATCH 116/130] fix train time calculation

---
 naslib/optimizers/discrete/bohb/optimizer.py | 2 +-
 naslib/optimizers/discrete/dehb/optimizer.py | 2 +-
 naslib/optimizers/discrete/hb/optimizer.py   | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 16f907e79..27d5f280d 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -264,7 +264,7 @@ def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index b02c2b5fb..96356c3ba 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -193,7 +193,7 @@ def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index d205ec008..9aed4ba7c 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -149,7 +149,7 @@ def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 

From 1abd9d15d7f988e39fcfe295bfac169255028f7a Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Tue, 14 Jun 2022 14:45:59 +0200
Subject: [PATCH 117/130] fix min budget

---
 examples/optimizers.ipynb                  | 19 ++++++++++---------
 naslib/optimizers/discrete/hb/optimizer.py |  2 +-
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index be501784d..50ec92552 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -293,13 +293,14 @@
     "    mean = incumbent_settings[1]\n",
     "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
     "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
+    "    std_error_2 = np.std(output[incumbent_settings[0]], axis=0, ddof=1) / np.sqrt(np.size(output[incumbent_settings[0]]))\n",
     "    time = time[incumbent_settings[0]]\n",
     "    # print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
     "    # print(f\"predictor: {predictor}, best config: {incumbent_settings[0]}\")\n",
     "    print('first mean', mean[0], 'last mean', mean[-1])\n",
     "    print('first std', std[0], 'last std', std[-1])\n",
     "    print('time ', time[-1])\n",
-    "    return mean, std, std_error, time"
+    "    return mean, std, std_error_2, time"
    ]
   },
   {
@@ -374,9 +375,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/3789807649.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/3727768184.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
       "  output = {key: np.array(item) for key,item in output.items()}\n",
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/3789807649.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/3727768184.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
       "  time = {key: np.array(item) for key,item in time.items()}\n"
      ]
     },
@@ -527,21 +528,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_22956/1044284299.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/51809283.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
       "Invalid limit will be ignored.\n",
       "  ax.set_ylim([-0.2, 1e-1])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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QZVt05UvgphQKhSbOStOreQl6SqeO0uvqqHvzm9FnSf0z19CrqlBm9upbxWKkOtrBMy+nm3nPvPzUvYYwFLMIxwqnVMmHbYPXIyyoHb1tfo+Gz6jgWI88SIZtpaTqUo8H1mSrSVQoAL/5E5In4l8sCzZvK67rSQxj8kqe8dGqq6l/q1sfr1jEyJ5WtGAQa3CQxMvby3pelUyWtX+XqTEvhEzSFiwb9LSmQteEKv/UdPvRhMX+3pTTr6lYXO9hQc3cuo2ZZQP6rSHak91529UaVQS1acZ0tC5EffFTqK6e7O3bdyNPbYF4aSeGKbtdp2WtnllSobMbTIuBJx9lwJMqrp/DV8GSRVMbQ9lQBD1BFgRbZuXsousY9fVlP4+dKE6t6zKzzAvDv2/RGnX6B+7gxo1FThRFkjJtEKElj5Dxe2Xmgj8nScSKcdXOz07YziseblvyPtp8i2dgVLNPwkqiMuxVvs9/C33X5BKCK5+X2Bc+WrStaSawlIWOzhFN62d7KGXFTiSoOv441/BfYcytR/BJMuzC7F24mh1DOpZKoZdw3jd0IZKw2duT/TSeMBUrFnipDVSmJ1RA89Fk1NFt9o/bLqlSPB99Zd4ImTFlFU47HrWnPa+qrxCSSOLvj0J1XWkHNw0sZRFJRdk3tBdECBoBtAznCK/hdeOgXMrGvFnJLHrXV3hzWxJDHC/atXWKZn95rj2SsFhYa1AfqlwZ3m8O8XzslTFu2QCmsvlm530AXNH4Gi6tf9VMD69yiCfALMKNfPvuEccG1VDr2KfyEQqgrrgUVi7Nv79MxM04CoWlrCzvwpSVpNnfzIq6mYkjKyfuSqYyqdxZsAzcs2v0SbVGN/nC6gNk2uyVaCR8dVBkqeNC2CnF3o4UeymsnhMNDN/oyWsDOgvrDAQmXaJ5KtQZ1ZxWfVTefXaGkDHV5JwjDjn8PqCIINIlo+UkpHdg/La//ytq5cw6EviN/Oq7hJWYke+by/xlXggZLc/TelAlSXR24tNGn1INO0k4tAZTm0TeLwHRjdxN499Y5Xh1qbQblK0UexIp9muC1xDaaqefWt7wC6EFU/t4NdHQ0LCxXSFTLE0N2O96I7JtHA+qZ19A4glIldY2OD0k72rWxaVUzAsh06pFuC3w95H3mpUiRAqiEMlo57Wi+MJb8Gg+TCOEQsNGIzmO0BFNI9iY6zkzwY9W0i0ymg1nXBlK2PRGxnpc+TQpysYjloWyFHYc8KU/XhGnOuYkAgEN0UkqV8hMijNPRJ15YsHd8v/ugAOd8OQW5B9umsGBFWZ4fbMrX8og0yR0+ukYiybnLedZvJjQ6ae7KyQXYJ4IGRGhPph5qQYwVnDYqgpDmZhWGL9KOKorZZHQ/MQ8LST1scZRM5kEjzYmW0o+MSPp/8bFUPTmpKSxlAJT8PomFjJKPCifD2UJssgRKtI/hPR0ggiWbY2Mzmv48Gn5V016epy/6f87Z3E4rRkZE8bFMiEnTgIFVIeKCro8pKmvhQOdTmYEc24I78if/zyl4/TaWgJHHlni0bjMReaFkNF9fkJt2YWndkQ0epJjn+yVcoyjAG1BiyZPgmC8i6R/EXuSzZgKllVrI09pViyO3roQKcKOYylrQsVErrlYRDDEYDBuUbs4WNTToW0pLFPRtHJUkA47ePh0H4ZmMJQa4sDQfnyBhrx9GP/ng2SSBCn+X/iH/Ns5/0bQM0GyRNOCofDY7e1dTsyJP8e2EQrOqyhwdeVlqL8+hVSQusy0LXTRqfdnr8aHfvc77HB4ymWzD952W1ZdHb25mQU33ojRWOTDisshwyH9Cx92YW5tGQ1CUwrClpC0hf/a7SdiFZ60r1oeZ3GTjuFvokoN8kCHhz/0hfDpwrp6Z8Jc6PXy2pBZVDXEyUStj6KAFFrCQvMm0PPE3mi6TkPrkhEBZFs2ZspmSW1+AQLgS/hoD7cXTFh5yqJT+M3u3wAQNaM8tOshFoWmGGQYTMLiJJAhgEwLunqL7sInHtZ7luOVGfzK2k4cFPUlSinT1ACXnFtRFhDTSiKah/q61Vnb69/+9kn3pSyLvVdd5QgnwOrvH9ln9fez7x/+gerXvnZa453o/C6VxyEtZJRS9wP3r1218pqRbcAXXw6wK1q86sY0QkCI7TFHg52wFM90x0f2r2owWVNb3sSHgsJWinyjjoeHULY9KuhEsC0Y6ovnae0QSSRIDSjiSRPdo+EJZo//vRvfy2mtp/GZv34GgB+/+ONSXcqUOWvxmbx/wzUTNywViSQ89+LMnW+OI7pO8403Ev3b35xs02nC//d/I38PPfTQbAxtDE8++eQCwzC+A2ygcpKVz1VsYItpmlcfd9xxnbk7D2khkw9N4DNro3TE8/mcZVPvyVYVnLsgxe87LZKaozbqiDqeab/bF8NnGKyomb5XWCE0XVC6gZaOJs1Sm0kMK5UCFJpuoGkCKHoP5FFdpYmk4sQGbDTdxPBr1K0YO/Y19WtYWbuSVwZeKfHVTI0dQzszsjjPAEpR0ujdCqTUxvnAhg0ENmzI2uZZuJDBBx6oqJWGYRjfWbhw4brm5uY+TdMqaXE557BtW7q6uo7o6Oj4DnBJ7v55IWRyA051gdbA5HXNr25OcX5dP121J4BofPrvHQwmbV7oT3Bk2F9WIWMDW3qclYlPFzY0+tGGJwgROne/gqYbtKxYiaYb+EPjT8ZWKomREjx64Yc4TTT++bR/pj3SPqturve8eA+PdTxGOFlYaJYFkQkdBQ8Fyv3Z1l56KbWXXlrWc0A6d9nxxxXbfIMrYEqDpmmqubl5oKOjY0O+/fNCyFTH97BwxyY6Vl057b4UzioB4LylVTy4awhTKYZNJbuHknxvax+2Urx7XT2raqdeDTKT+gzPsr6ExWDSHjknaYN8PBrBF01RFdTwjiM8chkv6YOu6Sypnt06IAuCTqXNvkQf7/n1e8bst2yLhaGFBDwB6nx1vOuId9FQwKFhUmhpIdM/WFx7pRxnhplcbU0T5ys0L+dZzRUwpSN9L/NOOvNCyAAsjz1OB9MXMjAavnZWaxVntVZl7bNsRW/CUQskrfJ8h3WB7f1jM84mEyn2tQ+ytqWGJbUTB5TOlTiGhaHRaPqYGcvbZs/QnpG/28PtXL728kmfp9pbzeq61aP3xTBg42FgF/k5dnTCQHhOCRkXl3Izb4RMvz3qIbSq/X8wrCgDocPoqD99Uv0IynnwKzA/axkT9//tDfN45+ikWO/TuXhFzcj73rjJA7uGAPDrwrlLq6jy6HgnsAPUePM7LSRsDZ9PxyxyUlQoRCAVsel5qXLTpK+3T+KNzRF6Uz1j9vWaPcQs5x6/FNsKOALnXx//1ymd65iqEzijLre8aHFIXx/6QD/KXzkZmIepJkSrNjbVv2mbeBsVqZokngIxUy7lQdf149asWRNTSqHruvrqV7+657zzzhuJD7/tttsW/Mu//MuSAwcObG5sbLQAHnjggeqLL774sB/84Afb3/a2tw0AnH322as/+tGPHrzooouGANrb242lS5ce+bnPfW7vxz/+8ZESpa2trRs3bNgQfeihh3YA/Nd//Vf9Aw88UHvfffftGm5z7rnnrurq6vJs3rz5heFtmzdv9l1zzTVtg4ODejKZlJNOOin8ox/9aHex1zkvhMzBeDW/iF3I0pRjh2kceAq/NYAtnhEho1txamI7qI7uYk/zhQVdezU7SePgU3n32ZqPA9poPM7LA9mR+60hI0vIRE2VJYT+1O7kmXnV4hAntgQwNGHxBLaVXASc4M0J26UdCHTBV1vZQZJe/JxffeGE7e7Z/yN+0/ngtM71dPhxng4/PvUODKCIfJqzwZWBi3mVNzt3pIrpRFIDHIwcpLU6fxnuyVLJ5a8rCZ/PZ7/wwgtbAe67776am266acl555034s547733NmzYsCFy9913111//fUjT1gtLS2pL3zhC4uGhUwumzZtqj/qqKMi99xzT0OmkAHYsmVL8Mknn/Qfd9xxY1xPu7u79S1btoSCwaC1detW7xFHHJEEuO6665Z96EMfOviOd7yjH+Cxxx6bRN6teSJkRG/GjJzP3r87T2r91S0END8d7c1se8UJDlvv+xUbQ98F4M8vX0jEdrYbxPBrYaJ2HTYeYJxgMqWwJcKRXp092ljHAv+gzb6H+p2mlqK/SqjXNPpyAt7+eCDCHw9EqNU0bmwqPkbDTKUI7z7IkK4Trfey5PAqPD4dQSNYWzun6stPhTctvpzTGs4kYU9+VXbvgR/yUvjQdlf+c/JpLGVTJUGatDrAUe/WxoPs7eig/WDX+B2Mg1EliCboms7h9Yfh1Utji5wvDAwM6LW1tSOPJ88//7wvGo3qX/3qV3d/7nOfW5QpZNatWxdNpVLys5/9rOYNb3jDGIPhPffc0/ClL31p77ve9a6VO3bs8KxatWok+vcf//EfD956662LfvnLX+7MPe7uu++uO/fcc/tbWlpSmzZtarj99ts7ADo7Oz3Lly8feWI+8cQT8+usC3BIC5nhYMylTYehlJ9U+jbd1/OFMW13W8dwWlBIqgAD8VG1giEWr224naDWx//0fJWYXTfheV+bLKx2sDOMrLX9iqvxERbFdo/FHwIpUplZoU3F4EuFY13yEyMC9ALb9/XjWWlgpUwWLF3CMUsb8Wekdpkwxc0cQ0RYHJja0/hHV9/E3tjuKQmokfMPDKLt24fyjqN2ElDBCTInlJjvRX9Gt+rnFWsfr1j7xg7JhBs8V7JOm1q6fzNpQ8wJCRhKRWnvGyiYrqgQ3qBBw6KqiRuWgf/3l/+3dHvf9pJ/KKvrV0f/+bR/3ltofyKR0NauXXtEIpGQ7u5uz4MPPvjS8L5NmzbVv+ENb+g9//zzw9dcc41/7969xtKlS0eE0Kc//en2m2++uTVXyGzfvt3T1dXlOfvss6OXXHJJ36ZNmxpuvfXWg8P7r7zyyt7vfve7zVu2bBnzFPCTn/yk4eabb25fvHhx6k1vetOqYSFz3XXXHbzwwgsPO+aYYyLnnHPOwHXXXdfT1NRUtD/6IS1khoMxlzUtvSbg30XTQidiPWHZxEyFJyt6voE/qI9Tr3bT0OoI6iPsX3CUcoIQU/hZtXALB6RoF0k0O0HSqMXWsj/PaEcKOzEqbKqUcHTS4Mikzn7dJjFsd07v32VYHNRtNOCERPHqM6+l01QTIhGLYtr2GFtN0kwR1woLMUMzMLRD+isygiYay4MrpteJL4VUry2837bRXnkFZdRN7zyT5HTvsfwi8XBBV2UF7ND2sMG/akr96xmlzDUDfAEdX27+unFw0iBNsWx2Cdjetz24pWfLjEu4THXZb3/729B73vOeFS+99NLzmqbx05/+tPGnP/3pdl3XufDCC/v++7//u/6mm24aWWpecMEF4ZtvvpmHHnooa9ybNm1quOSSS/oA3vnOd/ZeddVVbZlCxjAMPvShD3XcdtttCy+44IIRAbV3715j9+7d/te85jVhTdMwDEM9/vjj/hNOOCF+/fXX91x66aWDP//5z2vuv//+uu9///vNW7du3RoIBIoy/s6LGUTZQ1SHnmPj0XUA9MdN2iMmVd5c9dFKBllJK44xfsg+na1DtYCiu+Y4RGRkXzF4UgOEAyHivmx32ob8ZVwAaMuz7Q8v9PF4Z5J6n84bT5q4Vnr7w4OkhiwyEygrBZGkNeKYYNo6tb4FKPL/uJNmkoF4hKZgTd5UNi558HhQhYqVAVjWpDJhl4qL/a/iHN9JmMqk0+4lpkZXa1+J3g2ULlZGkKzVerEoW2FPU9DYU/TmXF2/OjqtE5eg33PPPTfS19dntLe3G/v37/fs3r3bd/755x8GkEqlZMmSJclMIQPwqU99qv2zn/3sIsMwRi78vvvua+jq6vL89Kc/bQBH1fXcc8/5Nm7cOPKhX3vttb1f/vKXF61fv35E7bVp06aGwcFBfenSpRsBwuGwvmnTpsYTTjhhP0BbW1vqhhtu6Lnhhht61qxZs/6JJ54InHHGGUVd37wQMrloIqgi5k1b89JdO7pyqY28hC/Vi6kH6C1Q8CsTJTpVsZ1UxXZnbBNivhZUjnE06WnA0vPb02rSwnAoabEvnMKrCc0BvaAL8nCuTpWxcvFowo6eSI4/Q+GHt4SZYF94kAODCVY2eKj1V7ZzgMv4BMUPAjVa9mcuaYf8UgkZpSbfl4iQSlh07i4yHqnQuVNjS2QUw3gqrZni6aef9tu2TUtLi/n5z3++5aMf/eiBz3/+8x3D+1tbWze+9NJLWTrIyy67bPCWW25Z3NXV5QF49tlnfZFIRO/s7Hx2uM2HP/zhxXfddVfDl770pfbhbT6fT1177bUHv/rVry489dRTh8BxMvjZz3728rnnnhsBeOGFF7znnXfeYV//+tf333vvvTUXX3zxkM/nU3v27DH6+/v1TBvNRMwjITP6xdcEZAq/qaXdv6Yh/DyDgbaihIxpVI2JdBRlEkwczNpmWGEGgmuJFRQyzgRvKvjCU87DzNmtIS5bVcApIC1krLgi2p4klbQwkgl0T341qhEwCCzwZwmthGkzYAqWpbBmT5NxaCECmo42OL3JtJRoCBYKlYijJTPGpUAZOio0trzFuAjsHZrinJ3x9fSIhxW1Kwo5eeZFJefWF3XYJgOOcP6P//iPXYZh8POf/7zh/vvvfzmz7QUXXNB31113NZxyyimZJbD4xCc+0f6Od7xjNcBdd93VcOGFF/Zl7r/88sv7rrjiipWZQgbg+uuv777zzjsXAbz44ove/fv3e1/96leP9L127dpkdXW19fvf/z7061//uubGG29c5vP5bIBbb71137Jly4r2oZTclCuHIksb6tQ/X3UlK044FXBch/sTJrUF4k0KceyB/2Rh5BmGvIv564qbAfBqTFud5DEHiXuaCBewCewcTHLnM91jtn/9zMUjfycsm1/tdlR5oQMWh/VOzrC/6NQFNB4xqopLmEl2D+3CtgMsqTFoDM2j55FykkiMn2Jhhrn2hWsxlcn5jefzxgVvHNkuySTa7j2Oim8KqJrqaY0rmoqyqm4luhT/G1XJJEted+aTSqnjJ2q7efPmXUcdddTYH5XLlNm8eXPTUUcd1Za7fd7MHD17XqFnz2iix1BDE0e85ny0IlL0D9O/4m0cTL2JFF5qvBotnQ/hi+7F9Deze8GYvHBFo0RHjfPItqLGy6eOa6YrZvK9bX15A9BTtuJ3+0Yfct6me2m1ir+2eHe2V5XAyOKv2IB3lyLwVZZrr5Ze9ipdg4wgUuX3Y23Mm4pqXGRoCH3rNshcrClQhgGh4sMr1ARBzy5zh3kjZHKJ9Hazbcdu/HWTyXGlMVywtt6MUDe0jYboVvqTyxiofR0AXnOI6uQBdBEGA21Y2sTR34alSCibsD663Nc1CBijdpvFIQ+LQx7+Yb3ww5f68/72DHFUagD2Bj8L65xzJxNxqhuaMHxj3Up3P7QfM2pi55MkabWiXUFP3i6lRdJJQEv1GatgEOuIddkbY3H0nTtRqnh1lqQi2NrQ5GK7KqgYnMso80LI+PxeVqxajK15CSc1enY7K5r9j/9pWv02LOpBC/rpUwbdcUdFuTz6Iqf2fg+An7d8ij7P4vG6AMBrm9jSy1BsNF2hR4Sq2iCGlr0aOaYKjjk27cGU7MHWvKSMaqo8Op89eSGf/JtjK0zo4K11Pl4V1bC0CKg8trp00GgqFmeoe1R7kLJTJMKDKFKEE0IwVviRUpsjOdBmCtE1gg0TewFWAiMrmQJehpNG11HVOaqyYNAxt0xCyCTi/ezzmGhFVJwdxf0eViLzQsh4fV5WrWnF1AP0WTUjQma6/Kb9MABqFizk8A2OGqRJPE4kJBDQIWlM/MUX5cdrR6lRjoBQwFDCQkOjxlf4IxJlATZWur5NvQJIB5JGDtA44KjPLGWjYqO6dQEGAqtJGVUjv8tYR5L9vx2tVKkUYAcQ0ehVI5dUgLFPwboUzMyTF00OncpRChPd089cmPQuTXyYfdUvopqKd82fNLqOWtA8qUOq7CYsLCqnAo3LVJkXQgbAFh1vqo8WGeLkUw5ncDDtIq5s4t4mbH3ySQ0PvvwCscF+LHPU0WIguIa/t36YvqRJzGgqqh8lOgk95+nPC4MCMl65YXHGLxmqjoBmE7M1Hu0P0OiFkxtS+AwhsxuPOUi9tY+UCtLra8YcAmWBGc7xhEt/PaYyVSoml0B+bvkFTUxqjlxRHS3URVvYOfjH2R5KFrqmo+etA+sy1zikhcxwWpklCxpQmodUOl9ToKGK4XIjhhkmElhGypi8N8xAx35ig/3EBvrY9vBoWVlLQcq20WQf6wM7WePfz3PRFexIjKY8WezpptXrqKcejxxOvqncVtA+Zmue69R0AivW4mloIaArYjYcSOg8N6iz1Jek3i8EDY3hEjOmXoNuJ/En97NwpU6nvxl7zCOjImWbWLaFwp6k2iI9riLFk2krAobgOUTmFNs0QYHh96F5KvcnZsYU4QPOA1L9zjbaY5NKSZVFbZuHYFPlXqvL7HFIfyuG08qsb1t8jdgplJYvGlvhT3TgSfXl2QeiFElPLSlP3Zh9Hr/jLWNbFuGe7OSCPs3kVS2v4BWLpb5utvdVk+wb9Sxa2LSbk6udGih/2dfIdFUrYpvULljIuU0xftcdwFRQbThqqGf6YXvEIG4Lr2mK4mjwdAw7hKkN0tm2qECvHmJmjKFkBK9efC4qpcCrw7Lq4gRTPKmoqYIG/6GiMBNSsTjBhiC+2um58paTyEGTF+5z3MDq9iznwJ6pC5kDj8VYcV5oyl9jw69RvdhA5lF2ifFS/T/00ENVN95449JwOKyBkz/sxhtv7Ab4yEc+sriqqsq67bbbRgLuWltbNz7xxBPbFi1aZE5UQmCmOaSFzDBKNAwrirLzPCorG48ZxkMYRDC1QJYxQbcTKM3IK2Ra1qzDTCQwk9nuv5atSNqKqDFAo/cgB5KNJALN+DLqeRiB0doovsYWpvrrTA31YycT2Olo5+Nrkxxfm23g7015eKTPsdscV5simPZi02zviJ6qyjM6wcdMm2g6xUfI0DBtp+RzsSiliE9Cma4JHIhAeyS/ikkXWF0nGHNsArIrPIo12KzTWbObBYPLS9Lfzv+b3jzWenKAhcdOKov8nKZQqv89e/YY7373u1fcc889O04//fRoe3u7ce65565ZsmRJ6vLLL8+b3r+Yfst9PYWYF0LGFoOIf+mE7XypfnQ7iiV+1EhiSIXHGiIQP4ASnbh3wYgQ8ldVs+rkM8b0E0nZtEdNtnEW2zK2Zyb02KbOYLgqUFWroKSwckml/x8yxj7p92z+O9EDu1F24QnNn1Fl9j/21IzZf2xzlHOWjI5ua2+C3+8fnTCWVemcvihB0XO8goQF8SJjgnUZPz5xKAm9sQTNwbpJORPMJpquE+vtI1FB0f35SB3/Av8Z/TIADVo9NVrxpSUAqqINnPL029DU9KeSgd2D+BuKKDegFLrPR/XihRO3nSNkpvq/4447Frz1rW/tOf3006MAixYtMj/3uc/tu+222xYXI2QK9TtbzAshg2gkvRO7lFq6j2C8HV0lkVTUSQJmJRERfJaFiE3c28xEq46AISytKjJbsrLxmQMIYBuBvHE1SsGecP4YgOE4AjsZp3fL44SWrMRXl13zZol/et+xPWENpepYOIkCamHTYml1aSpEDiQsPPqBkvQ1U+heD5rHU1HR/fk423s2v40/TEzF6FG99Fjj+xGOwbeTF49/nlByVDh5xccNDR8suov2xw2SQxrRLoO9fy7SMKcUundS821BHvqPryzt3ru75Kn+m5Yuj7722hsmnep/27ZtgSuvvDKrDOzpp58e3b59+8gy75vf/GbLT37yk5Efemdnp2eifmeL+SFkisTSgwyFVjmuwWmffl+qD48VwbAiiG1SE9k+Od/cQufSfCgRDCvK0bvuBGB302vpqj0hb/umVArDyq45aGo++gznu6Usi8jeV0j297Dw9POzjl3gs/nIigE6EmN/wF4ril27OmvbihovF3s0XuxP8FK/o3qLmGokADszx5lSKm+iTkHDVuOb/kWKi7ERzQYlBc9VqYhQku9KuXlL7Zv4W/RR7Kk6DHuBIPRaffSmhZSn1i46JYwnBMkhsE0hOTiZ+1UaB+fuvbuDHTterphU/8Uc+/73v/9grk1mon61WSpaOC+ETH9KI2GDr8h7rESH9A8k7luAZQ5RFYuQ9NQhJXBNFWWh246RVbdGja26ncSw8mfPDigLLEe15GDjs8NUL15Ksq+LVHgIZZlYiTiaPXbVs9iTYnGehUjI6iaCjYqM3pwFwFovHFWv8dl+xw37l7ucOIrb1vQSDq4cUSd+7dlevLqQa7KJmjZrar0gwqkLA1n2lL+0R7HSdp5Q2ha0qtbL8QvyP0wqW9gVFfSwRe4PpSUAzcFDxWFgdjg6cBRHByZO+DoRD4f/wC+HHgAgqZIEpDj7StN6C90DdpELbgVg23iCpbHfNC1dXpZU/5PpNzPV/9q1a2NPPPFEcLjcMcBf/vKX4OrVqyftmZHZb2tr66yozeaFkOlLCX8+aHBGwxTTTlg2qZQiZQseK0ahCBBBkdSrmdiIrwMKQRCls7PuAgDCnlaSZv7cVhYWUdNGBCwbgjoE1SALtU4WblzC3h0H6G7vQawUDZGXEUAr4kFSUybVHMy7r1kJ1dppDNmjnmUrwn9nn67TG3AKXNlKEU7lvx9b+5xV0HlLQnj1UUHwQl+SVE4amz+3R1le7aU5MPYrWe/XiZoamkjWyidpKQ7GIJxTh0QXWFIlbiaCGcYro9+TpEoSoDgh4K+FRScUvypRtsJKJKlbsWDszk8W3c0I46m0ZorMVP8f/ehHu04++eS1b3nLW/pOPfXUWEdHh37TTTctuemmmyatM87stxzjLoZ5IWQAYh6d6oap6ceNlEFoCGwtCuIhElpGPkHij7UTMMMoGQ5iVJh6IMOJYJTRgmI6Q9WjNWsKWTEWoo0kquyIpNCABHWj/XmcH7gCrGA1ZlxhlCDu5IaFO3kuVjNiWrD0wMhqTinFUU1+4hnFolK24qX+JMGsTAfZ9ypoCCNZ2RXE0scPJK28QsbpQcgV7l7dqYaSypmfBk1YHKJ4RwWXkuCV0Qekx6KPE9KnroHy4GG9fx1BrYCpRCmsOZ6rrFCq/+XLl6e+973v7Xzf+97XFolENKWUXHvttQff9ra3FWWEKtTvbFHUmUXkTOCvSikzZ7sBnKqUeqQcgysVC+jj3MgvaO5toLf5pBFVWLEovYaI9wjnjWiQN94GUsYSzAxVlSc1gCc5gKXnaZ9nCAv3/4ravmcxjSp2rL0uYwA2K7Y7+dD6Go4jFjqSVHpiFmUhysbrSec9UwpLQLDR0pLMxsiyDYgyRwThRCzzxVjmG12lG6ZClO2cFzinNd/Tam4NEjtLPrzviHpMHBuLZSu+/pyjx09Zzvt8KAUxM4HkST6jsNPb01Yj8TKL1XznDUJ2mQtfxkrmwfCvp93/xvgG3tvw7jwnFizLZHDv3HIGycWyrCcL7bvgggvCF1xwwbZ8++68884xF75///7niul3NihWvD0MLAI6c7bXpvdVdKx2i/TxmvDPYDsozUNfU37jekFEsPWJU7RbRgAyVASiLDzJ/uJPoyx0O4mdx6YSiDmx/0PmEHXpKpWeRC9V4Z0s2v8gz3mPo50gylbsfOw5NgZ2cnqt8x39XsdrqFrUSv1CJ3/Uid13k9ICPNXwlkkbpjVlsiTyNI2JKeZ/U4qUHqDf00q3HWS3OZp6Z9dQckQW7RlK8scDjkpbEzi6KcDh9WPdryHtjICGjU1PtJuImWTXwOhKxlZQ7YWaCeJJdU3Hk2fV6VKY1bXgT69aV/pWUqPVMGiXxm17v5lfiIiAr3rG7fQuU6TYX9RYXYVDIzBrkaRTwRcvwg+/VIg2qQRekaoV2JoPK090/VD1GgCSvtHSBIv2P0hVeCcAeobNI9o3RJKE8wgARAcGGRhMUtfSRJXZjUfFqUl2ErR6iRrZ7s4TjtFoxqPiGGrqqopQYj8Bc4AWzYuhHQ7Usq7K5HhfB34LQOixgoRTowLwyc4EJ7VMXKXRK3WYys7yHE7ajlt5a6jws5CtLPqT/QSN4t205zvhVHbu5iotxM0LPk3YDk+r3weHfsVjsSeI2lEOpvLbC13mDuMKGRH5ZfpPBdwtIpmh7TqwAfhrmcZWMp5TKxlehPXv38r2Dqj1KDbU22gCQ7VrCdesKfl5lWggYCRzMtzmLB5E2Vial8G69QzWrR/bkWjsXXH5mM0Hll5KzcBWxDbRvUtoDUeJRBKkLJs+3eZvg15s08RUOrZtYZoKw4wQsAax0dDMOKYzAIxiI/pFSBXpNVQI0+PDUHH8Zj9H8XfeX7+YmDJYG3Xsr4JNWK3hxJqF7E7VcjBmEjFtvvj0aCmCK9bUsiRPLFLQM1aHn7QUHl2ozVNPZxjLtomYYeJmfFrXVqlYyiLkmWQp5QnI9/yki06tPrmAzlyGj4+rOLd3f3FafbnMPhOtZIYDggToAzJd6JLAn4Fvl2FcJSeqfAQlwQZrGxusbc7o02swe5/B5uPvwPKUdglueqoZrMutLqiysiaDY7vxR/ZP2jna9FTT23TSyPsjT3Im1I6ISUAXEgoOvrIP6+AuAHwBIWYs58+1H8bUfCCC31bEozNrwFCik5IQKc2Z9I6rswCLCI63kNcK0+gP8q7Afh6LJLgrVriwnGUr/tSe7SlqCBzR4KchrVaUCTIKAOiaRmtoydQvqoKxlc3e8J4sAZoZwaRrxpi6RcUSSwFKYWiOI0YpWOqZODuHy9xhXCGjlHoPgIjsAr6klJpTqrHhLMzBhSv5hXYOb1G/Qs/z/KUpE2+yj1iJhUx6ELkbUDmbbDGQEgWWiYBlKoazl6mM7Mm2bSGaD1sz0ADdihNMdNKhFlNJtU8Ei6b4DkTZvFpvJ1hfzx61CCvDWeHw5F4ahkwStvB4Z9uYPnaHU7z9sLqR98UE3s+lQM/JoIvOwuAilLKxlIU56tpIyk4RSYUxCnlxjYMhcCCqsGyo98HymtLcvw2+9Vzf+AH6rP5JH/vxqfgwu5SVomwySqlbAUTkeGAV8IBSKiIiISCR63VWKQxnYT7isFXXrDnljTyt3sBAUvG+vzjqns8veZy3dH8NcLIYzxoiqBL5TuhAQ3C0r2hG0bN4NImeDmb0WFF85gBLBv9GSt9ATzrupRIwyZ7wTgj0cqTE6fdlPuEaRGyDpC14ZFSCWApshK6YySN7B9hQZ1DtcVym+yQ7kWkxiDjJQycjgEQz0DzFZ622lSJlK3x6+YJK/UZ+5/iknSSamlosYiBt8E9ZqqTFxUSENm8bbSXs02X2KNaFuQX4BXAijip2DfAKcCcQB64v1wBLimh4dLDSE/rz4VHbwqotXxrZnkuv0cx/N1zPoD6qtllZbXNMY2nUTI7tpjRPgZom1PozhIx/9CN+6a+bR/4+vKaTM1qdxKzbu0y2ducvdVBZ5PfMfH/G339qOJVnao8iZcOj3SlatvyMYGwvAkzV5SPXpXJCREPb8Br0tuMmbGor8GpCxLQxpvkdUOJkzc6ntfKKUOUd+/1O2WoyVZHzI5OqrOySZjglv2maouu6uvzyy3tuvvnmg7qu88ADD1RfccUVq1pbW0dSqt9+++17X//61w8Fg8FjotHo08Pbv/a1rzU+8cQToU2bNu35yEc+svjuu+9uamhoMBOJhJx66qlDmzZt2qPrs+cAXKx32ZeBgzjeZHsytt8DfL3Ugyon3oyHxT8NNEPaM9mnCht8g6kI0X2b+ZH1muztumJpVSnSzBho1lonnU2aWsPi6mUHWeyfXsBZqCa/GuTFwQUc37CfmOWhL1maRJaVwGHhl9keWklUd65bJlWfs0QoG2P/kzQuq5uwadxUBA0Nn2WPybItKKJGHYkiC+qlLEUCxljkU7bCp0neSpPxVJLBcIKUb/QgTRNCfl/Rq7dCrqcu45OZY2z//v3Gm9/85pWDg4P6l7/85QMAxx9/fPjhhx/ePtl+h/OaWZbFiSeeePiDDz5YffHFF5exvvb4FCtkzgHOUUr15XzxdgDLSj6qMmJocO5ik98eMHhFLeZDyQ9wjPZy3rY6Nlca/wdAFWOFUNQSXhwoxROCDoz1lHohHOAHx7w8rUVOTV0VJ559FAe7hkhZKiuH2J9YmW5U+EM0becYTwGjrm0rrDwzjFkgqDIfwy09hUL0lUK3EySNqqxjTD2UZXMCWKAUR6rnRjfUNNJjt/KKWcsvEyupHUdl9qHQM3hl9KHhq5GjsZWw3tPDcZ6xrrQGilpttHZPSgn7dneQikbxaX5qCrhyNHqt0RgeryKWUiwISFZgI4BhJ2hQAyi7SDVaodunQcJS5LOoemIpUpEBvInR759lQ7TuCMTwosvEBn1lO+dwmTqtra3md77znV2nnnrqEXfccUdJokwTiYQkEgmtsbFxTqT6DwDJPNubIc/sW2GoHKvv+9emuPqwVHpyOyb9ynsg6tHfIiiuatnOJc2PMVC7lvv3BXiur3S/KlEWmhUbWck8M+hMpv2mwXNDQY6smV7+vsYF9Rg1NfTGrLyuyv5UP147hkJjyDdaWE0pRSB2gOaAhhGoxfTmD4acLpGkRW/MIpCnXs4wuhXPWpV4rBjdVcswi0hd8s3nQ+xLZ6DuV4VXbTVrjsCX8czQ92QAUwmPJJfwSHKs59nSoMXHDx+dufdENL4bPsVZ7wNszX+eLx0zmHWejz1Tw9ktCS5qTWSttC29dCtM8eSXQaIlsVN2VmyWL9VHMrybJAYeHRrSCUid3HwhLC3b3pRMKGwNvMnS/SZsMTDL4YhTgKG/7G+MPt3ZBNDygWOyCnx1f3/LSiuc8vhX1w3Unr+iY3h7YvdgoP/+HcsA6i5etce3vGbE+9bsjxtG3eRqbBxxxBFJy7LYv3+/AfDEE09UDaeHAbjvvvt2rF+/PpGZNgacmjHnnXfeSMqZ4TIABw4c8L7qVa8aOPXUU6de8rQEFCtkHgHeDdyUfq9ERAc+AfyuDOMqKblCBpwVzcQItuZBt5M09T5BU+8TdDefwhvWXM0b2ko3Pt2MEBraOfKj+vaeBfysw5mpHu+vYhKLgjEEdZs1oThVXo2gN/9FL9n/KI19T5Myqti69oaR7ZYFR+/5IZqy6G4+lc5F50x9IOMh4yu1TFuRIDvjglelSKZMLFGFV0BpLm+L8dcuL+YE9zG3myafTUe8fLrsiCkMJJ2TPnzQx+KAzclNM5uPy/BoGCGFJ2MmELuKOpXCVEl0geqQ873R7BTC2PRZ8ZgilYDqcKm88xS2ZtBXd2SJ+psYqy/hS+0L55Vqyf2RkD2U9Bq1vqxlsB01jeFj7KiZNZeqVLHLz8IUUpdlqtlg1CYz/H5YXZZIJOTCCy9c+a1vfav+fe9736wZXYsVMh8H/igiJ+BYMe4A1uPElJ9WprFVBIN1G6jvfWrkfSi8u+znfPOinhEhc19HI/d1TC4qP5fVwRj/fPheaj35fYAysxVnTtieGVOBCKalSEh+KZCyFLV+DT1rnAaWYXHQsvFMEOOxPGSzPDT5BfenN0R4JazTnch/I4J69ngbfYpL2EKsa1S1VnfYWvx12XE+w7lDg7riwe5R4TmQKtUkPQnynNLSPFh4nAzbJoTT2nwRLwuqZEwp7pRP0ZeEcEY2GQ1YFBzbthC2BSNxtMrOKoExE+j1voRnSVXeVAXe1lDECnuTRnMg60ukBQ1z+BgtaGTndfRokzbWbt261avrOq2trebmzZsnPmACfD6fes1rXjP4yCOPVFe8kFFKbRWRjcC1QAInWfA9wL8rpdrLOL5ZZ8fh/4g/1k7r7vuo73sGf6yDDU9O3xdfaQYJv5NLTGwL3YqOJK1cAvzQHyRmjT/LDxHg6+Yb2KFax223PRrgiqcP440Le/Dl+e63mOdRV3U8phjs2NeUte+w0NUIit5kE137mvCoBFeEv0+L1c4X627JavvBgS8QssM84zueR6ov5pymAWo9FsHwLlr3/AyAvW1vJR5cnHWcV4e6QGFBEfJq1Pqy3Yg104OhxTkYT43Ja6QQgh5BS8+gCkU0NTUHjYV+ixVVGuOXX0uP01CcvaGZrmf2MLTXeRjxvdJN3ersbBKZk/E5Ch7hWABivb0MxDuYSSxskokh7AI52zI/lYQFg/78tjMvKms1mrBgICHkcWgbg7IhMQSjFc8Vup0iEhhHfer1Ub2kDa1E2YWrT2vtqT6ttSffvqZ3b8ibqM+3vCaWq1obZrKqsgMHDhjXXHPN8ve85z2dpSouZts2f/3rX6uOPvrostTLKZYJPyER8eBE9l+plPpM+YdUYYgQDy4mHmyFvmcQbPyJ0uQ/G056mY9Toai0o6/X/0qnf0XefV1JD4OpjI84708omyP4Q8F9zdLHEunGg4mJxg8PNGftv8EXpUYG2ReG7/S3sDfu5foVHU6iUNN5SJQMX9f67scxzAgJfzOejHQ6RnKQxu6/E65eTaRqRV73bqV5CJr9rNei9FevzdrXGTGJpuyRw2wbAh5HUE2WwaRNOKOvCTG8BNcfNSJkEv29HHzi0YLNFcAKR8gMHdjPwf4nJj3GUlBsBFH3xE1GmGQh5zzknb9HaFp/LIuOn7uKlGHbyrAL81vf+taez3zmMyPL4FybzCc+8Yn297znPROuSIZtMqZpyrp166If+9jHJu2FX0omFDJKqZSIrGCeeykeXHQuolJ4UtPLMKtZSXzxg1mp9kXZaHYiy4V5IjIF1IL4zrxtFkDZ8mM/aa0es+1n1mlUE+Mx25n0O9MeS6anmr4GZyI1jVGX6oaeJ/AluhmoXZeVs80wh2jsfpTG7kfpbHkV3S1njjmX0gxS3joCiR780eyM6M0K7EzVmwaaJeg5GjNbDCdnnRQWPlVeNaEtZwy1PuKLFzNwYGInIQEnHYEIT9cexfPV63h9xwM0pEbnkp8vvIheT/24/ZzU9zjrwy+MvP973Qn0eEfVdEErxon9jxOaYTVUObFi3dQ0j36o+eo2VTLjpeS/6KKLhoaGhp7Jty8zRgbgQx/6UA/pR8g777zzQL5SALNJsZ/KXcA1wMfKOJaKxvTWsK/trWXpO9fwXwz+WDvNHX9EsycfxT4dQhEnTGrFogU8uDC33IUjSH64bRkMgZXOn5PwL6B9yeuKPkfV0A4ABmsOn7DEQsrbMHajPtbUoIBc/YUnFUaUPcYNOhNNE4qP3R/l6AteQzIWw7YmVtNtetpmIKWT0rykNC8LzzyH1gxti7mjiUhi/OzQNRuOZUXj6IruwV0NvBLNvndbatbT5HX6XR1M8pbFA9jY9MZ7eCUS5De9jiO7Ljan1fewsTr7gUopNeKEYlrQWlP4vkVNRZ1fqPZR0P29IEphJAYK1jx66nfPM9QbQU8MUd3jCFbNTpEINudt7zK7FCtkQsDbReQ8nLDrLDW4UupDpR5YSVGg4jZoIMY0jatCyaLzM9HsBEYy+5HZ1oyC50p669m/7PUo0VEFiqiVg7XPfR5NmdQMbMWbyK88+aQVpN9joMcVXc8VnmR7tZVUL7sET122Tal7wekM1RxOylODbUyQ8Xkan4UjA8u3QPcGistW/cF1KX6z3xxxbKit8uPxjo7r6AabtlS+CIJRWmsNPMHRLMsLgkIsnaPsQGx0OduddH7yLQE10r7B5+XlVILe1Kg4/WPfAo5ekN/TTSmFbYInVFjIBExFnwleY9Q7bVJUF86l5qTsiWCjY/oct3rNTDh6UZeKo1ghsw4YdrFambOv8tVoNhC1nAKN05EPCiSo54ubHB+dcWsB25qfcHW2cViw0M3IuJkdxbbwJvumFE+gpLAAGw9L96OZYXyJHnyJ/Eae02FUTTfet8MC9jzGDt812dtFI+FfACJ44114UkPEgouxSxg3kj5RRUSrr6uzCWlJqgr41V/YOvnV6tvaRtVIrwzp/LXbM7KyBGgNZngaClQbJhtrw3TEvXQlvITNifWsMVOhAb48D25eQ7DLdGdFG64C6wqVuUCx3mVnl3sgZUVAiguMGRdl2aiYlV3wYCJsIKAhVYV/tErT8wY6przj6+FRCiIa2iTzk2pWHIU58SohD50LX01j99+zDPi5JG1h0NRR43hkLclwSlz18thqEeGqVexZ+TYaux+lvvdpUp4aXl77gUmXzh4PURbeWEfRpagnh0Yi0DyuvSdrLGUYwTArqy1WVhdOYamJzsJAgotb9/OXrgb+2NVMytaIpJJ4tPyCImnBvnT10daajASltoXfE0Av4eeUy4iQmU4AmcuMMbcsZbOM6NqkDenKsiFlo2LlmUai+uRroPjMTrzJblS+fDAT0B86kv7Q9IPk7tg6yJf1rxXcXxXeweFbvoBmO2oiIzXI4c/fMa79ZJjB2nUcaJ3YBpTUA2jJ6VVxLIRuxUn4GrCLcEe1lZPKZbbmTB2D5oCT6WFBYFRlFrMXsThUQDgFHNfwhKlYWj1q++mKdWHa5sjDQMyEwURpLkwHQj4ZcWXPDbKe1UzqLgUpNgvzw+TXKiictDLbgbuUUk/laTO/0QVMBbFSJkOfJinlBDJYszemv3IMlyT+mUWS7eh6sf5XLtIdl1/dHrVDCKAX6eTQ0PsUrz1wNV3UlWq4M8Dk67mUm397aeLEnMc3JPhkRjZyDW0kYsajO0XNYqkSCBnHAY82gxE1b7g3zFP3PzrSwBUylUmxK5ltwNuADuCx9LYTgIXAz4EzgH8UkfOVUhWXZkaUhSfVj617saZQnGla50ZGQ7wrBdtADM1Zmc0SS3xJnkyt4lmVXcfmd/YxPG2vYalM3rV/pbRzpu4kxwxIYvaNLfOAJ3p9xM0o/jzfcV2EUAl9UgYTip29ikR62rJSJr37JxO5U1nMtZT9U6VYIRMHvq+UuiFzo4jcASil1LEi8lXgs1RgLjNb8xHxLSIQ34/tcYzHIlJUFPehiBKKKxVZRq5ffIA/DNSSzJPiKcHx5CZsUrZCPELeYilpfImnIewImaubttGjz94EJFYS01dfVOyTaSvCCSuvq++AVkdCZrYUQ9yCsKnh1RQ1nvzfk51hg22DzvTxjRe8LPAr3rDcRBM9J/a/dNT4BIUiddgKPMrETB7aK5dKS9k/VYoVMu8CTs6z/T+BvwE3At/GSaJZcSjNQ1ILEbCTYMYcT0el8HgdXfJUPa3mLKLNTp2VDJo8Jm9qKiIFARCK7kRPDmAHqgg3HFawXdVAFNImliuHvlWKYU6PEkwHCuGvKz5M1NM0Zp+lecritKBEw9LGj0/6c6caETJ/7XT+rTIUpy9Q2DZYBfLQlQKjtoaqE0cLwmmZYQX/dnfZzjtbVErK/qlS7DdUcBJi5hZeOYJRx5gkFCigMUuIyMXAxUuWtFG7uI3AQJwAilQsSmRwEEShWXFAMI3JuwEDc3Q1JOV1Zyoxjf1/pSq2i2hgybhCJuFfgGJOXdqECIrTdt454+cd9C3mPzxX0psQGnyKDbUpREBCizE9IY6qN9ncb9IZF/qSGgrhlUFFmx+Gkib6NIWMrWx8ho+JPk0FhDzQHJr6p/67TduW9u4Pl1yP3tBaFT3nynV7C+2fayn7p8pkIv6/KyJrgMfT207ASfX//fT7VwFbSjq6aaKUuh+4/4gjjrpGGQGijU7dmGQ0Rr/tpAgK2AcIJDow7akpjy3TJBCcuboXpUBVRHTIZEjXM8lwm246+CfAprvlVSPbUr56XjnsH/DFxhYYq2RMG/pjJlpGLJVgs7H7f2dtTDWJA3wicbvzJsXIChFgV/0ZpPQQJ9QANfDHTg8JW2hN2BwxmCqJumzQhq1VG0lM8PBn2RBNQSQ59XP27g8HO3cPzfiPeK6l7J8qxQqZG3HKL38Yx9gPjhPAF4Evpd8/BPyqpKMrE95ggAXL2gDwRDR8EbC8tVPqq6/jAAo191Y004pKnVkOLLgIzBQq6KhwvIkedDNKQ8/j9DSfmpXxIOFf4ARyziGUUlimE5uayZaapdRGdhRtP1OQd4JXCuJmYSWDYSeJGA1YmoeW6Eu0RsZ/Vmzr+1PW+zUaznNAisll0JyARhVj8+LxUzlZtiKSsklMowxPQ2tVWbIUl6rfSknZP1WKDca0gNuB20WkJr1tMKfNntIPr/Jx0nLPNSWNoFlxdARLD1W8Pco0qlFiI4ZjRA+Gd9HY4zg56mYM0ztzaXXKgYgQyHcJnmaGqkqTj2u8cth2MkyieiWWEWQPF7K5ex/VZh+WEvZEdCwl7IvAB+P/QZA4umR/ZSw1kuOTTDcOGfnf5NDS6XBCqV6MCWxOhk5WldGpMJ5KqxKolJT9U2VSVkMROR5YBTyQfh8CEkpNMuR8hrHK6Eklmk4qnhiJQp4NbMvGG/AjFOeSbOkBIlVr8Mf3I3YSNUESykojHlhET9NJWHoAewbzts1ljHG+n4bmxLRo+nBC0xYSqgVDE+rTiSieP+jh6AEnM8PnjxqiKsPr7KsvBNkeHjuVNPss/ml9ZLyMSnk59ZUv0RjdTijZRVvPH4o+LjZBpuq5RqWl7J8qxQZjtgC/AE7EeWxfA7wC3Inj3nx9uQZYCsaTMaIUYqcQu3ACQiV6wXQm1Q2NzLZ9Y6Brct89JQYpoxbNk8CX7MBkjgmZ4OIxhc9cpkGOiU5EsHN+NHrOyiWTVdVWXiHTldAJm1LQDboQluZkHahKdrKx/X8mdexcYq6l7J8qxa5kvoxjk2kEMtVi9wBfL/WgZhLbCGJ7QgXlhGYnETuB6ctfAllKVMVuOsgU1V0JXxP+ZEc6Urq4PnQVY7pOhDYGtuYvOq+Xy0ww+gPQGF79j34nNtSZtPgj6AJBQ7F9SKc1YBEw4HWLExxZlyJmOe239Bv8oTMdHjCF568DtcfTFHlxRG3mMrcpVsicA5yjlOrLmdB2AMtKPqoZxPQ3Y/oL67311CDBvunX2y4nUzepaCQ99WhmrGjduSU+kr7mcZNfjoeg8CUO4k31Yeqhcdsq0VDaVKq5uEyWzLipfN+nOq+izutM+pYNf+z0UmUo3ro8jggsC40+eHQnRh8eprLG31t/KgdqjkVXxVvzz33xpkm1d5k5ihUyAZw4mFyacdRlLrPMsOF1ssT8S0s/mAmwND/e5PiBmIKFN9lLKp+QUTbVgy9R0/88tQNb2dP2VsI1heNnXCZAOSt2zXYmaUMlMZWNpkaFhS06w9lhf7zHTzglxK38X7jM9elUFcmW7sei+EwHtuiukKlQihUyj+BE89+Ufq9ERMeJk6m4NDKlp7K9ryp/fNnYmo+4fyKbio0nNVBgn7Bo/4MYZgRbdKoGXx4RMq2778OTGiBS1UbXwlePHOGLd7Jo3wMAHFx0HrHQzAvXSkVpOv7IfsAxAZhxi7ip8KRnB902GfAvJe51VMYdMY2dEYO1Nfn9fTIfdmYqe9FkSpe7zCzFCpmPA38UkRMAH3AHTgaAWuC0Mo2tpPTGRp9ylFJ4dY1q36FS6WBuCZlpI8KetiuoGXienuZTsIxRtZs/3okv0U3Kk12fR+wUweh+wEnD7zKKZWQHu6csiwR2hrU/Qr41SSEBkvlt7E9qNPjKb1uxy1ITyKUUFBsns1VENgLXAgnAj2P0/3elMqpPVSghj85xi0aDLfviKV7pi4xzRA6znExyYmTuhepMk3hwEfHgojHbo6GlpDy1JPwtWdttzUe4ysn4bBqVl1a/kijW5bjQryLz+C+/GOKm9WEWBcqbcao7dDhLBh6buKHLjDOhkBERD/Bn4Eql1GfKP6TSo4kQ8o4upxOzWEelLFR4MOWUmcJltS+5KO/2pL+JPSvfNvK+oftRqga3Y+k+9i9/01RHeEgiAknLxlJOgbDcSKThr1t7TOPHu/28uiVBs39U5CwNZv++dkf0sguZp5e8mx1Nr8FRuswNclP9A2zevNl3zTXXtA0ODurJZFJOOumk8I9+9KPdszXGUjChkFFKpURkBbMdDFJSip+9nBgZDSPRi2bFSAYWVaTrbSoZRzQNj9dbdFBmZZPnM1KUrHykN9ZNVfgVTCM0syUpMzMGVyhVHo1grZeUBV3R1Jh8qp70myFT489dXuKW8K6Vo7kbFwZsPrI2wp0vOGrMGbm9ojEYmHyV2ErjuuuuW/ahD33o4Dve8Y5+gMcee2zyNdIrjMkkyLwG+FgZx1KR2EaIoeZTAUVV19+pRFkbrK0Fu4rwQD/KrkgZWBJU1KJU8jNJLVHfYiwtgArP0MrWBoI6UuFe2Zo2/JiS31H9nIUJIqawL+ZoB5J5FimNvtGNs1VWei7S2dnpWb58+Ygn74knnjgnMy9nUqyQCQFvF5HzgCdxLIEjKKU+VOqBlZ3JJIgUIf0IWq7RTAvD4wE8aAU8bEwziW3PMRWhUqRSSczckssl0rocrD6Og9XpmiR2iV1fFSO1irI325X4jDJp1tVarKsd36aZ+SxQ6d+8j927eelLHUMlN9QdtrA6+sU3HTWpvGjXXXfdwQsvvPCwY445JnLOOecMXHfddT1NTU2VfgvHpVghsw54Kv33ypx9h8DPZm4iVoLMWVdTcURJVnwDAKk4oapqDG9pHqGVGCit/N48Qf0AplEzccNp0rL3J4SGXiTpb2HfyveNbNesOG0vfhGAnpbzGGgcrdtX2/MojQd/A8Cuw2/E1ke1Gote/g/8qV566k6hp/6Uso+//Ez+4UrLqCdjV3jG75c6hoKb9w1URL2O66+/vufSSy8d/PnPf15z//33133/+99v3rp169ZAIDBn59livcvOLvdAZpKpfuWVZmAkB6a9oBE7BaKjpqHXEqVQIpgZSQEtze8MTcte0Vi6QgvWohnTTyap2Qn01BApb2myA4+HeEP47BiiLJToWJ7yCBwdC91OoNmp9KowfX7NRE+vpHSNrH26xsg+wzCwM+5tVcJxlW4cePQQETKTJzPX2a8O+Hj44PQfcPy64o1L4xxeU9oH+8MWVpclu/FU+21ra0vdcMMNPTfccEPPmjVr1j/xxBOBM844Y05mYIZJZmGeq9ixFNHNXQTWNyLG1Cf2SMMxWYWzporYSaQEeh8lOnZGjEg43g4oDD37Y43Hoxi1i/GUYCWjpcJUdT+KZk7CBbxwb9hGYbtmrHYdKBvDjOAfyi3KWjqiteswvfVjhZgYDDSfDkAykB08mgwsHtmXu6rrDR6B7akmGlhetjFXAinbKbhmA6GcmcTQHKEQt4RY+lUK/u2lEIdVm1zcmqCtqjTCZrIqrXJy77331lx88cVDPp9P7dmzx+jv79czbTRzkXkhZKxwivCf9uM/vH5EyPjCSTwRE2VomPUZ+nNb4elxgvWsKg92YPQWaXFQho7yTi+6WOkVbvkdB6V5SYaWQgmErSfRhbKNrKJjAHpqyFntjZzUQlLRgjannBFi6yGYhDov3HhC/p40D71LLs67L169knh1rubYYW/ThRge79wrZDceeWLFfrgrwBO9Hpp9FjdvzH7o0AXeuzLKU32ekoSZPdoz+pt5acjgzhd0rl41t23i8Xhca2lpOXL4/bXXXntw3759nhtvvHGZz+d4Ttx66637li1bVtGlVCZiXgiZfDRv7aeqM06q3kffeaOuj5KyqX/YSa8xdEwTsTWjQZy+PWFCW3vpuWj5qKBRatQqleueOrxvBt1WU/EYpp4zGVvWlDM156J0L/ES5QnTu8MYyT5UTrS22EniNYePrnKUjW5GML0T1wvxxDvRrDj2DNiMpoRpQ77M3TrFR0FWCHra7pKwhfZY9jV5NJV2EHBWG0kLepITaxEW+u2sn8pwv8fWp3iky8PzA46wUQjf3jG3g2pt236ywK59MzqQMlOhv8TS4mkK0HTVBiRdQm8q863RG8fbHUMzFcZAklTz8AQIC+59BYDw+nqi6xtGjvHvHKLmiS6sgE7/qxZj1ZR3BVPT1IRt5XnoEUE3Ku+jjtcehlh5NAEijkAZtlkpG8tTU1SJbN0cQpvltDEFBbomYClULEfNYwJVle/anIuRvszBlMbnns+2m68ImXxk3agZYV9M58svjJ91G+DLxw6O9Avwha0hrAp3HHAZn8qbecqBCFqm2kuErrV19K20sQ0hnpHXDFsRP9mpEZ+sMjDT+zxK0XAgSjJo0B7Q8MZT1PgnMKSn9QR6zMK3P0K0zELG8HjAM3cqRVqeGsaEkxek2Ilm9iekmujLVCf3o8Sgs3E0SaeIZFvE06hS+WWXActWxJVFXLOImTY+Q9DS93hxsDI8a9+8LMaKKouoOQ8D+eYA80PI5FDjMzhuQ0vhBgXstamgH6s/wcaFNWztCtMTS4FSyOHOE3ak1kMsQ2D5QgbWshC9q2owfTp2rMh4DAWWslkQmlsVK8vL3PHgDMX30jj0JJZ4s4TMXEMXaAwa6KaBx2sQR0iaCn96qXF6c4pFAZuh1FjBGTKyP68FPpv3rJzYQSpXY/iuFbEJP/nV1RY1HkVXfPYfMFzGMi0hIyLbgDVKqTknrMareV7wmOU1sByqgNOXZdgHljcUPIaNjjCzIyli23oB8K2uw6gbFSDJPUOkOqOIoRE8upmUrfjbvn4AAi/0I7Yi1egj1TKqg9YHk/j2OcbW2MpqlH/0I/DtDaMPpbD9OvGVGR5Tpk3wJSd9fnJhALNhtF6H0RPHe9AxpEYPr8t64vbvGERLWFg1HhJLRtUiWszEv3MIgMTSEFb16ErN2xHF6E2gdCF2eF3WLZnKNWldfvSgYDVMLGw0Mwxq9mylomws8WBrHnRzEIDG/sdoGHgcgJeXX5fVfsW+TRjWEIPVa+loPmdkezC2lyXt94PArtbLiQZH6wO2dvwv9YPPsn/B+fTVHTOy3ZfopH7gWQC6G07CNKpH9tUPPONkqDaq6Wk4afxr8OggTooZXQSvD4YsCKfsUROkwOrqwquwzE8q5IFjGopb+WQed3RDIccqGeNYMcdMWvOG6QqHf8cpyTzvmKwh3Y6aRB/tABwbkdSPTvDJ3YPEnu1GfDqhYxagCXg0YTBh0rS1F81URA+rzZqQjcEkVVscoZVYHMTKmJD9u4fwHYiSqvNmCRmx1MgxQ0ZjlpDxdMdH9sXW1KIyhExw+wDGQJJ4ayhbyMStkWPMWm+2kDkQJbh9ANurjREyoclekwXGwSDeHi+RMxOocWpZpXxNWMbsxtUNrbma/ckkyXh8xMZfrYfQ0mqxsDfbHVqJoGFjqgARNbrPsPrRsJxZN2GhhmPnlYWedIRxMLyPXu+IgxLeWA9N/Y4w6/OuJ+Udvb/VAy9RE99BzLOAbv/xQDruyU4gWCSN9IOTpVBBG3xauo3Ctmxs28KybErkjTx1lI2ZSuIPZH/Ojb65s9qdT0xLyCil/q1UA3EZxdCE1Q1BXugqRSzK3MfbGUPvcSSLxAXlLzyZKN2Hpc+umlH31qIDmRY40x+lO+SoVYOL1mS1HzBfh2ZGiVetJNg8uk8fEgbtk7DFi7dhEZovvSpRCl9vAlvzIaEGqhtGVyv+gSB2n3P9wboqPP7qjP4C2Ekf4g2MHFN78AnqD/4egF0bbwXR0OP92GgYVV7QNSSlEHuAQLybMIKmj/ZZFpTCm+ovmFXCVopouB9PItOgp6ZcEtylvIiq+Fop0+e4I49Vf3vwj7M6BqXUaKZATbJWQspWI04CojtPj/3xFM8eHKLBm/6hCdn6AKVGM8poZLvM2Rmu04WOyd1nZ7hiT6W/QsfAWGO3pSZ1TXp/gtrNL2EbXpIrLFTw0P/OAogVwxfZX9Zz1B58mIb2hwDYdeQ/ozQPSoSEZzF6c/Vo8LJS9Hfug96diH9iL7/pIMpGUykGa9bm3W8ri/6DHXh9GepeO05tbA+nXPX5J5VSx090js2bN+866qijuks36skjIsddffXVB7/97W/vA7j55ptbwuGwfueddx74yEc+svjuu+9uamhoMC3LkltuuWXf29/+9oHM7cP9/PnPf37x73//e/Diiy8+7Ac/+MH2t73tbQMAZ5999uqPfvSjBy+66KIhgPb2dmPp0qVHfu5zn9v78Y9/vGv4+K985SuN3/jGN1oAlFLymc98Zv9wFujJsHnz5qajjjqqLXd7wZWMiDxMkdZWpdTctW7OEIU8iwBEG5t8UxenEFlvcuq2BaUgZdssrPIND2K4TPtYxlNoF9o31f4K3IdC/Vm1XlLr+jG949i+DkGUHiBes7q859AM4tUrUWIQq1kzEsSqEqbjQj7sRi5gGwEsoxpfvjifkqKR1KuwjfwrUltZ2LoPO2PFaqHmXPpxr9erHnzwwfr29vaORYsWjfmhv//97z942223HXzqqaf855xzzuGXX3755sztue1bWlpSX/jCFxYNC5lcNm3aVH/UUUdF7rnnnoZhIbNjxw7PHXfcseiZZ57Z1tjYaA0MDGjt7e0ltbGP19mWjL914O1AB/BoetuJwCLg7lIOyMWh2mdwytK6afWhgL/v7ce01ZQcHSoNI9k7+kYpTE/tpCL7XcaSqGojUdVWVFvLW0N/zeEEppnxwsVB13V15ZVXdn3uc59r+frXv15wyXrsscfGdV2no6Nj3C/7unXroqlUSn72s5/VvOENbxjM3X/PPfc0fOlLX9r7rne9a+WOHTs8q1atSrW3t3tCoZBdW+tEzdbW1tq1tbUlTWNTcNBKqQ8O/y0iX8apKXO9ytCvichXmOHABBFZB1wPNAG/U0r9x0yefybx6NN/MqsLGAzETQxNqPbN3IRsFVFERMSJWSqmYbR2A5LhMeaL7EWzE5Ub2T/XUaDiTtqlYaykhZUyZyV1v54r2Eo86/zXX3Y2/vzp/U3jtclN3f/k7r7Abfc/vyxf29cf09r9ntNW9Ex03o997GOdGzduXH/LLbd0FGrz+9//PqRpmhpe7Xzzm99s+clPftIIUFtbaz766KMvDbf99Kc/3X7zzTe35gqZ7du3e7q6ujxnn3129JJLLunbtGlTw6233nrw5JNPjjY1NaWWLl268bTTThu67LLL+gqthKZKsb/QK4FT1FgDzjeAv+NM+hMiIt8DLgI6lVIbMrafD3wVZ8X0HaXU7YX6UEptA94vIhqwCThkhUwpOLKlhn0DcbZ2D83oecNJixpf4SdeSymSpqIpNHGAqiQttH4f4MOs86F8OnqyHyPRO+GxLlNEF6yh7AdaO5rETljYhiNmlFI5GZXK9LypQBp8IyY/pXBsd5knB3R76g/g+/pivsmm+++PJo1Cxxzf1lDUD66hocF+85vf3HP77bcvCASya1QPC5NQKGRt2rTpFS2tpiykLgO44IILwjfffDMPPfRQ1rg2bdrUcMkll/QBvPOd7+y96qqr2m699daDhmHwyCOPvPzHP/4x+Jvf/Kbmk5/85NInnngidOeddx4oZvzFUKyQEWAj8FLO9o2TPN/3gX/DEQ5OxyI6jiv0eTg5ex4XkV/iCJzP5xz/XqVUp4hcAlwL/Pckzz8vqQsYrG6YOKVHKVmma7TWFPY1jqYsnjxQ3AOTMZCk/o/tAPS9apHj9iw6MocCNCsVb2QvNd1/R2yT3iWvG8lELZ6xDwhiWmi2PbIvkjDRNUe4mLYi04RrKwjogseYvmpNJUysvtHidUopCNuopOkIoCodS/PSF8yfsLQYltQHEkctqQ2P1yY3dX9d0GsWOmZJfSCRb3s+PvWpTx089thjj7j88suzHBHGEyYT9Nf+2c9+dpFhjEbE3nfffQ1dXV2en/70pw3gVOB87rnnfBs3bkxomsbZZ58dPfvss6MXXHDB4NVXX902G0Lme8B3RGQNzsoF4GTg48B/FXsypdQjItKWs/lEYLtS6hUAEfkf4FKl1OdxVj35+vkl8EsR+V/gh/naiMj7gPcBLF64mN6hPlKWiW3bBHwB+iMDtNQ1s6drP2sWr2DL7hc4su0INu98nqNWrOfZXVvZsHwtLx/YybLmVg72d1EXqiWWiKFpGh7dIByP0lhdz4Heg6xoWcoL+3awYfnhI30M/7tt78usXLic/T3tNNc2MhAdwudxnuATqSS1wWq6BnpobVzEKx27Wbd0zZg+tux+kbVLVrHz4F4WN7TQM9RHlT9Y1DXt2P/ijF9Tjb+aVzp2F7ymw5euJT60F9O7jHD4IMFAA4nEEIbHj21bKGVjGF6SiQg15misx+BgO74FK+mIhFmi2bTHErT4PPQkTWo9OhHTxq9rpGwbEUEXSNqKgK4xZFrUewy6EykW+L10xJMszPi3K5Gk3uthMGUS0nUStu04YOCsvHyaRsSyqPEY9CVTNPvG9tEZT9Lk89CXMqk2dGKWjVcTLOVMjh5NI27ZhAyNgZRFo9fgYCKV1UdHPDlj19QytJvm3T8GYGfdiYTqqgteU0zZmEBC2U7lSw0aQl6CHp24mX1NuyNxrJTCxsaHRgIbL0IKhYFgOYdnLUZUepuFMzGlUHjRSPq0kT58aCSxwaNB0IdKpBy7nKaIF5FAtRDvOW1FTzHqrUyOW14f+8UHTn9xyidN09LSYl188cV9P/zhD5uuuOKKSY0hH5dddtngLbfcsrirq8sD8Oyzz/oikYje2dn57HCbD3/4w4vvuuuuhg984APd+/bt85x++ulRgCeeeCLY2tpaUptMUS7MadXUjThqsUXpze04Kq47lFJFq2nTQuaBYXWZiLwJOF8pdXX6/TuBk5RSHyhw/FnAZYAPeFYp9e8TnbMSXJhdsklZNk+3D5IqwnajpWy8g873Plntwfbq+KP78Mf2OMZ/lylT1/sYa166E4Xw/MbbiYUK18AZjJt0R5OICAnToi7gYVltAG8e2+HBSIKhhIm/BCuZXBSKoe5uvF4vylRISB/xWLzkzW+bMy7MwWDwmGg0+jTA3r17jcMOO2zjtddee3DYhbmqqsrKXcnkc2H+xS9+sf3ll1/23XHHHS0PP/zwdoAf/OAHte94xztW33///S89/PDDVbFYTPvGN74x4lzw6KOPBq644oqVv/71r1++8sor2w4ePOjx+XyqoaEh9e1vf3vP+vXri16JDVPIhXnScTIiUgOglBrjvVDk8W1MQ8hMBVfIHIKE9yP9L4N/frk1lxyVNm6ka/VIx2MQ7wVfPWpR/tQzSime6hiidhxHkhkTMimFVM1NIXOoMek4mUJMVbiMw35gacb7JeltLi6FKaqAmcuE5MSWaNt/itb9LHbjeqwCQqaobqc7roo7kctUGS8Y81ngVUqpPhF5jnECM5VSRxbaVwSPA2tEZAWOcLkceNs0+nOZD4iWt1qjS4lJRZDOpwEbVb8Ogs1FHebO/S7DjLeSuQ8Y1svdW4qTiciPgLOAJhHZB3xGKfVdEfkA8BCOR9n3lFLPl+h8FwMXr1o+da8Tl9nHGkwQebQDpSB4dDOeBUHcaaw8WCd8CstOwXBJ7Fg3xuOfA8A84VOoIoXMjKFARSqjro1LfsYLxrw139/TQSl1RYHtDwIPluIcOf3eD9x/3JHHXlPqvl1mDjthEX+xDwD/6lpYEEyreVxBU3J8OY4UWeXEK6O4mgCarpFMpp+B3QVtReOGS7tUPKJraDVeZ75LezIl25MM/q4GxKbuDMHTODoZDj5hk9gHeggazhu1OShL0X2/MyOF1gnBw0ePie1UhDc7+xpeK+iB0X19j9iYveBphrrTRvszhxR9v3OOqT5O8C8dPSbyvCL6sgKB5kvnVk6tLIILSb36G4AGgeKrepRX/Auh+gZXuMwRxrPJjGuHyWSaNhkXl3ExGvw0XXlE1jZlK+yEM3nnmmZUEuw4SJ7SznY83SYnHaGyRvflohLOPpUbPaAy+svR2NimcvbNYfkCgO6FmrYxm2d7DSnI7A/CpSjGW8mUxA7j4lIO9CoP/rYEovvQcpL1elsE8Si03IQDAoFVzp9GTtyeUTO6L9dxzbdEMOoURk32rCbejP5ySqx4GoXAKjUyEVoxRfhZp/xBcE32ystlfqLr+nFr1qyJWZYlS5cuTfzkJz/Z2dTUZL344oveo446akNbW9vIY88HPvCBgx/4wAemHag5GxRlk3FxqTSMBj+1GwcdA3UgO1YmsEoIrBo7iYsm1JyQf3L3LhC8C/LvC60bW4oBQPcX7s+/RPAvyagZlIL4zvS5FoFnXtaTdcnE5/PZL7zwwlaAyy67rO2LX/xi8xe+8IUOgKVLlyaG9811DmmbjOtddgjjqUItPBHpehYVm9oDnijbsTPMQB0S0UELADLHQnwSA+hP3Qko7JWXolqOc7a79pCScvLJJ0eeffbZwGyPoxwULWRE5D3AFcAysivLopSqyFnc9S47hBEBXy2q5Viwp1jYrfs5x5gyA0JGDwnNl85BFZmdQjv4uPPnolMndWjSsvHqWnHlHGabR7/ZyOYfO6n+3/dwdj6yH7x5JZFuDyvPGuDcz4ym5N/7aIBffdJJ9X/B7XtYelJsZN/APoPaJUV9MU3T5OGHH66+6qqrRjIQ7N2717d27doRQ+RXvvKVPeeff/64CTwrlaKEjIh8DPgU8J/AmTgp/len//5S2Ubn4jIRnmlkl56lWjTJbsXQk85SoOZEwVNfwZOw5sGuWw1o4E1naBbB0GEwYWIrRZ1/rIeF36Pj1zWiSYuqGaxjNGX6dvs48FT+VP8dz4YY6vBS25qdzyvWZ4wcE+vLvshUbMInl0Qioa1du/aIgwcPelatWhV//etfP5JN5VBSlxX7CHcN8D6l1KeAFPBvSqlLgDuAwhn1XFxcxqBMMPucV66XW8Xhq8U662tYZ30FtXh0JbO2sYq1TSHHyysPVV6Dlqr85ZMrkvrlCRYfG2bxsWNXCwuPjLD42DCNa7L9DwP15sgxgfrsT9ITmDCoaNgms2fPnueUUtx+++0LpnkVFUmxjxhLgMfSf8eAmvTfP0pvd9VRLnOQ2VlBaD7wLk7/PXHNtorEa2h4lDp03IhPen8PJ70/v3Hv7fe8knf70pNiY1RrwxSpKgOorq62v/a1r+1585vfvPoTn/hEZ7HHzRWKXcl04JQ7BtgNnJL+ezWuCdDFZVJ46oX6MzXqz9TQQ5DsUiQPKqy4+1Oar5x22mmxtWvXxr71rW81wKhNZvj12c9+ds6ucopdyfweuAR4Cvgu8GUReQtwLPCTMo3NxaX8zPK8bkUZyRpQc7IQaJvd8YxB2ZAcAhTofjCyg4/ElYtTZriWzDC///3vtw//HY/Hn5r5EZWHcYWMiJyrlPotToVJDUAp9U0R6QNOw0mi+Z9lH+UUcV2YXcalEryeModQiRO2GcPzKyfloLXhGuzVbxjZJZVw/1wqnolWMr8RkV04q5f/Ag4AKKV+DPy4vEObPq4Ls0uloweg7lWCaKDXghVRJNLVlHxLycqhlmhXWEPpTANtGYGetiKWfgb2NIGnYXSf2a9IprX8gZUgxui++B4n9Y0WAn/r6HY7qbAipL3eMhNkVqIUdKl0JhIy64GrgA8Ct4jIb4DvAL+cTMllFxeX/Igh+BaNvk+0K4aeciZzo0HQM8LzYjsViT2gV2cLGRQjx1QdKXgyEiAku0f3+ZYKesYvPvqiItUD3oWjQkbZjuDp/a2i+RLQPF6sI68FBLthHQDatv9Ge/keJ6r0+LuyrmfVo/+IkexlYOGr2bH8vSPbG4a2cMwrTrTDE6tvYiB02Mi+9bu/ycL+vxP1LuBv6/519N4ok1c/ezUAOxZexq6WS0b2Le75A+v2fR+AP6+7k4TXrZBaqYxr+FdKbVNK3YjjXfZWnAX9T4D9IvIFETl8Bsbo4lJG3KfzTBIHIL5bgQVWDNAM7JUXY6+8COrSidqUjdjmaBBsxgpHlImmzLEZQ1FoI/uy9wh2et/Y59bhYyRnFSWZ/blUNEUZ/pVSJvBT4Kcishh4N/Ae4EYR+YtS6szyDdHFpVxUnk3BuwCa3+CMKzeLdM0JAscxdthaxjE5KWsCbYyUIJAcd+m6V4kz4ef0FxkuGVhA/qrGDVhrbBBtzFD6Fp+PZsWIV6/K2h73NrNzwcUAJDzZ2Um7ao4h7mkkZWTHQiq0kWP6Q2uy9g35l43ss/TcTKgulcSkQ3GVUgdE5BvAEHALjgOAi8vcowIN16JLwdxmmif/eEUEKRD3KIYgBX7lmndsf94mqD/bSaOv549/R7UcN5LDTNoHs/b1LLts9E1ydJUR9S1k++K35u3vYP0pHKw/ZewO0QoeMxhaxWBoVd59LpXFpISMiJwLvBd4PRDHCcb8TumH5eLiMhtofsHrLgxmhEKp/i3L4qqrrlr6l7/8pUZElNfrVffee++OtWvX5lY0mhNMGIwpIstE5DMishP4DbAIx6V5sVLqOqXU0+P3MHuIyMUi8q2BwYHZHopLxeLaZKaLewenxnBamZdffvn5uro684tf/GIzwHe+852Gjo4OzwsvvPD8Sy+9tPUXv/jF9sbGxjnraDVRnMxvgbOATuAu4LtKqe3jHVNJuC7MLuOibLCT5XPNNebBkqDyNI5zksxU/+3t7Z6WlpaUrjt601WrVqVmdXDTZCJ1WQS4DPhf12XZ5VBDBRcg8R5UGWokS6LfKSGgz63kZOaAIvKCU8EzdISMqQY6WZKWTTRVOg8wTZyUnD6jdEV5Dtz06aWJl18OlqzDNL41a6KLP/cveydql5vq/53vfGfvmWeeuXbt2rXVZ5xxxuC73/3untNOOy02UT+VyrhCRil16UwNxMVlxqlpQ+WpX18K1MEnESZMxFtx2PHRCp6BlYymwh2HhGkVjP5vqfaWbKFoK7CVoj9uEk2N3lsR0KfhxJF4+eVg/LnnCrg5lI9Cqf5XrVqV2r59+5b777+/+ne/+13NhRdeePimTZt2XHrppUMzPcZSMAcKPbi4uMwYOmhBHDVYEQu85qCXuGkX1JpVe0s/xSyqsrHTgstWEE1ZqGlIMt+aNdESDW1S/Q7bZIaGhrSzzjprze23377gn/7pnzoBAoGAestb3jL4lre8ZbClpSX105/+tM4VMi4uLnMeb5PQfEnxq4LWmrlvdypGpVVOclP9P/roo8ElS5ak2traUpZl8dxzzwU2btx4aKrLXFxcXFzKT2aq/5aWFvMf/uEflieTSQ3g6KOPjnzyk5+cs3VmXCHj4uLiMguMl+r/TW960+DYI+YmrpBxcSkXczRrcXy3InHAqXpZe3LpPe9c5heH9DfIDcZ0cZk8qT5FfDfE98z2SFwOBQ5pIaOUul8p9b7amtrZHoqLy5xB8wt6lVNSwMVlurjqMhcXlyxCa4XQWsfDzE4q7LRfk17lJPAcxooolOlkftarsouoWWlnWy2QnYjTTjj1agD0muzqmuaQAtvJPq0H3VQChwqukHFxcSlIoh0G/+bYlhovFIyM4MzBxxXJDvA0QsN5mYIEen7lHFN9vBBcPXpMbAeEn3X2LXizQEbgfv8jjnDyLYO6U10hc6jgChkXl3Ig4maOLAHRlxXxPQqzHxa8cVS7b8UU8T1OzKh3EdNOf+NSPg5pm4yLi8v08DZB7alC7amCFsjeFzrC2R7amD3Ba57RY7wt2cf4Wkf35aYJqD7G2R48LEOFNqBIdY0dlx2F8NOKoacVvb9TKHtuSvRPfOITC1evXr3+sMMOO2Lt2rVH/P73vw+deOKJhz/yyCMjudRefPFF75o1a9bP5jing7uScXFxKYgeEvRQ/n3eBQWKqBmCf1n+Y4xawSjgh+NbPLY//1IhvkvhXZj/mMCa/MfNBX7729+GHnroobrnnntuayAQUO3t7UYikZibFzMOrpBxcXGpWLwtQvMbGKN6NOqh+TJnNVSoYmils3//fk9DQ4MZCAQUwKJFi0qXrrqCcIWMi4tLRZPp0TayTROkVFUUfn7dUjq3ljzVPwuOiPL6fy+YF+31r3/94Oc///nFbW1tG04//fTBK664ovd1r3tdGODKK69c6ff7bYBUKiWaNnctG66QcXEpC3Pz6Xpe0rk1yIGnZjzVf21trb1ly5atv/71r6t/97vfVb/rXe9adfPNN+8D2LRp0ytnnnlmFBybzEUXXbRmpsdXKlwh4+LiMidJ9ShiO53YmqojBc0/RcG+4IiypPovpl/DMLjooouGLrrooqEjjzwy9t///d+NZRnLLHJICxkRuRi4eNXylbM9FBcXlxJjhiGWTikZXAvaVKsOjKPSKiebN2/2aZrGxo0bEwBPP/10YMmSJckXXnghMNGxc4lDWsgope4H7j/uyGOume2xuMwvBAWJfjDLXH65VEk4fXWgla6k8UwgOojPCUmaiwwODuof+tCHlg0ODuq6rqu2trbEXXfdtfvSSy9dNdtjKyWHtJBxcZktVN1qqFo628MoChnYAVYiXRJz7uBfIviXzFEJA5xxxhnRp59++oXc7Y899tiLme8PP/zw5Msvv/z8zI2stLhCxsWlHHhroMyLmFKhoh1IKjLbw5gWsZ1OZgCXymPu+sW5uLiUiLm7GhjGjkGqd7ZH4ZIPV8i4uLjMefQQeBdM6hDbtu25L10rhPS9tPPtmxdCRtkKK5LCjh2SAbUuLtNjrlrOM/AvF+pOm9R0tqWrq6vWFTTTx7Zt6erqqgW25Ns/L2wymlfHszBE6kB4tofi4lKBzL951jTNqzs6Or7T0dGxgXnysF1GbGCLaZpX59s5L4QMmqCHPKS0+fdjcnEpjvllND/uuOM6gUtmexzzgfkjwV354uJSAPfH4VI+5pGQcX9ILi55cX8bLmVkHgkZ97fk4lKQUmUOcHHJYd4IGREBBOX+mFxcXFxmjPlh+B/GEOyIOXUVtHKq/mn++XXbXFxcXKbKIT1bDmdhXr16NQC+5TXTcqIxBxKY3bHSDM7FpVIQYb55l7nMHIe0ukwpdb9S6n21tU5RcdE1xJjGK0+FPheXuY/7vXYpH4e0kHFxcXFxmV0OaXVZOVBJC9uYnGwWXZBJHuPiMnO4KxmX8uEKmUmg+Q2MpskVrVOmjR1JoVfNkbzvLvMP17ffpYy4QmYSaH4D76KqSR1jx00S0cEyjcjFpVS4hn+X8uDqcFxc5j3uSsalfLhCxsVl3uMKGZfy4QoZFxcXN62MS9lwhYyLy3zHNfy7lBFXyLi4zHdcGeNSRlwh4+Li4uJSNlwh4+Iy73GXMi7lwxUyLi7zHlfIuJQPNxjTZU6gTBs7Yc32MKaN5tURT4U922kesJMQ6ynQwAbxgL9uJkflcojgChmXOYGdsNBrvGjBufuVtaMm1mASvdKETNViVGhh4f2xbqTn+Zkbj8shxdz9xbrMO/QqL3r13M0BZ+lJrMHkbA8jPzKO4Btvn4vLBLhCxsVlJrEVViSVvU2BGBqaX5+dMRWFa7dxmRqukHFxmSG0kAf/mvox262hJKnOKFDJQsbFZWq4QsbFZYYQETDGrgjcWkMuhzKHtJARkYuBi4GoiGyb7fHMIWqBgdkeRBmYK9dVKeOcjXFM95zLSzUQl9Igah4kxhORbyml3jfb45grHKr3a65cV6WMczbGUSnX7lI65ss6/f7ZHsAc41C9X3PluiplnLMxjkq5dpcSMS9WMi4uLi4us8N8Wcm4uLi4uMwCrpBxcXFxcSkbrpBxcXFxcSkbrpBxcXFxcSkbrpApgIicJSJ/EpFvishZsz2eSudQvl+H8rWVA/d+uWRS0UJGRJaKyMMislVEnheR66fR1/dEpFNEtuTZd76IvCgi20Xkk+nNCggDfmDfVM87k4iIX0QeE5HN6ft16zT6qsj7JSK6iDwtIg9Mo4+KvLZSIyJ1InKviLwgIttE5JQp9jMv7pdLmVBKVewLWAQcm/67GngJOCKnzQKgOmfb6jx9nQkcC2zJ2a4DO4CVgBfYDBwBaOn9LcAPZvteFHm/BKhK/+0BHgVOPpTuF/AR4IfAA3n2zelrK8O9ugu4Ov23F6hz75f7mulXRa9klFLtSqmn0n8PAduA1pxmrwJ+LiI+ABG5Bvh6nr4eAXrznOZEYLtS6hWlVBL4H+BSpZSd3t8H+EpxPeVGOYTTbz3pV24g1Jy9XyKyBHgd8J0CTebstZUaEanFEQ7fBVBKJZVS/TnN3PvlUnbmTO4yEWkDjsF5Oh9BKXWPiKwAfiwi9wDvBc6bRNetwN6M9/uAk0TkMuC1QB3wb1Mf+cwiIjrwJLAa+Hel1KF0v74CfBxnVTuGOX5tpWYF0AX8l4gchfOduF4pFRlu4N4vl5lgTggZEakC7gNuUEoN5u5XSv2riPwP8B/Aqoyn+SmjlPop8NPp9jPTKKUs4GgRqQN+JiIblFJbctrMufslIhcBnUqpJ8czJs/FaysTBo6K64NKqUdF5KvAJ4H/l9nIvV8u5aai1WUAIuLBETA/SH9587U5A9gA/Az4zCRPsR9YmvF+SXrbnCatGnkYOD933xy9X6cBl4jILhy1zKtF5O7cRnP02srBPmBfxkr2Xhyhk4V7v1zKTUULGRERHJ3yNqXUnQXaHAN8C7gUeA/QKCKfncRpHgfWiMgKEfEClwO/nN7IZwcRaU6vYBCRAI7q44WcNnPyfimlPqWUWqKUakuf8/dKqXdktpmr11YOlFIdwF4ROTy96Rxga2Yb9365zAiz7Xkw3gs4Hcdw/SzwTPp1YU6b04CNGe89wDV5+voR0A6kcJ7yrsrYdyGO59oO4NOzfd3TuF9HAk+n79cW4OY8beb8/QLOIr932Zy/thLfp6OBJ9Lfh58D9e79cl8z/XKzMLu4uLi4lI2KVpe5uLi4uMxtXCHj4uLi4lI2XCHj4uLi4lI2XCHj4uLi4lI2XCHj4uLi4lI2XCHj4uLi4lI2XCHjUhZEpE1ElIgcP4tjeFhErpyt85cKEQmLyLuLbHuPiHy0zENycSkaV8gcoojI99OTvBIRU0T2iMh/iEh9Gc71BxHJTYS4F6dUwzOlPl8xiMjrcFKe/GA2zj+L3AZ8Op2F2cVl1nGFzKHNb3Em+jbgauBi4BszcWKllKWU6lBKmTNxvjxcD3xfOQlD5w1KqeeAV4B3TNTWxWUmcIXMoU0iPdHvU0r9Bvgx8JrhnenVTlaFSRG5JbMC4nAbEbleRPaLSJ+I/JeIBIf349QluS5j5dSWqy4TpySvEpELRORJEYmJU6J3iYi8SpxqnuH0uRpzxvQecaqjxkXkJRH5sIgU/O6KSDNwLnB/zvZ/SB8fF5FuEXlIRIyM/eOeR0Rq06vB9nSbbSLy1oz9l4nIcyKSEJG9IvLpdP694f27ROSfROQ/RWRQRPaJyMdyxrg6vTKMi1Nx8qI813eziOxOn6dDRDblNPklcEWh++PiMpPMiVT/LtNHRFbiZGROTeHwM3ByV52Lo4L6CU6+qs/jrBgOw0nEeVO6fRfZ2XkzuRW4ARjAqXD5YyAOvA+wgHuAW4APpsd9DY4K6IM4NVE2AN9OX0ehWiWnAwmc/G2k+zke+HfgXcCfcWqdvDpj/7jnSQuLB4F6nGSSLwGH45QYRkSOS4/9szgquhOA/wQGyS4E9mGcbMdfBC4AviYif1ZK/S0t0H6GU+zrFCAIfJWMwl8i8kbgRhwh8hxOdcuTc67/MeCfRCSglIoVuEcuLjPDbCdPc1/leQHfB0ycWusxnESjCvhwTpsHco67hYwyu+k2ewE9Y9u3gd9mvP8D8G85/bSlz3d8+v1Z6fevzWjzgfS2Y8c5/x7gnTl93wBsHefabwB252y7DEewVRc4Ztzz4GS0toF1BY7/AU5m6Nx7uS/j/S7gRzltXgb+Kf33a3AE7bKM/cNJYt+dfv8R4EXAM871H5k+ZtVsfw/dl/ty1WWHNo/gZOI9Eedp+kHga1PoZ6vKtm0cwHmCngrPZvx9MP3vcznbFsCI2msp8J9pVVpYRMLA7cCqcc4RwFkdZfJ/wG5gp4j8QETeJSLVkzjPMUC7UmpbgXOuA/6Ss+3PQKuI1BS4fsi+l+uA/UqpPRn7H8URbsPcg7N62iki3xWRN0u6fHIGw6uXQIGxurjMGK6QObSJKqW2K6WeU0p9CEf9klkZ0QYk5xhPnn5yVWyKqX93MvtSAEqp3G3DfQ//+34cYTn82gCsH+cc3ThqrdFOlRrCKdr1FpxVy6eAF0Rk8TTOUyyZqc6ndS+VUntx1HT/gKOKuwN4UkRCGc0a0v92TX6oLi6lxRUy84tbgU+kJ1ZwJqFFOW2OnkK/SUCfxrjyopQ6iPOkvyotLLNe4xz6NNAsIk05/ZlKqd8rpT6Fo1IKARcVeZ6ngUUisq7AObfh1GfJ5HQcddlQkZe8DWflk2nPOpGc36lSKq6U+l+l1IdxbD/rc869AWdFdBAXl1nGNfzPI5RSfxCRrcA/Af8I/B74uIi8F0e1dhnOZLVvkl3vAk4UkTYcG1BvqcaMYyT/uoj046j7PDgrklal1OcLHPM00Ikzyf8cIO2ltQrnOnuBs4FqnIm9mPP8Dkd1dZ+IfBjH8L8aCCmlfo6zonhcRG7BcWg4Afgoo84QxfBbHAeKTelzBIAv49jWSF/Hu3F+t4/i3Ou34qyOXs7o5wzgoUmc18WlbLgrmfnHHcBVIrJcKfUQzurmX3A8qtqYWhzNl3BWM1txVkfLSjNUUEp9B3gv8E5gM/AnHE+0neMcYwHfA96esbkfeD2jE/mNwNVKqT8Vcx6llI3jDfYX4G4c4fRVwJve/xTwZuCNOF5tt6dfhTzg8o3bBt6A87t8FNiE462WyLmOq9Lj25I+32VKqZ0AIuJP9/HtYs/r4lJO3MqYLockIrIAR+idMDwBzwdE5DrgUqXUayZs7OIyA7grGZdDEqVUJ87KpGSrqjlCinSMkYtLJeCuZFxcXFxcyoa7knFxcXFxKRuukHFxcXFxKRuukHFxcXFxKRuukHFxcXFxKRuukHFxcXFxKRuukHFxcXFxKRv/H4AWI9Z7YDVtAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -553,7 +554,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -565,7 +566,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -606,7 +607,7 @@
     "        color = pred_plot_dict[tag]['color']\n",
     "        fmt = pred_plot_dict[tag]['fmt']\n",
     "        ax.plot(x, mean, label=label, color=color, linestyle=fmt, linewidth=2.5)\n",
-    "        ax.fill_between(x, mean-2*sem, mean+2*sem,\n",
+    "        ax.fill_between(x, mean-50*sem, mean+50*sem,\n",
     "                        color=color, alpha=0.2)\n",
     "\n",
     "        if add_sub_axes:\n",
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index 9aed4ba7c..b49771eba 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -31,7 +31,7 @@ def __init__(self, config):
         self.min_budget = self.config.search.min_budget
         self.eta = self.config.search.eta 
         self._epsilon = float(self.config.search.epsilon) 
-       
+        self.min_budget = min(self.min_budget, self.max_budget)
         s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
         # set up round sizes, fidelities, and list of arches
         for s in reversed(range(s_max + 1)):

From 892299a5b9afece00e6e0c2e087113131747e1bc Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Tue, 14 Jun 2022 15:37:42 +0200
Subject: [PATCH 118/130] implement kde for asr

---
 naslib/optimizers/discrete/bohb/optimizer.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 27d5f280d..5c67f6c62 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -146,6 +146,15 @@ def fit_kde(self, round):
                     self.vartypes += [5]  # depend on the encoding of search spaces
                 else:
                     self.vartypes += [7]
+        elif self.config.search_space == "asr":
+            good_enc = np.array([sum(m.arch.get_compact(), []) for m in good_models])
+            bad_enc = np.array([sum(m.arch.get_compact(), []) for m in bad_models])
+            self.kde_vartypes = ""
+            self.vartypes = []
+            for _ in range(len(good_enc[0])):  # we use unordered discrete variable
+                self.kde_vartypes += 'u'
+                # TODO: Adapt this
+                self.vartypes += [2]  # depend on the encoding of search spaces
         self.vartypes = np.array(self.vartypes, dtype=int)
         good_enc = self.impute_conditional_data(good_enc)
         bad_enc = self.impute_conditional_data(bad_enc)

From 3b806c1dbe51a21fecc2d2ce408d0e4ee2c0c6f1 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 14 Jun 2022 18:52:41 +0200
Subject: [PATCH 119/130] adapt kde to asr

---
 naslib/optimizers/discrete/bohb/optimizer.py |  9 ++++--
 naslib/search_spaces/nasbenchasr/graph.py    | 31 ++++++++++++++++++++
 2 files changed, 37 insertions(+), 3 deletions(-)

diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 5c67f6c62..5b3787f2c 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -151,10 +151,13 @@ def fit_kde(self, round):
             bad_enc = np.array([sum(m.arch.get_compact(), []) for m in bad_models])
             self.kde_vartypes = ""
             self.vartypes = []
-            for _ in range(len(good_enc[0])):  # we use unordered discrete variable
+            for i in range(len(good_enc[0])):  # we use unordered discrete variable
                 self.kde_vartypes += 'u'
-                # TODO: Adapt this
-                self.vartypes += [2]  # depend on the encoding of search spaces
+                if i == 0 or i == 2 or i == 5:
+                    self.vartypes += [6]
+                else:
+                    self.vartypes += [2]
+                
         self.vartypes = np.array(self.vartypes, dtype=int)
         good_enc = self.impute_conditional_data(good_enc)
         bad_enc = self.impute_conditional_data(bad_enc)
diff --git a/naslib/search_spaces/nasbenchasr/graph.py b/naslib/search_spaces/nasbenchasr/graph.py
index 47560409c..ccef197cb 100644
--- a/naslib/search_spaces/nasbenchasr/graph.py
+++ b/naslib/search_spaces/nasbenchasr/graph.py
@@ -99,6 +99,37 @@ def sample_random_architecture(self, dataset_api):
         compact = m
         self.set_compact(compact)
         return compact
+    def model_based_sample_architecture(self, dataset_api=None, minimize_me=None, good_kde=None, vartypes=None):
+        """
+        This will perform a model-based architecture sampling and update the edges in the
+        naslib object accordingly.
+        """
+        num_samples = 128
+        random_fraction = 0.33
+        best = np.inf
+        best_vector = None
+        for i in range(num_samples):
+            idx = np.random.randint(0, len(good_kde.data))
+            datum = good_kde.data[idx]
+            vector = []
+            for m, bw, t in zip(datum, good_kde.bw, vartypes):
+                if np.random.rand() < (1 - bw):
+                    vector.append(int(m))
+                else:
+                    vector.append(np.random.randint(t))
+            val = minimize_me(vector)
+            if val < best:
+                best = val
+                best_vector = vector
+        if best_vector is None or np.random.rand() < random_fraction:
+            self.sample_random_architecture(dataset_api=dataset_api)
+        else:
+            for i in range(len(best_vector)):
+                best_vector[i] = int(np.rint(best_vector[i]))
+            best_vector = [[best_vector[0],best_vector[1]],
+                           [best_vector[2],best_vector[3],best_vector[4]],
+                           [best_vector[5],best_vector[6], best_vector[7], best_vector[8]]]
+            self.set_compact(best_vector)
 
 
     def mutate(self, parent, mutation_rate=1, dataset_api=None):

From 8e2b6e80f779a5bd5d8dc4de6b9837f0ec60ad15 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Wed, 15 Jun 2022 21:04:15 +0200
Subject: [PATCH 120/130] set fix seed for asr query

---
 examples/optimizers_HPO.ipynb              | 5371 +++++++++++++++-----
 naslib/optimizers/discrete/sh/optimizer.py |    2 +-
 naslib/search_spaces/nasbenchasr/graph.py  |    3 +-
 3 files changed, 4080 insertions(+), 1296 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 8b1a26b76..4315081c1 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -9,18 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -29,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,52 +66,54 @@
     "    results_dict = {}\n",
     "    root = os.path.expanduser(root)\n",
     "    for search_space in search_spaces:\n",
-    "            #print(search_space)\n",
-    "            for optimizer in optimizers:\n",
-    "                #print(optimizer)\n",
-    "                optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
-    "                #print(optimizer_path)\n",
-    "                if not os.path.exists(optimizer_path):\n",
+    "        print(search_space)\n",
+    "        for optimizer in optimizers:\n",
+    "            print(optimizer)\n",
+    "            optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
+    "            #print(optimizer_path)\n",
+    "            if not os.path.exists(optimizer_path):\n",
+    "                continue\n",
+    "            for hpo_folder in os.listdir(optimizer_path):  #change seed and hpo_folder\n",
+    "                #print(hpo_folder)\n",
+    "                if hpo_folder[:7] != 'config_':\n",
     "                    continue\n",
-    "                for hpo_folder in os.listdir(optimizer_path):  #change seed and hpo_folder\n",
-    "                    print(hpo_folder)\n",
-    "                    hpo_seed = int(hpo_folder.split('_')[-1])\n",
-    "                    values = []\n",
-    "                    for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
-    "                        result_path =   optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
-    "                        #print(result_path)\n",
-    "                        if not os.path.isfile(result_path):\n",
-    "                            print(\"what?\")\n",
-    "                            continue\n",
-    "                        result = json.load(open(result_path))\n",
-    "                        valid_accs = result[1]['valid_acc']\n",
-    "                        value = np.max(valid_accs)\n",
-    "                        \n",
-    "                        #print(value)\n",
-    "                        if  np.isnan(value):\n",
-    "                            #print(type(value))\n",
-    "                            continue\n",
-    "                        value = float(value)\n",
-    "                        #print(value)\n",
-    "                        values.append(value)\n",
-    "                   \n",
-    "                   \n",
-    "                       \n",
-    "                    # had to add these if statements because file structure is in a different order\n",
-    "                    if hpo_seed not in results_dict:\n",
-    "                        results_dict[hpo_seed] = {}\n",
-    "                    if search_space not in results_dict[hpo_seed]:\n",
-    "                        results_dict[hpo_seed][search_space] = {}\n",
-    "                    if optimizer not in results_dict[hpo_seed][search_space]:\n",
-    "                        results_dict[hpo_seed][search_space][optimizer] = {}\n",
-    "                    results_dict[hpo_seed][search_space][optimizer] = [np.mean(values), np.std(values)]\n",
+    "                hpo_seed = int(hpo_folder.split('_')[-1])\n",
+    "                values = []\n",
+    "                for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
+    "                    result_path = optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
+    "                    print(result_path)\n",
+    "                    if not os.path.isfile(result_path):\n",
+    "                        print(\"what?\")\n",
+    "                        continue\n",
+    "                    result = json.load(open(result_path))\n",
+    "                    valid_accs = result[1]['valid_acc']\n",
+    "                    value = np.max(valid_accs)\n",
+    "                    \n",
+    "                    #print(value)\n",
+    "                    if np.isnan(value):\n",
+    "                        #print(type(value))\n",
+    "                        continue\n",
+    "                    value = float(value)\n",
+    "                    #print(value)\n",
+    "                    values.append(value)\n",
+    "                \n",
+    "                \n",
+    "                    \n",
+    "                # had to add these if statements because file structure is in a different order\n",
+    "                if hpo_seed not in results_dict:\n",
+    "                    results_dict[hpo_seed] = {}\n",
+    "                if search_space not in results_dict[hpo_seed]:\n",
+    "                    results_dict[hpo_seed][search_space] = {}\n",
+    "                if optimizer not in results_dict[hpo_seed][search_space]:\n",
+    "                    results_dict[hpo_seed][search_space][optimizer] = {}\n",
+    "                results_dict[hpo_seed][search_space][optimizer] = [np.mean(values), np.std(values)]\n",
     "                   \n",
     "    return results_dict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,11 +176,11 @@
     "# TODO: adapt this for multi-fidelity\n",
     "\n",
     "ss_dict_lined = {'nb201_c10':'NB-201\\n CIFAR10'}\n",
-    "\"\"\"\n",
     "ss_dict_lined = {'nb101':'NB-101\\n CIFAR10', \n",
     "                 'nb201_c10':'NB-201\\n CIFAR10', \n",
     "                 'nb201_c100':'NB-201\\n CIFAR100', \n",
     "                 'nb201_im':'NB-201\\n ImageNet', \n",
+    "                 'nb311_c10':'NB-311\\n CIFAR10',\n",
     "                 'darts':'DARTS\\n CIFAR10',\n",
     "                 'nlp':'NB-NLP\\n TreeBank',\n",
     "                 'asr':'NB-ASR\\n TIMIT',\n",
@@ -210,7 +203,6 @@
     "                 'transmacro_normal':'TNB-Macro\\n Surface', \n",
     "                 'transmacro_auto':'TNB-Macro\\n Autoenc.', \n",
     "          }\n",
-    "\"\"\"\n",
     "# TODO: adapt this for multi-fidelity\n",
     "ss_dict_oneline = {'nb101':'NB-101 CIFAR10', \n",
     "                 'nb201_c10':'NB-201 CIFAR10', \n",
@@ -246,9 +238,10 @@
     "    'nb201_c10':'nasbench201/cifar10', \n",
     "    'nb201_c100':'nasbench201/cifar100', \n",
     "    'nb201_im':'nasbench201/ImageNet16-120', \n",
+    "    'nb311_c10':'nasbench311/cifar10',\n",
     "    'darts':'darts/cifar10', \n",
     "    'nlp':'nlp/LM-task', \n",
-    "    'asr':'asr/TIMIT', \n",
+    "    'asr':'asr', \n",
     "    'transmicro_obj':'transbench101_micro/class_object',\n",
     "    'transmicro_scene':'transbench101_micro/class_scene',\n",
     "    'transmicro_jigsaw':'transbench101_micro/jigsaw',\n",
@@ -273,6 +266,7 @@
     "                 'nb201_c10':'NB-201', \n",
     "                 'nb201_c100':'NB-201', \n",
     "                 'nb201_im':'NB-201', \n",
+    "                 'nb311_c10': 'NB-311',\n",
     "                 'darts':'DARTS',\n",
     "                 'nlp':'NB-NLP',\n",
     "                 'asr':'NB-ASR',\n",
@@ -315,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "scrolled": true
    },
@@ -324,1216 +318,4017 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_86/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_86/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_86/8/errors.json\n",
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+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_86/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_86/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/8/errors.json\n",
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+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/bohb/config_72/5/errors.json\n",
+      "dehb\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/8/errors.json\n",
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+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_36/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_9/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_9/0/errors.json\n",
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+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_26/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_21/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_28/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_17/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_10/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_44/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_43/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_88/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_75/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_81/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_86/5/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/9/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/0/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/7/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/6/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/1/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/8/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/4/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/3/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/2/errors.json\n",
+      "/Users/lars/Projects/results/all_experiments/asr/dehb/config_72/5/errors.json\n"
      ]
     }
    ],
@@ -1541,17 +4336,11 @@
     "# do not accidentally run this twice.\n",
     "# takes >30 min to load all these results\n",
     "if True:\n",
-    "    search_spaces = ['nb101', 'nb201_c10', 'nb201_c100', 'nb201_im', 'darts', 'asr', 'nlp',\n",
-    "                     'transmicro_obj', 'transmicro_scene', 'transmicro_jigsaw', \n",
-    "                     'transmicro_room', 'transmicro_segment', 'transmicro_normal', \n",
-    "                     'transmicro_auto', \n",
-    "                     'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
-    "                     'transmacro_room', 'transmacro_segment', 'transmacro_normal', \n",
-    "                     'transmacro_auto', 'mr_cls', 'mr_3ddet', 'mr_seg', 'mr_video']\n",
-    "    search_spaces = ['nb201_c10','nb201_c100','nb201_im']\n",
+    "    search_spaces = ['nb201_c10','nb201_c100','nb201_im', 'nb311_c10', 'asr']\n",
+    "    search_spaces = ['asr']\n",
     "    optimizers=('sh', 'hb', 'bohb','dehb')\n",
     "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
-    "    root = '~/plotting_data/bbo/'\n",
+    "    root = '/Users/lars/Projects/results/all_experiments/'\n",
     "\n",
     "\n",
     "    results = get_hpo_results(optimizers, search_spaces, root=root)"
@@ -1559,38 +4348,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'nb201_c10': {'sh': [90.87899999999999, 0.2048145502643775],\n",
-       "  'hb': [91.053, 0.23639162421710094],\n",
-       "  'bohb': [91.17559998706055, 0.19551327291051013],\n",
-       "  'dehb': [91.37559999267577, 0.19945886851307323]},\n",
-       " 'nb201_c100': {'sh': [71.89999996093749, 0.9765654354455887],\n",
-       "  'hb': [72.31199997070311, 0.7659869445931258],\n",
-       "  'bohb': [71.58099999999999, 0.9223063482379389],\n",
-       "  'dehb': [71.63199999999999, 0.7419811318355743]},\n",
-       " 'nb201_im': {'sh': [44.55666663004557, 1.101216496499461],\n",
-       "  'hb': [44.829999959309895, 0.9529428291015308],\n",
-       "  'bohb': [44.986666653442384, 0.9239949405195867],\n",
-       "  'dehb': [44.24666666768392, 1.0534546359944508]}}"
+       "{'asr': {'sh': [0.9688516855239868, 0.0],\n",
+       "  'hb': [0.9688516855239868, 0.0],\n",
+       "  'bohb': [0.9688516855239868, 0.0],\n",
+       "  'dehb': [0.9688516855239868, 0.0]}}"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results[20]\n"
+    "results[12]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2153,11 +4934,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "a100edf3d46bc2e6c79c61e7e0e4c85fe6b13f4c7883d33d138476068eadc537"
-  },
   "kernelspec": {
-   "display_name": "Python 3.7.13 ('naslib_exercises')",
+   "display_name": "Python 3.8.9 ('jupyter-venv')",
    "language": "python",
    "name": "python3"
   },
@@ -2171,7 +4949,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.8.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "86605511fd47612766282e128cf9cb9d4e63c6452a0637c1eb09019680aeca68"
+   }
   }
  },
  "nbformat": 4,
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index 97004bd2b..c57f2f5e4 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -91,7 +91,7 @@ def new_epoch(self, epoch, round, i):
         
         if self.process < i: # re-init for each new process
             del self.current_round
-            del  self.next_round
+            del self.next_round
             del self.round_number
             del self.prev_round
             del self.process
diff --git a/naslib/search_spaces/nasbenchasr/graph.py b/naslib/search_spaces/nasbenchasr/graph.py
index ccef197cb..0dcc94113 100644
--- a/naslib/search_spaces/nasbenchasr/graph.py
+++ b/naslib/search_spaces/nasbenchasr/graph.py
@@ -55,7 +55,8 @@ def query(self, metric=None, dataset=None, path=None, epoch=-1,
             Metric.TRAIN_TIME,
             Metric.RAW,
         ]
-        query_results = dataset_api["asr_data"].full_info(self.compact)
+        # seed has to be set manually, otherwise one would get different metric values for the same architecture!
+        query_results = dataset_api["asr_data"].full_info(self.compact, seed=1234)
 
 
         if metric != Metric.VAL_ACCURACY:

From 28e700416f9f81c551e2398c30194dd18c7d7783 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 17 Jun 2022 18:29:38 +0200
Subject: [PATCH 121/130] comment in hb optimizer

---
 naslib/optimizers/discrete/hb/optimizer.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index b49771eba..a58923cd6 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -70,24 +70,26 @@ def compute_epochs(self):
         return self.round_sizes, self.rounds[::-1]
 
     def new_epoch(self, epoch, round, i):
+        # round - bracket
+        # epoch - number of architectures in bracket so far
         if self.process < i: # re-init for each new process
             del self.current_round
-            del  self.next_round
+            del self.next_round
             del self.round_number
             del self.prev_round
             del self.process
             self.current_round = []
             self.next_round = []
-            self.round_number = 0
+            self.round_number = 0 # index to fidelity
             self.prev_round = 0
             self.process = i
             self.clean_history()
 
-        if self.prev_round < round:  # reset round_number for each new round
+        if self.prev_round < round:  # reset round_number for each new round # check if new bracket
             self.prev_round = round
             self.round_number = 0
 
-        if epoch < self.round_sizes[round][0]:
+        if epoch < self.round_sizes[round][0]: # check if first fidelity of bracket
             # sample random architectures
             model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
@@ -100,8 +102,8 @@ def new_epoch(self, epoch, round, i):
             self._update_history(model)
             self.next_round.append(model)
 
-        else:
-            if len(self.current_round) == 0:
+        else: 
+            if len(self.current_round) == 0: # fidelity is full
                 # if we are at the end of a round of hyperband, continue training only the best 
                 logger.info("Starting a new round: continuing to train the best arches")
                 self.round_number += 1
@@ -110,7 +112,7 @@ def new_epoch(self, epoch, round, i):
                 self.next_round = []
 
             # train the next architecture
-            model = self.current_round.pop()
+            model = self.current_round.pop() # architecture to train
             """
             Note: technically we would just continue training this arch, but right now,
             just for simplicity, we treat it as if we start to train it again from scratch

From f6533b00ab9d3a175adb25dae173d16afd832c29 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 17 Jun 2022 19:12:45 +0200
Subject: [PATCH 122/130] delete obsolete files

---
 examples/epochs_calc.py                       |  32 -
 examples/multifidelity                        |   6 -
 .../nas_predictors/discrete_config_BOHB.yaml  |  21 -
 .../discrete_config_Bananas.yaml              |  87 ---
 .../nas_predictors/discrete_config_DEHB.yaml  |  22 -
 .../nas_predictors/discrete_config_HB.yaml    |  19 -
 .../nas_predictors/discrete_config_SH.yaml    |  41 --
 naslib/defaults/trainer_multifidelity_old.py  | 615 ------------------
 .../discrete/bohb/optimizer._old.py           | 192 ------
 naslib/optimizers/discrete/bohb/parameter.txt |   7 -
 naslib/optimizers/discrete/bohb/plannung.txt  | 141 ----
 naslib/optimizers/discrete/hb/old.py          | 176 -----
 .../optimizers/discrete/sh/optimizer_old.py   | 226 -------
 13 files changed, 1585 deletions(-)
 delete mode 100644 examples/epochs_calc.py
 delete mode 100644 examples/multifidelity
 delete mode 100644 naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
 delete mode 100644 naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml
 delete mode 100644 naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
 delete mode 100644 naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
 delete mode 100644 naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
 delete mode 100644 naslib/defaults/trainer_multifidelity_old.py
 delete mode 100644 naslib/optimizers/discrete/bohb/optimizer._old.py
 delete mode 100644 naslib/optimizers/discrete/bohb/parameter.txt
 delete mode 100644 naslib/optimizers/discrete/bohb/plannung.txt
 delete mode 100644 naslib/optimizers/discrete/hb/old.py
 delete mode 100644 naslib/optimizers/discrete/sh/optimizer_old.py

diff --git a/examples/epochs_calc.py b/examples/epochs_calc.py
deleted file mode 100644
index 705b725aa..000000000
--- a/examples/epochs_calc.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# this script calculates the amount of budget needed to create nice plots for Successhive Halving :D
-
-from math import log
-from math import ceil
-from math import floor
-
-max_budget = 128
-eta = 2
-number_archs = 128
-
-def calc_sh_budget():
-    i = 1
-    budget = 0.0
-    while i <= max_budget:
-        print("fidelity: {}".format(i))
-        budget = budget + (i / max_budget) * (number_archs / i)
-        i = i * eta
-    print(budget/2)
-
-def calc_hb_budget():
-    i = 1
-    s = floor(log(max_budget, eta))
-
-    n = ceil(int(b / max_budget / (s + 1)) * eta ** s)
-    r = max_budget * eta ** (-s)
-    # in a loop
-
-def main():
-    calc_hb_budget()
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/multifidelity b/examples/multifidelity
deleted file mode 100644
index a62e8d54b..000000000
--- a/examples/multifidelity
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="utf-8" standalone="no"?>
-<svg height="253" width="322" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <g>
-        <image xmlns="http://www.w3.org/2000/svg" x="0" y="0" height="100%" width="100%" xmlns:xlink="http://www.w3.org/1999/xlink" 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"/>
-    </g>
-</svg>
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
deleted file mode 100644
index a7f435c51..000000000
--- a/naslib/benchmarks/nas_predictors/discrete_config_BOHB.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-seed: 0
-optimizer: bananas
-search_space: nasbench201
-dataset: cifar10
-out_dir: run
-
-search:
-  seed: 0
-  checkpoint_freq: 10 # 1000
-  min_fidelity: 1
-  fidelity: 200
-  budgets: 4000
-  eta: 3
-  max_budget: 512
-  min_budget: 12
-  epsilon: 1e-6
-  predictor_type: var_sparse_gp
-  min_bandwith: 0.001  #bandwidth is done by algorithm, but we want also a minimum bandwidth at the end train 0 to 0.01
-  top_n_percent: 0.1   #number of archiekteurs that are consider as good train 0.05 to 0.6 but I think log scale 
-  min_points_in_model: 7  #number of points where we start to train a model min dimesnsions + 1, here 7. Can be trained or fix. 
-  n_process: 1
\ No newline at end of file
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml b/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml
deleted file mode 100644
index 170905ba1..000000000
--- a/naslib/benchmarks/nas_predictors/discrete_config_Bananas.yaml
+++ /dev/null
@@ -1,87 +0,0 @@
-seed: 0
-optimizer: oneshot
-search_space: darts
-dataset: cifar10
-out_dir: run
-
-experiment_type: single
-predictor: oneshot
-test_size: 200
-train_size_list: [8, 12]
-train_size_single: 2
-fidelity_list: [5]
-fidelity_single: 5
-
-search:
-  seed: 0
-  checkpoint_freq: 1000
-  epochs: 50
-  fidelity: -1
-
-  # GDAS
-  tau_max: 10
-  tau_min: 0.1
-
-  # RE
-  sample_size: 10
-  population_size: 30
-  
-  # LS
-  num_init: 10
-  
-  # BANANAS
-  k: 10
-  num_ensemble: 3
-  acq_fn_type: its
-  acq_fn_optimization: mutation
-  encoding_type: path
-  num_arches_to_mutate: 2
-  max_mutations: 1
-  num_candidates: 100
-  
-  # BP
-  predictor_type: bananas
-  debug_predictor: False
-
-
-  # additional params
-  batch_size: 64
-  learning_rate: 0.025
-  learning_rate_min: 0.001
-  momentum: 0.9
-  weight_decay: 0.0003
-  warm_start_epochs: 0
-  grad_clip: 5
-  train_portion: 0.9
-  data_size: 25000
-
-  cutout: False
-  cutout_length: 16
-  cutout_prob: 1.0
-  drop_path_prob: 0.0
-
-  unrolled: False
-  arch_learning_rate: 0.0003
-  arch_weight_decay: 0.001
-  output_weights: True
-  
-
-evaluation:
-  checkpoint_freq: 5000
-  batch_size: 96
-  learning_rate: 0.025
-  learning_rate_min: 0.00
-  momentum: 0.9
-  weight_decay: 0.0003
-  epochs: 600
-  warm_start_epochs: 0
-  grad_clip: 5
-  train_portion: 1.
-  data_size: 50000
-
-  cutout: True
-  cutout_length: 16
-  cutout_prob: 1.0
-  drop_path_prob: 0.2
-  auxiliary_weight: 0.4
-
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
deleted file mode 100644
index 3307473f8..000000000
--- a/naslib/benchmarks/nas_predictors/discrete_config_DEHB.yaml
+++ /dev/null
@@ -1,22 +0,0 @@
-seed: 0
-optimizer: bananas
-search_space: nasbench201
-dataset: cifar10
-out_dir: run
-
-search:
-  seed: 0
-  checkpoint_freq: 10 # 1000
-  fidelity: 200
-  budgets: 4000
-  eta: 3
-  max_budget: 512
-  min_budget: 12
-  epsilon: 1e-6
-  predictor_type: var_sparse_gp
-  top_n_percent: 0.3
-  enc_dim: 6   #dimensions of the output fix.
-  max_mutations: 1 #nummber of gens that mutate if it is mutating range(0 to enc_dim) but maybe to make it better (1 to 4)??
-  crossover_prob: 0.5   #probabilty that a gen is choosen from mutate range(0.4 to 0.9)
-  mutate_prob: 0.1 #probability of mutation  range(0.05 to 0.7)
-  n_process: 1
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml b/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
deleted file mode 100644
index 2373d7ca5..000000000
--- a/naslib/benchmarks/nas_predictors/discrete_config_HB.yaml
+++ /dev/null
@@ -1,19 +0,0 @@
-seed: 0
-search_space: asr
-dataset: cifar10
-out_dir: run
-
-search:
-  seed: 0
-  checkpoint_freq: 10 # 1000
-  fidelity: 200
-  budgets: 400
-  eta: 3
-  n_process: 100
-  max_budget: 39
-  min_budget: 12
-  epsilon: 1e-6
-  predictor_type: var_sparse_gp
-
- 
-  
diff --git a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml b/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
deleted file mode 100644
index 750fcbff5..000000000
--- a/naslib/benchmarks/nas_predictors/discrete_config_SH.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-config_id: 0
-dataset: None
-optimizer: sh
-out_dir: run
-search:
-  acq_fn_optimization: mutation
-  acq_fn_type: its
-  budget_max: 128
-  budget_type: epoch
-  budgets: 16000
-  checkpoint_freq: 5000
-  crossover_prob: 0.5
-  debug_predictor: false
-  enc_dim: 6
-  encoding_type: path
-  epochs: 200
-  epsilon: 1.0e-06
-  eta: 2
-  fidelity: 39
-  k: 10
-  max_budget: 39
-  max_mutations: 1
-  method: random
-  min_bandwith: 0.001
-  min_budget: 1
-  min_fidelity: 1
-  min_points_in_model: 7
-  mutate_prob: 0.5
-  n_process: 1000000
-  num_arches_to_mutate: 1
-  num_candidates: 50
-  num_ensemble: 3
-  num_init: 10
-  number_archs: 128
-  predictor_type: test
-  population_size: 50
-  predictor: var_sparse_gp
-  sample_size: 10
-  top_n_percent: 0.1
-search_space: asr
-seed: 0
diff --git a/naslib/defaults/trainer_multifidelity_old.py b/naslib/defaults/trainer_multifidelity_old.py
deleted file mode 100644
index de547b048..000000000
--- a/naslib/defaults/trainer_multifidelity_old.py
+++ /dev/null
@@ -1,615 +0,0 @@
-import codecs
-from naslib.search_spaces.core.graph import Graph
-import time
-import json
-import logging
-import os
-import copy
-import torch
-import numpy as np
-
-from fvcore.common.checkpoint import Checkpointer
-
-from naslib.search_spaces.core.query_metrics import Metric
-
-from naslib.utils import utils
-from naslib.utils.logging import log_every_n_seconds, log_first_n
-
-from typing import Callable
-#from .additional_primitives import DropPathWrapper #Can be causes issues
-
-logger = logging.getLogger(__name__)
-
-
-class Trainer(object):
-    """
-    Default implementation that handles dataloading and preparing batches, the
-    train loop, gathering statistics, checkpointing and doing the final
-    final evaluation.
-
-    If this does not fulfil your needs free do subclass it and implement your
-    required logic.
-    """
-
-    def __init__(self, optimizer, config, lightweight_output=False):
-        """
-        Initializes the trainer.
-
-        Args:
-            optimizer: A NASLib optimizer
-            config (AttrDict): The configuration loaded from a yaml file, e.g
-                via  `utils.get_config_from_args()`
-        """
-        self.optimizer = optimizer
-        self.config = config
-        self.epochs = self.config.search.epochs
-        self.lightweight_output = lightweight_output
-
-        # preparations
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-        # measuring stuff
-        self.train_top1 = utils.AverageMeter()
-        self.train_top5 = utils.AverageMeter()
-        self.train_loss = utils.AverageMeter()
-        self.val_top1 = utils.AverageMeter()
-        self.val_top5 = utils.AverageMeter()
-        self.val_loss = utils.AverageMeter()
-
-        n_parameters = optimizer.get_model_size()
-        logger.info("param size = %fMB", n_parameters)
-        self.errors_dict = utils.AttrDict(
-            {
-                "train_acc": [],
-                "train_loss": [],
-                "valid_acc": [],
-                "fidelity": [],
-                "arch": [],
-                "valid_loss": [],
-                "test_acc": [],
-                "test_loss": [],
-                "runtime": [],
-                "train_time": [],
-                "arch_eval": [],
-                "params": n_parameters,
-            }
-        )
-
-    def search(self, resume_from="", summary_writer=None, after_epoch: Callable[[int], None] = None, report_incumbent=True):
-        """
-        Start the architecture search.
-
-        Generates a json file with training statistics.
-
-        Args:
-            resume_from (str): Checkpoint file to resume from. If not given then
-                train from scratch.
-        """
-        logger.info("Start training")
-
-        np.random.seed(self.config.search.seed)
-        torch.manual_seed(self.config.search.seed)
-
-        self.optimizer.before_training()
-        checkpoint_freq = self.config.search.checkpoint_freq
-        if self.optimizer.using_step_function:
-            self.scheduler = self.build_search_scheduler(
-                self.optimizer.op_optimizer, self.config
-            )
-
-            start_epoch = self._setup_checkpointers(
-                resume_from, period=checkpoint_freq, scheduler=self.scheduler
-            )
-        else:
-            start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq)
-
-        if self.optimizer.using_step_function:
-            self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(
-                self.config
-            )
-        e = start_epoch
-        # TODO: good name for "epochs"
-        while e < self.epochs:
-
-            start_time = time.time()
-            # return function_eqalisation difference time to run or number of elovations
-            used_budget = self.optimizer.new_epoch()
-
-            if self.optimizer.using_step_function:
-                for step, data_train in enumerate(self.train_queue):
-                    data_train = (
-                        data_train[0].to(self.device),
-                        data_train[1].to(self.device, non_blocking=True),
-                    )
-                    data_val = next(iter(self.valid_queue))
-                    data_val = (
-                        data_val[0].to(self.device),
-                        data_val[1].to(self.device, non_blocking=True),
-                    )
-
-                    stats = self.optimizer.step(data_train, data_val)
-                    logits_train, logits_val, train_loss, val_loss = stats
-
-                    self._store_accuracies(logits_train, data_train[1], "train")
-                    self._store_accuracies(logits_val, data_val[1], "val")
-
-                    log_every_n_seconds(
-                        logging.INFO,
-                        "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
-                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()
-                        ),
-                        n=5,
-                    )
-
-                    if torch.cuda.is_available():
-                        log_first_n(
-                            logging.INFO,
-                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
-                            n=3,
-                        )
-
-                    self.train_loss.update(float(train_loss.detach().cpu()))
-                    self.val_loss.update(float(val_loss.detach().cpu()))
-
-                self.scheduler.step()
-
-                end_time = time.time()
-
-                self.errors_dict.train_acc.append(self.train_top1.avg)
-                self.errors_dict.train_loss.append(self.train_loss.avg)
-                self.errors_dict.valid_acc.append(self.val_top1.avg)
-                self.errors_dict.valid_loss.append(self.val_loss.avg)
-                self.errors_dict.runtime.append(end_time - start_time)
-            else:
-                end_time = time.time()
-                # TODO: nasbench101 does not have train_loss, valid_loss, test_loss implemented, so this is a quick fix for now
-                # train_acc, train_loss, valid_acc, valid_loss, test_acc, test_loss = self.optimizer.train_statistics()
-                # only for debugging is it but out
-                (
-                    train_acc,
-                    valid_acc,
-                    test_acc,
-                    train_time,
-                ) = self.optimizer.train_statistics()
-                train_loss, valid_loss, test_loss = -1, -1, -1
-
-                self.errors_dict.train_acc.append(train_acc)
-                self.errors_dict.train_loss.append(train_loss)
-                self.errors_dict.valid_acc.append(valid_acc)
-                self.errors_dict.valid_loss.append(valid_loss)
-                self.errors_dict.test_acc.append(test_acc)
-                self.errors_dict.test_loss.append(test_loss)
-                self.errors_dict.runtime.append(end_time - start_time)
-                self.errors_dict.train_time.append(train_time)
-                self.train_top1.avg = train_acc
-                self.val_top1.avg = valid_acc
-            self.checkpointer.save(e) # define the name in accurate , also 
-            # TODO: change step into save for checkpointer
-            anytime_results = self.optimizer.test_statistics()
-            if anytime_results:
-                # record anytime performance
-                self.errors_dict.arch_eval.append(anytime_results)
-                #log_every_n_seconds(
-                #    logging.INFO,
-                #    "Epoch {}, Anytime results: {}".format(e, anytime_results),
-                #    n=5,
-                #)
-
-            self._log_to_json()
-            self._log_and_reset_accuracies(e, summary_writer)
-            if after_epoch is not None:
-                after_epoch(e)
-
-            e += used_budget
-
-        self.optimizer.after_training()
-        self._log_optimizer_stats()
-
-        if summary_writer is not None:
-            summary_writer.close()
-
-        logger.info("Training finished")
-
-    def evaluate_oneshot(self, resume_from="", dataloader=None):
-        """
-        Evaluate the one-shot model on the specified dataset.
-
-        Generates a json file with training statistics.
-
-        Args:
-            resume_from (str): Checkpoint file to resume from. If not given then
-                evaluate with the current one-shot weights.
-        """
-        logger.info("Start one-shot evaluation")
-        self.optimizer.before_training()
-        self._setup_checkpointers(resume_from)
-
-        loss = torch.nn.CrossEntropyLoss()
-
-        if dataloader is None:
-            # load only the validation data
-            _, dataloader, _ = self.build_search_dataloaders(self.config)
-
-        self.optimizer.graph.eval()
-        with torch.no_grad():
-            start_time = time.time()
-            for step, data_val in enumerate(dataloader):
-                input_val = data_val[0].to(self.device)
-                target_val = data_val[1].to(self.device, non_blocking=True)
-
-                logits_val = self.optimizer.graph(input_val)
-                val_loss = loss(logits_val, target_val)
-
-                self._store_accuracies(logits_val, data_val[1], "val")
-                self.val_loss.update(float(val_loss.detach().cpu()))
-
-            end_time = time.time()
-
-            self.errors_dict.valid_acc.append(self.val_top1.avg)
-            self.errors_dict.valid_loss.append(self.val_loss.avg)
-            self.errors_dict.runtime.append(end_time - start_time)
-
-            self._log_to_json()
-
-        logger.info("Evaluation finished")
-        return self.val_top1.avg
-
-    def evaluate(
-        self,
-        retrain: bool = True,
-        search_model: str = "",
-        resume_from: str = "",
-        best_arch: Graph = None,
-        dataset_api: object = None,
-        metric: Metric = None,
-    ):
-        """
-        Evaluate the final architecture as given from the optimizer.
-
-        If the search space has an interface to a benchmark then query that.
-        Otherwise train as defined in the config.
-
-        Args:
-            retrain (bool)      : Reset the weights from the architecure search
-            search_model (str)  : Path to checkpoint file that was created during search. If not provided,
-                                  then try to load 'model_final.pth' from search
-            resume_from (str)   : Resume retraining from the given checkpoint file.
-            best_arch           : Parsed model you want to directly evaluate and ignore the final model
-                                  from the optimizer.
-            dataset_api         : Dataset API to use for querying model performance.
-            metric              : Metric to query the benchmark for.
-        """
-        logger.info("Start evaluation")
-        if not best_arch:
-
-            if not search_model:
-                search_model = os.path.join(
-                    self.config.save, "search", "model_final.pth"
-                )
-            self._setup_checkpointers(search_model)  # required to load the architecture
-
-            best_arch = self.optimizer.get_final_architecture()
-        logger.info("Final architecture:\n" + best_arch.modules_str())
-
-        if best_arch.QUERYABLE:
-            if metric is None:
-                metric = Metric.TEST_ACCURACY
-            result = best_arch.query(
-                metric=metric, dataset=self.config.dataset, dataset_api=dataset_api
-            )
-            logger.info("Queried results ({}): {}".format(metric, result))
-        else:
-            best_arch.to(self.device)
-            if retrain:
-                logger.info("Starting retraining from scratch")
-                best_arch.reset_weights(inplace=True)
-
-                (
-                    self.train_queue,
-                    self.valid_queue,
-                    self.test_queue,
-                ) = self.build_eval_dataloaders(self.config)
-
-                optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
-                scheduler = self.build_eval_scheduler(optim, self.config)
-
-                start_epoch = self._setup_checkpointers(
-                    resume_from,
-                    search=False,
-                    period=self.config.evaluation.checkpoint_freq,
-                    model=best_arch,  # checkpointables start here
-                    optim=optim,
-                    scheduler=scheduler,
-                )
-
-                grad_clip = self.config.evaluation.grad_clip
-                loss = torch.nn.CrossEntropyLoss()
-
-                self.train_top1.reset()
-                self.train_top5.reset()
-                self.val_top1.reset()
-                self.val_top5.reset()
-
-                # Enable drop path
-                best_arch.update_edges(
-                    update_func=lambda edge: edge.data.set(
-                        "op", DropPathWrapper(edge.data.op)
-                    ),
-                    scope=best_arch.OPTIMIZER_SCOPE,
-                    private_edge_data=True,
-                )
-
-                # train from scratch
-                epochs = self.config.evaluation.epochs
-                for e in range(start_epoch, epochs):
-                    best_arch.train()
-
-                    if torch.cuda.is_available():
-                        log_first_n(
-                            logging.INFO,
-                            "cuda consumption\n {}".format(torch.cuda.memory_summary()),
-                            n=20,
-                        )
-
-                    # update drop path probability
-                    drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
-                    best_arch.update_edges(
-                        update_func=lambda edge: edge.data.set(
-                            "drop_path_prob", drop_path_prob
-                        ),
-                        scope=best_arch.OPTIMIZER_SCOPE,
-                        private_edge_data=True,
-                    )
-
-                    # Train queue
-                    for i, (input_train, target_train) in enumerate(self.train_queue):
-                        input_train = input_train.to(self.device)
-                        target_train = target_train.to(self.device, non_blocking=True)
-
-                        optim.zero_grad()
-                        logits_train = best_arch(input_train)
-                        train_loss = loss(logits_train, target_train)
-                        if hasattr(
-                            best_arch, "auxilary_logits"
-                        ):  # darts specific stuff
-                            log_first_n(logging.INFO, "Auxiliary is used", n=10)
-                            auxiliary_loss = loss(
-                                best_arch.auxilary_logits(), target_train
-                            )
-                            train_loss += (
-                                self.config.evaluation.auxiliary_weight * auxiliary_loss
-                            )
-                        train_loss.backward()
-                        if grad_clip:
-                            torch.nn.utils.clip_grad_norm_(
-                                best_arch.parameters(), grad_clip
-                            )
-                        optim.step()
-
-                        self._store_accuracies(logits_train, target_train, "train")
-                        log_every_n_seconds(
-                            logging.INFO,
-                            "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
-                                e, i, train_loss, scheduler.get_last_lr()
-                            ),
-                            n=5,
-                        )
-
-                    # Validation queue
-                    if self.valid_queue:
-                        best_arch.eval()
-                        for i, (input_valid, target_valid) in enumerate(
-                            self.valid_queue
-                        ):
-
-                            input_valid = input_valid.to(self.device).float()
-                            target_valid = target_valid.to(self.device).float()
-
-                            # just log the validation accuracy
-                            with torch.no_grad():
-                                logits_valid = best_arch(input_valid)
-                                self._store_accuracies(
-                                    logits_valid, target_valid, "val"
-                                )
-
-                    scheduler.step()
-                    self.checkpointer.step(e)
-                    self._log_and_reset_accuracies(e)
-
-            # Disable drop path
-            best_arch.update_edges(
-                update_func=lambda edge: edge.data.set(
-                    "op", edge.data.op.get_embedded_ops()
-                ),
-                scope=best_arch.OPTIMIZER_SCOPE,
-                private_edge_data=True,
-            )
-
-            # measure final test accuracy
-            top1 = utils.AverageMeter()
-            top5 = utils.AverageMeter()
-
-            best_arch.eval()
-
-            for i, data_test in enumerate(self.test_queue):
-                input_test, target_test = data_test
-                input_test = input_test.to(self.device)
-                target_test = target_test.to(self.device, non_blocking=True)
-
-                n = input_test.size(0)
-
-                with torch.no_grad():
-                    logits = best_arch(input_test)
-
-                    prec1, prec5 = utils.accuracy(logits, target_test, topk=(1, 5))
-                    top1.update(prec1.data.item(), n)
-                    top5.update(prec5.data.item(), n)
-
-                log_every_n_seconds(
-                    logging.INFO,
-                    "Inference batch {} of {}.".format(i, len(self.test_queue)),
-                    n=5,
-                )
-
-            logger.info(
-                "Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(
-                    top1.avg, top5.avg
-                )
-            )
-
-    @staticmethod
-    def build_search_dataloaders(config):
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode="train"
-        )
-        return train_queue, valid_queue, _  # test_queue is not used in search currently
-
-    @staticmethod
-    def build_eval_dataloaders(config):
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode="val"
-        )
-        return train_queue, valid_queue, test_queue
-
-    @staticmethod
-    def build_eval_optimizer(parameters, config):
-        return torch.optim.SGD(
-            parameters,
-            lr=config.evaluation.learning_rate,
-            momentum=config.evaluation.momentum,
-            weight_decay=config.evaluation.weight_decay,
-        )
-
-    @staticmethod
-    def build_search_scheduler(optimizer, config):
-        return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer,
-            T_max=config.search.epochs,
-            eta_min=config.search.learning_rate_min,
-        )
-
-    @staticmethod
-    def build_eval_scheduler(optimizer, config):
-        return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer,
-            T_max=config.evaluation.epochs,
-            eta_min=config.evaluation.learning_rate_min,
-        )
-
-    def _log_and_reset_accuracies(self, epoch, writer=None):
-        logger.info(
-            "Epoch {} done. Train accuracy (top1, top5): {:.5f}, {:.5f}, Validation accuracy: {:.5f}, {:.5f}".format(
-                epoch,
-                self.train_top1.avg,
-                self.train_top5.avg,
-                self.val_top1.avg,
-                self.val_top5.avg,
-            )
-        )
-
-        if writer is not None:
-            writer.add_scalar('Train accuracy (top 1)', self.train_top1.avg, epoch)
-            writer.add_scalar('Train accuracy (top 5)', self.train_top5.avg, epoch)
-            writer.add_scalar('Train loss', self.train_loss.avg, epoch)
-            writer.add_scalar('Validation accuracy (top 1)', self.val_top1.avg, epoch)
-            writer.add_scalar('Validation accuracy (top 5)', self.val_top5.avg, epoch)
-            writer.add_scalar('Validation loss', self.val_loss.avg, epoch)
-
-        self.train_top1.reset()
-        self.train_top5.reset()
-        self.train_loss.reset()
-        self.val_top1.reset()
-        self.val_top5.reset()
-        self.val_loss.reset()
-
-    def _store_accuracies(self, logits, target, split):
-        """Update the accuracy counters"""
-        logits = logits.clone().detach().cpu()
-        target = target.clone().detach().cpu()
-        prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
-        n = logits.size(0)
-
-        if split == "train":
-            self.train_top1.update(prec1.data.item(), n)
-            self.train_top5.update(prec5.data.item(), n)
-        elif split == "val":
-            self.val_top1.update(prec1.data.item(), n)
-            self.val_top5.update(prec5.data.item(), n)
-        else:
-            raise ValueError("Unknown split: {}. Expected either 'train' or 'val'")
-
-    def _prepare_dataloaders(self, config, mode="train"):
-        """
-        Prepare train, validation, and test dataloaders with the splits defined
-        in the config.
-
-        Args:
-            config (AttrDict): config from config file.
-        """
-        train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(
-            config, mode
-        )
-        self.train_queue = train_queue
-        self.valid_queue = valid_queue
-        self.test_queue = test_queue
-
-    def _setup_checkpointers(
-        self, resume_from="", search=True, period=1, **add_checkpointables
-    ):
-        """
-        Sets up a periodic chechkpointer which can be used to save checkpoints
-        at every epoch. It will call optimizer's `get_checkpointables()` as objects
-        to store.
-
-        Args:
-            resume_from (str): A checkpoint file to resume the search or evaluation from.
-            search (bool): Whether search or evaluation phase is checkpointed. This is required
-                because the files are in different folders to not be overridden
-            add_checkpointables (object): Additional things to checkpoint together with the
-                optimizer's checkpointables.
-        """
-        checkpointables = self.optimizer.get_checkpointables()
-        checkpointables.update(add_checkpointables)
-        self.checkpointer = utils.Checkpointer( 
-            model=checkpointables.pop("model"),
-            save_dir=self.config.save + "/search"
-            if search
-            else self.config.save + "/eval",
-            # **checkpointables #NOTE: this is throwing an Error
-        )
-
-    
-        if resume_from:
-            logger.info("loading model from file {}".format(resume_from))
-            # TODO: maybe there is an implementation error, because the variable resume_from will not be used if resume equals to True
-            checkpoint = self.checkpointer.resume_or_load("resume_from", resume=True)
-            if self.checkpointer.has_checkpoint():
-                return checkpoint.get("iteration", -1) + 1
-        return 0
-
-    def _log_optimizer_stats(self, filename="sh_stats.json"):
-        """Log extended statistics to json file"""
-        if not os.path.exists(self.config.save):
-            os.makedirs(self.config.save)
-        with codecs.open(
-            os.path.join(self.config.save, filename), "w", encoding="utf-8"
-        ) as file:
-            json.dump(self.optimizer.optimizer_stats, file, separators=(",", ":"))
-
-    def _log_to_json(self):
-        """log training statistics to json file"""
-        if not os.path.exists(self.config.save):
-            os.makedirs(self.config.save)
-        if not self.lightweight_output:
-            with codecs.open(
-                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
-            ) as file:
-                json.dump(self.errors_dict, file, separators=(",", ":"))
-        else:
-            with codecs.open(
-                os.path.join(self.config.save, "errors.json"), "w", encoding="utf-8"
-            ) as file:
-                lightweight_dict = copy.deepcopy(self.errors_dict)
-                for key in ["arch_eval", "train_loss", "valid_loss", "test_loss"]:
-                    lightweight_dict.pop(key)
-                json.dump([self.config, lightweight_dict], file, separators=(",", ":"))
diff --git a/naslib/optimizers/discrete/bohb/optimizer._old.py b/naslib/optimizers/discrete/bohb/optimizer._old.py
deleted file mode 100644
index 5a5668920..000000000
--- a/naslib/optimizers/discrete/bohb/optimizer._old.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import numpy as np
-import torch
-import copy
-import math
-from collections import defaultdict
-from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
-from naslib.search_spaces.core.query_metrics import Metric
-
-from collections import defaultdict
-
-
-class BOHB(MetaOptimizer):
-    """
-    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
-    """
-
-    # training the models is not implemented
-    using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(BOHB, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-        self.sh_config = copy.deepcopy(config)
-        self.tpe_config = copy.deepcopy(config)
-        self.performance_metric = Metric.VAL_ACCURACY
-        self.dataset = config.dataset
-        self.method = config.search.method
-        #self.fidelit_min = config.search.min_fidelity
-        self.budget_max = config.search.budget_max
-        self.eta = config.search.eta
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
-        self.sampled_archs = []
-        self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s = self.s_max + 1
-        self.sh = None
-        self.first = True # TODO: think about a more ellegant solution 
-        self.b = (self.s_max + 1)* self.budget_max
-        self.encoding_type = config.search.encoding_type
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-        self.ensemble = None
-        
-    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
-        self.search_space = search_space.clone()
-        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
-        self.dataset_api = dataset_api
-        if self.method == "tpe":
-            #self.tpe_config.search_space = self.search_space.clone()
-            #self.tpe_config.dataset_api  = self.dataset_api
-            self.ensemble =   Ensemble(
-                    predictor_type= "tpe",  
-                    num_ensemble= 1,
-                    encoding_type= self.encoding_type,
-                    ss_type = self.tpe_config.search_space,
-                    config_tpe = [self.search_space.clone(),self.dataset_api, self.dataset],
-                    hyperparams = self.tpe_config,   #replace with config maybe or not 
-
-                    )
-
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        after https://arxiv.org/pdf/1603.06560.pdf
-        """
-        # TODO: rethink because of s
-        if  self.sh == None or self.sh.end:
-            #if sh is finish go to something diffrent as initial budget
-                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-            self.s -= 1
-            if self.sh != None:
-                self.sampled_archs.extend(self.sh.sampled_archs)
-            if self.s < 0:
-                print("HB is finish, allready not defined what to do") # TODO define what to do 
-                return math.inf #end the thing
-            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-            r = self.budget_max * self.eta ** (-self.s)
-            self.sh_config.search.number_archs = n
-            self.sh_config.search.min_fidelity  = r 
-
-            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
-            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-        
-        budget = self.sh.new_epoch()
-
-        self.update_optimizer_stats()
-        return budget
-                
-    def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
-
-    def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
-        all_sampled.extend(self.sh.sampled_archs)
-        return max(all_sampled, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
-        
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
-
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-
-        )
-    def train_model_statistics(self, report_incumbent=True):
-
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
-        )
-    
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
-        """
-        self.optimizer_stats[self.s] = self.sh.optimizer_stats
-
-
-
-    def test_statistics(self):
-        return False
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
-
-    def get_op_optimizer(self):
-        return self.weight_optimizer
-
-    def get_checkpointables(self):
-        return {"model": self.history}
diff --git a/naslib/optimizers/discrete/bohb/parameter.txt b/naslib/optimizers/discrete/bohb/parameter.txt
deleted file mode 100644
index 7466ae26b..000000000
--- a/naslib/optimizers/discrete/bohb/parameter.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-p = 0.15 #percentage of random sampeling
-N_min = 0 # mimum of nodes that needs to sample
-num_samples = 64 # num_essemels as ide
-min_points_in_model = 6
-top_n_percent = 15
-min_bandwith = 1e-3
-random_fraction = 0.15
diff --git a/naslib/optimizers/discrete/bohb/plannung.txt b/naslib/optimizers/discrete/bohb/plannung.txt
deleted file mode 100644
index f42446157..000000000
--- a/naslib/optimizers/discrete/bohb/plannung.txt
+++ /dev/null
@@ -1,141 +0,0 @@
-for bohb, hyperbamd can be completly be the same. But for succesive halving we sample totally different.
-because instead of random sampling we use a model. That could be into succesive halving or in other method. 
-I comment how i think we can change it. TPE needs to be one a file, like in bananas. we need a predictor which i have to look, and we need TPE  to discide which we are sample. Like in bananas
-
-Code: https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/config_generators/bohb.py
-
-def get_config(self, budget):
-		"""
-			Function to sample a new configuration
-			This function is called inside Hyperband to query a new configuration
-			Parameters:
-			-----------
-			budget: float
-				the budget for which this configuration is scheduled
-			returns: config
-				should return a valid configuration
-		"""
-		
-		self.logger.debug('start sampling a new configuration.')
-		
-
-		sample = None
-		info_dict = {}
-		
-		# If no model is available, sample from prior
-		# also mix in a fraction of random configs
-		if len(self.kde_models.keys()) == 0 or np.random.rand() < self.random_fraction:
-			sample =  self.configspace.sample_configuration()
-			info_dict['model_based_pick'] = False
-
-		best = np.inf
-		best_vector = None
-
-		if sample is None:
-			try:
-				
-				#sample from largest budget
-				budget = max(self.kde_models.keys())
-
-				l = self.kde_models[budget]['good'].pdf
-				g = self.kde_models[budget]['bad' ].pdf
-			
-				minimize_me = lambda x: max(1e-32, g(x))/max(l(x),1e-32)
-				
-				kde_good = self.kde_models[budget]['good']
-				kde_bad = self.kde_models[budget]['bad']
-
-				for i in range(self.num_samples):
-					idx = np.random.randint(0, len(kde_good.data))
-					datum = kde_good.data[idx]
-					vector = []
-					
-					for m,bw,t in zip(datum, kde_good.bw, self.vartypes):
-						
-						bw = max(bw, self.min_bandwidth)
-						if t == 0:
-							bw = self.bw_factor*bw
-							try:
-								vector.append(sps.truncnorm.rvs(-m/bw,(1-m)/bw, loc=m, scale=bw))
-							except:
-								self.logger.warning("Truncated Normal failed for:\ndatum=%s\nbandwidth=%s\nfor entry with value %s"%(datum, kde_good.bw, m))
-								self.logger.warning("data in the KDE:\n%s"%kde_good.data)
-						else:
-							
-							if np.random.rand() < (1-bw):
-								vector.append(int(m))
-							else:
-								vector.append(np.random.randint(t))
-					val = minimize_me(vector)
-
-					if not np.isfinite(val):
-						self.logger.warning('sampled vector: %s has EI value %s'%(vector, val))
-						self.logger.warning("data in the KDEs:\n%s\n%s"%(kde_good.data, kde_bad.data))
-						self.logger.warning("bandwidth of the KDEs:\n%s\n%s"%(kde_good.bw, kde_bad.bw))
-						self.logger.warning("l(x) = %s"%(l(vector)))
-						self.logger.warning("g(x) = %s"%(g(vector)))
-
-						# right now, this happens because a KDE does not contain all values for a categorical parameter
-						# this cannot be fixed with the statsmodels KDE, so for now, we are just going to evaluate this one
-						# if the good_kde has a finite value, i.e. there is no config with that value in the bad kde, so it shouldn't be terrible.
-						if np.isfinite(l(vector)):
-							best_vector = vector
-							break
-
-					if val < best:
-						best = val
-						best_vector = vector
-
-				if best_vector is None:
-					self.logger.debug("Sampling based optimization with %i samples failed -> using random configuration"%self.num_samples)
-					sample = self.configspace.sample_configuration().get_dictionary()
-					info_dict['model_based_pick']  = False
-				else:
-					self.logger.debug('best_vector: {}, {}, {}, {}'.format(best_vector, best, l(best_vector), g(best_vector)))
-					for i, hp_value in enumerate(best_vector):
-						if isinstance(
-							self.configspace.get_hyperparameter(
-								self.configspace.get_hyperparameter_by_idx(i)
-							),
-							ConfigSpace.hyperparameters.CategoricalHyperparameter
-						):
-							best_vector[i] = int(np.rint(best_vector[i]))
-					sample = ConfigSpace.Configuration(self.configspace, vector=best_vector).get_dictionary()
-					
-					try:
-						sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
-									configuration_space=self.configspace,
-									configuration=sample
-									)
-						info_dict['model_based_pick'] = True
-
-					except Exception as e:
-						self.logger.warning(("="*50 + "\n")*3 +\
-								"Error converting configuration:\n%s"%sample+\
-								"\n here is a traceback:" +\
-								traceback.format_exc())
-						raise(e)
-
-			except:
-				self.logger.warning("Sampling based optimization with %i samples failed\n %s \nUsing random configuration"%(self.num_samples, traceback.format_exc()))
-				sample = self.configspace.sample_configuration()
-				info_dict['model_based_pick']  = False
-
-
-		try:
-			sample = ConfigSpace.util.deactivate_inactive_hyperparameters(
-				configuration_space=self.configspace,
-				configuration=sample.get_dictionary()
-			).get_dictionary()
-		except Exception as e:
-			self.logger.warning("Error (%s) converting configuration: %s -> "
-								"using random configuration!",
-								e,
-								sample)
-			sample = self.configspace.sample_configuration().get_dictionary()
-		self.logger.debug('done sampling a new configuration.')
-		return sample, info_dict
-
-
-bad_kde = sm.nonparametric.KDEMultivariate(data=train_data_bad,  var_type=self.kde_vartypes, bw=bw_estimation)
-good_kde = sm.nonparametric.KDEMultivariate(data=train_data_good, var_type=self.kde_vartypes, bw=bw_estimation)
diff --git a/naslib/optimizers/discrete/hb/old.py b/naslib/optimizers/discrete/hb/old.py
deleted file mode 100644
index da8703a0f..000000000
--- a/naslib/optimizers/discrete/hb/old.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import numpy as np
-import torch
-import copy
-import math
-from collections import defaultdict
-from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
-from naslib.search_spaces.core.query_metrics import Metric
-
-from collections import defaultdict
-
-
-class HyperBand(MetaOptimizer):
-    """
-    This is a Hyperband Implementation, that uses the Sucessive Halving Algorithm with different settings.
-    """
-
-    # training the models is not implemented
-    using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-    ):
-        """
-        Initialize a Successive Halving  optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(HyperBand, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-        self.sh_config = copy.deepcopy(config)
-        self.performance_metric = Metric.VAL_ACCURACY
-        self.dataset = config.dataset
-        self.budget_max = config.search.budget_max
-        self.eta = config.search.eta
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type #is not for one query is overall
-        self.sampled_archs = []
-        self.history = torch.nn.ModuleList()
-        self.s_max = math.floor(math.log(self.budget_max, self.eta))
-        self.s = self.s_max + 1
-        self.sh = None
-        self.first = True # TODO: think about a more ellegant solution 
-        self.b = (self.s_max + 1)* self.budget_max
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-       
-        
-    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
-        self.search_space = search_space.clone()
-        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
-        self.dataset_api = dataset_api
-
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        after https://arxiv.org/pdf/1603.06560.pdf
-        """
-        # TODO: rethink because of s
-        if  self.sh == None or self.sh.end:
-            #if sh is finish go to something diffrent as initial budget
-                    #n = ((self.b ) / (self.budget_max))* ((self.eta**self.s)/(self.s + 1))
-            self.s -= 1
-            if self.sh != None:
-                self.sampled_archs.extend(self.sh.sampled_archs)
-            if self.s < 0:
-                print("HB is finish, allready not defined what to do") # TODO define what to do 
-                return math.inf #end the thing
-            n = math.ceil(int(self.b / self.budget_max / (self.s + 1)) * self.eta ** self.s)
-            r = self.budget_max * self.eta ** (-self.s)
-            self.sh_config.search.number_archs = n
-            self.sh_config.search.min_fidelity  = r 
-
-            self.sh = SH(self.sh_config,  esemble = self.ensemble) #should be in config 
-            self.sh.adapt_search_space(self.search_space, dataset_api= self.dataset_api)
-        
-        budget = self.sh.new_epoch()
-
-        self.update_optimizer_stats()
-        return budget
-                
-    def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
-
-    def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        all_sampled = copy.deepcopy(self.sampled_archs) #This is because we also want the running into accound, but not later
-        all_sampled.extend(self.sh.sampled_archs)
-        return max(all_sampled, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
-        
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
-
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-
-        )
-    def train_model_statistics(self, report_incumbent=True):
-
-        
-        best_arch = self.sampled_archs[self.fidelity_counter -1].arch
-        best_arch_hash = hash(self.sampled_archs[self.fidelity_counter -1])
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-            self.fidelity,
-            best_arch_hash,
-        )
-    
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        TODO i have to expand the dictionary such that we keep track of all parallel sh evaluations
-        """
-        self.optimizer_stats[self.s] = self.sh.optimizer_stats
-
-
-
-    def test_statistics(self):
-        return False
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
-
-    def get_op_optimizer(self):
-        return self.weight_optimizer
-
-    def get_checkpointables(self):
-        return {"model": self.history}
diff --git a/naslib/optimizers/discrete/sh/optimizer_old.py b/naslib/optimizers/discrete/sh/optimizer_old.py
deleted file mode 100644
index 777f4e2da..000000000
--- a/naslib/optimizers/discrete/sh/optimizer_old.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import numpy as np
-import torch
-import random
-import math
-
-from collections import defaultdict
-from naslib.predictors import predictor
-from naslib.predictors import ensemble
-from naslib.predictors.ensemble import Ensemble
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-from naslib.search_spaces.core.query_metrics import Metric
-
-
-class SuccessiveHalving(MetaOptimizer):
-    """
-    Optimizer is randomly sampling architectures and queries/trains on the corresponding fidelities.
-    After that, models will be discarded according to eta.
-    DONE: Implement training
-    """
-    using_step_function = False
-
-    def __init__(
-        self,
-        config,
-        weight_optimizer=torch.optim.SGD,
-        loss_criteria=torch.nn.CrossEntropyLoss(),
-        grad_clip=None,
-        esemble = None,
-    ):
-        """
-        Initialize a Successive Halving optimizer.
-
-        Args:
-            config
-            weight_optimizer (torch.optim.Optimizer): The optimizer to
-                train the (convolutional) weights.
-            loss_criteria (TODO): The loss
-            grad_clip (float): Where to clip the gradients (default None).
-        """
-        super(SuccessiveHalving, self).__init__()
-        self.weight_optimizer = weight_optimizer
-        self.loss = loss_criteria
-        self.grad_clip = grad_clip
-        self.performance_metric = Metric.VAL_ACCURACY
-        self.dataset = config.dataset
-        self.end = False
-        self.fidelity = config.search.min_fidelity
-        self.number_archs = config.search.number_archs
-        self.eta = config.search.eta
-        self.budget_max = config.search.budget_max 
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.budget_type = config.search.budget_type  # is not for one query is overall
-        self.fidelity_counter = 0
-        self.sampled_archs = []
-        self.history = torch.nn.ModuleList()
-        self.end = False
-        self.old_fidelity = 0
-        self.method = config.search.method
-        #right now only for testing 
-        if self.method == "tpe":#
-            self.ss_type= config.search_space
-            self.encoding_type = config.search.encoding_type
-            self.N_min =  config.search.N_min #This has to be higher then 
-            self.ensemble = esemble
-        self.optimizer_stats = defaultdict(lambda: defaultdict(list))
-
-    def adapt_search_space(self, search_space, scope=None, dataset_api=None):
-        assert (
-            search_space.QUERYABLE
-        ), "Successsive Halving is currently only implemented for benchmarks."
-        self.search_space = search_space.clone()
-        self.scope = scope if scope else search_space.OPTIMIZER_SCOPE
-        self.dataset_api = dataset_api
-    
-
-    def new_epoch(self):
-        """
-        Sample a new architecture to train.
-        # TODO: with this kind of architekeur, in evaluation only the last fideltiy
-        """
-        # TODO: error is occuring on fidelity: 128 on architecture number: 1
-        # fidelity is not calculated corretly and therefore epoch is maybe not correct 
-        model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-        #model.arch = self.search_space.clone()
-        #TODO is num_init needed 
-        if len(self.sampled_archs) < self.number_archs:
-            #model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-            model = self.sample(self.method)
-            
-        else:
-            model = self.sampled_archs[self.fidelity_counter]
-
-#             return(ranks < self.num_configs[self.stage])
-
-        model.accuracy = model.arch.query(
-            self.performance_metric,
-            self.dataset,
-            epoch=int(self.fidelity),
-            dataset_api=self.dataset_api,
-        )
-
-        budget = (self.fidelity - self.old_fidelity) / self.budget_max
-        # DONE: make query type secure
-        if self.budget_type == 'time':
-            #TODO also for predictions
-            # DONE: make dependent on performance_metric
-            model.time = model.arch.query(  # TODO: this is the time for training from screatch.
-                self.performance_metric,
-                self.dataset,
-                epoch=int(self.fidelity),
-                dataset_api=self.dataset_api,
-            )
-            budget = model.time
-        elif not(self.budget_type == "epoch"):
-            raise NameError("budget time should be time or epoch")
-        # TODO: make this more beautiful/more efficient
-        # DONE: we may need to track of all ever sampled archs
-        if len(self.sampled_archs) < self.number_archs:
-            self.sampled_archs.append(model)
-        else:
-            self.sampled_archs[self.fidelity_counter] = model
-        self.update_optimizer_stats()
-        self.fidelity_counter += 1
-        # DONE: fidelity is changed for new epoch, what make the wrong values in the dictonary
-        self._update_history(model)
-        if self.fidelity_counter == self.number_archs:
-            self.old_fidelity = self.fidelity
-            self.fidelity = math.floor(self.eta*self.fidelity)
-            self.sampled_archs.sort(key=lambda model: model.accuracy, reverse=True)
-            if self.fidelity > self.budget_max:
-                self.end = True
-            elif(math.floor(self.number_archs/self.eta)) != 0:
-                self.sampled_archs = self.sampled_archs[0:math.floor(self.number_archs/self.eta)]
-
-            else:
-                self.end = True
-                self.sampled_archs = [self.sampled_archs[0]]  # but maybe there maybe a different way
-            self.number_archs = len(self.sampled_archs)
-            self.fidelity_counter = 0
-        # TODO: budget equals
-        return budget
-       
-    def _update_history(self, child):
-        if len(self.history) < 100:
-            self.history.append(child)
-        else:
-            for i, p in enumerate(self.history):
-                if child.accuracy > p.accuracy:
-                    self.history[i] = child
-                    break
-
-    def get_final_architecture(self):
-        """
-        Returns the sampled architecture with the lowest validation error.
-        """
-        return max(self.sampled_archs, key=lambda x: x.accuracy).arch
-
-    def train_statistics(self, report_incumbent=True):
-
-        if report_incumbent:
-            best_arch = self.get_final_architecture()
-        else:
-            best_arch = self.sampled_archs[-1].arch
-
-        return (
-            best_arch.query(
-                Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api
-            ),
-            best_arch.query(
-                Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api
-            ),
-        )
-
-    def update_optimizer_stats(self):
-        """
-        Updates statistics of optimizer to be able to create useful plots
-        """
-        arch = self.sampled_archs[self.fidelity_counter].arch
-        arch_hash = hash(self.sampled_archs[self.fidelity_counter])
-        # this dict contains metrics to save
-        metrics = {
-            "train_acc": Metric.TRAIN_ACCURACY,
-            "val_acc": Metric.VAL_ACCURACY,
-            "test_acc": Metric.TEST_ACCURACY,
-            "train_time": Metric.TRAIN_TIME
-        }
-        for metric_name, metric in metrics.items():
-            metric_value = arch.query(
-                metric,
-                self.dataset,
-                dataset_api=self.dataset_api,
-                epoch=int(self.fidelity)
-            )
-            self.optimizer_stats[arch_hash][metric_name].append(metric_value)
-        self.optimizer_stats[arch_hash]['fidelity'].append(self.fidelity)
-
-    def test_statistics(self):
-        best_arch = self.get_final_architecture()
-        return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api)
-
-    def get_op_optimizer(self):
-        return self.weight_optimizer
-    
-    def get_checkpointables(self):
-        return {"model": self.history}
-
-    
-    def sample(self, method):
-        #is right now in tpe
-        if method == "random" or  len(self.sampled_archs) < self.N_min:
-            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
-            model.arch = self.search_space.clone()
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api) 
-        else:        
-            xtrain = [m.arch for m in self.sampled_archs]
-            ytrain = [m.accuracy for m in self.sampled_archs]
-            train_error = self.ensemble.fit(xtrain,ytrain,self.fidelity)
-            _, info_dict  = self.ensemble.query_tpe("test", self.fidelity)
-            model = info_dict["model"]
-        return model

From 3ffd401476577ea6896c14fd68ba9eef8ad60cb3 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Fri, 17 Jun 2022 21:25:57 +0200
Subject: [PATCH 123/130] code cleanup for trainer and multifidelity methods

---
 naslib/defaults/trainer_multifidelity.py     | 133 ++++++++-----------
 naslib/optimizers/discrete/bohb/optimizer.py |  80 ++++++-----
 naslib/optimizers/discrete/dehb/optimizer.py |  54 ++++----
 naslib/optimizers/discrete/hb/optimizer.py   |  73 +++++-----
 naslib/optimizers/discrete/sh/optimizer.py   |  93 +++++--------
 5 files changed, 192 insertions(+), 241 deletions(-)

diff --git a/naslib/defaults/trainer_multifidelity.py b/naslib/defaults/trainer_multifidelity.py
index f8e5f60a6..bb559254e 100644
--- a/naslib/defaults/trainer_multifidelity.py
+++ b/naslib/defaults/trainer_multifidelity.py
@@ -24,8 +24,7 @@ class Trainer(object):
     Default implementation that handles dataloading and preparing batches, the
     train loop, gathering statistics, checkpointing and doing the final
     final evaluation.
-    If this does not fulfil your needs free do subclass it and implement your
-    required logic.
+    This trainer is purposed for multi-fidelity optimizers.
     """
 
     def __init__(self, optimizer, config, lightweight_output=False):
@@ -39,7 +38,7 @@ def __init__(self, optimizer, config, lightweight_output=False):
         self.optimizer = optimizer
         self.config = config
         self.lightweight_output = lightweight_output
-        self.n_process = config.search.n_process #need to be a parameter
+        self.n_process = config.search.n_process
 
         # anytime
         computed_epochs = self.optimizer.compute_epochs()
@@ -78,7 +77,6 @@ def __init__(self, optimizer, config, lightweight_output=False):
              'params': n_parameters}
         )
 
-
     def search(self, resume_from=""):
         """
         Start the architecture search.
@@ -96,7 +94,6 @@ def search(self, resume_from=""):
         checkpoint_freq = self.config.search.checkpoint_freq
         if self.optimizer.using_step_function:
             self.scheduler = self.build_search_scheduler(self.optimizer.op_optimizer, self.config)
-        
             start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq, scheduler=self.scheduler)
         else:
             start_epoch = self._setup_checkpointers(resume_from, period=checkpoint_freq)
@@ -126,10 +123,12 @@ def search(self, resume_from=""):
 
                                 log_every_n_seconds(logging.INFO,
                                                     "Round {}: Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
-                                                        round, e, step, train_loss, val_loss, self.scheduler.get_last_lr()), n=5)
+                                                        round, e, step, train_loss, val_loss,
+                                                        self.scheduler.get_last_lr()), n=5)
 
                                 if torch.cuda.is_available():
-                                    log_first_n(logging.INFO, "cuda consumption\n {}".format(torch.cuda.memory_summary()),
+                                    log_first_n(logging.INFO,
+                                                "cuda consumption\n {}".format(torch.cuda.memory_summary()),
                                                 n=3)
 
                                 self.train_loss.update(float(train_loss.detach().cpu()))
@@ -147,7 +146,6 @@ def search(self, resume_from=""):
                         else:
                             end_time = time.time()
                             train_acc, valid_acc, test_acc, train_time = self.optimizer.train_statistics()
-                            
                             train_loss, valid_loss, test_loss = -1, -1, -1
 
                             self.errors_dict.train_acc.append(train_acc)
@@ -157,18 +155,14 @@ def search(self, resume_from=""):
                             self.errors_dict.test_acc.append(test_acc)
                             self.errors_dict.test_loss.append(test_loss)
                             self.errors_dict.runtime.append(end_time - start_time)
-                           
                             self.train_top1.avg = train_acc
                             self.val_top1.avg = valid_acc
                             if train_time > 0:
                                 self.search_time += end_time - start_time
                                 self.search_time += train_time
                             else:
-                                #this if we have no time only epochs
-                                #self.fidelities[self.round_number]
-                                #if work proberly has to be check 
-                                #ofcourse now we doesn't factor in runetime of optimizer, but this not hat large comapre to training time normaly
-                                _, latest_arch_epoch  = self.optimizer.get_latest_architecture()
+                                # case if we do not work with train_time as budget, like in NASBenchASR
+                                _, latest_arch_epoch = self.optimizer.get_latest_architecture()
                                 self.search_time += 1 * latest_arch_epoch
                                 train_time = 1 * latest_arch_epoch
                             self.errors_dict.train_time.append(train_time)
@@ -176,7 +170,7 @@ def search(self, resume_from=""):
 
                         anytime_results = self.optimizer.test_statistics()
                         if anytime_results:
-                           # record anytime performance
+                            # record anytime performance
                             self.errors_dict.arch_eval.append(anytime_results)
                             log_every_n_seconds(logging.INFO, "Round {} Epoch {}, Anytime results: {}".format(
                                 round, e, anytime_results), n=5)
@@ -203,8 +197,9 @@ def search(self, resume_from=""):
                         self._store_accuracies(logits_train, data_train[1], 'train')
                         self._store_accuracies(logits_val, data_val[1], 'val')
 
-                        log_every_n_seconds(logging.INFO, "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
-                            e, step, train_loss, val_loss, self.scheduler.get_last_lr()), n=5)
+                        log_every_n_seconds(logging.INFO,
+                                            "Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}".format(
+                                                e, step, train_loss, val_loss, self.scheduler.get_last_lr()), n=5)
 
                         if torch.cuda.is_available():
                             log_first_n(logging.INFO, "cuda consumption\n {}".format(torch.cuda.memory_summary()), n=3)
@@ -233,18 +228,15 @@ def search(self, resume_from=""):
                     self.errors_dict.test_acc.append(test_acc)
                     self.errors_dict.test_loss.append(test_loss)
                     self.errors_dict.runtime.append(end_time - start_time)
-                    
+
                     self.train_top1.avg = train_acc
                     self.val_top1.avg = valid_acc
                     if train_time > 0:
                         self.search_time += end_time - start_time
                         self.search_time += train_time
                     else:
-                        #this if we have no time only epochs        
-                        #self.fidelities[self.round_number]
-                        #if work proberly has to be check         
-                        #ofcourse now we doesn't factor in runetime of optimizer, but this not hat large comapre to training time normaly
-                        _, latest_arch_epoch  = self.optimizer.get_latest_architecture()
+                        # case if we do not work with train_time as budget, like in NASBenchASR
+                        _, latest_arch_epoch = self.optimizer.get_latest_architecture()
                         self.search_time += 1 * latest_arch_epoch
                         train_time = 1
                     self.errors_dict.train_time.append(train_time)
@@ -256,18 +248,17 @@ def search(self, resume_from=""):
                     # record anytime performance
                     self.errors_dict.arch_eval.append(anytime_results)
                     log_every_n_seconds(logging.INFO, "Epoch {}, Anytime results: {}".format(
-                            e, anytime_results), n=5)
+                        e, anytime_results), n=5)
 
                 self._log_to_json()
                 self._log_and_reset_accuracies(e)
                 if self.search_time > self.budgets:
-                    gc.collect() 
+                    gc.collect()
                     return
 
             self.optimizer.after_training()
         logger.info("Training finished. Total Budgets[s]: {}".format(self.search_time))
 
-
     def evaluate_oneshot(self, resume_from="", dataloader=None):
         """
         Evaluate the one-shot model on the specified dataset.
@@ -310,15 +301,14 @@ def evaluate_oneshot(self, resume_from="", dataloader=None):
         logger.info("Evaluation finished")
         return self.val_top1.avg
 
-
     def evaluate(
-            self, 
-            retrain=True, 
-            search_model="", 
+            self,
+            retrain=True,
+            search_model="",
             resume_from="",
-            best_arch=None, 
+            best_arch=None,
             dataset_api=None
-        ):
+    ):
         """
         Evaluate the final architecture as given from the optimizer.
         If the search space has an interface to a benchmark then query that.
@@ -336,7 +326,7 @@ def evaluate(
 
             if not search_model:
                 search_model = os.path.join(self.config.save, "search", "model_final.pth")
-            self._setup_checkpointers(search_model)      # required to load the architecture
+            self._setup_checkpointers(search_model)  # required to load the architecture
 
             best_arch, _ = self.optimizer.get_final_architecture()
         logger.info("Final architecture:\n" + best_arch.modules_str())
@@ -358,10 +348,11 @@ def evaluate(
                 optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
                 scheduler = self.build_eval_scheduler(optim, self.config)
 
-                start_epoch = self._setup_checkpointers(resume_from, 
-                    search=False, 
+                start_epoch = self._setup_checkpointers(
+                    resume_from,
+                    search=False,
                     period=self.config.evaluation.checkpoint_freq,
-                    model=best_arch,    # checkpointables start here
+                    model=best_arch,  # checkpointables start here
                     optim=optim,
                     scheduler=scheduler
                 )
@@ -404,7 +395,7 @@ def evaluate(
                         optim.zero_grad()
                         logits_train = best_arch(input_train)
                         train_loss = loss(logits_train, target_train)
-                        if hasattr(best_arch, 'auxilary_logits'):   # darts specific stuff
+                        if hasattr(best_arch, 'auxilary_logits'):  # darts specific stuff
                             log_first_n(logging.INFO, "Auxiliary is used", n=10)
                             auxiliary_loss = loss(best_arch.auxilary_logits(), target_train)
                             train_loss += self.config.evaluation.auxiliary_weight * auxiliary_loss
@@ -416,12 +407,10 @@ def evaluate(
                         self._store_accuracies(logits_train, target_train, 'train')
                         log_every_n_seconds(logging.INFO, "Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
                             e, i, train_loss, scheduler.get_last_lr()), n=5)
-                        
-                        
+
                     # Validation queue
                     if self.valid_queue:
                         for i, (input_valid, target_valid) in enumerate(self.valid_queue):
-                            
                             input_valid = input_valid.cuda().float()
                             target_valid = target_valid.cuda().float()
 
@@ -465,19 +454,16 @@ def evaluate(
 
             logger.info("Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}".format(top1.avg, top5.avg))
 
-
     @staticmethod
     def build_search_dataloaders(config):
         train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(config, mode='train')
         return train_queue, valid_queue, _  # test_queue is not used in search currently
 
-
     @staticmethod
     def build_eval_dataloaders(config):
         train_queue, valid_queue, test_queue, _, _ = utils.get_train_val_loaders(config, mode='val')
         return train_queue, valid_queue, test_queue
 
-
     @staticmethod
     def build_eval_optimizer(parameters, config):
         return torch.optim.SGD(
@@ -487,49 +473,48 @@ def build_eval_optimizer(parameters, config):
             weight_decay=config.evaluation.weight_decay,
         )
 
-
     @staticmethod
     def build_search_scheduler(optimizer, config):
         return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer, 
-            T_max=config.search.epochs, 
+            optimizer,
+            T_max=config.search.epochs,
             eta_min=config.search.learning_rate_min
         )
 
-
     @staticmethod
     def build_eval_scheduler(optimizer, config):
         return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer, 
-            T_max=config.evaluation.epochs, 
+            optimizer,
+            T_max=config.evaluation.epochs,
             eta_min=config.evaluation.learning_rate_min
         )
 
-
     def _log_and_reset_accuracies(self, epoch, *args):
         if args:
             r, i = args[0], args[1]
-            logger.info("Process: {}, Round {} Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
-                i,
-                r,
-                epoch,
-                self.train_top1.avg,
-                self.val_top1.avg,
-                self.errors_dict['runtime'][-1],
-                self.errors_dict['train_time'][-1],
-                sum(self.errors_dict['runtime']) +
-                sum(self.errors_dict['train_time'])
-            ))
+            logger.info(
+                "Process: {}, Round {} Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
+                    i,
+                    r,
+                    epoch,
+                    self.train_top1.avg,
+                    self.val_top1.avg,
+                    self.errors_dict['runtime'][-1],
+                    self.errors_dict['train_time'][-1],
+                    sum(self.errors_dict['runtime']) +
+                    sum(self.errors_dict['train_time'])
+                ))
         else:
-            logger.info("Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
-                epoch,
-                self.train_top1.avg,
-                self.val_top1.avg,
-                self.errors_dict['runtime'][-1],
-                self.errors_dict['train_time'][-1],
-                sum(self.errors_dict['runtime']) +
-                sum(self.errors_dict['train_time']),
-            ))
+            logger.info(
+                "Epoch {} done. Train accuracy (top1): {:.5f}, Validation accuracy: {:.5f}, Query time: {:.5f}, Training time: {:.5f}, Accumulated time: {:.5f}".format(
+                    epoch,
+                    self.train_top1.avg,
+                    self.val_top1.avg,
+                    self.errors_dict['runtime'][-1],
+                    self.errors_dict['train_time'][-1],
+                    sum(self.errors_dict['runtime']) +
+                    sum(self.errors_dict['train_time']),
+                ))
         self.train_top1.reset()
         self.train_top5.reset()
         self.train_loss.reset()
@@ -537,7 +522,6 @@ def _log_and_reset_accuracies(self, epoch, *args):
         self.val_top5.reset()
         self.val_loss.reset()
 
-
     def _store_accuracies(self, logits, target, split):
         """Update the accuracy counters"""
         logits = logits.clone().detach().cpu()
@@ -554,7 +538,6 @@ def _store_accuracies(self, logits, target, split):
         else:
             raise ValueError("Unknown split: {}. Expected either 'train' or 'val'")
 
-
     def _prepare_dataloaders(self, config, mode='train'):
         """
         Prepare train, validation, and test dataloaders with the splits defined
@@ -566,7 +549,6 @@ def _prepare_dataloaders(self, config, mode='train'):
         self.train_queue = train_queue
         self.valid_queue = valid_queue
         self.test_queue = test_queue
-    
 
     def _setup_checkpointers(self, resume_from="", search=True, period=1, **add_checkpointables):
         """
@@ -586,14 +568,13 @@ def _setup_checkpointers(self, resume_from="", search=True, period=1, **add_chec
         checkpointer = utils.Checkpointer(
             model=checkpointables.pop('model'),
             save_dir=self.config.save + "/search" if search else self.config.save + "/eval",
-            #**checkpointables #NOTE: this is throwing an Error
+            # **checkpointables #NOTE: this is throwing an Error
         )
 
         self.periodic_checkpointer = PeriodicCheckpointer(
             checkpointer,
             period=period,
             max_iter=1)
-        #max_iter=self.config.search.epochs if search else self.config.evaluation.epochs
 
         if resume_from:
             logger.info("loading model from file {}".format(resume_from))
@@ -602,7 +583,6 @@ def _setup_checkpointers(self, resume_from="", search=True, period=1, **add_chec
                 return checkpoint.get("iteration", -1) + 1
         return 0
 
-
     def _log_to_json(self):
         """log training statistics to json file"""
         if not os.path.exists(self.config.save):
@@ -616,4 +596,3 @@ def _log_to_json(self):
                 for key in ['arch_eval', 'train_loss', 'valid_loss', 'test_loss']:
                     lightweight_dict.pop(key)
                 json.dump([self.config, lightweight_dict], file, separators=(',', ':'))
-
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 5b3787f2c..49ea87aa5 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -1,27 +1,24 @@
 import collections
-import os
-import math
-import logging
-import torch
 import copy
+import logging
+import math
 import numpy as np
+import os
+import statsmodels.api as sm
+import torch
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
-
 from naslib.search_spaces.core.query_metrics import Metric
-import statsmodels.api as sm
-
-from naslib.utils.utils import AttrDict, count_parameters_in_MB
 from naslib.utils.logging import log_every_n_seconds
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
 
 logger = logging.getLogger(__name__)
-    
-        
+
+
 class BOHB(MetaOptimizer):
-    
     # training the models is not implemented
     using_step_function = False
-    
+
     def __init__(self, config):
         super().__init__()
         # Hyperband related stuff
@@ -44,8 +41,8 @@ def __init__(self, config):
             self.rounds.append(s)
             round_sizes = []
             fidelities = []
-            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
-            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon)  # initial number of configurations
+            r = self.max_budget / self.eta ** s  # initial number of iterations to run configurations for
             for i in range(s + 1):
                 n_i = math.floor(n / self.eta ** i + self._epsilon)
                 r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
@@ -71,7 +68,6 @@ def __init__(self, config):
         self.prev_round = 0
         self.process = 0
 
-
     def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         assert search_space.QUERYABLE, "Hyperband_simple is currently only implemented for benchmarks."
         self.search_space = search_space.clone()
@@ -79,11 +75,9 @@ def adapt_search_space(self, search_space, scope=None, dataset_api=None):
         self.dataset_api = dataset_api
         self.max_training_epoch = self.search_space.get_max_epochs()
 
-
     def compute_epochs(self):
         return self.round_sizes, self.rounds[::-1]
 
-
     def impute_conditional_data(self, array):
         return_array = np.zeros(array.shape)
         for i in range(array.shape[0]):
@@ -107,7 +101,6 @@ def impute_conditional_data(self, array):
             return_array[i, :] = datum
         return return_array
 
-
     def fit_kde(self, round):
         budget = self.fidelities[round][0]
         good_models = self.kde_models[budget]['good']
@@ -118,10 +111,10 @@ def fit_kde(self, round):
             bad_enc = np.array([encode_101(m.arch, encoding_type='adjacency_cat') for m in bad_models])
             self.kde_vartypes = ""
             self.vartypes = []
-            for _ in range(len(good_enc[0])-5): # adj encoding + one-hot ops list
+            for _ in range(len(good_enc[0]) - 5):  # adj encoding + one-hot ops list
                 self.kde_vartypes += 'u'
                 self.vartypes += [2]
-            for _ in range(len(good_enc[0])-5, len(good_enc[0])):  # adj encoding + one-hot ops list
+            for _ in range(len(good_enc[0]) - 5, len(good_enc[0])):  # adj encoding + one-hot ops list
                 self.kde_vartypes += 'u'
                 self.vartypes += [3]
         elif self.config.search_space == "nasbench201":
@@ -136,8 +129,12 @@ def fit_kde(self, round):
         elif self.config.search_space == "darts":
             from naslib.search_spaces.darts.conversions import convert_naslib_to_compact, \
                 make_compact_mutable, convert_mutable_to_vector
-            good_enc = np.array([convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in good_models])
-            bad_enc = np.array([convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in bad_models])
+            good_enc = np.array(
+                [convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in
+                 good_models])
+            bad_enc = np.array(
+                [convert_mutable_to_vector(make_compact_mutable(convert_naslib_to_compact(m.arch))) for m in
+                 bad_models])
             self.kde_vartypes = ""
             self.vartypes = []
             for i in range(len(good_enc[0])):  # we use unordered discrete variable
@@ -157,7 +154,7 @@ def fit_kde(self, round):
                     self.vartypes += [6]
                 else:
                     self.vartypes += [2]
-                
+
         self.vartypes = np.array(self.vartypes, dtype=int)
         good_enc = self.impute_conditional_data(good_enc)
         bad_enc = self.impute_conditional_data(bad_enc)
@@ -171,9 +168,8 @@ def fit_kde(self, round):
         g = self.bad_kde.pdf
         self.minimize_me = lambda x: max(1e-32, g(x) / max(l(x), 1e-32))
 
-
     def new_epoch(self, epoch, round, i):
-        if self.process < i: # re-init for each new process
+        if self.process < i:  # re-init for each new process
             del self.current_round
             del self.next_round
             self.current_round_ = []
@@ -190,7 +186,7 @@ def new_epoch(self, epoch, round, i):
 
         if epoch < self.round_sizes[round][0]:
             # sample random architectures
-            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             budget = self.fidelities[round][0]
             if round == 0:
@@ -214,8 +210,8 @@ def new_epoch(self, epoch, round, i):
 
             model.epoch = min(self.fidelities[round][0], self.max_training_epoch)
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
             self._update_history(model)
             self.next_round.append(model)
@@ -244,8 +240,8 @@ def new_epoch(self, epoch, round, i):
             model = copy.deepcopy(model)
             model.epoch = min(self.fidelities[round][self.round_number], self.max_training_epoch)
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
             self.kde_models[self.fidelities[round][self.round_number]]['good'].append(model)
             self._update_history(model)
@@ -253,15 +249,14 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
-    
+
     def clean_history(self):
         best_arch = max(self.history, key=lambda x: x.accuracy)
         self.history = torch.nn.ModuleList()
         self.history.append(best_arch)
 
-
     def get_final_architecture(self):
-        
+
         # Returns the sampled architecture with the lowest validation error.
         best_arch = max(self.history, key=lambda x: x.accuracy)
         return best_arch.arch, best_arch.epoch
@@ -275,21 +270,24 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                       epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                                epoch=self.fidelities[self.prev_round][
+                                                    self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
-            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
-            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            train_time, 
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api,
+                            epoch=best_arch_epoch - 1),
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            train_time,
         )
-    
+
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()
         return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
-    
     def get_op_optimizer(self):
         raise NotImplementedError()
 
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 96356c3ba..b04aa1fe3 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -1,25 +1,22 @@
 import collections
-import math
-import logging
-import torch
 import copy
+import logging
+import math
 import numpy as np
+import torch
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
-
 from naslib.search_spaces.core.query_metrics import Metric
-
-from naslib.utils.utils import AttrDict, count_parameters_in_MB
 from naslib.utils.logging import log_every_n_seconds
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
 
 logger = logging.getLogger(__name__)
-    
-        
+
+
 class DEHB(MetaOptimizer):
-    
     # training the models is not implemented
     using_step_function = False
-    
+
     def __init__(self, config):
         super().__init__()
         # Hyperband related stuff
@@ -46,8 +43,8 @@ def __init__(self, config):
             self.rounds.append(s)
             round_sizes = []
             fidelities = []
-            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
-            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon)  # initial number of configurations
+            r = self.max_budget / self.eta ** s  # initial number of iterations to run configurations for
             for i in range(s + 1):
                 n_i = math.floor(n / self.eta ** i + self._epsilon)
                 r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
@@ -65,7 +62,6 @@ def __init__(self, config):
         self.dataset = config.dataset
         self.history = torch.nn.ModuleList()
 
-
         self.epochs = self.compute_epochs()
         self.current_round = []
         self.next_round = []
@@ -85,10 +81,10 @@ def compute_epochs(self):
         return self.round_sizes, self.rounds[::-1]
 
     def new_epoch(self, epoch, round, i):
-        if self.process < i: # re-init for each new process
-            #to save ram for experiements
+        if self.process < i:  # re-init for each new process
+            # to save ram for experiements
             del self.current_round
-            del  self.next_round
+            del self.next_round
             del self.round_number
             del self.prev_round
             del self.process
@@ -106,7 +102,7 @@ def new_epoch(self, epoch, round, i):
 
         if epoch < self.round_sizes[round][0]:
             # sample random architectures
-            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
             budget = self.fidelities[round][0]
             if round == 0:
@@ -172,13 +168,14 @@ def new_epoch(self, epoch, round, i):
 
     def _update_history(self, child):
         self.history.append(child)
+
     def clean_history(self):
         best_arch = max(self.history, key=lambda x: x.accuracy)
         self.history = torch.nn.ModuleList()
         self.history.append(best_arch)
 
     def get_final_architecture(self):
-        
+
         # Returns the sampled architecture with the lowest validation error.
         best_arch = max(self.history, key=lambda x: x.accuracy)
         return best_arch.arch, best_arch.epoch
@@ -192,21 +189,24 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                       epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                                epoch=self.fidelities[self.prev_round][
+                                                    self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
-            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
-            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            train_time, 
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api,
+                            epoch=best_arch_epoch - 1),
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            train_time,
         )
-    
+
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()
         return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
-    
     def get_op_optimizer(self):
         raise NotImplementedError()
 
@@ -214,4 +214,4 @@ def get_checkpointables(self):
         return {'model': self.history}
 
     def get_model_size(self):
-        return count_parameters_in_MB(self.history)
\ No newline at end of file
+        return count_parameters_in_MB(self.history)
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index a58923cd6..ccaed1c78 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -1,25 +1,22 @@
 import collections
+import copy
 import logging
 import math
-import torch
-import copy
 import numpy as np
+import torch
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
-
 from naslib.search_spaces.core.query_metrics import Metric
-
-from naslib.utils.utils import AttrDict, count_parameters_in_MB
 from naslib.utils.logging import log_every_n_seconds
+from naslib.utils.utils import AttrDict, count_parameters_in_MB
 
 logger = logging.getLogger(__name__)
-    
-        
+
+
 class HB(MetaOptimizer):
-    
     # training the models is not implemented
     using_step_function = False
-    
+
     def __init__(self, config):
         super().__init__()
         # Hyperband related stuff
@@ -29,8 +26,8 @@ def __init__(self, config):
         self.fidelities = []
         self.max_budget = self.config.search.max_budget
         self.min_budget = self.config.search.min_budget
-        self.eta = self.config.search.eta 
-        self._epsilon = float(self.config.search.epsilon) 
+        self.eta = self.config.search.eta
+        self._epsilon = float(self.config.search.epsilon)
         self.min_budget = min(self.min_budget, self.max_budget)
         s_max = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
         # set up round sizes, fidelities, and list of arches
@@ -38,8 +35,8 @@ def __init__(self, config):
             self.rounds.append(s)
             round_sizes = []
             fidelities = []
-            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon) # initial number of configurations
-            r = self.max_budget / self.eta**s # initial number of iterations to run configurations for
+            n = math.ceil((s_max + 1) * self.eta ** s / (s + 1) - self._epsilon)  # initial number of configurations
+            r = self.max_budget / self.eta ** s  # initial number of iterations to run configurations for
             for i in range(s + 1):
                 n_i = math.floor(n / self.eta ** i + self._epsilon)
                 r_i = min(math.floor(r * self.eta ** i + self._epsilon), config.search.fidelity)
@@ -72,7 +69,7 @@ def compute_epochs(self):
     def new_epoch(self, epoch, round, i):
         # round - bracket
         # epoch - number of architectures in bracket so far
-        if self.process < i: # re-init for each new process
+        if self.process < i:  # re-init for each new process
             del self.current_round
             del self.next_round
             del self.round_number
@@ -80,7 +77,7 @@ def new_epoch(self, epoch, round, i):
             del self.process
             self.current_round = []
             self.next_round = []
-            self.round_number = 0 # index to fidelity
+            self.round_number = 0  # index to fidelity
             self.prev_round = 0
             self.process = i
             self.clean_history()
@@ -89,22 +86,22 @@ def new_epoch(self, epoch, round, i):
             self.prev_round = round
             self.round_number = 0
 
-        if epoch < self.round_sizes[round][0]: # check if first fidelity of bracket
+        if epoch < self.round_sizes[round][0]:  # check if first fidelity of bracket
             # sample random architectures
-            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api)   
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api)
             model.epoch = self.fidelities[round][0]
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
             self._update_history(model)
             self.next_round.append(model)
 
-        else: 
-            if len(self.current_round) == 0: # fidelity is full
-                # if we are at the end of a round of hyperband, continue training only the best 
+        else:
+            if len(self.current_round) == 0:  # fidelity is full
+                # if we are at the end of a round of hyperband, continue training only the best
                 logger.info("Starting a new round: continuing to train the best arches")
                 self.round_number += 1
                 cutoff = self.round_sizes[round][self.round_number]
@@ -112,7 +109,7 @@ def new_epoch(self, epoch, round, i):
                 self.next_round = []
 
             # train the next architecture
-            model = self.current_round.pop() # architecture to train
+            model = self.current_round.pop()  # architecture to train
             """
             Note: technically we would just continue training this arch, but right now,
             just for simplicity, we treat it as if we start to train it again from scratch
@@ -120,23 +117,22 @@ def new_epoch(self, epoch, round, i):
             model = copy.deepcopy(model)
             model.epoch = self.fidelities[round][self.round_number]
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
             self._update_history(model)
             self.next_round.append(model)
 
     def _update_history(self, child):
         self.history.append(child)
-    
+
     def clean_history(self):
         best_arch = max(self.history, key=lambda x: x.accuracy)
         self.history = torch.nn.ModuleList()
         self.history.append(best_arch)
 
-
     def get_final_architecture(self):
-        
+
         # Returns the sampled architecture with the lowest validation error.
         best_arch = max(self.history, key=lambda x: x.accuracy)
         return best_arch.arch, best_arch.epoch
@@ -150,21 +146,24 @@ def get_latest_architecture(self):
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.prev_round][self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                       epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                                epoch=self.fidelities[self.prev_round][
+                                                    self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
-            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
-            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            train_time, 
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api,
+                            epoch=best_arch_epoch - 1),
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            train_time,
         )
-    
+
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()
         return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
-    
     def get_op_optimizer(self):
         raise NotImplementedError()
 
diff --git a/naslib/optimizers/discrete/sh/optimizer.py b/naslib/optimizers/discrete/sh/optimizer.py
index c57f2f5e4..2cfa2614c 100644
--- a/naslib/optimizers/discrete/sh/optimizer.py
+++ b/naslib/optimizers/discrete/sh/optimizer.py
@@ -1,46 +1,21 @@
-import collections
 import logging
 import math
 import torch
 import copy
-import numpy as np
 
 from naslib.optimizers.core.metaclasses import MetaOptimizer
 
 from naslib.search_spaces.core.query_metrics import Metric
 
-from naslib.utils.utils import AttrDict, count_parameters_in_MB
-from naslib.utils.logging import log_every_n_seconds
-
-from naslib.search_spaces.nasbench201.conversions import convert_naslib_to_str
+from naslib.utils.utils import count_parameters_in_MB
 
 logger = logging.getLogger(__name__)
-    
-        
-import collections
-import logging
-import math
-import torch
-import copy
-import numpy as np
-
-from naslib.optimizers.core.metaclasses import MetaOptimizer
-
-from naslib.search_spaces.core.query_metrics import Metric
-
-from naslib.utils.utils import AttrDict, count_parameters_in_MB
-from naslib.utils.logging import log_every_n_seconds
-
 
 
-logger = logging.getLogger(__name__)
-    
-        
-class  SuccessiveHalving(MetaOptimizer):
-    
+class SuccessiveHalving(MetaOptimizer):
     # training the models is not implemented
     using_step_function = False
-    
+
     def __init__(self, config):
         super().__init__()
         # Hyperband related stuff
@@ -49,23 +24,23 @@ def __init__(self, config):
         self.fidelities = []
         self.max_budget = self.config.search.max_budget
         self.min_budget = self.config.search.min_budget
-        self.eta = self.config.search.eta 
-        self._epsilon = float(self.config.search.epsilon) 
+        self.eta = self.config.search.eta
+        self._epsilon = float(self.config.search.epsilon)
         self.min_budget = min(self.min_budget, self.max_budget)
-        times_of_split  = math.floor(math.log(self.max_budget / self.min_budget, self.eta)  + self._epsilon )
+        times_of_split = math.floor(math.log(self.max_budget / self.min_budget, self.eta) + self._epsilon)
         # set up round sizes, fidelities, and list of arches
-        
-        n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (times_of_split + 1) + self._epsilon) # initial number of configurations
-        r = int(self.max_budget / self.eta**times_of_split) # initial number of iterations to run configurations for
-        for i in range(times_of_split + 1):
-           
+
+        n = math.ceil((times_of_split + 1) * self.eta ** times_of_split / (
+                    times_of_split + 1) + self._epsilon)  # initial number of configurations
+        r = int(self.max_budget / self.eta ** times_of_split)  # initial number of iterations to run configurations for
+        for _ in range(times_of_split + 1):
             self.round_sizes.append(n)
             self.fidelities.append(r)
             # n= 2*3 ** 1/2  for i in range(s + 1):
             n = math.floor(n / self.eta)
             # TODO: maybe this can be replaced by search space get_max_epoch()
             r = min(math.floor(r * self.eta), config.search.fidelity)
-        
+
         self.performance_metric = Metric.VAL_ACCURACY
         self.dataset = config.dataset
         self.history = torch.nn.ModuleList()
@@ -88,8 +63,8 @@ def compute_epochs(self):
         return [self.round_sizes], [0]
 
     def new_epoch(self, epoch, round, i):
-        
-        if self.process < i: # re-init for each new process
+
+        if self.process < i:  # re-init for each new process
             del self.current_round
             del self.next_round
             del self.round_number
@@ -102,24 +77,23 @@ def new_epoch(self, epoch, round, i):
             self.prev_round = 0
             self.process = i
 
-        
         if epoch < self.round_sizes[self.round_number]:
             # sample random architectures
-            model = torch.nn.Module()   # hacky way to get arch and accuracy checkpointable
+            model = torch.nn.Module()  # hacky way to get arch and accuracy checkpointable
             model.arch = self.search_space.clone()
-            model.arch.sample_random_architecture(dataset_api=self.dataset_api)   
+            model.arch.sample_random_architecture(dataset_api=self.dataset_api)
             model.epoch = self.fidelities[self.round_number]
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
-            
+
             self._update_history(model)
             self.next_round.append(model)
 
         else:
             if len(self.current_round) == 0:
-                # if we are at the end of a round of hyperband, continue training only the best 
+                # if we are at the end of a round of hyperband, continue training only the best
                 logger.info("Starting a new round: continuing to train the best arches")
                 self.round_number += 1
                 cutoff = self.round_sizes[self.round_number]
@@ -127,7 +101,6 @@ def new_epoch(self, epoch, round, i):
                 del self.next_round
                 self.next_round = []
 
-
             # train the next architecture
             model = self.current_round.pop()
             """
@@ -137,8 +110,8 @@ def new_epoch(self, epoch, round, i):
             model = copy.deepcopy(model)
             model.epoch = self.fidelities[self.round_number]
             model.accuracy = model.arch.query(self.performance_metric,
-                                              self.dataset, 
-                                              epoch=model.epoch, 
+                                              self.dataset,
+                                              epoch=model.epoch,
                                               dataset_api=self.dataset_api)
             self._update_history(model)
             self.next_round.append(model)
@@ -153,7 +126,7 @@ def clean_history(self):
         self.history.append(best_arch)
 
     def get_final_architecture(self):
-        
+
         # Returns the sampled architecture with the lowest validation error.
         best_arch = max(self.history, key=lambda x: x.accuracy)
         return best_arch.arch, best_arch.epoch
@@ -164,25 +137,27 @@ def get_latest_architecture(self):
         latest_arch = self.history[-1]
         return latest_arch.arch, latest_arch.epoch
 
-
     def train_statistics(self):
         best_arch, best_arch_epoch = self.get_final_architecture()
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
-        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api, epoch=self.fidelities[self.round_number - 1]) if self.round_number > 0 else 0
+        train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                       epoch=latest_arch_epoch)
+        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
+                                                epoch=self.fidelities[
+                                                    self.round_number - 1]) if self.round_number > 0 else 0
         train_time = train_time - previous_train_time
         return (
-            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch-1), 
-            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch), 
-            train_time, 
+            best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api,
+                            epoch=best_arch_epoch - 1),
+            best_arch.query(Metric.VAL_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            best_arch.query(Metric.TEST_ACCURACY, self.dataset, dataset_api=self.dataset_api, epoch=best_arch_epoch),
+            train_time,
         )
-    
+
     def test_statistics(self):
         best_arch, epoch = self.get_final_architecture()
         return best_arch.query(Metric.RAW, self.dataset, dataset_api=self.dataset_api, epoch=epoch)
 
-    
     def get_op_optimizer(self):
         raise NotImplementedError()
 

From e4c47ad7e6e52ecb0b92abf861bf3d1a4c815650 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Sat, 18 Jun 2022 10:56:38 +0200
Subject: [PATCH 124/130] adapt train time in nasbenchasr

---
 examples/optimizers.ipynb                    | 255 ++++---------------
 naslib/optimizers/discrete/dehb/optimizer.py |   4 -
 naslib/search_spaces/nasbenchasr/graph.py    |   5 +
 3 files changed, 60 insertions(+), 204 deletions(-)

diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index 50ec92552..b917fd911 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -39,7 +39,7 @@
     "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
     "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
     "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
-    "benchmark_datasets = ['cifar10', 'cifar100', 'ImageNet16-120', ]\n",
+    "benchmark_datasets = ['asr', ]\n",
     "# benchmark_datasets = ['ImageNet16-120', ]\n",
     "# TODO: What of this am I really needing?\n",
     "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
@@ -112,19 +112,22 @@
     "    'npenas': 'NPENAS',\n",
     "}\n",
     "\n",
-    "val_gts = {'cifar10': 91.61, \n",
+    "val_gts = {'cifar10': 97.61, \n",
     "           'cifar100': 73.49, \n",
     "           'ImageNet16-120': 50.0, \n",
     "           'nasbench311': 95.8, \n",
     "           'nasbench111':95.6, \n",
-    "           'nasbenchnlp':100.7}\n",
+    "           'nasbenchnlp':100.7,\n",
+    "           'asr': 100,\n",
+    "           }\n",
     "\n",
     "datasets = {'cifar10': 'cifar10',\n",
     "           'cifar100': 'cifar100', \n",
     "           'ImageNet16-120': 'ImageNet16-120', \n",
     "           'nasbench311': 'cifar10', \n",
     "           'nasbench111':'cifar10', \n",
-    "           'nasbenchnlp':'ptb'\n",
+    "           'nasbenchnlp':'ptb',\n",
+    "           'asr':'asr'\n",
     "           }"
    ]
   },
@@ -330,7 +333,7 @@
     "def save_all_results():     \n",
     "    # that was/is very very shity code!\n",
     "    # key_choice = 'nasbench311'\n",
-    "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
+    "    folder = os.path.expanduser(os.path.join(base_path, 'asr'))\n",
     "    all_results = {dataset:save_results_to_dict(folder, predictors, dataset) for dataset in benchmark_datasets}\n",
     "    return all_results"
    ]
@@ -352,150 +355,27 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " saving cifar10\n",
-      "predictor: ls\n",
-      "first mean 0.04324999999999997 last mean 0.000870000000000047\n",
-      "first std 0.021849313490359377 last std 0.000946625585963078\n",
-      "time  891204.295082887\n",
-      "predictor: bananas\n",
-      "first mean 0.04324999999999997 last mean 0.003050000000000012\n",
-      "first std 0.021849313490359377 last std 0.0017024981644630083\n",
-      "time  876740.4151002569\n",
-      "predictor: re\n",
-      "first mean 0.04324999999999997 last mean 0.0004000000000000625\n",
-      "first std 0.021849313490359377 last std 0.0\n",
-      "time  968633.8430881497\n",
-      "predictor: rs\n",
-      "first mean 0.04324999999999997 last mean 0.005819999999999979\n",
-      "first std 0.021849313490359377 last std 0.0023714130808444043\n",
-      "time  761624.9727487558\n"
+      " saving asr\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/3727768184.py:101: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  output = {key: np.array(item) for key,item in output.items()}\n",
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/3727768184.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  time = {key: np.array(item) for key,item in time.items()}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "predictor: bohb\n",
-      "first mean 0.24727200008300776 last mean 0.0013320001391601579\n",
-      "first std 0.11420437960697061 last std 0.0007157206395425516\n",
-      "time  1102489.6275617096\n",
-      "predictor: dehb\n",
-      "first mean 0.3576000000567627 last mean 0.00031600016113279145\n",
-      "first std 0.14031822252285087 last std 0.0007581451321007654\n",
-      "time  1102614.4726861517\n",
-      "predictor: hb\n",
-      "first mean 0.43363200006103514 last mean 0.003290000000000006\n",
-      "first std 0.08783890716983626 last std 0.0018619613314996453\n",
-      "time  1101775.6664992971\n",
-      "predictor: sh\n",
-      "first mean 0.43363200006103514 last mean 0.0010760001538085648\n",
-      "first std 0.08783890716983626 last std 0.0005408733542267523\n",
-      "time  1100574.386521101\n",
-      "predictor: npenas\n",
-      "first mean 0.04324999999999997 last mean 0.0005900000000000603\n",
-      "first std 0.021849313490359377 last std 0.0005699999999999931\n",
-      "time  946390.188622475\n",
-      "\n",
-      " saving cifar100\n",
-      "predictor: ls\n",
-      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
-      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
-      "time  1870579.6545982368\n",
-      "predictor: bananas\n",
-      "first mean 0.07454000036010736 last mean 0.004780000341796821\n",
-      "first std 0.028682370761335663 last std 0.002698444125281455\n",
-      "time  1858928.1182050698\n",
-      "predictor: re\n",
-      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
-      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
-      "time  2024720.5091238003\n",
-      "predictor: rs\n",
-      "first mean 0.07454000036010736 last mean 0.010660000280761651\n",
-      "first std 0.028682370761335663 last std 0.00712168501714301\n",
-      "time  1509874.2697715748\n",
-      "predictor: bohb\n",
-      "first mean 0.5971599999999999 last mean 0.004929999999999936\n",
-      "first std 0.05310638756307945 last std 0.003036461756716197\n",
-      "time  2003797.8242489465\n",
-      "predictor: dehb\n",
-      "first mean 0.43855999999999995 last mean 0.005079999999999941\n",
-      "first std 0.10679414965249734 last std 0.001905150912657582\n",
-      "time  2003974.1317677882\n",
-      "predictor: hb\n",
-      "first mean 0.5971599999999999 last mean 0.004280000427246052\n",
-      "first std 0.05310638756307945 last std 0.0030925073913141074\n",
-      "time  2001546.2954940004\n",
-      "predictor: sh\n",
-      "first mean 0.50251 last mean 0.0046399999999999445\n",
-      "first std 0.08918686506431316 last std 0.0022415173432298262\n",
-      "time  2002205.9176952029\n",
-      "predictor: npenas\n",
-      "first mean 0.07454000036010736 last mean 0.0023000006103515605\n",
-      "first std 0.028682370761335663 last std 4.336808689942018e-19\n",
-      "time  2072520.2831745134\n",
-      "\n",
-      " saving ImageNet16-120\n",
-      "predictor: ls\n",
-      "first mean 0.1281666671702067 last mean 0.027066667521158848\n",
-      "first std 0.042031271577528125 last std 0.0006110104655917387\n",
-      "time  5713274.493876268\n",
-      "predictor: bananas\n",
-      "first mean 0.1281666671702067 last mean 0.03093333388264974\n",
-      "first std 0.042031271577528125 last std 0.00320693704693154\n",
-      "time  5093832.737680004\n",
-      "predictor: re\n",
-      "first mean 0.1281666671702067 last mean 0.026666667277018226\n",
-      "first std 0.042031271577528125 last std 6.938893903907228e-18\n",
-      "time  6223506.154484801\n",
-      "predictor: rs\n",
-      "first mean 0.1281666671702067 last mean 0.035033333760579426\n",
-      "first std 0.042031271577528125 last std 0.004188476714839849\n",
-      "time  4552233.556291293\n",
-      "predictor: bohb\n",
-      "first mean 0.40754999999380115 last mean 0.03556666660563151\n",
-      "first std 0.042707405685386175 last std 0.00217332313519604\n",
-      "time  6510118.864055392\n",
-      "predictor: dehb\n",
-      "first mean 0.3650333333396912 last mean 0.03314999985758464\n",
-      "first std 0.03514476407331037 last std 0.000616666684812266\n",
-      "time  6511342.426820518\n",
-      "predictor: hb\n",
-      "first mean 0.38350000001207984 last mean 0.03566666705322265\n",
-      "first std 0.040044558503294796 last std 0.003467307816906263\n",
-      "time  6513290.2482745955\n",
-      "predictor: sh\n",
-      "first mean 0.38350000001207984 last mean 0.03340000009155271\n",
-      "first std 0.040044558503294796 last std 0.0010598741974143196\n",
-      "time  6504810.671534024\n",
-      "predictor: npenas\n",
-      "first mean 0.1281666671702067 last mean 0.028466667419433583\n",
-      "first std 0.042031271577528125 last std 0.0025612496835876377\n",
-      "time  3225795.3340755766\n"
+     "ename": "KeyError",
+     "evalue": "'asr'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 10'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=0'>1</a>\u001b[0m base_path \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/Users/lars/Projects/results/all_experiments/\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=2'>3</a>\u001b[0m results \u001b[39m=\u001b[39m save_all_results()\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=4'>5</a>\u001b[0m \u001b[39m# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'wb')\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=5'>6</a>\u001b[0m \u001b[39m# pickle.dump(results, result_file)\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=6'>7</a>\u001b[0m \u001b[39m# result_file.close()\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=7'>8</a>\u001b[0m \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=8'>9</a>\u001b[0m \u001b[39m# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=9'>10</a>\u001b[0m \u001b[39m# results = pickle.load(result_file)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=10'>11</a>\u001b[0m \u001b[39mprint\u001b[39m\n",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 8'\u001b[0m in \u001b[0;36msave_all_results\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msave_all_results\u001b[39m():     \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=1'>2</a>\u001b[0m     \u001b[39m# that was/is very very shity code!\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=2'>3</a>\u001b[0m     \u001b[39m# key_choice = 'nasbench311'\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=3'>4</a>\u001b[0m     folder \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexpanduser(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(base_path, \u001b[39m'\u001b[39m\u001b[39masr\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=4'>5</a>\u001b[0m     all_results \u001b[39m=\u001b[39m {dataset:save_results_to_dict(folder, predictors, dataset) \u001b[39mfor\u001b[39;00m dataset \u001b[39min\u001b[39;00m benchmark_datasets}\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=5'>6</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m all_results\n",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 8'\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msave_all_results\u001b[39m():     \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=1'>2</a>\u001b[0m     \u001b[39m# that was/is very very shity code!\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=2'>3</a>\u001b[0m     \u001b[39m# key_choice = 'nasbench311'\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=3'>4</a>\u001b[0m     folder \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexpanduser(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(base_path, \u001b[39m'\u001b[39m\u001b[39masr\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=4'>5</a>\u001b[0m     all_results \u001b[39m=\u001b[39m {dataset:save_results_to_dict(folder, predictors, dataset) \u001b[39mfor\u001b[39;00m dataset \u001b[39min\u001b[39;00m benchmark_datasets}\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=5'>6</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m all_results\n",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 7'\u001b[0m in \u001b[0;36msave_results_to_dict\u001b[0;34m(folder, predictors, key_choice)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m saving\u001b[39m\u001b[39m'\u001b[39m, key_choice)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=3'>4</a>\u001b[0m \u001b[39mfor\u001b[39;00m _, predictor \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(predictors):\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=4'>5</a>\u001b[0m     mean, std, std_error, runtime \u001b[39m=\u001b[39m get_results(predictor, folder, metric\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mvalid_acc\u001b[39;49m\u001b[39m'\u001b[39;49m, key_choice\u001b[39m=\u001b[39;49mkey_choice)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=5'>6</a>\u001b[0m     key \u001b[39m=\u001b[39m predictor\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=6'>7</a>\u001b[0m     results_dict[key] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mlabel\u001b[39m\u001b[39m'\u001b[39m:pred_label_dict[predictor], \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=7'>8</a>\u001b[0m                          \u001b[39m'\u001b[39m\u001b[39mkey\u001b[39m\u001b[39m'\u001b[39m:key, \u001b[39m'\u001b[39m\u001b[39mmean\u001b[39m\u001b[39m'\u001b[39m:mean, \u001b[39m'\u001b[39m\u001b[39mstd\u001b[39m\u001b[39m'\u001b[39m:std, \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=8'>9</a>\u001b[0m                          \u001b[39m'\u001b[39m\u001b[39mstd_error\u001b[39m\u001b[39m'\u001b[39m: std_error, \u001b[39m'\u001b[39m\u001b[39mruntime\u001b[39m\u001b[39m'\u001b[39m: runtime}\n",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 6'\u001b[0m in \u001b[0;36mget_results\u001b[0;34m(predictor, path, metric, key_choice)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=7'>8</a>\u001b[0m \u001b[39mfor\u001b[39;00m dataset_folder \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mlistdir(path):\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=8'>9</a>\u001b[0m     dataset_path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(path, dataset_folder)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=9'>10</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m(dataset_folder \u001b[39m==\u001b[39m datasets[key_choice] \u001b[39mand\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39misdir(dataset_path)):\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=10'>11</a>\u001b[0m         \u001b[39mcontinue\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=11'>12</a>\u001b[0m     dataset_paths\u001b[39m.\u001b[39mappend(dataset_path)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'asr'"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function print>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "base_path = '/Users/lars/Projects/results/4_experiments'\n",
+    "base_path = '/Users/lars/Projects/results/all_experiments/'\n",
     "\n",
     "results = save_all_results()\n",
     "\n",
@@ -508,6 +388,21 @@
     "print"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filepath='/Users/lars/Projects/results/plot_files/nbasr.pickle'\n",
+    "result_file = open(filepath, 'wb')\n",
+    "pickle.dump(results, result_file)\n",
+    "result_file.close()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
+    "# results = pickle.load(result_file)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -517,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -528,55 +423,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3060/51809283.py:71: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
-      "Invalid limit will be ignored.\n",
-      "  ax.set_ylim([-0.2, 1e-1])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 324x252 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 324x252 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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MGbmjg4MD4KxRZYymu1PeCaxRegEO111wAF6FDhMOJyuFg8PocARVhsQCHWz704/Y/sx9GIkYcdMimjDHRMA4Iuro4r333jvcU3BwmFA4gipTzATS5YZ9O4nUbWRTQ4DXtrfSFcnA3TzpJCFNCytqYEWNwQluHY3qqGHx4sWHewoODhMKR1BliuZCdXuQuYXEN7xCnkdBFQIzXUb1dAhBbHsXrfevo/X+dYPd0x05ddTgFE50cBgdjqDKFNPWnBR/NqK7nc76LXZzJpqQXTOxz8svBStiYEUMR1AdRTiFEx0cRofj9Zcpap+Hn1BUEpEgOoJ9HRFaA/21I0tCdYGPXK+9j+LWcE/NQ8lxIRSwwgaK1770gRf3EtveRfyEMvIvm37ozsfhsPHOO+845j8Hh1HgCKpMMfvWoqQEiSTXqxMzTGIDSst3hhPk+/ReQQV2ZV9XmR+hlWJ2xFA89qWP7egCILqp/RCchMN4YNGiRYd7Cg4OEwpHUGWK2v9SCSlRFYHPNfgSDhRcw+FfUoYVNfDMLjzgKTpMDDZt2uTEUTk4jAJHUGVKSj0qYatUw5LpkpP/+DLMUAJXpVP64Whh8uTJh3sKDg4TCkdQZYgVCtD85go2lMyjzFPAMdbQhRTtAN5RHsBxpjhq2LdvH1OnTj3c03A4QFatWlWiadr9wDwcx7QDxQLWGYbxyWOPPbZ54EZHUGWILxjjlDfXcYKykZvO+AKxvVEumzt0/1EHAjuC6qihoKDgcE/BYQzQNO3+srKy2cXFxR2Koji/4APAsizR0tIyp7Gx8X7g4oHbnaeAUaJbJldt/jd1zV1jMl58X5Dwqia6/rULOYq1LYeJSzgcPtxTcBgb5hUXF3c7QurAURRFFhcXd2Frp4O3H+L5TDh66lG15/hYNakGgNPqV2M11WMm0teUGk25DqMpTGRNK+EVTY6gOkpQFOdnd4SgOEJq7Ehey7Q/DucXMwJSyqellJ/y+fx0Lp2HhUBBsmDbWnb95S62/emHg177Hvspzbs2ZXYAVdhl6nXFEVRHCbo+iqz7Dg7DoKrqsbNmzZozc+bMOXPmzJn9r3/9y5+6/Y477ihxu92L29ra1J62Z555JlsIcewf//jH3J62M888c9ozzzyT3fO+oaFB0zRt8Y9+9KPi1PEqKyvnn3feeb0LrL///e/zP/CBD9Sm9jnnnHOmLly4sF/m5dWrV7uXLFkyc9asWXOmTJky96qrrqoZzXk6gipThKC6tIRArl3qY050D5Y3C+nyDHqJYBfNLc0YZhrBIwRWzMQMJzDDCVxTcym4chYlNx+DmuU6xCflcDgIjkG1aAcHALfbbW3atGnD5s2bN/z3f/93/Te/+c2q1O2PPvpowbx580IPPfRQXmp7aWlp4oc//GH5UOMuX748f+HChaFHHnlk0ILqunXrfKtWrfKk26+1tVVdt26dPxAIqBs2bOi9od14442Tbr755qZNmzZt2LFjx/ovfOELgxwmhsMRVJkibaGTIyIAFMkgqtuT/uVSCQYjdEUSg5wqtDw3nml5uCfl4J6Ug6cmF8WvOdrUUURRUdHhnoLDEUhXV5eam5tr9Lxfv369OxwOq3fccUf9ww8/3E/gzJ49O5ydnW0+8cQTgwvkAY888kjBXXfdtbepqUnfvn17PxPAf/3XfzXdfvvtaYXcQw89lHfOOed0XnbZZe3Lly/vPWZzc7NeU1MT73m/ZMmSyGjOzfH6yxTF1pwtIVCRmFJgSIEm0pioVRXR3cyLL73E9BIfWV6N3OJKCkurEaqCSFfC3pFTRw11dXVOTaojjO+89p3qbR3bfGM97rT8aeH/Xvrfe4faHovFlFmzZs2JxWKitbVVf/bZZ7f0bFu+fHn+ZZdd1n7++ecHb7jhBs/evXu16urqXkH2rW99q+GWW26pvOyyy7pTx9y2bZve0tKin3nmmeGLL764Y/ny5QW33357U8/2a6+9tv23v/1t8bp161KL9AHw8MMPF9xyyy0NFRUViSuuuGLqnXfe2Qhw4403Nl144YUzjjnmmNDZZ5/ddeONN7YVFRUNHeMzAEdQZYppf77bRRkzaGC9OYXv7pnJdcV1zPX1N+UovizEng0kdm1gr1ugJ0LkLjqBwktvSD+2IrASJlY4gfBoCMUpSX8kM23atMM9BYcxZlvHNt+6tnWHPGq/x/QH8O9//9t//fXXT96yZct6RVF4/PHHCx9//PFtqqpy4YUXdvzhD3/I/+Y3v9nSs+8FF1wQvOWWW3juuef6zXv58uUFF198cQfANddc0/6JT3yiNlVQaZrGzTff3HjHHXeUXXDBBb1Cbu/evdru3bs973vf+4KKoqBpmlyxYoXn+OOPj37uc59ru+SSS7qffPLJnKeffjrvgQceKN6wYcMGr9ebkTOKI6gyJZmU1pK2ZiWAuFR5N5Q7SFAJbxbCmwWGRLgFWrQDwzAGjghAbFcX7Q9tRBqS7DOryDq5AuF2PpYjmfXr17Nw4cLDPQ2HMWRa/rSDEnMwmnHPOeecUEdHh9bQ0KDV19fru3fvdp9//vkzABKJhKiqqoqnCiqAb3zjGw3f/e53yzVN6xUYjz32WEFLS4v++OOPF4Bttlu7dq17/vz5vW7On/3sZ9t/+tOfls+dO7fXhLd8+fKC7u5utbq6ej5AMBhUly9fXnj88cfXA9TW1iY+//nPt33+859vmz59+tyVK1d6Tz311IzOz7kjZkpPUlrF1laV5JpVTA69zKcCkbgkHhOo7VE2NgyOvVJbQ/jCthCTgDQkUpeOVnUE4wipI4/hzHOHinfffddjWRalpaXGD37wg9IvfelL+37wgx809myvrKycv2XLln4eW5dffnn3bbfdVtHS0qIDrFmzxh0KhdTm5uY1PX2+8IUvVDz44IMFd911V0NPm9vtlp/97Gebfv7zn5edfPLJAbAdN5544omt55xzTghg06ZNrnPPPXfGPffcU//oo4/mLFu2LOB2u+WePXu0zs5ONXXNaiQcZ4pMSWpUMhkjpSQXlTZGsrh973Ru3zud5kR/rz1VEyAhYSlEozG6wsbglyrors2iY5IfxacR3xsgtqNzcAVghyMGp3Ciw1jRs0Y1a9asOVdeeeWU//mf/9mlaRpPPvlkwYc+9KHO1L4XXHBBx4MPPjjIi+9rX/taQ2NjowvgwQcfLLjwwgs7UrdfeeWVHT3aVSqf+9znWk3TFACbN2921dfXu84666xQz/ZZs2bFs7Ozzf/85z/+f/zjHzkzZ86cO3PmzDnnnnvujNtvv71u0qRJ6c1MaRCjTvVzlDKtolT++NMfpvY/L+BpMpBZcOHZPwLRJ+u/UbGNMtfgIGArHEToXqZe9l9pxzbaI7S0hzljmh2yYIYSeKblpXe6cHBwOKgIIVZJKY8bqd/q1at3LVy4sPVQzOloYfXq1UULFy6sHdjumP4yRPhzyDnlEpo2vUNNUxsiCN/a8ABNWj4Ai9iBd2uEdiWLXd5SihZMpSDLjr0TikCa6bNY2B0EQoJpSRRh5wmUUoKUo8py4TAxWLVqlVPl18FhFDiCKkNc3ixKZh7H1nMuhXd/C8ApWzcM6ldBJ/Oo46k2nYJlswGQQoXEMOZYATHT4rU9HSBAxkx0y8Dj0TiuJh/N0ayOKBwh5eAwOpw7YIYYybx+08+7gRdnzCIhVEyh9L4Q0n4lKQ119u2sKkMLKlOiN4aY0hClNGxQ4NXJd2sU+F1EEyahWMahBg4ThLVr1x7uKTg4TCgcjSpDVK3PUaL2+g+zbf1bqEWDg7MTf3ieuW17cKeUrlc0F1Y8ipmIo+oD0iSZFnn/rgcg3hyh88zKvmMKQUc4Tq7PyQ13JDFjxozDPQUHhwmFo1FliJGI0xaK0RaK0R2zYAivvFjSO7A82s7WnXVs313PzvoGDFMSj3YP3kFXMPJs4RWr7h8v6HNpNHZFCEQTg/dzmLDs2bPncE/BwWFC4WhUGeL3ujl1uu2V969NOgHsOKmBTNf3AVAa6OD9f/1Xb/tb1ZNJXBLAmz0gz5sQdF5Ui7KtE6vIC0DO6jYMVUVUZdEdjbOmroul05z8cEcKpaWlh3sKDg4TCkejyhDTNFEVgaoIFDH0ZWstzk7bPrupkZY3n2XH83/sfe168RFikSC4VOLFXiyfhhIxcDeF8b1hx9bl+9yjL2vvMK7p7Ow83FNwOEIYrszHc889lzV//vzZkydPnjt58uS5d911V+/T7he/+MWKW265pd8TU2Vl5fyGhgZtpHEPB45GNQJCiGXAstra2t42RTBk6fjIKefz67wAhfEuNspJXL73RRbU7cJvxLCCXRDsy04huzsJVE0nu2AKRjyKomnkvdeOGrMwogZmPIYlJVbMwIgN495+AGi61ptw1+HQ4PGkrZDg4DBqUnP9PfbYYznf/OY3q84999zNe/bs0a677rrJjzzyyPZTTjkl3NDQoJ1zzjnTq6qqEldeeeWI5cmHGvdgn89QOIJqBKSUTwNPL1q0qDejrFAEQ0U3aZpK9tRaHmir5ISsDnKDIagD1bKw/NnoKU4ZpmnQ9cIjdKGgxMFSIGbV4CMPGZGEHn0OpCRhSjZkH4Sbm4Da42eQM+tEyK0cub+Dg8O4JbXMx09+8pOSD3/4w22nnHJKGKC8vNz4/ve/X3fHHXdUZCKohhr3cOEIqgyxrL46HEIRQ2pUACdkd3JCdicAMiURfiIW6ieo1AJb8xYGELZQNEEHBm2JRiy/QranECkhZppY+V4UoeDRxk5gBVs6iIQTeCNh8PS5z+tup4DjwSQajR7uKTiMMc/9z8+qW/fuHvMyH0XVNeHzPvv5UZf52Lhxo/faa69tS+17yimnhLdt2+bteX/vvfeWPvzww4U975ubm/WRxj1cOIIqQ1Q11TyWebaItXottdgJi7cG/WSpg7/LeSQoEn2mPUWoxC2DjmgnIIkZQHcAgGn51ahjZKrTXC4a1u5kz/YAcX8ZAEIozDvvVHLy09ZTcxgD8vLyDvcUHMaY1r27fY3bt46bMh+Z7PuZz3ym6Y477ugt31FZWTl/pHEV5fC4NTiCKkNSy3QoqsAaTqVKYY9VCawA4F/NFeyNDPb4OiurlUtdDSClnU4JcGuuPlcXKYlEIWrEiWVb+FxjI6g8uX6ET2C6c0nklAAQbGrGjDvu8AeTpqYmcnKcB4EjiaLqmoNS5mM046aW+Zg1a1Zk5cqVvquvvrqzZ/trr73mmzZt2qgq6w4ct7Ky8rCYAB1BlSEuV585TNM8CCOzjBFd/tze/yuCrezNTuOaLLCFkgley4fbcGNGoNvfCYBbtzW4mCmx5BiXAhYCkTKmBCzTKTd8MJk0adLhnoLDGDOcee5QkVrm40tf+lLLiSeeOOtDH/pQx8knnxxpbGxUv/nNb1Z985vf3Hcg4x6MeWeCI6gyJHVdoWjqfPa9+TxWYz2kTRprIbNyUf3ZLJjUp3md3/kKM8q7e0uF6Fl+vL5s8rUEUlcQEUlxZxlFsTIS4Thr/APLQQgMc6x91QVC9he6luWkbTqYbNmyhfnz54/c0cFhBHrWksBOZt1T5qOmpibxu9/9buenPvWp2lAopEgpxWc/+9mmj3zkIxk5Ugw17uEioyMLIU4DXpdSGgPaNeBkKeXLB2Ny4wmvt3cNEn92HrnnXofLCKKmEVRdO9aQ2LEO/NnMyFNJeFT0qMmJm7dx4uZtvf1MofDcZWdTMqmKd0M5NITd5BgaPqJYlokayuEYf182CwEY1thrVKQKKgnWmAtDh1QcIeUwVpimOWRxswsuuCB4wQUXbEy37e677x6kWdXX1/cmoRxu3MNBpiLyBaAcaB7QnpvcdsQH4oTDfaZij66QU1BC3CxCV1SyPP0vY7i1DiMlxVK0TEPfNVhLUaXFWS++wPqCSeSRi8/0AIJK0UKlsMvcbFem4hK2cLKkRPBPuoVBQOTQKMpQBAgh8MowlaZtfahXq4mIPqeNEquBHKubhNDYrU7tbZdeL6csqCNWeSqR3Bm9x2irayQet70Ac4sL8GX7URXhlBwZI5wyHw4OoyNTQTVUiGshEErTfsTh8/Xd+IuzPRRne9jTFmJ32+C1TlV3I1PWfXZfcDHm9jWoCYNdaikWCue99SbugIGn3eDY9h1DHreUlWnb/XRSRvqccdPoTNvuBebTv86bHg7gPn4fq5iHFBpmTLD7HdsT1eoOkH/SErwVpSyozKM4251mVIfR4ggpB4fRMaygEkI8lfxXAg8JIVLTI6jAPOD1gzS3cUWqRtWDrirEDYtwvP8aY0L21zxc7iyYczIAPXmzt1iNLHhzK5iSsPQQT/koPMTxCFuj6ZTZpD4j5IgwChZxqROhL6ZKw8Qv7DmGpA8jRcn1EsUlElgoBKSdCSUnbj9ftG3Mhs1hZvA1ADq8ObRduoyiSeW0aTnkeDSkUDDH2uR4FONoVA4Oo2MkjaonYEwAHUCqa2MceBX4zUGY17gjVaPqwe/RKMv1DFI121UNKxKFtsbeNuHxo/j78gC6F5zK5gWnDhqzVXTzS/czBJuuINFxMogEWTNuRyi2MLwqdjbT4mWUZZWRmh8jDgzlx5quEtZ7j7/Ioi1Jbc0CF/b4paF29qzbQWWJTk5UQGIaAnDk1NjhCCkHh9ExrKCSUl4PIITYBdwlpTwqzHzpiEQGhx/keHTmVuYOavcbM1nZdgYetx0IZXS1Yu3eAv70CWtTmRGYyu/2fYcXpMV9SpwKTyfHRk/lH74X7LHE2Hjk5Z9zAitzcxEp3owL1r+JbpmIRIKEKwd3125cndshy8IsmjUmx3WA1atXs3DhwsM9DQeHCUNGa1RSytsBhBDHAVOBZ6SUISGEH4gN9AY8EhlNItG8ojJy559FUZYbXVXoatpJy95tGeWz8Ma9FMdKuBSTyTVvowjoFPn8I7ndoEdQSUaTIWMg/hwv/rOP6dcW2rKavFgQ1TBAKBh6HtaOHcjtG9i6tYH2ogGlRiyLSYtmk19SsN/zOBqZO3fu4Z6Cg8OEIqN8GEKIUiHEm8DbwB+BnqjVu4GfHKS5jStio8henu93UeB3YSY9/xTNBRmazuJanLAaJKFH7CztgCb7PqYEBy/GKZ7MQ6gadmYKNTcX6c/B0tyo0S6s7jas7jbMYDdGJEp3QxNbX1nBu39/mbX/eQPTCRTOiG3bto3cycEhA3rKcUybNm3uzJkz59x6662lpmnfI5555pns7OzsRbNmzZrT83ryySezAXw+X7+n1F/84heF11577SSwS4CUlJQsmDVr1pzJkyfP/ehHPzqpZ8zDRaZefz8FmrC9/FJdzR4B7hnrSY1HUjNTZIKuCoJRE7emoLm8iAxTLrXkNNKq7gO3giEFIVNlVbiUeHQJit5OwmV/YaSU6WONRzNHoxtfohlFxun0ziSRrE6sJNNFCUVBdbmQ0o8V3Ed3sBEwUd0ePDnF5JWWkzAF0rLo2teEZRioqpPQdiSqqqoO9xQcjhBSc/LV19drH/zgB6d0d3erP/3pT/cBHHfcccEXXnhh1E9GPXkATdNkyZIlM5999tnsZcuWBcZ6/pmSqaA6GzhbStkxIJZmO3BU5IMxEglIDJP1WnVBSsLGPJ+LQDRMIGqQ5csBXccy4rZ2lSHboj7+p6k2+a4agF2lb7BEH3KXUVEcXs3U9icAeLP6doxk5Hll8x7q//h3TJcbz9LFFJbnIV19Ac9GLEx3Sz1asAtX2Rz8Xg9KINgvw7zD0LS2tpKVdcjzlzoc4VRWVhr333//rpNPPnnOT37yk1GnSkpHLBYTsVhMKSwsnBBlPrykdx4rBiZszQIhxKXA+4Ec4LdSyn8O1VeNdcDrQyiPlgGzLoLyvowDNYV+NEWwvSWEouqI4krYuQnL3X+tSxSWIgamJpFA1KJWhMlVEnRZfZJpdUxhpr6ds0P5yBQzoHCrKPro4q4Tiu2qHlez0awIsaQwyo8GyN9jJ2Bel4jDVef3C/ZVPX4Uy4eMtNO1dz17sqZhtAep6AhT63WKAo6EI6SOTAKv1ReG320uAii96Zh+RQZbH1g3xQwmdM+0vK7c8yf3ugPHdnd7O5/ePgkgb9nUPe6anF6vLaMzqml5nlEJiDlz5sRN06S+vl4DWLlyZVZPKiSAxx57bPvcuXNjqSmSwK45de655/amV+opAbJv3z7X6aef3nXyySePOpntWJKpoHoZuA74ZvK9FEKowNeA5zM9mBAiD7gfO/5KAh+XUr6R6f4p4/wOuAhollLOG7DtfODn2HFe90sp7xxqHCnlk8CTQoh84C5gaEEVbIAXvjv0pF77GVzzV/D1OBZIXIYXS9o37qLjzyUypd9UCW5cgQx1IXJ7S8IgpEC1VJACr2Jwa/5muiyN74VzMGIVWLESniz+F1MCk5ksCmwXdQlIiVTTLDkmM1eko803l33Zp7A77zwM1U/kpCXsfDnG1MRuZAikqeCJpHf0FIpA+ArIS3RhelQ6FUE8ccT71IwJiYSTnf5IxOyIuRN1wbRPIfH6kN8KxF1arrvfYrcVNrSefayw0e9+LBPWAdfUGMr0l2oyBHuNauXKlb3l5ntMf7FYTFx44YVT7rvvvvxPfepTHQc6n/0lU0H1VeAlIcTxgBvbgWIudgqlpaM43s+Bf0gprxBCuIB+wUlCiBIgIqUMpLRNk1IOvNAPAL8Elg/YXwV+BZwL1AErkkHLKvCDAWN8XErZkxLq28n99h8jCr8/D3JtEx3SIs9XjPf4n4B/OjlF1eQUVffbxUokCL31HFa0rretPDGLKsPWzFZrT6IKSQlQqWjsBszIZEJbv81LrpWUyR34NPsSvhQvZpccbFYUAKpAKDBdC3Kyu71324a4n99bH0TEdCxFh9yZKBdN4zexz7D9hRLiTQrWcFnihUAKBSElQkpM6eQIzATHRHpkoua7Y3pVVjDdNlelP2QGXXGt2NvPAqX4NKNnH8Wn9c+lqiuj/qJs2LDBpaoqlZWVxurVq0e7+yDcbrd83/ve1/3yyy9nj3tBJaXcIISYD3wWiAEebEeKX0kpGzIZQwiRC5yGrZkhpYwz2Jx4OvAZIcSFUsqYEOIG4HLgggHzeVkIUZvmMEuAbVLKHclj/hm4REr5A2wNbOCcBHAn8Hcp5TtDzHsZsGx6VRG879bBHRKR/ppWV1+2f727ntKdTxAo/Wq6oSmftxRr9gn92jzvtMJbtvwMnHIhPQ5/x9Qb7N7c9z2W1dOJTK6mLLcWgK1v7+HVvZ1pj9PjcahOnc+FZ/Tl+tv36hb+vaZtUPfb1Wu4WnkOgNrOfbT+6kFkUijZf+1XzOXBXLoY76KkUud4/WVEuuBxh4lP9tLKtuyllYN/UEDRdfPS5klz1+REBpoJexit2W/fvn3aDTfcUHP99dc3j1WBQ8uyeP3117MWLVp0UOptZcqIgkoIoWNnoLhWSpnmTp0xk4EW4PdCiIXAKuBzqUHEUspHhBCTgb8IIR4BPo6tHWVKJZBaF6YOOGGIvgD/DzgHyE1qbvcO7CClfBp4ev78eTew5Ib0o/gKYPWfQEm5nHvfAmkh4iG6IiObejRF4HdrmJNyCERNpJRML8qjx0d9aoHE0F7mX222pbUy71Q7+FdVEYBXV8lxD/FxSgmKwKtriJTqwDlF+eS4OxCWgRT2F1uaJo/I9zHJ3ciJrMdlGVQEWoae+GPb6fjbP4jklbEuJ4f2ugZKZ00f1M3v0vBmWPAxy6OhpzNjHiG0t7eTn59/uKfhcATQs9ZkGIZQVVV++MMfbrv11lt7q/YOXKP62te+1nD99dePqBn1rFEZhiFmz54d/spXvjIwIfkhRcgMzDVCiGbgFCnllv0+kB0s/CawVEr5lhDi50C3lPI7afr+GbgQmCqlTHuXTGpUz6SuUQkhrgDOl1J+Mvn+GuAEKeVN+zvvHhYvXizfeSet0gVGDCIDPvt7FkM8RPfCT9J+6h3Djm1JyY7WEKXZ9npWojmElbDQDIucN+3vXHhWHs+rb3N/k73kdqv1HWY3leHRPHSdNby7sxVOoBX5ULMGmwaVWCe+5vcwvPbaWvOGHcTauwhHLQKvvMPiyEr8MkwLheyg1i6yKCWz6wc/BL58/rXkTp3CpPPPQoj+giZmmv1SPg1FwrTw6ApaiqDSFMHC6rwjRnhFIpF+ZWMcxhdCiFVSyuNG6rd69epdCxcubB2pn0PmrF69umjhwoW1A9szXaN6ELgB+MoBzKEOqJNSvpV8/yjw9YGdhBCnYjtbPAHcCoxGyNTT48dtU5VsO2B6yl6kRXNDdln/NsX21MtxCXKK/Gl26kNKyZ72FM26535uSVwttkk7WmuiZfVpJL6oRm6nwLbEjoAiMLvjaQUVAwSKoilIC3KLssm97HQq9r1DdryDcMG5lOScCIA/Vo/akGD1unwIRJm32/YQ1KXE7VLw6QrqAE/GrFHU6DRMq1/UWWckjmlJRunUOG7ZuXMnc+bMGbmjg4MDkLmg8gMfFUKci22y6+cKJqW8eaQBpJSNQoi9QoiZUsrN2LFZG1L7CCGOAe7DXk/aCfyfEOK7UspvZzjPFcD0pPmwHrgS+EiG+w7LaFIoAdBjYgs2Dd8P2ytPEbZmpQhBr6RSBPFi+7iWR0MRfXfqgCdOZ75EEyqt4Tay3FnkNBv419rOEl1Ly7D8trAUw97hFVKTvQtFtU2FSZ40l3INf+azDcezfZ8tjM9X93Kv/iqFx89llXIR/NYWVMRjIOlX4mR/0AZoTqoQWEeQo8asWU7eRAeH0ZCpoJoN9Ni9pgzYNpo7yP/DFj4uYAdw/YDtPuBDUsrtAEKIa0k6X6QihPgTcAZQJISoA26VUv5WSmkIIW4CnsP29PudlHL9KOY3JOnKfAxLz3rVxqegfQcUDLxs/VEVhbZQDEUIjHAcyzBRNBXzjIpe93K1rU/gfFf5Hh+e/xlmeBYQjTRSJsrIjbvQO23NT6QUbkSAjBjE6roZiGJG0NvDGK5krSkpkCn5nlZ4zuKetlPppM/r9kRhX9JnEosp9KUcJx5DApZpwRgFJUOv9/0Rw3vvvcfixYsP9zQcHCYMmXr9nTkWB5NSvgcMafuVUr424H2CNGVEpJRXDTPGs8Cz+z/L9IzaU8tb0KdN7Xp1REG1oCoXI1kCPq7rWFGDXcEocVPi1mxB5VH6z+GJ1t/znZpfoQiBKU0sr0aswu4jNVsr8W7pROuMY/o0QnPSLOALzXZfVxWsuIkMJsDoE1QXFYTI07wEjBgxGWdNyMUf4udytfpvlocWUGxm0+Nho4fbUaPd7Ht9JWiDtThFVSlfvAB9P4KCjySNyhFSDg6jI/OFg6OcUWtUl/0v3Hea/b85stdftqdPBYl741hS0G1aNARjuJNCp0Av5vSc9/NS99/sfjJGl9FOlpqLYRkkSrx0lfRfpHc1RnA3hokXeQjPS5PlXLiQug/FDIHLjaKAGQoSQ4IA3ePh3Pw+IXEV8Le2bK5p+wbtMpsbymLEFA23ZVCyazvmsTNwR02i3lIspf+aWLS1jXgsNmpBZUnJu3s6k2bRgfOHAp+Opip4dZXSnL6xXdr4dL5wCic6OIyOjASVEOIF0pv4JHYKpW3Ag0PFIh0JjFqjyk3xxLP2L/Nwrkdjb3df5hJd0VmafT6lehUPt/0vAO1GMzlqHoaVXhhaXluzCRxfnP4gQiGSMxNv92YUaeDP8eA5bjY6Ebq6JaF97Wi+/sLv/YVhGrJK+P5eP1lqlJjmwh03mNSxj+jvl9P88avJVyJ05c5Bir6vmKIGkfuRvaLI78YaQqFKmBZdYQNLSgIxg+0tdryllJJjJuWT5xt/SXIdIeXgMDoyfeTcCCwGKkh67wHlybZm4FTgLSHE2QdjkuOB0a9RpZi+hhAiQyE0BQyJOy6RUQsrYmJFTArJY5pnMqWyvLdvW6QJJSYwh4jVCs3JJzwjF8sz9DOJpWcTKjyOUOFxREtOhJlnIyYfR7Y7hpDpx63UI/y+aiWVxg52FfcJZY+ZoLUxgmIZKNZgoWQaoxdUQghUJf3Lo6tkeTRyvDqVeV4K/W4K/W4URQwp3A43Q4Y5ODiMkolWrmN/ydT0FwUekFJ+PrVRCPETQEopFyfjor7LKHL/TSRGrVGlBv+muWEPh17sQy/24ZaSbK+gPZKgPLdPq/H4C1E7VExp8teuP9CkN7LIXMJkpg2KVLL8OsFFdsHDrHdbcdeHMH0anWdVpnSSFD67BwSEK/3Ey/wYWRWYvlKE0UysvXPQHP1mM8eE7Pjol077NP/afAxfeucvALzYKLhTn4YUFogwqhBcN9lFDQJpmOxuC/HuEFk0dEVw4pRCsj36AZnuxvOS1qJFiw73FByOEsZbuY79JVNB9THgxDTt/wu8AXwZ2+nhurGZ1vgjGo2O3CkVNcXtbZSCqgchBMfV5PPq1lbCcXsMn0tDFSoF7lJaonYm/zdb/0WDvovjwieiDoiLQgKqQHGrKDETNWwg09z/1bA9fta2LtpPKge3hru8iko1hJAWcU8hMsWP3R3KhWREnFfGWVHa5yBwxcbnOWPXyt73mgD3KxDKyqOrqIDNfslvX9055Hnf+/IOTp1exFfPOzA37kyC2Q8HmzZtcuKoHA4p46Vcx/6SqaAS2Elotw5on0NfeGqcjOvYTjykAZveHEX9MWnSc5tt2dVAm7YFiYI3S8PlVdDdKi5PZhGseUSIRyXhuEmXKUlEwpxTeQUvNvyVhvBuANpkC2qZF13t7xcuYwZGawRQiRd7kYrAcg8+bqzMR6zKT7jUi67bkszIn4rIrcHTtgGPGUXqfVql8E2i/qTbsEzJca5JnP56kLii4bIMJgWbmRRMn3HltadKaF18OplbnY88Jk+efLin4DDGPL98Y3V7fXDMkzgWVGaFz7529t6htk+0ch37y2gyU/xWCDEdO6gW4HjsMh8PJN+fDqwb09mNI4IdYV79y6pR7CGZlWv/V7z9N+xdV8fa+PnEpQe3pwmXN4rXP7qHm4QlaYuYdMsYxaeeyVVV3+aV1sdZ0f53wkY3reEwXn1wap5ELIEiJaLSC5XJ7dH+a09dS2zzoBU10SN2fxuBm2zyoi29aZGkoiI1L+FSO9IgB7isuJG1S99H3qYNyTRL2NnVk88xtW17UKWFKxrmGHeUZZ84g4GJMxOmxWvb24glTKryvZiW5N29HZiWpDrfR0XekZF2aN++fUydOnXkjg4Thvb6oK95d+CQFxqbaOU69pdMBdWXsUvRfwHoyRXUCPwYu44T2EG2fx/T2Y0jpKUTiwzhOTcUuX3/Lvb+nXCijNWRi4lHS5AdFvHAQ0hz9KnCFCC4bQPV55xOZTRIXch2ye547zksLW9QfxGDwsJpqDnuQdtcHi+u1PU3TUXPc6OkaF1BUUBnqIVcoYJlogcbMd25WJqvLwWTolBx7BS0k2cihIpqRDA0P8GsWntu3/8WReEO9HAAXYBPE6j64K/f++f3OYpEEya3P23/Bq8/uZbLF4+uhPvWZtsD0J3MzOHTVRRl5HyDB5uCgjRhAg4TmoLKrIOSXXysxh0v5Tr2l0wDfk3schh3CiFykm3dA/rsGfvpjSdMpNU5qj2e77yeE7KfwK90IYSk3LWBdqMaQ7ppTMxE99QizbTla4YkkVwrsxIGu//+PHnAeZQCsI9nGK7+dFbu4BukqmpMmrEQX3Y+3oiX3K58pC7omBNB6rZW1R2z2NttkOVSAIkn2o1qNqGZYbxWgJntf6ctOpM6awqqrpFfVoHQdDzRFlwJ+zfR6fZSFO7AG+okN7gFy1iKqg+fvkJNESqJUbrw5ftcdITjrK3vQgDRhMWxtfkUZQ0W1oeacDjsZE8/whjOPDceGC/lOvaXUQX8JjOgTwWeSb73AzEp5YRcoBsNvjyVEy6uHLljP65ii/F+5q/7JLrRyVTPW0z12B4I68Pnsm/mF5lx/CWjGrFh+1Zef/gh5H4U3wt2tadtX/vmvwCYkr2Q44vOB+CNP/2GyAAhOnDV6X3lWyjxBMl2RTBbJXva7N9A056dzF16JjFPX+XiqMe2RniiEVQzgRZuQRVDxH7pPqTqQksRVOYoa10pQlDo7xNKHaE4naF4ryeh36X1E4SHkrGqFeTgMBLjrVzH/pJpwG8p8FfswoQSmI6dq+9ubNf1zx2sCY4X3D4vx7xvxMz/aVFiZ8LGJ/q1lepbac8uombBjFGNVbPgGCpnz6Vt7x62NW0kKCP8odUe+8zsEzin+AxSEzjEwmH2rl2NbukwwHm9uWEnsZRS8zEzTEesCYmFzCCF45qOck4s2kOhK9yvtxFqwxXZR8LXJ9hjXltQ1XTUs2LVXo5buA6Xf3CGCmHGiOVNJeGvRGoeVEVgWpJ39nQSS6Z2mleZy/G1fdrh1qYAXZEE00uzyfWm19K8LpW6zgj1nRGiCYslkwvI9x+eYGB9BE3SwSFTwuHwu6nvb7755jagDeDuu+/ed/fddw9nZJkwZKpR/RR7jaoQSDXxPQLcM9aTGo9IaaFlWPhvEJffC5svscvVr/gN1K9CFQliof1TRKtnzaVqxmyUpnI6Yh00v/RnAGJT8olVF/Wr+6QrOheddyFqGp90S1rUbVxHPGRrQvG2EN1dYdAEi+YvszvFLchSBjnptUQsAoFONiXq2eLyIQpNSpq76NqxmcsnraOwdQ3vVn6VhJoNQCI7u3ff49e8zrv/qaKkLGfQnIQ00Y3NgCCYVYUmyjGBzU0BNjfZ4R8S+gmqv6zcy1s7bW1xQVUuLlUhx6Nz1QmTKEumVPLoKp7kWlVbKDaqTMpjTTAYpKio6DDOwMFhYpGpoDobOFtK2SH651vbDkwa81mNQzRtVFbS/ugemHeZ/f+OF6F+FW4RIqvpX/DGS/s1pAAqY+1o8SAeoRGVBgkMirJL+vXrjHbiycnBraZfm5l98um92pOZMEk0BLCiZq8zhRUxkKbFgM8dS0qagmGadr2CouWAW0Nq2yltfZUKbwAsyKl/nb2KnS7IU1tJYIOX7ITtHWvFJZrLB0gUr4ZQUx8CClGNCIXBJt7vsvhbotoO4E1OQRtgstve0qcVrqnr9czF61L5zOmDvevSpQw8lDhCysFhdGR69/Vix0kNpBjb9HfEM2zhxNGg2QLDp3ZyYvQW21dyPylMvu70F/O1/EK2791Du97/4+iOxtnYsBtNs81cRbU+XMOkUzJEFNkRRbgURJaG8NiqlEtx4dH6m+oqc/3kxQuxVC/BuKRTcbG+s5RSTxDFV4CYNoeyYjuarAzYgmDeX/8EgIwnwLKQpkR6BholwdS8mHlePpSo54SKWgxNw+fWmVbkh+L+3pe3LZvDL1/Y1pt9fk97mLhp0TlEWqnDHQdcV1fn1KRycBgFmQqql7GzTnwz+V4KIVTsOKojMmXSQNzuMfIWq1kK7zw4NmMlOTvUwhd3fYcOs5o1L3cN2r6Lut7/fRWCaddrgzQkAEyJqwmyN0H2Zmg9A+IlkLASFHqKmJLXP1BVUwR5Hg10F6pmobpdWCj8u3E6ADlduzjliqW9/d05fW7w0jBAUWAkpxCfmzwzgmEqJMISI9QCbi+av69qck2hnx9fsbD3/ef+8i47WkLEjfR5zaSE+s4w2R7tsJS3nzZt2iE/poPDRCZTQfVV4CUhxPGAG/gJdqaKXGDpcDseKYw6hdJQLPwwm7e4WP9y/QEPdYz/SSZ77Pjr6d5XeDs4cjHj8D6JO5iNv2Twgr7SapD9eF/UgU/34XZrJKw4rpcCdOg70Kfm4JreFyBmtgnidRZmjYW7oBB3VjaxoL2WFOzspDvWsw4nUVOrJHd00dzQgTQlikdFpAgMIexVtkRWDl6XD5fHixQakViC7cEEemuQKX3hVoNwJ2th9ThfxAyTtfVdLKzKQ0+uX9V3RMnz6lQX+Ice6CCxfv16Fi5cOHJHBwcHIPM4qg1CiPnAZ4EY4MF2pPiVlLLhIM5v3OD1jl1WhBlXXIqoaSLQHhu+o5Qk4ibxiGlXzR1AnXUyk7fbNS0bctfzatGjaYc5PvskKozJtG20zZe7/hNi7pV5g/pZRRqhS/woTSboAivXFh6q0ND3RkjQTbunm0BBn4aW39KGd8ck2KGSe04nSy5ayqbX3iG4L8LFk/4LXgLvsdBRHCOQUnLj+LWvwNpXhj9/4B81S9hwziQuqc0m1+NCGiqxITSlHj592hQShkVO0gMwHLMDhz90XDXXnFiDS1PI8WjUd0QPi6ByhJSDw+gYUVAJIXTgVeBaKeWtI/U/0hBCLAOWVVWNLivCCGMyY0nZiP2klFimRA4T7Cp/5EUYEWZOrWL+5Ore9ogR4ckdttv6kjkzmJqzgLaNbQC0rI2xb2qYwpmDzZlWtoLmVsDdZxpUEFh5KkjQ/S6yXX0Cxx2zs8ZYBXHyPFngycIsMggE9uFW3iNmLUJToCbPi3dSMVv9BZSG0sdzpeP83W/z7vbFbM6fw5RsFZdQ0FoaYWNSWGXnQlV1v32mFvfPZPPMWvtZam1dJ92RCty6gkdXMUYZmzVWOIUTHcYKn893zEAX9dWrV7tvuOGG2u7ubjUej4sTTjgh+Kc//Wn34ZrjWDCioJJSJoQQk0lfOPGIR0r5NPD0cccdd8OhPrYQAlUbwUUtGTya78nhgqnn41JtIdId6+4VVGvb1hCTUXxZC1CDtvlty5MBpOgeNJzljhM4bh3S1XcTV1CYM3cuxXrSiWFvn2NJLKcNzxQDt1/HSgrU6YlVFJXX0RlvYlXHxyjdm0uZu5BQR4DQ6aeyPqijYwtJwxVGuFSEIvp5VEx+4R+4kybED7/zV9o3vEBcgap8D80nnA4VSff0PTshFOhL5ZSGhjp7nI2NAT7627dwaQpfPGcGiyflDX9tDxKOkHI4mNx4442Tbr755qarr766E+Dtt9+e8EkyR5OU9gbgKwdxLuOaURdOPFQk617pCAzL6BVUWko9rBVNK1jRtAJ1zsPc8PZdve1CDhaCatRN3quDb6T1QD3pytjUAuDPSlC4LxmQa9ZQRB0utZNN0Tib3m1BX9vj5JFLji8PNTsPhEBYEiRIYQtmnzsGUhL9wLW4lv8aISVTuhuY0p20MDdCOBhGLjnGjhfLK4DY8B6Zs7UIr6QEgsUNi7d2tjG3MofdbaF+fXO9+kGvCuxoVA4Hk+bmZr2mpqb3R7FkyZIJmTE9lUwFlR/4qBDiXGAV0O/XLaW8eawnNt4YdeHEQ4WwHQdUBEZKJiuf7uOE8hN4q+Gt3jZTTfDgsd9mZssSdLO/2c9jZDG3af/9YkJBnVDQXhPqUj/Cm+KDBMwSdK+XPLWOKwq/BsDzXf+PnbGZdGdg/Zsz9zyyGtbQITVCEspDreTGw8Q6u4gmLLy6Cppmv4bh/PIw+VMq6ZA6f1qxh0DUQGKbNPe29/2GTUuiqXDilKKDml7JEVJHHl95dHX1lsbAmN8kZpRlh398xcJR5RG88cYbmy688MIZxxxzTOjss8/uuvHGG9uKioomZmnfJJkKqtlAT/3sKQO2HRUmwUhknD6UJE1eulAIx8OoQu3NTPHpBZ/m+rnXD4p/klKm/dS6dxu2w0XKtq3BTbwXHbq8SW37PGo65/Zr6zIr+k8RiVuxNVJVmMk2EwVbsJropKtPtaFoGRQtI47k0awYH1z7MOftfouKYCvm7V+3y83X1CIv+QBimPpWejzKKf4u8Hp5SkhbLwwEyNm3q39HKWnSvDQW+qnMP3gPJmvXrmX+/PkHbXyHQ8+WxoBvdV3XIS/zkY7Pfe5zbZdcckn3k08+mfP000/nPfDAA8UbNmzY4PV6J+y9OlOvvzMP9kTGOx7P4Lx04wLF1qi8219gzmlfomtA7co2sw1N0fqZAofCOx1KpvX/LseaffxpxetD7rOx9HWEFIg0KZruLP44xXEvWqKbppZTaWtoprn9BaKxlczNbeD8is0APFX3YZpiCgEzAQi8OVdhpSxYuRB8JOghJ5kbUJUWanuLffqtzbDq7WHPSyoq1tUfRy5cjEdYeBTQTQOCAzLXRyP4/DkEYgc3x/KMGaPL7+gw/plRln1Q1gb2d9za2trE5z//+bbPf/7zbdOnT5+7cuVK76mnnjpO1y9G5gDyAh1djFlmirGmRwAFmyh/9JOUf/rlfpvfbXp3VCXZBwYCLyxZyBeP/SINoZGjEJRYgNb2bTzXtQkAq8QkP7sAKCDI9exbsYnmbW8AJqlqW1e8ie64rWm5FIMrC76LIWfyfGMOnZaG6T0HgLVZBeyedjoF0W4mh3ZT25GZ96CwTGLvvE1XRQW3T9HIL69AiDR5GwXoApo6ghjxBC5VYXpp9uB+Ix5QDEgJ1Z89e/Ywffr00Y/rMG4ZrXnuYPLoo4/mLFu2LOB2u+WePXu0zs5ONXXNaiLiCKoMOaBcfweT2lNhjZ2Ulsa1g7cLMsqEPhSKUFhSviSjvsJMsLV1Pc+tuBOAtd27iFh9vw9lhou5yiJCTd0osQLWWtkopqBwcg05msYUax3z5NsIJIHERgSzCSdqcCd9lpSsU7m/wtYYP9gNM4Pt5HXtBDm0m/m0XX/HHe8m0KSy9sWdSLOBiplzmXXSwsHZORQVd0crSlcXUSStMRNvsX/UWdaF7sJ3/HFDCqvS0tJRjefgMBTRaFQpLS1d0PP+s5/9bFNdXZ3+5S9/eZLb7bYAbr/99rpJkyZN6FJM4/TuO/4wzXG6FnnZvWBEYMNf7Ru2ZfW6rAMIxAEJqtEgVR2vry8P3//V/2dQn3zNzzePv4Jqj10yRQQtpodBaqBFK4lv2YOluFmffTmFRXHK9xWwNmBnpHUJHTveHOKqi5ivkibf8DXCJtW/ijvejTfaSkW3BlTT/W4nL+55CV+uwjEFLtxqUmCpCsycg8tnBwHHowmifhdq/uiCgs32dvtzGEJQdXZ2kpMzOHO8g8NosSxrqAXkuiHaJySOoMqQcVvsTggoW2ALKgDLAKVPA1CEQnukHU09gI9a2u7u2e6RzWCl/lKKvEW0RlrTbu8wQvy25T/cOecT9vQVC2ElHwKyKtm3+DYAiuIJChMmnqYpeP2SvYkOYnRjp+EFzddBrowRDYcxjDiqomBJgaaogMCSkoRUkaotpHMCe5i34Xcjzj9SUk7nx+2QuUhCIj0KSmsy3VSKAiYGpdG1cwgKVSADQfRcPyIlMDq1dyAUoi4cQEpJXnEp2UXFg8ZycHDowxFURwJqSt4+KwH03SCn508nkZs+i3imhBNhtnZszaivpmjcdfpdNO5+GYxYb7Z4CXxjoy0o6iOtBIzkuq4bSGMJU6JRCte/gV9dhUssJmdGPrHcGLxnb39M+BG5bZw9s5lwJMEUr4JFhIqsSlyqTtxIEIxbhLSF8K/Ma8fpLU2se+4tFGlhGXUgR+/taagK8qnM3Nv9eQV85Lt3kVNcMnJnB4ejFEdQZYi1H6XfDxlKiqAy+wslr+bFy4EFpuvJ8U0rM/OnruhMz6lBi4eRet+xryg/lUcbXiFgRrjmnR8NO0ahafLinnrQ4Ve5L/JHzU9XJAbiFpC2IH60exKPBkrxT/0pXjXAteYCrtKm4VY9uFWTbDUKH76cxJmn0B23WLNuHc3b3IRd1fzVHyeWoiRfvONVLtr5Bpq0OPfdA6i9AkR0ldemVxHXR/55hTrb2fHOChad9/4DOqaDw5HMAQkqIcRGYLqU8ogXeOowXlyHnVTX8weX9X8/6QQ47wcHVC1QUzR8mo+okVkGeQuLULSTMqH3Wx2r9WXuRBBKmW9c6aZbgy/vu5YEHu5H0tQzsnQT2vZ1rOrf85usd5leupAFuaWU+0og0gVlk4lnuYg0NONdeAKKuQ+lo5xlEqIpzx6lnkbgjYznNxzehMnC4ipCM5Ihh7oLtL7vj2GaKFKy6TW7aKZpHJjG6+BwpHOgAuZX9CwaHOEYxjh2mvH0ld2gcU3/bfvegTmX2QJrP9EVnWPLMs+mIKVkVeuOQdrdSQVz+IZyJS2xzkwG4b6KKHF01LDgBl3hmLoZFMosTtHDfK9yNa/u6gs0zuk4kXDWZt4IbaHQm02L2Uml4qa4YyNKZzO+jn0sLJ7CnGXz2FUfJhA28eopKpV1PDvWu8kLdSJjULdP4pKCgY8nniwoqhFIoCJ3QCB1OEz3o48BMLViEr6FJ2CFwyi+/hrt3nicckWkCKpx/N1ycBgHHJCgklL+cqwmMt5xuQ5u/rcDYtb7YfYl0JayjmREoX2H/X/nHiiZc2jmonkQmo6ialjxMFaK67gAluTNTF+0cSgSEqXZsJ0R5sUxG7sJGgafqKrg4sJuvrrK9p6bG5rFCsBruig080gk4tQpERplA1a0lXxhURxrAbOLWHYJIiuPLHf/r39+xSm9///kzU7e7Tb4cY2P5tX9vSa7821PykBW/zRUwhWiFFtQ1e21iG7zUlaRS1WJByUlJVONaaKmmFG7W1to2b0TAEVTKaioHt01cnA4wjniTXZjxZgVTjwYuLPgw8v7t7VuhV/aLuA8/slDN5eCKfDJ58nyFBDpbsTS+gt4U5roio5Xz3DdTBdYpfbXVBgSxROgPZDAlYBSl0mlCvUm9OhusXACpdvCbajo2T4sv0pc04nGQ1h6DsSCqJG9yHgX5BXBgPRSaCrbQrA1bAuSHzVFue+yPNa9HKHbrpJC8y5bcLXQv56YsPr8Qtxv/wPx3muEjAhbJk2l/JRkyiQBzaEQJT4f2ZEYAa+b1f/8G7vX2BnKFEXlpA99lKnHHI8+XrOhOIwbhBDHfvKTn2z6zW9+Uwdwyy23lAaDQfXuu+/e98UvfrHioYceKiooKDBM0xS33XZb3Uc/+tGu1PaecV599dXNb775pm/ZsmUz/u///m/bRz7ykS6AM888c9qXvvSlposuuigA0NDQoFVXVy/4/ve/v/erX/1qS8/+P/vZzwp//etflwJIKcWtt95a35O9fSwYUlAJIV4gwzx+UsqzxmpC45WxLJx4SMgqBc1rx1gdStp3wJ8/yozJp0H3PnD1z5kXSkToiHXgHigghkAYCSx3FoGppyJ1FZmjoMRBCoGISlSXCRGVdd5dAOS3laF05gIWTAmgdEnMuAuJGyNogHShRwP4A3VYsVZETYpJM5GA7iClWXkEDfurLwBNV5h9kpd3n48QH+ZySkUjofnRjRDueBfueDJj/JY2Orb0pXnSgQ7gZEXw/OwaDE2ls7Ev88fffvZDTv3IdRz7/ktRx2ugucO4wOVyyWeffTa/oaGhsby8fJAN+TOf+UzTHXfc0fTOO+94zj777JlXXnnl6tT2gf1LS0sTP/zhD8t7BNVAli9fnr9w4cLQI488UtAjqLZv367/5Cc/KX/vvfc2FhYWml1dXUpDQ8OYfnGHG2xdyv8q8FGgEehJx70EKAceGssJjVfGbZmPofDkwFV/6ouvOthEOmDDk/b/e163X2nwJ1+jJWFG6Jh0IrjAcBkYEoQJLjxouIgRwyUlWqeC2pGF1C2sqUGISXRPNsFSQZ2S/B3n+OnOdtPR1UnY6ltHU+JRfHVbMLI9HJtdxKqAl4RpEW/rQNEUjj1doEiJaUlihmRWWV8OUpEsUxKdez2R11/HkoL4e0Mn8wVQLUmhdyHR7ELQNYKdbYDESuzhlT8+QHdrMyd/8KMZXR9V03GP1wz/DgcNVVXltdde2/L973+/9J577qkfqt/ixYujqqrS2Ng4rACZPXt2OJFIiCeeeCLnsssuG1Sw7pFHHim466679n7sYx+bsn37dn3q1KmJhoYG3e/3W7m5uSZAbm6ulZubO6Ypm4actJTy//X8L4T4KXZNqs/JlMRxQoifQZrIxyOQcVvmYzimnmm/DgVSwm/fB3XDJ4jdXypXP0rl6kcBmJfSfiaAB7DA3Cn4K+9icBpBK8zDXc+zLG8JWbsKyBU5kJdAFtm/H5dbYkS6KAxvpit7BobmB1yoBbVo0sKTXFPqSMAHVpmAydWTfVwxyYcAgsE4a9r7vvqqgDnVeeRdeD7+RQtQs3MIhCRv/7sJI2ybCFta7J9OQcdG5m58EICandvZOu0YIt5i9ORXTFoRYl33sfmNVwm2t414baQlkdJi8YUX480aOuOFlBKhKPjz8vHl5A7ZrwehKM5a2Sj4/Ws7C598t75ouD4Dy3as2t3hvePp9ZPS9b30mMrW65dOHvEL8JWvfKV5/vz5c2+77bbGofr85z//8SuKInu0rnvvvbf04YcfLgTIzc013nrrrS09fb/1rW813HLLLZUDBdW2bdv0lpYW/cwzzwxffPHFHcuXLy+4/fbbm0488cRwUVFRorq6ev7SpUsDl19+ecdQGtn+kql6di1wkhyc3fTXwJvA58ZyUuORCadRHWqEgOuehdaNtsHYTMC+90Cmib2yEuAt6M38PiRmDP7+NYiHhu+XRBWSy/kPTxesoF2DqTtgA89ySfsZqKiQHYYmeyxLSlyhLGLmhUSmWkQKAJeGVV2OKyEojHZBV/+HQsuTg5FnB+YW5PU/dnswRtyVQ5bLixmoxwoF8eo6py/NQ8lyoWa5CAZM/vrnNmLh/N79itrWkRWo440T70Amr4dQvCC8RAPdbF/5Fpmy892VaLrLToorAOyqySJZCkYI+6Mx4nHmn3kuLu/QD19SSgoqq5hxwtJhhZXmcqOM59CNQ0hdR8Q92lIfneG4NtQ+x9UWpKtUOoiCggLrgx/8YNudd95Z4vV6+wV89ggkv99vLl++fEdPhp2hTH8AF1xwQfCWW27hueee6zev5cuXF1x88cUdANdcc037Jz7xidrbb7+9SdM0Xn755a0vvfSS75///GfO17/+9eqVK1f677777swj7UcgU0ElgPnAlgHtR01RnQmpUR1qNN1O59RD2XzSLnPWr7QLPmruwdsGctl9sOlvYCaFRtt26Nxtj5s/mUZXDe+1whnBv+HB7rMsPOD3rT1t/40kX0lygZABDeHPYOVLLAnBhMXZLxrMxM2kMg+NBX3OIPMMC2t3O91ZKrEcDb+uoiqCrKhGfluMOCEsn440JWZnB0LXkQkTLeFH6Nn4XHDFlX4igXl0ppQa9cQ7OXvtbSS8eQQDJhFvMZunziXOMA48MgDSXtdKRPtOykhkZnFZ/a+/Z9Tv5Yd+T8WMWWm3WaZJxYzZnPiBKx3NC6jK98YWVuUGh+szsGxHns9lDLVPVb43lq49Hd/4xjeaFi9ePOfKK6/sl7tsOIE0wngN3/3ud8s1Tev9AT/22GMFLS0t+uOPP14AdiXhtWvXuufPnx9TFIUzzzwzfOaZZ4YvuOCC7k9+8pO1h0NQ/Q64XwgxHVuDAjgR+Crw+7GazHhm3BZOHM9oQ7j0KzqEWkEdYrvu7XPCyK2EEz415CHKgLntYZ5fNYfTdvwEL3GEoiKFREqJif0C8Iq+r7uwDAQSVW2iMl+npNSDlJJ1jfa9wY3gJJ/F3sq+z33pvxSeJ8BtSWn3xZO85PssFnSVUv5eJ5a3C8Oj454yBRmNkmiIEW8LYYbiaEUC4VJwaSrbI0Fm/+mPBP75T9p//wAAVkc7akc7uUBuYDcd+TNpKD972Mt78uWFlE3xEAuFWPP8P5CWlVSbZF9pF5lMSZxs2/nuymHHHEg0GGDHOyuG3L5r9TsUVdcw/YSTRzXukcj1Sye3ZWKqS+XYmvzIX286ZfOBHru0tNRctmxZxx//+Meiq666alRzSMfll1/efdttt1W0tLToAGvWrHGHQiG1ubm5N1DzC1/4QsWDDz5YcNNNN7XW1dXpp5xyShhg5cqVvsrKykOzRjWArwLN2Ca+7yfbGoA7gZ+M5YTGK+O2cOJEpHSunQcwHZEOaN85yFtwOFRFoaNwMfOTuQRvPz3KklKT3A1v845Vzzek7RdUqRWiJSsB/6BuM/MjQbb6tvP14HeR623zlWFINlcsJk/LZ0bZUnx6Snoqov0WZP+wOo6iSNxGA2VYfHH2JAqzdLRcH+RCbFs98V3279V/XAW+hbbz+kLDQAXM7m6M9g4Su3cDYCZM4uvtUi1aBllApMyjfFoNABUzZ48YOCxNk3gkQt3mDSNqQJtff5n6TeuHzGgSTzGFt9btdgTVOOBb3/pW44MPPphRhuPUNSqAv/71r9sG9vna177WcPXVV08DePDBBwsuvPDCjtTtV155ZcdVV1015VOf+lTbl7/85aqmpibd7XbLgoKCxG9+85s9B3o+qYjRFNUDEELkAEgpB3mEHMnMmzdPrlu3buSODgdGLAh73gSkXbZE1cGbP+wuzd1RXtoZ5Csv2ULh9mnvcvW+n6FaCTrceZxWMdjB4H8bmjk5GqVBVflLTp8pPi4E//T7aNI0riv6PNM9SdcNBVxR2NQR52dr0n/1r60u5NuX9GXLCL6+j/A7zXgXFOE/oRzP1DwANm3axMzp0wm9/gZqft+5WbEYez5qe/lp512Kdtr7wDRwLToWkXTukBY8efe7ACxZNpnj3z952Guzv5hGgkBrK3KIWl+WZfHAFz/b+/4j37ub8mlHRuViIcQqKeVxI/VbvXr1roULF6YvE+CwX6xevbpo4cKFtQPbR+3rfrQJqB7GdWaKIwl3FtScbGfWMGLQvMG+O4uhy6wUZ3s4vkYF7PjDek8tcXcu3kgrUf8kzsiaQ7vVxqcbNzI92MEDtfPwuSMQjVJumny+o7+D0lfbOwkIgb77a70aWA/nIJhfcDa/UK8H7ES92zotoqZks2Gwor6TsiwP1bkesk6uIOvkCsxQAldFnzCsqqqyHR50HaO5GeHSUfPy+xVazMr3kDezBKOlGZfWhVBTaozZ1j02v7qXpo1DOnohPN5+tclGQgiYc0oFtfOLUDWdvLLyYftrLjdG3NaMn7v351x3168yPpaDw2gYLuB3DXC6lLJDCLGWYYJ/pZQLhtp2pDCuc/0dabh8faa/tu0jCiohIN/bd5PfG3bx9IL/xpUIkuVTudzjIxi1WNy4i6xYHV/at4v2eR/Aeuu3KFb6hLDZUoJMv+3s8N944pgi4oqGS3iR286mI2RR6NZIhA22B7tp64gwJc+LX1OQhmW7kFsSoQhaW1vJqq3Fd+xirEiE8KpVGB3tpFo3Ov/8ZzoffhhXTQ3FX/0qirvP8UTXIR6Hrg6Dro7hvpejX1fduboVt09DUW0XQUVTqJrVp/V5/DrHnl+DN9vFcRddypuP/wWAQFvLUEM6OBwww2lUj0FvjphHD8FcDjlCiEuB9wM5wG+llP8cqu+4LZx4pKPqYJn9M8KnIUvvW0v5+w4/f98Btl8fXDQtwAnFIbLathL0lhEvX0Ro0hJ2VixEjXQhTAMsAyO3gtxN/+C5Pf/GSvNcNjMeZ0nU/km81/ZvupIa0OXHFPOBqdchE7aZLJYw6QgnWCcslGicREsE0RxA6Ao51TnkKC6iCROPrqPqOt4FC5DJCtJaWRlGY1JLsiziO3di1NfjXbSodx4LjxOsfy/CiGb7oTZLC1St3/pTJNQXRhAL9xd+m9/sr7Wtf6Weky6dir/wNKrnw77N6zGNlkFZ4BVFRTi/G4cxYLiA39vT/X+gCCFUYCVQL6W8aD/H+B1wEdAspZw3YNv5wM+xs2ncL6W8c6hxpJRPAk8KIfKBu4AhBdVo1/IcxghVzygNlKoI5hRpbGgdrGE8sy2b1+p8lJ11L9G4RXmeTj6gh1qRmhsjuwKRiKDGAgRrT2ZXQR5/a3gDA4mUkPSb4wNdnSyJ2p6+WcJDVzLDYF1oF7u7dpPnySPPnYdXV/F4dSwJ+oYgWW80ocYs9p1RQdfebvaG2/EEdUr8PeZkjZ6fYtmtd9H93GOYnR2EXnwRgNZf/xqR4syTD/Slz02P2dpK/sc+hnv69EHbhNuF6uvT0KRp4T3uRFb8ax8djbaTRHdblLa6IJqrT9AYcav37ysP9yRArkT3VwJw702voGrNeH070fVW8sorOPvjn8Ht259cJA4OfRyORGKfAzZiazH9EEKUABEpZSClbZqUcqBHygPAL4F+mViTQvBXwLlAHbBCCPEUttD6wYAxPi6lbE7+/+3kfg7jDdUFicwCfh+6rJCH3u1AVRQQ8OO3+jznOqIqf9vm4rzaGI0dUfxGHF/DRspX/5HG4z9BaMpSTN2LSET4aP58rs6eQWjSCciUWK+8Dc/A87bT662us/gCbxAyujGsBG0x2yM4z50H2OmUVGELUDVmYXpVdL9GrkfDCMG+um46NQUzSwdFkO/RKctyI3wV5Fx4KVa0q1dQme3t+3Xp2n/zm/QbFIWyW2/FM9d2/DCaGrHaWjj+tHxsMZie915p5q1/NAy5HcA0Sgh2lyClSXsr7PzSmxnFWAkhmHFCGWdfO3vEvg5HH8OtUQ27LpVKpmtUQogqbFPb94AvpulyOvAZIcSFUsqYEOIG4HLgggHHe1kIUZtm/yXANinljuTx/gxcIqX8AbYGNnA+AtvF/u9SyneGmPMyYNnkyQfHu8phBFQXZFhducCrcmaNjqYoaKrg3FqdvQGLG/4ewpLw5BYPr+x18Y1FDUx/41tolu0lqO7bCFOWAiB1L6buRQ23o0U6MHyFSNV2Ubf0Ppf5k7b+hlnV06nHwNsVpSPWwv+t3YsqVrOoKp8PLa7A79IQeQmE3oJV4EFHIa5UU9KlUvSuHQtZd24lIY9KTEJ5npfQmw0YrTG8i3Tyrrye2Ob1mV0nIZLJzCSRVSOksbIsGm+9FbWgALDNjQXXfxJlYOJlAYqrb+1vViHUXK5ipiit0WiY55/fQDRRDUpf9iD7mRHbeTODu4hEsun1BqYsKEJzKWgulbKpuU4gsQMwvEZ1MNalfoYdk5WdbqOU8hEhxGTgL0KIR4CPY2tHmVIJ7E15XwcMVzHw/wHnALlJze3eNHN6Gnh6/vz5N4xiHg5jheqCeMD2XtO8I6Zd0oSSNNMKhBBMylE5rVrlxT32GkxbROGuNSXkHvd1irq2s883GW9uNvmx/s6sIh4mv2E9snIRht8ONwnW9P8qPbC3x/z1KPAoXwD70W4v/b+FKtAGPA+hvHlsKr67d5NXUxEulbhpYbRHMTpjyASYHVHcCxbinjsPlDyQAuESqP6+G7dMSMygLQUUNyju5LaPfZbojl1YXYM1sY7f39P7f4+mZra3E35nPb7j+sdCCSFQS7L7rWVlDVC4coEPTylHmiaGIdm4Nko0YrF21esgJTm5BRROnYGiaQih4MvORRvgQdvRGGLvRjtE59l71/a2zz+jktOunDnoHBz6UFX12OnTp0dM0xTV1dWxhx9+eGdRUZG5efNm18KFC+fV1tb2mhVuuummpptuuumAg4EPBxmtUY0FQoieNaVVQogzhjnuj5Ka0P8AU6WUw6YkORCklL8AfpFJX71f4KfDIcObB3k1EAtBV53tvi5Uu6pxmqdtTRO0BRO4tGR+O+BbJ/tZUBzhF6vsNaXGkE6rmE/ljEWUxAzcusL0/KzeWCWALe1bMBMx2/EgieXys/Wah5n2hw8jMjM2DMLfuQ6mR2hbYucMjOomprToiCaoSyTwRQxkjka4shpZ6sa9YzPmagPiAr1SwTe373todEvC79jqjf94HXyiVwMxu6owOyrJOklHzU5xbXffSvj1l5CWAVYXsQ22YIhv24Cr6mSkCWqOQC9RMSOmLXhHUGpUTQdNR3PDMSfaWtm6t94mkYjTHoX2lAQ+Lq+PT/ziN/2S4rY3hPjT7YNzGtZv6cz4uh6tuN1ua9OmTRsALr/88tof//jHxT/84Q8bAaqrq2M92yY6h3KNailwsRDiQux81zlCiIeklFendhJCnIqdIPsJ4FbgplEcox6oTnlflWw7YOLxMc0I4pAp3jz7lYhAOPnRBhohMkBbiIfBk0OZ2yBP2NqT1Lw0hS06QwlOr9LY0Ab/3mULq8c2W6xoEGTLCCcWR5meq6N6c9AUO6GrS3VhJiKDkurG86rYecW9eJs28pcdT9MW68CveSn1lFKqTQEjjxW7BcF4/7v7x6u3MrvxBQD+5/XXmDWpguqqWui0+4UTkhctaRuv1QTICLF6SVndLo41ZuNCp7Wrm+7tUSwg26WSE3WjYZsjZTyA1W0LTysqwNQBFTMkUVPsF8IzCX3KR0CBnLNd1N94HQCRFW8Q396IlKC4BIpX2O707v1LOLu0tYVYLEJLto9tZQV91y8SZt0L/6J8+szeBw2B4OxrC+lut4XujnfDtNUn6G4J89Qv+srFiJ4AMtEnkBVFRXW5mDQ3n8kLChgOVdWO6GKUJ554YmjNmjUTrHBeZmQsqIQQ1wNXAZOAfrq7lHLKSPtLKb8BfCM51hnAl9MIqWOA+7DXk3YC/yeE+K6U8tsZTnMFMD1pPqwHrgQ+kuG+w+KkUDrM6F477x/0/e0hEYEu+3kkO/nCjEP7DvLLKzBMiw0N3dyw0N0rqFY2mqxsNLlV+yMX7H0O+a7C9rk3E689i1llObhVNwEzSiLQQFyamLq398bakV8F+VW8GFjN9q4el+xWMFu5ed4PWHh8BQlTYll9WldX0yuQFFT36z+CBqhXTmPFSbcB0OOyISKgdBhIj4IlJcrkGezKS5BIBMnKzseVV4phWbQlJJ2GZEG1DytooleXoCQ99MygQXh9KyCxQmHMzj5hK2M9Hob2zV6vmkSizs52Y7bvBCCzFcHh8SVf+eEYtZdfQVATvPny8wC88scHht1X852N5l6IkZDs3ZBZZe2tK5qJdf0G5FAeohK3z8+yL3yDmgWLMj2N0fHWvYWs/ou9UPepF/rn7/u/D04h1Koz5Ywuzrm1z99/71te/v51u8zHBXfuofqEvhPoqtPIrcoogNMwDF544YXsT3ziE72ZMvbu3eueNWvWnJ73P/vZz/acf/75B81CdTDJSFAJIb6CLWT+FzgNu7zHtOT/d43hfHzAh6SU25PHvRa4Ls18/gScARQJIeqAW6WUv5VSGkKIm4DnsFcGfielzHA1enicMh/jGN0LRdP6t1kmdNejKwJdUcn36jR2Rzl/ss4rdQkkEEu5BYTKTsDncdEeMzBjIWr8VUhPMcSDdseyeeDu76j6hWO/wE/f+SnbO7f3tv1i/Te4cc5/U+abZBepStKdNw2J6GcyLNr3Fn97L4gQML/aQ3WhDiJZWThmoQL4cgj5IByP4PYX4db96IAqJYGEibsizzbPWRKRPJ6aBYUfKQGdfuZMAM9CiO3chtnSRKJdJ+t9ywi//iI9JUH6rp9d50pxq6CNPhbK7OwksWsXADWTpyOKinjvnbeJBkeuXGHG1qNokxBKJvkeFYTQEUJFKH6kOfTvNBYOseHl5w+eoOrY7WbfO+nLfDSu8RNodJFb2T/JZaRD690n0tH/fpyIjHjhY7GYMmvWrDlNTU361KlTo5deemnvYuvRaPq7AfiUlPLRpCD4pZRyhxDiO0DNaA8qpXwReDFN+2sD3ieAQT62Usqrhhn7WeDZ0c5pJJwyHxON/jfomsIspIRPzI9z42IXHl3lR29GeHr3SezTJ3HlMWdjubKwgjHif/1/uKJtdJzzU0qqpkCozTY5DXDkOL36dCqyKljZtJLvv/X93vZt3evQlf4lTNo0jSdOv5Ot7+xhbudKLlLfwi1jfHnLfwGgbJEU+40hl4MkErfiQhF9czAsy9aKNAXRe8YS8moxJ12AjFt2o1CR+b0P1nh8CWSVbRTxTj4T9/RZKB4vIqXsfWSjQWKfhXBDznn946AUl4riGf7WEXrjDVp+YuerlqaJGg5x6cUfZvfOHgcUWyBLCUphAUpubm+zRII0sbO1yV7fY4lEWpZdABIwjARtdRYNO2yP3JLJ56J7omTlBVHV/nrhtpVvEAuFSMQyrpwxevJrYlQsTq+xlC0IkV0Rp3B6fxXRm2/07uPN76896d4RldueNapAIKCcccYZ0++8886Sb3/7280j7TfRyCgprRAiDMySUu4RQjQD75NSvieEmAa8LaUc3jh8BDBnzhy5YcMR8XBydCAl7HqlL0u7ZYLmoiHuZU9bmDyfi5+ujPCPHbbp7snLs/G7BMKIMu2ZK4j5KwhMvpCik6+2S5J4csCTD8X9E69u69hGe6ydrW17uXPlbSNOywhO49KGCn6gPTTWZ7x/KDrhpf+DzJnczz09tCJEdHMMNMg+2RaOZkgSWW+h+ATZS/2ouUPnvwy9+SYtd/UZW3xLllD8la8Mcjc3AwH0slLc+xn+sW9rB0/85N1B7XmlPoyEScX0PHRdZdd7D9G29z3cPj/55RVICeXTZ3LWdZ8alD1jIiWl9fl8x4TD4XcBXnvtNe8HP/jBabt37167Y8cO10UXXTR969atY2JROlQcaFLaRqAI2APsBk4C3sM2/x0VKRscjWqCIQRMOrnPay/SDo3rKM/10BqMYUmJnnJ/+tU7US6cqjO7wPaqa5n9MYKeUtRwAqQfAmHcXa34dE+/dE5qoBkt1sF0PRuP6idqDh+crGVt4/mpW/h9ZzaVCQNp+rFipQgljuJuBhHHipUjjRxAorgbQbGFbY6WwxRfLUKAR+sz1RmmxKUp+FveQ0Q7R3edrAR62xskJKBWgcs2b7pqXUAC4RK92pMVs5BRCzMqiawPk3XCELcPVUErKenXFH77bczOTrSUbPE9HEjWl+JJOfhyXYQHVGPubLJNgFvesl0ONf044D1i4RCN222trmnHVjxZ2VTOnEPZtOl4/KMqzjvuWLp0aWTWrFmR++67r+Ccc84JDlyjuvrqq1snqraVqUZ1P1AnpbxNCPEZ4KfYBRQXAw9LKY/4GCNHo5rgRDqhbgVIi/XNMUw9hz0Bi5v+PXjx/e35fyVeNJ/GnLmIpDRImBb5aoSZxf2dauqD++iOdeNV3ewumcKG9i3EEhaRhCQSl/086OOGJGZIcmJhyBv6pvjye8Xs2Jc21BAAn0tw4znlLKi0DRnRhEUgaqApkGNJPEZfTJgabWeyq4Mi/4BqykYUnvj0oLEtbzlm9lT7/4jR62UHgCkwAh4UTwKhmpjZkwnplwIKwmWheGwznV7sRXFrRNauIfTmm0TetgOQK++5B728f0Z2MxBAKy3BM2VEf6whiccNulsihLvivPOP3cRjJi27A2guBdOwkwG7fQo+3zMkYlECbS3EQv0fKHy5eXzyl79Fd7knlEZ1pLFfGpUQ4hwp5b+BT4Fd70BKea8QogPb3fwxbAeLIx5Ho5rgePNgyhkQ6aAgtIpgqINpPoXKLJ36YP+Htb/nXcnJpTr5sS7ydjyD4SmgveIsgtLHjnAyjklCjteF5c3HUAQi0k1Ny05qcIEiMXOziZb1S0OJaUk21kdRBOjDOCgEq0Ls2Dd0NR2XJsjJ6mZXV/8+MSNGSPOj6n3rWCEri6bsQt4/a3F/s5uU8OKd0LGz3xhKpAElMnSaJLcKJJKv6BvErEqi8YXoU8vxzM3GjBjIhEVsn4GSdzxaZRdgC6r6m28eFPsmNI38a6/B8+UvD3nMkXC5NIoqs6ESJs3prQWIlJKX/7yFdS/VAwof+a5tijQSCe6/6eOEOvvqAIa7OunYV09J7f4LTIeDx0imv38KIXYBv8UuOb8PQEr5F+AvB3dq44toNDM3WYdxjKpDVgnli84HMwE7X+LxK3J4fnuA1a2Sv2y0zUcv7zFQ2rZzYlYD3vYN+FpWA2BUvY9Q1Hb1jpuSQNRAahaNwRDNqhvFUPHrPpAW7pZmrK63yPPZQkOqLhK5FWQZBqG2CDLXjerLwlLdg6Z5ykw/x03xsqUhjmFJXmp4iqZIHYXuUk4uvozWgEm2O5u9bQnW7o1y4SJb+8qWWZgDnMvdumBjUyt1Ha9QkuPikjnHoyqqLTA++W/Y9rxtHl31e+jcy0iWfAmIQJ8gK1B+bEdF1oOlnA2AlZDE6+3fi8/dTc7S9LkKQ41uOrf76X7mGYpvvnnY4+4vPSW+jLjFzjV9pUhOv+52Wvfsoqu5ni1v2GEDa19cT0HFhEzccMQzkqCaC3wCO9XQbUKIfwL3A09JOSAS8gjHKZx4BCEEaC7QfXhDzczOhpq8nF5B9fzuBAmlhYvd9+GTAdpmfJh4Ti2eFE3FIyEYMzDjfgq0ySSkQVOwiUaznWxXNkLmEY+YuEICv1uhJieOu2UrMyxJmw6Rpm4SpTMIZ1emnaJHV1gwyTYzbjQaae9cg+Yuoap8CZOr3LQHfYRjFv9cGyQStzhrbhb5fhWV/p6JPpfKtKIi4oakOxrAklZfH38RLPyw/f+iIR1p+186QMbCcPccRKxfZXKUvXaclAJoKdPwVpOWnOoouZPDGIkg5n0XZ3T8HqTmJTHpA1ieZOV1Rbdf/TpJrLakI4hh8eyv1zKYStw5djjnpjcBRnafdzj0DCuopJQbgS8LIb4OXIyde+9hoE0I8SB2nNLm4cY4Ukgk0hfRc5jA1JyMpWxAdG2l2CdYVKLyXrP9/PWytZDF0f/lf8/3oyp29oRyKVF6TFcCsjx95TkASrJy2N65HYsYCioeTWJZJt1xk6Ci2Qn5dAgGNcJqnOyuXbiiHXTn1g47TTVZZbg91sz9m78HQCIwB6N5GZDPK5vDvF23meJpf6I2exaTs2b127/cV0ORuwbDMlnXtg5VZJZtwpIWNdk15Hv7O0AItw/56VeQO1+C7r3wzvK+QpemRJr2uhCGRJox29onBCgK0hRoib5EiL6ipB2x9Y2M5pSKq/E//d5LVy6JhZ/DrDnffm8YFOYdlQXJjzgy8vqTUhrA48DjQogK7CDc67GF2GtSytMO3hTHB5qWqYOkw4RBCFDdJOJR9Egrdy31EBDZvLI3wd0ropgSPvn3vkX3D+Vv5kszWuiefGG/YbJ3/5tEVgXRwjkUegqJmX2xOpaURBMSt3DhTWpkUZ9BMFaApVnkhtrIim5EUwWamn7daiFZDPQx1nw7iNP38BSP5tMVb2d12+usbnudgZxfdTVTsuYikRgys2rVgXiATR2bcHUNYU2onG2/Zr8PVVGZWzQXXdF7Y8Esw8RoaMKKRkns2Y1WaK8fxaWJ/sYdUPc2VjQKQqBkFaDofWZHaYFMpqESuiRVtqpGesc1Ee/CteIOus1KpJ6HlJLqMi/nnC4Jdlv9YtRkIoFSWIhWVkE40ElX8z5a6xU6GvYvZZTDwWXUd18p5T4hxK+xdeTbsJ0qjnisDEtNOEwsPMW1RCdlEbC6yG5fQx4Rji/NQ1cgWbCXArp5wPVDZof3oK82+VHnWSgCllZpzC0UFG14AC3Wye6zfkleTm3/A0jojCQIhQTTq/MQAoxIgLLqnhtiLYZl0RpMkOdLn/j4A/kzmJ47l/pIAwEZYVN4B240lDlPsaHhNOqbZiDNLIJb7Exj7rIn0XPW9RvjH3UPAYKqwh8yJX9SRtfG5XFhWJkJte5YN6ZloqeY3xRNxV1dgRWPYzTWI3rtgSrGad+l5Z5fE3rpP+kHHAbVVUZ2dQRFswWbrzhOdlXfGrL12OdpeMvWArXSUqp/9jPEgKTSViiEmpeLZ2Y1dnrQ+QC0NwS56ahwD5tYjEpQCSHOwTb/XQpEgT9hr1k5OExIPC4Nr9tFSziXrrwTKOheT5nVwX1netgctPP7vbBbJdjqRVdMolLnsc32WtYjm+JcUVzPXbFOAJ54ewcr3CXkWp3kW+20+KZy6XQXtbkagUiC1mAMXVUwJeR7+m6cEmgPJ+gIxvtrVQL8Lg1FEdR4K/FJHa/uY2nOMQBoiRD/lCX8IZmdXJq2y/v7qz/GghoVwzL4xdrvEAvMQHG1orib+OOmP1CdU5XRtXGpLs6sPpMyf9mIfVWh9lZCToth2tpTClpZ+RCdh8eMK3Ru78uW0b4ZNI/J9EvtC5E3OULOpAjxbg1ohofOQxkgqBRA4oGP/axfe4G/iInEUGU+TNPkE5/4RPVrr72WI4SQLpdLPvroo9tnzZo1IbNrjyiohBCTsM1812GnS3oJ2139USnlUeMK5xRwO3KZXZ5DwrToDCfYEamh1GswSa9nsq8Dw5XNiRU+vv7CdwgEOgelOPpnSy53qlfSJPN5q7WWfRgsEo382n0L97W9n//a+VGqshV+fpabhvrd+Lp3kDBMdpQuxtJSyst7deKmSWrqp/ZgnKauKC5VEDLitEQNfFqfuc9rGcwvinHJsaU0dBpYyZjIfI8HK6Hz5tYw5q47iEaTNau8u1jLvaxrS+dUkJ6/bvsrx5WOGFJEtiubuBXHp6cJ4zAtFLUZs6uOmuwa8rx5AOReehGuydUYra1oWWniykYI8bQSyRpezWDFIGQ+hV+1658qKnh6MxIZ2M/VAwnAXz464rmNZ4Yq83H//fcXNDY26ps2bVqvqirbt2/Xc3JyJqxZaKQ4qn9jJ39tBh4EfpumLPxRgWP6O3Lxu+2fQZ7PRWNXKXFNQfpL0EMN6N178Ht93HNeb1524qbkx29F2NNtATn8k0sB20v7LGsvJ5vrwYSZwnYaqAtYhEyVGcGNlL33UwB2lt+P4bXHi8RMuqI9N9W+u7OiCnzJMhu6VNGEgqYILAsMS9IWMFGMMFU5FuV5/QORm7oMXKogGO0bz4rU4iIXoQx+qE4VwEJA1OwLhF7ZtDKj6zgjfwanVaVZrtaA2bPoinVB3jS8WX2alLu2hsjadWgF+5GFzbRItEfofi6GAnQzHbX8CbTgVqTQiO9uwIqH7fWtFEVV98Rw+4bKsj5xSS3z0dDQoJeWlibUpH/+1KlTJ7Q32EgaVQi7FPzfjjZ39IE4zhRHB0LYokJqXkx3Hi5rO1qoGdOTh1RtpwKXKvjWyUMEgMuZFK95kXDrZCLGJEh6cG9sM5mRRikXZgKfZgFKb8l7exyLHN2024VG0DAxVFBEHGkmsCyDSFSgheqpNluJqX6asqaA5kJLpniaWqZyzSlZvLQxyp42WxBePenHFGbb2zXVzrthWX1GOyHs2KvsnFYeXP8ggfjw7tpSSvYE7DIh3fFuO0ZrCBRFGZQu6YAsFaqCXuxHLTAQqol3fhFx5VP0iOHu5jh4wT1Fx7fQFuTxfQkSJkRkG+6iMK7qGoSmIFwK4pnPwb7BeQMzYd83v1Ud27p1zLMCuKdPD1d8/3t7R+o3sMzHNddc037aaafNmjVrVvapp57afd1117UtXbp0wkrnkdzTLzlUExnvOIUTjw48ukpHyP6sFfwUVJ6Ku3MrSjzQK6iGRSi0LPwsAPV7E/CafW/4wRsRFl9wHLEzfkFHIEqWx/aAq3jjVnyta4gUzqXu1B/2DqNFWpn8z48D0HTMzVDzPqbnTwegYOOfKNnyZwJ5M3h26tfJ1S1c8S7wFdJt9n9wLsnWmFoWYU8yjrWxyyBmyLSFe+OGpCRXw62pVGdX8+0TRy4DZ0mLj/zNLvn2+NbHeXbn0IULdEXniulXcHJlSsn77iBK43pk1+DA53RoQmNSziQU0aci+RZbJBpjmB3BfpkvFJ8H4TcQPoVEi537L/yeiYyBWuiBnEJkrAYRF4TfaMGI3kXBZRG4ffROzLGtW33RtWsPebLAocp8TJ06NbFt27Z1Tz/9dPbzzz+fc+GFF85cvnz59ksuuWRCBoo5akKGuN2Z/ZAcJjYLq2235lDcZNWudizVjan6UKzOUY+1oLi/drGqw8fCkmysfEkkJgCLEjPzhKw98U9ZHRsBUDQPPt1L3JJoJMh35ZKjDS7wutkdAewbdVOXQXVheoEbiVtIOeLSUD8UoVDgKaA92k7MjPVzzU/Hv/b8q7+g8nmxFs7K+KDt8QBTy2aiKyPfuvwnxQm+tArhFVg905I6IMA0AYnQFMzOGDJuktgXIbS3IrOJDMA9ffpBKVg30rjDlfnwer3yQx/6UPeHPvSh7tLS0sTjjz+e5wiqIxwnhdLRgxACr67i1lU6I3Fchoo/FMAMdOMvHiLNQhryPAoPvj+Lj/3NLjd055uDLS8XK6dTIeZyVkE5re191nXN8BKbfC0ALWIqgZRtRvaxzG5ZzT7fLCzVQ9g0aQooxN0RTG3wHb8yqxS7YDa0BKPsbu1vxc/xCty6IJaQBKLQ0AndkQA+j4JLdeHR3QigIl/HlSZH4ecWf47X6l/DkkOv465pXUNzuJnWSCvP7XpuyH4jkevO5cSC89C0kbVbxTTxLpyWFEo2eqkJFghVok8qRcvPIhpMENvWCYAcsiLY8GRinjuYZGdnW7/4xS/2fPCDH5z2ta99rfmtt97yVVVVJWpraxOmabJ27Vrv/Pnzj0zTn0MfXu/gJ1WHIxdVEZw4JZng1CoiGpvO+jf+QbizBZk0O+mqwOPq+QkpWK7Blp9Sv6DIK2iNpFcZnrJs7eLetcDagSVCzk/5P3XbOfZrC7Cl596TjV2FZ3g21FlsqBus9VTle8j1akgpqSrwUp6VjU6CblrRRIxQTKLrGn7PYEFV6ivloikXUeAt6F0fG8iv3/s1zeFmumJd/N/G/xtxnsPh1/zMyJ8xckeAwgHvU6qPmHIH1WaC0kWlFFcvwmyPohV4mKiklvkoLS01Pv3pT9fE43EFYNGiRaGvf/3rE7LEBziCKmOcUvRHMYqC2+OldvZiiNnrIIZpUdcRIZjUIrzBvSRMjdQUCglDoqmC753iZnNnn3WrIxgjP8tNxJDc++7+V5ytEi18QXsUgeSriU9hHMDPua4jSl3S8WNzU5hjJxXhVbOYV2qbwtpDceaX5JDvS6/JrG9djylNtCHmcFLFSaxoXEHEOPCH+s0dm/G7/CN3HAHLskiYCfYG93Js6bF4SwZKtfFPT9HEHv7zn//0emVfccUVR0z+KEdQZYhT5uPoRghBYXltv7aSVA+2nVHQs/qVq48kTCIJE9kc5IKpqTf4vv/PnKSzqX3/HGpLAwHO3/QKAIm5HybsKup3fICYYdHUbQcaqwq4NJXdAXhyx9DjmpZEQRA3Jd0hC11TMU2FWFxBePs8E3VV9HrtKYpCOBEesgji7ILZ3HPWPQjEsJ6BQxExInz237aTylPbn+Kp7U+Neoyh+OCMDzKvaB4ebeJqU0c6jqDKEEejchhIP9dqRbOrCPcWGYzj09zoUuJKdKFGXb0lPeo64lTlu5CqmwKvysmVQ9emGo6sur4H5uMmFyHTrNuYpkIgBgnDpC2UINst0AX0CyxKQ31nGE1V2N4cQBEKQpFsbQowvTQbl6ZgSZhdlk1lvv0AV+ItoUN0DOtuHjEiRBPR/dKG3Kqb6uxq9gbGfinokS2PML9oPpdNv2zMx3YYGxxBlSGORuUwLNUnQk+ooZQQ7QQpEZZFONxOxIhSqNpmvspiP8KIoIcaSXjykemyOWRAsOo0tpcehxrrwHL5+pkde1BUyHVB3LBojQaxVJ05xZJ7Tg7SpRUR9tvOIX9dXc/6lGKND7yxe9BYlx9TiUfTKMp24dJUmgMxXMn8fV6liOL8MvQhEusCtEZaWdW0Kn2SiAz40rFfYmf3Tryat7fy8v5SF6zjDxv+0Pt+c/tRUQRiwuIIqgyJRCasw4zDoUDtX/ID3c6PpwFzZ5Xwzu4Oosly8K17t1FcPh1XYA969x6MNMUTM0IoWLoPS/ehhZtR4kEsVzaGr7i3i5IIo4UaUU2JauYAOkII8tyCLDVMRLe9la+c4+NPlsWGxuCQh2voinLSlEK6IglCsQjNgQh728N2gLSEhVW5VOT3OR25tf6CM9uVzcLihcPnBBwBn+7Dp/uGdNrIlIUlC7lg8gW9MWBjsXbmcPBwBFWGeDyO/dph/8jx6KiK7YChqQr5ZZOQmotY3nSUeABlP26SwjIQZgypukGAHmqg4s07CFSfSfOim3r7edo3UvnGrQDsm3M7Ec8sNEXBVN24jC5c3bYWVWCE+dxxc2k0imgKxLGkZG1DCMuC9Y0humMmwWicaCyOCuS4BJZl0hGMErcgmrAwLYs97baJXEoozHKnuLNL3JqKruaQ49XJ9qTPFD8STaEmumJd/QJ+9xdLWqhCxZQmj297nNuX3n7AYzocHBxBlSFOZgqHAyHPq9MajCGEINZWj7swWdk3ax4xw6Iid5ThD1KihZtwt2+i4o3b8HQN4x2RxK0phKREj3fiincipEkweyoAmtDJCuxgmhAkQgr/rtfZ1KXSGe8TCGv3BVi7bxMAAskHauNcUmsS8ZUTQKFU+ihQ85CKTkTxEowZkHRqNEwLCYRjJlNL/PstqBYULxjSYWO0xMwYZkpmuPea3sOjeRBu4cSijDMcQZUhTq4/hwNhXmUuPffX7lI3OTk5vdtW13XSFhq9m7oWE2THEmw47rv/v707D4+zug89/v29s2hGu2RJXrBBxguLbbxAWC5gCCmUgIkpDW3JAiShl9wbSOAmDeSWBtonfUifkPamBBIgTYAkhUBCeIAS3LCkBho2b1h4wQZjW/ImWaul2ed3/3hfSaNBkkfSWCOZ3+d55rHmXc45I8/MT+e85z0/ylobEE0TC9cRifQvo9RddCyRJbcC0OKbSk8syQm7HqX+gJs2/ukz/t3r6RW5qzYohEOwo9uhve9vsw8vuKQI/91cxKeOTxDq3kPboRQl6iMcD6C+EFq3hAG3XDnu+olBn8P+zig98f4AkValqjhARThIVcnwN/I64nx47adRKnaKuez4y3j6/acBOBg7SLmWI45Mmg/7LbfcMu03v/nNFMdx1HEc7r333p233nrrzLvuumv38uXLewC2bt0aXLFixbxt27Zl59+cNCbNf0ihpVIf6TV5zRiJSN+EwM7ODiorK/r2LT22aoizDiMSgF2lUFIDx08fsM5dvypgtvvjgUM0H4oRaK+DA9A69UymV4aJJNzVGs56+XP4U1Hem3YJM875Am1RZXVTmtkdr3FN1wN0ajFXxm9nP+5K551xeG9vC7XJvTzYNJdja0r48rJiSuIHCe99A2dAc5TolIWUBEqIOkXEk/2rWCRSaVoPxSkKRDhxen8AF9wV7X1OniLTIJZNXdYXqFY3rqbYXwzKpPiwP//88yWrVq2q3Lhx46ZwOKx79+71x2KxozIfkQWqHDnO2MfEjYE8Xu/0BSFYAtFOSEahZIq7bQgzq4qZWh5Cqj9NPBCnqHwWc2r7V9NwvECXSKXpiSepCvlZOcdHbbOPys3dVEo3flJUh6A1qlyvjzNr+07+xLeOn8f+jlW7ylm1q4fZ0s58aaQ0KOwoO420+Egmk5QENjOtWDj/1FOYO60/UIcCPkqL/LT2xGlo6ujbHk2kOXvulFEPE+aiKGMiyxPbnjhi9RwJTU1Ngerq6mQ4HFaA6dOn55aOeRL6SAcqEbkcuBQox8219Z+FbZExIxAsgdnnuj83rYFkDIa5lzYUcAgFHAhNgXNvJAgMuKNp2edIJRMkUzPpjCb6EjEGKWHXlHPwOcI3jynhrXaHX2xJs0A+4ALf+g/Vc5HzFt8KPALAic0/I4obDIpIEsfP+tbN3LdyOonSGeClrhcRppQMnP14sDvGvvYoXUVJHAdqy0J5712dNeMsasO1NEeax1bQk1+ZxYFN+b+Hpe7kHi6/Z9Cbxy6//PLOO++8c0Z9ff3Cc845p/Oqq65qvfTSSw8BXH311ceHQqE0QCKRkMn+h/a4BSoRCQGrgSKv3l+r6u2jLOunwArggKouzNp3MfAD3I/sT1T1u0OVo6pPAk+KSBVwFzBkoLLEiSZfjsgCx74QHDoA8ez1Aoegabf35WXbBWDJZ/EBM3sStDV1UFbkpyTkh+mns2v6KXRGkpQHfFxQDkvrHHbFbuVnHbt5eXeCBp0KwMXHCZXNgPdxOb4c9sfdXtrbvmv5ID2VKzr/gbtf6iEVbAHHTxqYVlXOBSfPYEppf7AKB3zs7XB/Vz2JFMvnBygO5vcrqyZcw1OXP8Vre17ryzx8BmeMvKADm4rZs3Zc03xUVFSkGxoaNj333HNlL7zwQtk111wz59vf/nYjwMMPP/x+9jWq8Wxbvo1njyoGXKCqh0QkALwiIr9T1dd6DxCROiCiql0Z2+YOklX4QeCHwMOZG0XEB9wDXAg0Am+KyFO4QevOrDK+qKq9izTe5p03pN5MmcaMVWVlZf4LrTsRaubmfnykHXa/AemMyzH+IPiClPhhfk2QxrYInV0xivwOPk0Si8cRdb8ywg6Ulwq+8lm80g3d3W5kem6nsmjBx3m9fCkiwt+UFIM4JDtbYT3UO/v5H9LAM7vPAjKn5Xfx8BtNfP/KxYQD7metPBzom1yR7IqxZmcb5aEA/oxelQLTykPUlI0+DY/P8RHyhwgPkiIlZ3UnH5mlaw5Trt/vZ8WKFV0rVqzoOuWUUyI///nPJ9+ChTkYt0Cl7pzS3rsJA94je57pecCXReQSVY2JyF/jZhj+ZFZZq0WkfpBqTge2q+r7ACLyKLBSVe/E7YENIO56L98Ffqeqa4drfzJ51A7/mnG2f//+AbP+8sLxfWidv2GV1ED92f3PY53QtQ8QAg5MLQtRHgrQEUnQ2N4DyRghjVPhT+MIxBJpItE0xf4kl05VXthd2VfU994p5cYlFSyr6x9uSntte85/AYeooY4002mmPaZ0aTGtuL+Prz++oe8cnyPccdkClsyqpLasiEQqTTQxcJ7DoVgSv0+oLB76OpaIHHbIMJFO0BZxV+UVR0Z+UWyI4bkjacOGDUWO47Bo0aIYwLp168IzZ86Mb9my5aibXj+u16i8Hs8aYC5wj6q+nrlfVR8XkdnAr0TkceCLuL2jXB0DZL5hGmHYfvyNuDkTKrye248HafNlwGVz5swZQTOMGdqxxx5b6Ca4Qa24uv95cTVU1Q84JOw9alJub+nNHW1EkilUlVgixY6Wbmp8UfzBbi6dF+E/tvX/8d/c0UOgIs67HQ5vNAeo9jtUnPcIU8Nhvgb4Y+0seulmKHKnun89fj1PpAdm1k2llVe2tzCzKkxpkZ9QwPehJZocERpbI+zrGHo41RFh2XFVlBYN/nVX5CtiSd2SvhUzUrFUjuOnhdXZ2en76le/emxnZ6fP5/NpfX197KGHHtq5cuXKo+7LSvJ189yIKhWpBH4L3KiqDYPsfxS4BJijqoNe5fR6VM9kXqMSkU8DF6vqdd7zzwNnqOoNg5UxEgsWLNB33pm0tyGYCWTjxo0sWrSo0M0YsUg8RU88yaFokvZInPW72wkHfai3MkV3IolPhPJAmoAjOCK8/F4bv317PwD/64RDLKr1k/KFINLGZW9/ha5jltNy8jXsihWzvTVJMlxDOljGj15tojPa33sKBRzu/LNTmFs38stAB7tjLDuuivIcZw+KyBpVPe1wx23YsOGDxYsXt4y4QWZIGzZsqFm8eHF99vaCzPpT1XYReQk3M9yAQCUi5wILcQPZ7cBIgkwTkJmCdaa3bcwscaLJl8kYpADCQR/hoI8ppUVMS4ao8SY+dEaTNLX18EFLNxVevqpkOk0yDdF0/5Db3kQJS5MHSTlBnKIS3pl/Az6fQ1vEwQkEOKkqgS+1G1SYV1rEmmj/11M0kebmx9azfF4N/oxeVcARLlowjflTy4Zs91gXsDWFN56z/mqBhBekwrhDev+UdcxS4H7c60k7gF+KyHdU9bYcq3kTmOcNHzYBfwV8Jh/ttzQfJl/WrFnDqaeeWuhmjEmR39eX4uMYcO/PcoTSoH/AzLyFMyr4XcM+ABpT1bzVqbRFU1SGAiRrziboE0jBvB2/YFrrG5RG9vC7sx7lMycqy/Z30+zU8uTW/pG41ds+3IHZvK+Lez6zbJjWjv+okcmv8exRTQce8q5TOcBjqvpM1jHFwF+o6nsAInI1cG12QSLyCHA+UCMijcDtqvpvqpoUkRuAVbgz/X6qqnkZr7M0HyZfJnuQGkx1SZBjKsM0tbkz+cIBX98khqDfIZ5M89bONt7CoTcX1qUzo1wy2/25KNJMaWQPaRxCAR+hAJw/I02rJDh+yhR+vyNKS0+KVLo/6Bzocped2tXaw8p7Xum7YTlbZXGAez67jDNmH5UT4j4SxnPW39vA0sMc82rW8wTwwCDHXTVMGc8Cz46ymUOyHpXJl6OhRzWYKcVBkinlYHcMR4SQN838EyfW9fWqet10VjVzi9o4rsLHQw0x/it9BecHywhKiocakgQ0zpRUB+f5Xmb5/KVcfN5JxGoWDCjjxS0H+Jfn3wUgrfTdoJyt5VCcVQ37jkSgSqfTaXEcx7pseZBOp4W+O/AG+kivTDES1qMy+XI0BimA2vIQteUhNuxuIxJP9/V+rl8+h8+feRyZcSQcdGfwKfDqi6+xp2s6j3G1u7ND+WHgR6zwubdYrnXOHHQZw+XzaoglUzR3Db2g75Prm0iklGjiiNyw39Dc3HxybW1thwWrsUmn09Lc3FxB1pyFXhaocmSJE02+bNiwgcWLFxe6GUdMaVGAzmiEVLz/uzsST1FdEhw0A3BlSYhYKgqaRry0G+tZwArcQKWIl3+rP9WOOn78PodPLpw+bFt+v2k/7ZEE8VT+A1Uymbxu3759P9m3b99CesczzWilgYZkMnndYDstUOXIEieafFmwYMHhD5rE5tSVMidrGvnanW1DDs3906eX9D9J995YfxZ/fP8TRHetJa0hyt77AzPevh9HhPb6S4hVzSVRNmuw4gbozdvYE4nS0dUNviBBv0M4OPaVZk499dQDwKfGXJA5LAtUOYrFRp4vyJjBbN++nRNPPLHQzRhXPp/Q1hXH77iBKKVKaTDw4YCRkWK+YsZcyuPNVFUHKOo4QPUONx3HzlmfwmnZQbplJ1X7/pvqA3/E5wj7TrtlQFG1b/+Ye1OdPO9bxINbVvD6918C8XHlnCS3nlsLvoC7QkfFzJGt6mHGnQWqHAWDwyd0MyZXM2fOLHQTxt1J08pJ1vUPv+1u7aG5K054mOXe00VVdE87k8qSJJUhB/XSz8+fWeeuHA+kD75ESdsmAKKBgXm9gu3bOZ0D7HKqiSeEg1GANB9/9zuwfQtMXQjnfRMObgME9jfAvIvz+rpNfligypGt9WfypaWlhdLScV1ou+CCfodgxmWcsqIAje0RWnuGm4MgJINVaIngm38hLLoCgDAKkgR/EYmKafRUub1TzbpfKloxh0OBagLpaVxY6qexS9l8sH+1i61tyq/eLucbZ5bhE/BvfgZ5+3EkbzmETb4UZAmlyWjJkiW6fv36QjfDHAVaWlqoqakpdDMKSlVJpnP77vFrEunYTd+0wXg3dO52U5vkoKk9woGuCP+118//21jEJ5w11Ek7B7SSF9LuDMyb/L/mY7KVs33vUPW9nh1t3YnjR/XCzBFhPaocWUA3+ZJIJArdhIITEQK+XDsuQZiSsc5qOg1Tco8jM2YpU5NJZjc2sLGjm60Hl/GOwr6e/voPaZiz/e+wOrWIlKy1C1YTjAUqY8aZJeEcI8eBoqHX9ssmuF90lScu54H5SnskgaqybX8Xz655j3g8Srdeyf09JyCJCH5njQWqCcaG/nK0dOlSXbduXaGbYY4CbW1tVFVVHf5AM77SKUj04A+Xr0umdbjFA804s5vUcmSTKUy+tLa2FroJZjCOD4rKSOngy/iYwrFAlaNAYORJP40ZzIwZMwrdBGMmFQtUOYrH44c/yJgc7Nixo9BNMGZSsUCVI1tCyeTLR21VCmPGygJVjizNh8kXux/PmJGxQJUjS/Nh8mXZMptQZsxIWKDKkfWoTL6sWbOm0E0wZlKxQJUj61GZfDlaEycac6RYoMqR9ahMvqxdu7bQTTBmUrFAlSPrUZl8WbJkSaGbYMykYoEqR9FotNBNMEeJLVu2FLoJxkwqFqhyZIkTTb7Mnj270E0wZlKxQJUjS81g8mXPnj2FboIxk4oFqhz5/ZYRxeRHdXV1oZtgzKRigSpHlkPI5IvNIDVmZCxQGTPOHMc+dsaMhH1iciSSa9psY4ZnKWOMGRnL8JsjEekCtha6HSNQAXRMsnpGW9ZIz8v1+MMdN9r9NUBLDvVPJJPt/TSWcuapakUe2mDyRVXtkcMDeKvQbRhhe++fbPWMtqyRnpfr8Yc7brT7J9t7Kd//z+NRz1jKGa/Xao/cHzb0d/R6ehLWM9qyRnperscf7rix7p9MJtv7aSzlHE3/b0cFG/rLkYi8paqnFbodZvKz95IxI2M9qtzdX+gGmKOGvZeMGQHrURljjJnQrEdljDFmQrNAZYwxZkKzQGWMMWZCs0A1CiJyvoi8LCI/FpHzC90eM7mJiCMi/ygid4vINYVujzETjQUqj4j8VEQOiEhD1vaLRWSriGwXkVu9zQocAkJA43i31Ux8I3w/rQRmAgns/WTMh9isP4+ILMcNPg+r6kJvmw94F7gQ9wvkTeAqYIuqpkVkKvDPqvrZAjXbTFAjfD99CmhT1ftE5Neq+ukCNduYCcl6VB5VXQ20Zm0+Hdiuqu+rahx4FFipqr05P9qAonFsppkkRvJ+wg1abd4xqfFrpTGTg2UDHN4xwO6M543AGSJyBfCnQCXwwwK0y0xOg76fgB8Ad4vIucDqQjTMmInMAtUoqOoTwBOFboc5OqhqD/ClQrfDmInKhv6G1wTMyng+09tmzGjY+8mYUbBANbw3gXkiMltEgsBfAU8VuE1m8rL3kzGjYIHKIyKPAH8EThCRRhH5kqomgRuAVcBm4DFVfaeQ7TSTg72fjMkfm55ujDFmQrMelTHGmAnNApUxxpgJzQKVMcaYCc0ClTHGmAnNApUxxpgJzQKVMcaYCc0ClTHGmAnNApWZkESkXkRURE4rYBteEpGrC1V/vojIIRG5NsdjHxeRrx/hJhkzIhaozKBE5EEvUKiIJEVkl4j8SESqjkBdfxCR7FXodwPTgfX5ri8XInIp7rp8vyxE/QX0D8DfikhFoRtiTC8LVGY4z+MGi3rgOuAy4N7xqFhVU6q6z1t2qBC+Bjyoqh+p/FCquhF4H/hcodtiTC8LVGY4MS9YNKrqfwK/Ai7q3en1up7JPEFE7shMv957jIh8TUSaRKRNRH4mIsW9+4HzgK9k9ODqs4f+ROR87/knRWSNiERE5GURmSki54nIBm+I6xkRmZLVpi+IyCYRiYrIuyJys4gM+d4XkVrgT4Cns7Zf750fFZEWEVklIv6M/cPWIyIVXq90r3fMZhH5y4z9V4jIRhGJichuEflbEZGM/R+IyG0icp+IdHprCP5NVhvnej3UqJfyfsUgr+/bIrLTq2efiDycdchTuJmHjZkQLB+VyYmIHA9cDCRGcfq5wF7cL/9ZwGO4KdnvxO25zAe2AP/XO76ZgekwMv09cBPQAfw7bvCMAv8TNzvu48AdwI1eu/8adzjrRmANsBB4wHsdQyW9PAeIAZkB9zTgHuAa4BXcpJkXZOwfth4v4DwLVAFf8F7/CUDIO/9Ur+3fwR1u/BhwH9AJ3J3RtpuB24HvAZ8E/lVEXlHVP3pB8be42YLPAopxkzL2ZaEWkT8HvoEbiDYCdcCZWa//DeA2EQmramSI35Ex40dV7WGPDz2AB4EkcAiIAOo9bs465pms8+4AGrKO2Q34MrY9ADyf8fwPwA+zyqn36jvNe36+9/xPM465wdu2bJj6dwGfzyr7JmDTMK/9JmBn1rYrcINj2RDnDFsPcCGQBk4a4vxfAi8O8rtszHj+AfBI1jHbgNu8ny/CDdbHZuw/x/sdXes9/z/AViAwzOs/xTtnTqHfh/awh6ra0J8Z1mpgCXA67l/1zwL/OopyNunAaz17cP+SH423M37e7/27MWtbHfQN4c0C7vOGBQ+JyCHgu8CcYeoI4/bSMv0e2AnsEJFfisg1IlI2gnqWAntVdfMQdZ4EvJq17RXgGBEpH+L1w8Df5UlAk6ruytj/Om6A7PU4bi9uh4j8m4hcKSJFDNTbiwoP0VZjxpUFKjOcHlXdrqobVfWruENJf5exPw1I1jmBQcrJHi5URv/eyyxLAVQ1e1tv2b3/fhk34PY+FgILhqmjBXeIrr9Q1S5gGfAXuL2nbwFbRGTGGOrJVWYunjH9LlV1N+6Q4/W4w4rfB9aISEnGYdXev80jb6ox+WeByozE3wO3eF/O4H6RTc86Zskoyo0DvjG0a1Cquh+3xzHHC7gDHsOcug6oFZGarPKSqvqiqn4Ld3isBFiRYz3rgOkictIQdW4Gzs7adg7u0F9Xji95M24PLPP63ulkfc5VNaqq/6GqN+NeC1uQVfdC3J7ZfoyZAGwyhcmZqv5BRDYBtwH/G3gR+KaIfBF3mPAK3C+8xhEW/QFwuojU414Ta81Xm3EnHtwtIu24Q5cB3J7RMap65xDnrAMO4AaKJwG82XNzcF9nK/BxoAw3OORSzwu4w3C/EZGbcSdTzAVKVPVJ3J7NmyJyB+4kkY8BX6d/gkkunsedlPKwV0cY+Bfca414r+Na3M/967i/67/E7aVtyyjnXNwsxMZMCNajMiP1feBLInKcqq7C7WX9I+5Mt3pGd5/VXbi9qk24vbRj89NUUNWfAF8EPg9sAF7GnSG4Y5hzUsBPgc9mbG4HLqc/GHwDuE5VX86lHlVN487SexX4BW6A+wEQ9PavBa4E/hx3tuF3vcdQMxMHa3ca+DPcz/XrwMO4swhjWa/jS177Grz6rlDVHQAiEvLKeCDXeo050iwVvTGDEJE63MD5sd4v8Y8CEfkKsFJVLzrswcaME+tRGTMIVT2A20PKW+9ukkjg3YNmzERhPSpjjDETmvWojDHGTGgWqIwxxkxoFqiMMcZMaBaojDHGTGgWqIwxxkxoFqiMMcZMaP8fuDugUWgGt3oAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 324x252 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot performance vs runtime\n",
     "\n",
@@ -630,17 +479,23 @@
     "    if key_choice == 'cifar10':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([2e-4, 1e-2])\n",
-    "        ax.set_xlim([2.0e5, 7.6*1e5])\n",
+    "        # for nasbench201\n",
+    "        # ax.set_ylim([2e-4, 1e-2])\n",
+    "        # ax.set_xlim([2.0e5, 7.6*1e5])\n",
+    "\n",
+    "        # for nasbench311\n",
+    "        ax.set_ylim([2.5e-2, 4.2e-2])\n",
+    "        ax.set_xlim([1.9e4 *7 / 7, 1.8e6])\n",
     "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
-    "        ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
+    "        # ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
+    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
     "    elif key_choice == 'cifar100':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([0.0020, 0.3])\n",
-    "        ax.set_xlim([1e4, 1e6])\n",
+    "        ax.set_ylim([0.002, 0.03])\n",
+    "        ax.set_xlim([1e5, 1e6])\n",
     "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
@@ -648,9 +503,9 @@
     "    elif key_choice == 'ImageNet16-120':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([-0.2, 1e-1])\n",
     "        # ax.set_ylim([0.0025, 0.3])\n",
-    "        ax.set_xlim([1.0e5, 5e6])\n",
+    "        ax.set_ylim([2.7e-2, 6.8e-2])\n",
+    "        ax.set_xlim([1.5e5, 3.2e6])\n",
     "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
     "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
@@ -658,8 +513,8 @@
     "    elif key_choice == 'nasbench311':    \n",
     "        ax.set_xscale('log')\n",
     "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([.035, .066])\n",
-    "        ax.set_xlim([400, 2100])\n",
+    "        # ax.set_ylim([.035, .066])\n",
+    "        # ax.set_xlim([400, 2100])\n",
     "        # ax.set_ylim([2e-3, .028])\n",
     "        # ax.set_xlim([5e4, 4*10e5])\n",
     "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index b04aa1fe3..e62b99217 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -191,10 +191,6 @@ def train_statistics(self):
         latest_arch, latest_arch_epoch = self.get_latest_architecture()
         train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
                                        epoch=latest_arch_epoch)
-        previous_train_time = latest_arch.query(Metric.TRAIN_TIME, self.dataset, dataset_api=self.dataset_api,
-                                                epoch=self.fidelities[self.prev_round][
-                                                    self.round_number - 1]) if self.round_number > 0 else 0
-        train_time = train_time - previous_train_time
         return (
             best_arch.query(Metric.TRAIN_ACCURACY, self.dataset, dataset_api=self.dataset_api,
                             epoch=best_arch_epoch - 1),
diff --git a/naslib/search_spaces/nasbenchasr/graph.py b/naslib/search_spaces/nasbenchasr/graph.py
index 0dcc94113..b383230ce 100644
--- a/naslib/search_spaces/nasbenchasr/graph.py
+++ b/naslib/search_spaces/nasbenchasr/graph.py
@@ -64,6 +64,11 @@ def query(self, metric=None, dataset=None, path=None, epoch=-1,
                 return query_results[metric_to_asr[metric]]
             elif (metric == Metric.PARAMETERS) or (metric == Metric.FLOPS):
                 return query_results['info'][metric_to_asr[metric]]
+            elif (metric == Metric.TRAIN_TIME):
+                if epoch == -1:
+                    return self.get_max_epochs() + 1
+                else:
+                    return 1 * epoch
             elif metric in [Metric.TRAIN_ACCURACY, Metric.TRAIN_LOSS,
                             Metric.TRAIN_TIME, Metric.RAW]:
                 return -1

From 91a2ccc8f66216afcbaedd2da919df763bcce2ee Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 20 Jun 2022 16:02:32 +0200
Subject: [PATCH 125/130] removing prints

---
 examples/optimizers.ipynb                    |  117 +-
 examples/optimizers_HPO.ipynb                | 5157 ++++--------------
 naslib/optimizers/discrete/bohb/optimizer.py |    7 +-
 naslib/optimizers/discrete/dehb/optimizer.py |    2 +-
 4 files changed, 1073 insertions(+), 4210 deletions(-)

diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index b917fd911..b17a78883 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -39,6 +39,7 @@
     "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
     "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
     "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
+    "# predictors = ['bohb', 'dehb', 'hb', 'sh', ]\n",
     "benchmark_datasets = ['asr', ]\n",
     "# benchmark_datasets = ['ImageNet16-120', ]\n",
     "# TODO: What of this am I really needing?\n",
@@ -118,7 +119,7 @@
     "           'nasbench311': 95.8, \n",
     "           'nasbench111':95.6, \n",
     "           'nasbenchnlp':100.7,\n",
-    "           'asr': 100,\n",
+    "           'asr': 1.00,\n",
     "           }\n",
     "\n",
     "datasets = {'cifar10': 'cifar10',\n",
@@ -241,6 +242,10 @@
     "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
     "        elif key_choice == 'nasbenchnlp':\n",
     "            val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
+    "        elif key_choice == 'asr':\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0])]\n",
+    "            for ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])))\n",
     "        else:\n",
     "            # TODO: is this scaling really necessary?\n",
     "            val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
@@ -253,9 +258,12 @@
     "        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
     "        train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
     "        total_time = [i+j for i,j in zip(runtime, train_time)]\n",
-    "        if predictor in {'rs', 'ls', 're', 'bananas', 'npenas'}:\n",
+    "        if predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and not key_choice == 'asr':\n",
     "            # continue\n",
     "            total_time[:] = [x * 200.0 for x in total_time]\n",
+    "        elif predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and key_choice == 'asr':\n",
+    "            total_time = train_time\n",
+    "            total_time[:] = [x * -40 for x in total_time]\n",
     "        # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
     "        if metric == 'valid_acc':\n",
     "            incumbent = val_incumbent\n",
@@ -355,23 +363,70 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " saving asr\n"
+      " saving asr\n",
+      "predictor: ls\n",
+      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
+      "first std 0.27409130929712305 last std 0.0\n",
+      "time  16000\n",
+      "predictor: bananas\n",
+      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
+      "first std 0.27409130929712305 last std 0.0\n",
+      "time  16000\n",
+      "predictor: re\n",
+      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
+      "first std 0.27409130929712305 last std 0.0\n",
+      "time  16000\n",
+      "predictor: rs\n",
+      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
+      "first std 0.27409130929712305 last std 0.0\n",
+      "time  16000\n"
      ]
     },
     {
-     "ename": "KeyError",
-     "evalue": "'asr'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 10'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=0'>1</a>\u001b[0m base_path \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/Users/lars/Projects/results/all_experiments/\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=2'>3</a>\u001b[0m results \u001b[39m=\u001b[39m save_all_results()\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=4'>5</a>\u001b[0m \u001b[39m# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'wb')\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=5'>6</a>\u001b[0m \u001b[39m# pickle.dump(results, result_file)\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=6'>7</a>\u001b[0m \u001b[39m# result_file.close()\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=7'>8</a>\u001b[0m \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=8'>9</a>\u001b[0m \u001b[39m# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=9'>10</a>\u001b[0m \u001b[39m# results = pickle.load(result_file)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000009?line=10'>11</a>\u001b[0m \u001b[39mprint\u001b[39m\n",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 8'\u001b[0m in \u001b[0;36msave_all_results\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msave_all_results\u001b[39m():     \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=1'>2</a>\u001b[0m     \u001b[39m# that was/is very very shity code!\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=2'>3</a>\u001b[0m     \u001b[39m# key_choice = 'nasbench311'\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=3'>4</a>\u001b[0m     folder \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexpanduser(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(base_path, \u001b[39m'\u001b[39m\u001b[39masr\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=4'>5</a>\u001b[0m     all_results \u001b[39m=\u001b[39m {dataset:save_results_to_dict(folder, predictors, dataset) \u001b[39mfor\u001b[39;00m dataset \u001b[39min\u001b[39;00m benchmark_datasets}\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=5'>6</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m all_results\n",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 8'\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msave_all_results\u001b[39m():     \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=1'>2</a>\u001b[0m     \u001b[39m# that was/is very very shity code!\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=2'>3</a>\u001b[0m     \u001b[39m# key_choice = 'nasbench311'\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=3'>4</a>\u001b[0m     folder \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexpanduser(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(base_path, \u001b[39m'\u001b[39m\u001b[39masr\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=4'>5</a>\u001b[0m     all_results \u001b[39m=\u001b[39m {dataset:save_results_to_dict(folder, predictors, dataset) \u001b[39mfor\u001b[39;00m dataset \u001b[39min\u001b[39;00m benchmark_datasets}\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000007?line=5'>6</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m all_results\n",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 7'\u001b[0m in \u001b[0;36msave_results_to_dict\u001b[0;34m(folder, predictors, key_choice)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m saving\u001b[39m\u001b[39m'\u001b[39m, key_choice)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=3'>4</a>\u001b[0m \u001b[39mfor\u001b[39;00m _, predictor \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(predictors):\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=4'>5</a>\u001b[0m     mean, std, std_error, runtime \u001b[39m=\u001b[39m get_results(predictor, folder, metric\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mvalid_acc\u001b[39;49m\u001b[39m'\u001b[39;49m, key_choice\u001b[39m=\u001b[39;49mkey_choice)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=5'>6</a>\u001b[0m     key \u001b[39m=\u001b[39m predictor\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=6'>7</a>\u001b[0m     results_dict[key] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mlabel\u001b[39m\u001b[39m'\u001b[39m:pred_label_dict[predictor], \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=7'>8</a>\u001b[0m                          \u001b[39m'\u001b[39m\u001b[39mkey\u001b[39m\u001b[39m'\u001b[39m:key, \u001b[39m'\u001b[39m\u001b[39mmean\u001b[39m\u001b[39m'\u001b[39m:mean, \u001b[39m'\u001b[39m\u001b[39mstd\u001b[39m\u001b[39m'\u001b[39m:std, \n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000006?line=8'>9</a>\u001b[0m                          \u001b[39m'\u001b[39m\u001b[39mstd_error\u001b[39m\u001b[39m'\u001b[39m: std_error, \u001b[39m'\u001b[39m\u001b[39mruntime\u001b[39m\u001b[39m'\u001b[39m: runtime}\n",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb Cell 6'\u001b[0m in \u001b[0;36mget_results\u001b[0;34m(predictor, path, metric, key_choice)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=7'>8</a>\u001b[0m \u001b[39mfor\u001b[39;00m dataset_folder \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mlistdir(path):\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=8'>9</a>\u001b[0m     dataset_path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(path, dataset_folder)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=9'>10</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m(dataset_folder \u001b[39m==\u001b[39m datasets[key_choice] \u001b[39mand\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39misdir(dataset_path)):\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=10'>11</a>\u001b[0m         \u001b[39mcontinue\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers.ipynb#ch0000005?line=11'>12</a>\u001b[0m     dataset_paths\u001b[39m.\u001b[39mappend(dataset_path)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'asr'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3829/1081133750.py:108: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  output = {key: np.array(item) for key,item in output.items()}\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3829/1081133750.py:109: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  time = {key: np.array(item) for key,item in time.items()}\n"
      ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predictor: bohb\n",
+      "first mean 0.5410241097211838 last mean 0.02777442932128906\n",
+      "first std 0.23560936207907718 last std 0.00033180661295487476\n",
+      "time  16011.08962893486\n",
+      "predictor: dehb\n",
+      "first mean 0.5410241097211838 last mean 0.02910938858985901\n",
+      "first std 0.23560936207907718 last std 0.0013909191444725138\n",
+      "time  16034.51386833191\n",
+      "predictor: hb\n",
+      "first mean 0.5410241097211838 last mean 0.027595114707946778\n",
+      "first std 0.23560936207907718 last std 0.00043575140860873865\n",
+      "time  16018.907649040222\n",
+      "predictor: sh\n",
+      "first mean 0.5410241097211838 last mean 0.02686458230018616\n",
+      "first std 0.23560936207907718 last std 0.0004565035557138738\n",
+      "time  16000.764096736908\n",
+      "predictor: npenas\n",
+      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
+      "first std 0.27409130929712305 last std 0.0\n",
+      "time  16000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function print>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -390,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,9 +478,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 324x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot performance vs runtime\n",
     "\n",
@@ -541,6 +609,17 @@
     "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
     "        ax.set_title('NAS-Bench-NLP11 PTB', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nasn11_ptb.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    \n",
+    "    elif key_choice == 'asr':\n",
+    "        # ax.set_xscale('log')\n",
+    "        # ax.set_yscale('log')\n",
+    "        ax.set_ylim([0.026, 0.04])\n",
+    "        ax.set_xlim([700, 16000])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
+    "        ax.set_title('NAS-Bench-ASR', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nbasr.pdf')\n",
+    "        ax.set_xlabel('Epochs', fontsize=14)\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n"
    ]
   },
diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 4315081c1..77c3b46b4 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,9 +66,9 @@
     "    results_dict = {}\n",
     "    root = os.path.expanduser(root)\n",
     "    for search_space in search_spaces:\n",
-    "        print(search_space)\n",
+    "        # print(search_space)\n",
     "        for optimizer in optimizers:\n",
-    "            print(optimizer)\n",
+    "            # print(optimizer)\n",
     "            optimizer_path = os.path.join(root, file_dict[search_space], optimizer)\n",
     "            #print(optimizer_path)\n",
     "            if not os.path.exists(optimizer_path):\n",
@@ -81,7 +81,7 @@
     "                values = []\n",
     "                for seed in os.listdir(optimizer_path + '/' + hpo_folder):\n",
     "                    result_path = optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
-    "                    print(result_path)\n",
+    "                    # print(result_path)\n",
     "                    if not os.path.isfile(result_path):\n",
     "                        print(\"what?\")\n",
     "                        continue\n",
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,6 +211,7 @@
     "                 'darts':'DARTS CIFAR10',\n",
     "                 'nlp':'NB-NLP TreeBank',\n",
     "                 'asr':'NB-ASR TIMIT',\n",
+    "                 'nb311_c10': 'NB-301 CIFAR10',\n",
     "                 'mr_3ddet':'NB-MR KITTI',\n",
     "                 'mr_cls':'NB-MR ImageNet',\n",
     "                 'mr_seg':'NB-MR City',\n",
@@ -241,7 +242,7 @@
     "    'nb311_c10':'nasbench311/cifar10',\n",
     "    'darts':'darts/cifar10', \n",
     "    'nlp':'nlp/LM-task', \n",
-    "    'asr':'asr', \n",
+    "    'asr':'asr/asr', \n",
     "    'transmicro_obj':'transbench101_micro/class_object',\n",
     "    'transmicro_scene':'transbench101_micro/class_scene',\n",
     "    'transmicro_jigsaw':'transbench101_micro/jigsaw',\n",
@@ -309,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -318,4017 +319,907 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "asr\n",
-      "sh\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/9/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/0/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/7/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/6/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/1/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/8/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/4/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/3/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/2/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_36/5/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/9/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/0/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/7/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/6/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/1/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/8/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/4/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/3/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/2/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_9/5/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/9/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/0/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/7/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/6/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/1/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/8/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/4/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/3/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/2/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_31/5/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/9/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/0/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/7/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/6/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/1/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/8/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/4/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/3/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/2/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_7/5/errors.json\n",
-      "/Users/lars/Projects/results/all_experiments/asr/sh/config_38/9/errors.json\n",
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-      "what?\n",
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-      "dehb\n",
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+      "what?\n",
+      "what?\n"
      ]
     }
    ],
@@ -4337,7 +1228,7 @@
     "# takes >30 min to load all these results\n",
     "if True:\n",
     "    search_spaces = ['nb201_c10','nb201_c100','nb201_im', 'nb311_c10', 'asr']\n",
-    "    search_spaces = ['asr']\n",
+    "    # search_spaces = ['asr']\n",
     "    optimizers=('sh', 'hb', 'bohb','dehb')\n",
     "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
     "    root = '/Users/lars/Projects/results/all_experiments/'\n",
@@ -4348,19 +1239,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'asr': {'sh': [0.9688516855239868, 0.0],\n",
-       "  'hb': [0.9688516855239868, 0.0],\n",
-       "  'bohb': [0.9688516855239868, 0.0],\n",
-       "  'dehb': [0.9688516855239868, 0.0]}}"
+       "{'nb201_c10': {'sh': [91.266, 0.14058449416631907],\n",
+       "  'hb': [91.237, 0.19375499993548587],\n",
+       "  'bohb': [91.28399998876954, 0.16047927996122993],\n",
+       "  'dehb': [91.46839998706055, 0.15957393322422234]},\n",
+       " 'nb201_c100': {'sh': [72.83599995605468, 0.4194091327042208],\n",
+       "  'hb': [73.04799997802733, 0.3842082780699827],\n",
+       "  'bohb': [72.273, 0.3776254758355177],\n",
+       "  'dehb': [72.381, 0.46007499388686585]},\n",
+       " 'nb201_im': {'sh': [46.12666665445964, 0.30397370344054225],\n",
+       "  'hb': [46.273333309936525, 0.21643574019383016],\n",
+       "  'bohb': [45.981666661580405, 0.32909388676447],\n",
+       "  'dehb': [46.31166666056315, 0.3130539611625373]},\n",
+       " 'nb311_c10': {'sh': [94.36338508817775, 0.05634092470148716],\n",
+       "  'hb': [94.26923521643793, 0.05639211939481929],\n",
+       "  'bohb': [94.28346731038836, 0.08095619478093283],\n",
+       "  'dehb': [94.26727583389199, 0.05562582737382367]},\n",
+       " 'asr': {'sh': [0.9391645073890686, 0.00866653351374373],\n",
+       "  'hb': [0.9391645073890686, 0.00866653351374373],\n",
+       "  'bohb': [0.9391645073890686, 0.00866653351374373],\n",
+       "  'dehb': [0.9391645073890686, 0.00866653351374373]}}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4371,7 +1278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4382,7 +1289,7 @@
     "            'transmacro_obj', 'transmacro_scene', 'transmacro_jigsaw', \n",
     "            'transmacro_room', 'transmacro_segment', 'transmacro_normal', 'transmacro_auto']\n",
     "\n",
-    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im']"
+    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im', 'nb311_c10', 'asr']"
    ]
   },
   {
@@ -4394,18 +1301,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "result_folder = 'plots_nov10/'\n",
+    "result_folder = 'plots_naslib/'\n",
     "# specify the seed that represents the default config:\n",
     "default_config = 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4476,6 +1383,8 @@
     "    #ax.set_title('Scaled Accuracy of NAS Algorithms', fontsize=14)\n",
     "    ax.set_title('NAS Algorithms', fontsize=14)\n",
     "    \n",
+    "    # ax.set_ylim([0.87, 1.01])\n",
+    "\n",
     "    if save:\n",
     "        plt.savefig(result_folder + save, bbox_inches = 'tight', pad_inches = 0.1)\n",
     "    #print(results)"
@@ -4483,26 +1392,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.9136920570379896, 0.9136920570379896, 0.0, 0.7449831365356921, 0.32551943616346357, 0.8749777952235025, 0.4047238461303686, 1.0]\n",
-      "[0.9432892249527459, 0.9432892249527459, 0.5552929918256772, 0.8539697404079082, 0.0, 0.6880907372400793, 0.01748582230624769, 1.0]\n",
-      "[1.0, 1.0, 0.5865384398995577, 0.8572485205808359, 0.39497041376109165, 0.9741124224823862, 0.0, 0.8498520704419449]\n",
-      "[91.395999984375, 91.395999984375, 90.367, 91.20599999999999, 90.73359998535156, 91.3523999831543, 90.82279999096679, 91.49319998876953]\n",
-      "[72.611, 72.611, 71.78999997070312, 72.42199997070313, 70.61499999999998, 72.071, 70.652, 72.731]\n",
-      "[45.63499999186198, 45.63499999186198, 44.70333327433268, 45.31333332417806, 44.27166665445964, 45.57666665039062, 43.38166665344239, 45.29666665649414]\n"
+      "[0.0, 0.9837089326419766, 0.8851479823472495, 0.9004208577623538, 0.9481401064322367, 0.9742058122329813, 0.9697257672864026, 1.0]\n",
+      "[0.0, 1.0, 0.9909803289368035, 0.9971905892109975, 0.8781605796244293, 0.9893538370545633, 0.9569717580955202, 0.9847700724530541]\n",
+      "[0.0, 1.0, 0.949310899727513, 0.9693996670117478, 0.9138051849225757, 0.9778089243083572, 0.9614576043946761, 0.9976640991715666]\n",
+      "[0.0, 1.0, 0.9540009841099787, 0.9540009841099787, 0.901702602051951, 0.9373531208241037, 0.9772525033885768, 0.9772525033885768]\n",
+      "[0.8860806085516122, 1.0, 0.6101329843431573, 0.7215281043880724, 0.3949428233214571, 0.653175106276154, 0.0, 0.1443015311489578]\n",
+      "[88.63199999194335, 91.5303999831543, 91.23999999999998, 91.285, 91.42559999389648, 91.5023999892578, 91.48919998486329, 91.57839998388673]\n",
+      "[66.322, 73.085, 73.0239999645996, 73.06599995483397, 72.26100000000001, 73.013, 72.794, 72.982]\n",
+      "[39.56666666056314, 46.70166666666667, 46.33999993591309, 46.48333329060872, 46.086666660563154, 46.54333334147135, 46.42666667378744, 46.68500001424154]\n",
+      "[92.47606450965773, 94.51213530185342, 94.41847804913, 94.41847804913, 94.31199484094256, 94.38458182094116, 94.46581978840733, 94.46581978840733]\n",
+      "[0.9728365659713745, 0.9731354176998138, 0.9721126556396484, 0.9724048852920533, 0.9715481340885163, 0.972225570678711, 0.970512056350708, 0.970890611410141]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 90.72x144 with 1 Axes>"
+       "<Figure size 151.2x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -4512,9 +1425,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 90.72x144 with 1 Axes>"
+       "<Figure size 151.2x144 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -4524,7 +1437,9 @@
     }
    ],
    "source": [
-    "plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
+    "# plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
+    "optimizers=('sh', 'hb', 'bohb','dehb')\n",
+    "plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True, save='multi-fidelity-zoomed-HPO.pdf')\n",
     "plot_default_hpo(results, ss_group_full, optimizers, scale_type=None, one_line=True, label=True) #, save='optimizers_appendix.pdf')\n",
     "\n",
     "\n"
@@ -4539,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4644,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4678,7 +1593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4713,61 +1628,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im'] "
+    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im', 'nb311_c10', 'asr'] "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "config_0\n",
-      "config_1\n",
-      "config_2\n",
-      "config_65\n",
-      "config_85\n",
-      "config_9\n",
-      "config_28\n",
-      "config_59\n",
-      "config_60\n",
-      "config_87\n",
-      "config_58\n",
-      "config_9\n",
-      "config_0\n",
-      "config_1\n",
-      "config_2\n",
-      "config_65\n",
-      "config_85\n",
-      "config_9\n",
-      "config_28\n",
-      "config_59\n",
-      "config_60\n",
-      "config_87\n",
-      "config_58\n",
-      "config_9\n",
-      "config_0\n",
-      "config_1\n",
-      "config_2\n",
-      "config_65\n",
-      "config_85\n",
-      "config_9\n",
-      "config_28\n",
-      "config_59\n",
-      "config_60\n",
-      "config_87\n",
-      "config_58\n",
-      "config_9\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#have to load different results\n",
     "norm_matrices = []\n",
@@ -4786,89 +1658,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "num valid 100\n",
-      "num valid 100\n",
-      "num valid 100\n",
       "num valid 100\n"
      ]
     },
     {
-     "ename": "TypeError",
-     "evalue": "plot_matrix() got an unexpected keyword argument 'hparam'",
+     "ename": "NameError",
+     "evalue": "name 'norm' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_9423/2558175469.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mavg_scaled_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, save='bbo_matrix_avg.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_kt_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_spaces\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis_r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhparam\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, save='bbo_corr_matrix.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: plot_matrix() got an unexpected keyword argument 'hparam'"
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb Cell 24'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=12'>13</a>\u001b[0m kt_mat \u001b[39m=\u001b[39m compute_matrix_stand(results_stand, search_spaces, optimizers, optimizer\u001b[39m=\u001b[39moptimizer)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=13'>14</a>\u001b[0m \u001b[39m#norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=14'>15</a>\u001b[0m norm_matrices\u001b[39m.\u001b[39mappend(norm)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=15'>16</a>\u001b[0m kt_matrices\u001b[39m.\u001b[39mappend(kt_mat)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=16'>17</a>\u001b[0m kt_matrix \u001b[39m=\u001b[39m scale_matrix(kt_mat, mean_div\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'norm' is not defined"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
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",
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -4905,29 +1714,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[ 1.        ,  0.12386693,  0.10568158],\n",
-       "        [ 0.12386693,  1.        , -0.0165503 ],\n",
-       "        [ 0.10568158, -0.0165503 ,  1.        ]]),\n",
-       " array([[ 1.        ,  0.12252777,  0.0331289 ],\n",
-       "        [ 0.12252777,  1.        , -0.00830929],\n",
-       "        [ 0.0331289 , -0.00830929,  1.        ]]),\n",
-       " array([[1.        , 0.11976836, 0.02144988],\n",
-       "        [0.11976836, 1.        , 0.03865534],\n",
-       "        [0.02144988, 0.03865534, 1.        ]]),\n",
-       " array([[1.        , 0.06899342, 0.02952478],\n",
-       "        [0.06899342, 1.        , 0.07582652],\n",
-       "        [0.02952478, 0.07582652, 1.        ]])]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kt_matrices"
    ]
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index 49ea87aa5..c6c0d7c2a 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -192,8 +192,8 @@ def new_epoch(self, epoch, round, i):
             if round == 0:
                 model.arch.sample_random_architecture(dataset_api=self.dataset_api)
             else:
-                print("budget: {}, the number of good enc: {}".format(budget, len(self.kde_models[budget]['good'])))
-                print("budget: {}, the number of bad enc: {}".format(budget, len(self.kde_models[budget]['bad'])))
+                logger.info("budget: {}, the number of good enc: {}".format(budget, len(self.kde_models[budget]['good'])))
+                logger.info("budget: {}, the number of bad enc: {}".format(budget, len(self.kde_models[budget]['bad'])))
                 if epoch == 0 and \
                         len(self.kde_models[budget]['good']) >= self.min_points_in_model and \
                         len(self.kde_models[budget]['bad']) >= self.min_points_in_model:
@@ -234,9 +234,6 @@ def new_epoch(self, epoch, round, i):
             Note: technically we would just continue training this arch, but right now,
             just for simplicity, we treat it as if we start to train it again from scratch
             """
-            print(self.fidelities[round])
-            print(self.round_number)
-            print(self.fidelities[round][self.round_number])
             model = copy.deepcopy(model)
             model.epoch = min(self.fidelities[round][self.round_number], self.max_training_epoch)
             model.accuracy = model.arch.query(self.performance_metric,
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index e62b99217..5c19aea3d 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -109,7 +109,7 @@ def new_epoch(self, epoch, round, i):
                 model.arch.sample_random_architecture(dataset_api=self.dataset_api)
             else:
                 if len(self.de[budget]['promotions']) > 0:
-                    print('promotion from budget: {}, length: {}'.format(budget, len(self.de[budget]['promotions'])))
+                    logger.info('promotion from budget: {}, length: {}'.format(budget, len(self.de[budget]['promotions'])))
                     model = self.de[budget]['promotions'].pop()
                     model = copy.deepcopy(model)
                     arch = self.search_space.clone()

From 00b7c7b02c9ca911ab1038f46b3576f174ca86c7 Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 21 Jun 2022 10:06:56 +0200
Subject: [PATCH 126/130] plots generatisation

---
 examples/optimizers_HPO.ipynb | 393 +++++++++++++++++-----------------
 1 file changed, 195 insertions(+), 198 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 77c3b46b4..27e877a01 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -1231,7 +1231,7 @@
     "    # search_spaces = ['asr']\n",
     "    optimizers=('sh', 'hb', 'bohb','dehb')\n",
     "    #optimizers=('rs', 're', 'ls', 'npenas',  'hb', 'bohb','dehb')\n",
-    "    root = '/Users/lars/Projects/results/all_experiments/'\n",
+    "    root = '/home/till/all_experiments_smaller'\n",
     "\n",
     "\n",
     "    results = get_hpo_results(optimizers, search_spaces, root=root)"
@@ -1239,27 +1239,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'nb201_c10': {'sh': [91.266, 0.14058449416631907],\n",
-       "  'hb': [91.237, 0.19375499993548587],\n",
-       "  'bohb': [91.28399998876954, 0.16047927996122993],\n",
+       "  'hb': [91.237, 0.1937549999354859],\n",
+       "  'bohb': [91.28399998876952, 0.16047927996122993],\n",
        "  'dehb': [91.46839998706055, 0.15957393322422234]},\n",
        " 'nb201_c100': {'sh': [72.83599995605468, 0.4194091327042208],\n",
-       "  'hb': [73.04799997802733, 0.3842082780699827],\n",
+       "  'hb': [73.04799997802735, 0.3842082780699827],\n",
        "  'bohb': [72.273, 0.3776254758355177],\n",
        "  'dehb': [72.381, 0.46007499388686585]},\n",
-       " 'nb201_im': {'sh': [46.12666665445964, 0.30397370344054225],\n",
-       "  'hb': [46.273333309936525, 0.21643574019383016],\n",
+       " 'nb201_im': {'sh': [46.12666665445964, 0.3039737034405422],\n",
+       "  'hb': [46.273333309936525, 0.2164357401938302],\n",
        "  'bohb': [45.981666661580405, 0.32909388676447],\n",
-       "  'dehb': [46.31166666056315, 0.3130539611625373]},\n",
+       "  'dehb': [46.31166666056315, 0.31305396116253725]},\n",
        " 'nb311_c10': {'sh': [94.36338508817775, 0.05634092470148716],\n",
-       "  'hb': [94.26923521643793, 0.05639211939481929],\n",
-       "  'bohb': [94.28346731038836, 0.08095619478093283],\n",
+       "  'hb': [94.26923521643795, 0.05639211939481929],\n",
+       "  'bohb': [94.28346731038835, 0.08095619478093283],\n",
        "  'dehb': [94.26727583389199, 0.05562582737382367]},\n",
        " 'asr': {'sh': [0.9391645073890686, 0.00866653351374373],\n",
        "  'hb': [0.9391645073890686, 0.00866653351374373],\n",
@@ -1267,7 +1267,7 @@
        "  'dehb': [0.9391645073890686, 0.00866653351374373]}}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1278,7 +1278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1301,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1392,40 +1392,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.0, 0.9837089326419766, 0.8851479823472495, 0.9004208577623538, 0.9481401064322367, 0.9742058122329813, 0.9697257672864026, 1.0]\n",
-      "[0.0, 1.0, 0.9909803289368035, 0.9971905892109975, 0.8781605796244293, 0.9893538370545633, 0.9569717580955202, 0.9847700724530541]\n",
-      "[0.0, 1.0, 0.949310899727513, 0.9693996670117478, 0.9138051849225757, 0.9778089243083572, 0.9614576043946761, 0.9976640991715666]\n",
-      "[0.0, 1.0, 0.9540009841099787, 0.9540009841099787, 0.901702602051951, 0.9373531208241037, 0.9772525033885768, 0.9772525033885768]\n",
-      "[0.8860806085516122, 1.0, 0.6101329843431573, 0.7215281043880724, 0.3949428233214571, 0.653175106276154, 0.0, 0.1443015311489578]\n",
-      "[88.63199999194335, 91.5303999831543, 91.23999999999998, 91.285, 91.42559999389648, 91.5023999892578, 91.48919998486329, 91.57839998388673]\n",
-      "[66.322, 73.085, 73.0239999645996, 73.06599995483397, 72.26100000000001, 73.013, 72.794, 72.982]\n",
-      "[39.56666666056314, 46.70166666666667, 46.33999993591309, 46.48333329060872, 46.086666660563154, 46.54333334147135, 46.42666667378744, 46.68500001424154]\n",
-      "[92.47606450965773, 94.51213530185342, 94.41847804913, 94.41847804913, 94.31199484094256, 94.38458182094116, 94.46581978840733, 94.46581978840733]\n",
-      "[0.9728365659713745, 0.9731354176998138, 0.9721126556396484, 0.9724048852920533, 0.9715481340885163, 0.972225570678711, 0.970512056350708, 0.970890611410141]\n"
+      "[0.0, 0.9837089326419765, 0.8851479823472483, 0.9004208577623529, 0.9481401064322361, 0.974205812232986, 0.9697257672864024, 1.0]\n",
+      "[0.0, 1.0, 0.9909803289368057, 0.9971905892109996, 0.878160579624427, 0.9893538370545611, 0.9569717580955222, 0.9847700724530541]\n",
+      "[0.0, 1.0, 0.9493108997275121, 0.969399667011749, 0.9138051849225748, 0.9778089243083563, 0.9614576043946751, 0.9976640991715656]\n",
+      "[0.0, 1.0, 0.954000984109979, 0.954000984109979, 0.9017026020519446, 0.9373531208241042, 0.9772525033885769, 0.9772525033885769]\n",
+      "[0.8860806085516122, 1.0, 0.6101329843431573, 0.7215281043880724, 0.3949428233214571, 0.653175106276154, 0.0, 0.1443015311489578]\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 151.2x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'plots_naslib/multi-fidelity-zoomed-HPO.pdf'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_4834/4026343565.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# plot_default_hpo(results, ss_group_full, optimizers, scale_type='0-1', one_line=True, label=True) #, save='optimizers_appendix.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sh'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hb'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bohb'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'dehb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplot_default_hpo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_group_full\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'0-1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mone_line\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'multi-fidelity-zoomed-HPO.pdf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplot_default_hpo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mss_group_full\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mone_line\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, save='optimizers_appendix.pdf')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_4834/460307967.py\u001b[0m in \u001b[0;36mplot_default_hpo\u001b[0;34m(results, ss_group, optimizers, offset, scale_type, one_line, label, save)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_folder\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'tight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0;31m#print(results)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    977\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    978\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    980\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3044\u001b[0m                         ax.patch._cm_set(facecolor='none', edgecolor='none'))\n\u001b[1;32m   3045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3046\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3047\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3048\u001b[0m     def ginput(self, n=1, timeout=30, show_clicks=True,\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2323\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2324\u001b[0m                         \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2325\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   2326\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2327\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1646\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1648\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1650\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0;31m# Early return in the simple, non-deprecated case (much faster than\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0;31m# calling bind()).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m         \u001b[0marguments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_varargs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py\u001b[0m in \u001b[0;36mprint_pdf\u001b[0;34m(self, filename, dpi, bbox_inches_restore, metadata)\u001b[0m\n\u001b[1;32m   2781\u001b[0m             \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2782\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2783\u001b[0;31m             \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPdfFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2784\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2785\u001b[0m             \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewPage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, metadata)\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moriginal_file_like\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell_base\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 654\u001b[0;31m         \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_opened\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    655\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/naslib_exercises/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    449\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'plots_naslib/multi-fidelity-zoomed-HPO.pdf'"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 151.2x144 with 1 Axes>"
       ]
@@ -1454,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1462,23 +1465,18 @@
     "plot a matrix where entry [i,j] is the regret of a predictor \n",
     "tuned on search space i and evaluated on search space j.\n",
     "\n",
-    "Some of the matrix code is different types of scaling so that we can see the trends better.\n",
+    "Some of the matrix code is different types of scaling so that we can see the trends better\n",
     "\"\"\"\n",
-    "def replace_if_nan(num, replacement=0):\n",
-    "    if np.isnan(num) or np.isinf(num):\n",
-    "        return replacement\n",
-    "    return num\n",
-    "\n",
-    "def compute_matrix_stand(results, ss_group, optimizers, optimizer):\n",
+    "def compute_matrix(results, ss_group, optimizers, optimizer):\n",
     "\n",
     "    # first, compute a list of all seeds that have full info\n",
     "    hpo_seeds = []\n",
     "    for hpo_seed in results.keys():\n",
     "        valid = True\n",
     "        for search_space in ss_group:\n",
-    "            for opt in optimizers:\n",
+    "            for pred in optimizers:\n",
     "                if not (search_space in results[hpo_seed] and \\\n",
-    "                        opt in results[hpo_seed][search_space]):\n",
+    "                        pred in results[hpo_seed][search_space]):\n",
     "                    valid = False\n",
     "        if valid:\n",
     "            hpo_seeds.append(hpo_seed)\n",
@@ -1496,19 +1494,19 @@
     "        # 0-1 scaling:\n",
     "        min_acc, max_acc = np.min(raw_seed_results[i]), np.max(raw_seed_results[i])\n",
     "        scaled_seed_results[i] = [(m - min_acc) / (max_acc - min_acc) for m in raw_seed_results[i]]\n",
-    "        for j in range(len(scaled_seed_results[i])):\n",
-    "            scaled_seed_results[i][j] = replace_if_nan(scaled_seed_results[i][j], 0)\n",
-    "        #normed_seed_results[i] = copy.deepcopy(raw_seed_results[i])\n",
-    "        \n",
+    "\n",
     "    # compute the matrices\n",
-    "  \n",
+    "    div_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
     "    kt_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
-    "    \n",
+    "    scaled_matrix = np.zeros((len(ss_group), len(ss_group)))\n",
+    "\n",
     "    for i, ss_1 in enumerate(ss_group):\n",
     "        for j, ss_2 in enumerate(ss_group):\n",
-    "            kt_matrix[i][j] = replace_if_nan(kendalltau(scaled_seed_results[i], scaled_seed_results[j])[0], 0)\n",
+    "            div_matrix[i][j] = scaled_seed_results[i][best_seeds[j]] / scaled_seed_results[i][best_seeds[i]]\n",
+    "            scaled_matrix[i][j] = scaled_seed_results[i][best_seeds[i]] - scaled_seed_results[i][best_seeds[j]]\n",
+    "            kt_matrix[i][j] = kendalltau(raw_seed_results[i], raw_seed_results[j])[0]\n",
     "\n",
-    "    return  kt_matrix\n",
+    "    return div_matrix, scaled_matrix, kt_matrix, scaled_seed_results\n",
     "\n",
     "def scale_matrix(matrix, mean_div=2.5):\n",
     "    # further scaling of the results\n",
@@ -1531,24 +1529,24 @@
     "\n",
     "    return scaled_matrix\n",
     "\n",
-    "def plot_matrix(matrix, ss_group, save=None, cmap='viridis', title=\"No title specifics\"):\n",
+    "def plot_matrix(matrix, ss_group, save=None, cmap='viridis', hparam=False, title = \"Optimizers\"):\n",
     "    coords = matrix\n",
     "    cellsize = 5\n",
     "    fig, ax = plt.subplots(1,1,figsize=(cellsize*1.2, cellsize*coords.shape[0]/coords.shape[1]))\n",
     "    im = ax.pcolormesh(coords, edgecolor='k', linewidth=0.5, cmap=cmap) \n",
-    "   \n",
-    "    plt.title(title)\n",
-    "\n",
-    "       \n",
+    "    if hparam:\n",
+    "        plt.title('HParam. Corr. for {}'.format(title))\n",
+    "    else:\n",
+    "        plt.title('Generalizability for {}'.format(title))\n",
     "    plt.gca().invert_yaxis()\n",
     "    ax.set_xticks([])\n",
     "    ax.set_xticklabels('')\n",
     "    ax.set_xticks([c+0.5 for c in range(coords.shape[0]-0)], minor=True)\n",
-    "    ax.set_xticklabels([ss_dict_mat[ss] for ss in ss_group], minor=True, rotation=90)\n",
+    "    ax.set_xticklabels([ss_dict_oneline[ss] for ss in ss_group], minor=True, rotation=90)\n",
     "    ax.set_yticks([])\n",
     "    ax.set_yticklabels('')    \n",
     "    ax.set_yticks([c+0.5 for c in range(coords.shape[1]-0)], minor=True)\n",
-    "    ax.set_yticklabels([ss_dict_mat[ss] for ss in ss_group], minor=True)\n",
+    "    ax.set_yticklabels([ss_dict_oneline[ss] for ss in ss_group], minor=True)\n",
     "    fig.colorbar(im, ax=ax)\n",
     "    \n",
     "    if save:\n",
@@ -1559,170 +1557,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ss_dict_mat = {'nb101':'NB-101', \n",
-    "                 'nb201_c10':'NB-201', \n",
-    "                 'nb201_c100':'NB-201', \n",
-    "                 'nb201_im':'NB-201', \n",
-    "                 'darts':'DARTS',\n",
-    "                 'nlp':'NB-NLP',\n",
-    "                 'asr':'NB-ASR',\n",
-    "                 'mr_3ddet':'NB-MR',\n",
-    "                 'mr_cls':'NB-MR',\n",
-    "                 'mr_seg':'NB-MR',\n",
-    "                 'mr_video':'NB-MR',\n",
-    "                 'transmicro_obj':'TNB-Micro', \n",
-    "                 'transmicro_scene':'TNB-Micro', \n",
-    "                 'transmicro_jigsaw':'TNB-Micro', \n",
-    "                 'transmicro_room':'TNB-Micro',\n",
-    "                 'transmicro_segment':'TNB-Micro', \n",
-    "                 'transmicro_normal':'TNB-Micro', \n",
-    "                 'transmicro_auto':'TNB-Micro', \n",
-    "                 'transmacro_obj':'TNB-Macro', \n",
-    "                 'transmacro_scene':'TNB-Macro', \n",
-    "                 'transmacro_jigsaw':'TNB-Macro', \n",
-    "                 'transmacro_room':'TNB-Macro',\n",
-    "                 'transmacro_segment':'TNB-Macro', \n",
-    "                 'transmacro_normal':'TNB-Macro', \n",
-    "                 'transmacro_auto':'TNB-Macro', \n",
-    "          }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "plot a matrix where entry [i,j] is the regret of a predictor \n",
-    "tuned on search space i and evaluated on search space j.\n",
-    "\n",
-    "Some of the matrix code is different types of scaling so that we can see the trends better.\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def compute_matrix(results, best_configs, optimizer, search_spaces):\n",
-    "    \"\"\"\n",
-    "    best_configs: denodes the best config for every search space\n",
-    "    \n",
-    "    \"\"\"\n",
-    "    matrix= np.zeros((len(search_spaces), len(search_spaces)))\n",
-    "    for i, ss_1 in enumerate(best_configs):\n",
-    "        for j, ss_2 in enumerate(best_configs): \n",
-    "            #print(\"i:{},j:{},ss_1:{},ss_2:{}\".format(i,j,ss_1, ss_2))\n",
-    "            #print(\"i:{},j:{},ss_1:{},ss_2:{}\".format(i,j,ss_1, ss_2))\n",
-    "            print(results[ss_2][search_spaces[j]][optimizer][0])\n",
-    "            matrix[i][j] =  results[ss_2][search_spaces[j]][optimizer][0] - results[ss_1][search_spaces[i]][optimizer][0]\n",
-    "    return matrix \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ss_group_full = [\"nb201_c10\",'nb201_c100', 'nb201_im', 'nb311_c10', 'asr'] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#have to load different results\n",
-    "norm_matrices = []\n",
-    "kt_matrices = []\n",
-    "optimizer_matrix = []\n",
-    "results_stand = results\n",
-    "root = '~/plotting_data/transfer/'\n",
-    "optimizer_dict = {}\n",
-    "results_transfer = get_hpo_results(optimizers, search_spaces, root=root)\n",
-    "\n",
-    "best_configs = [[0,0,0],\n",
-    "                [9, 85, 65],\n",
-    "                [59,60,28],\n",
-    "                [58,9,87]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "num valid 100\n",
+      "num valid 100\n",
+      "num valid 100\n",
       "num valid 100\n"
      ]
     },
     {
-     "ename": "NameError",
-     "evalue": "name 'norm' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb Cell 24'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=12'>13</a>\u001b[0m kt_mat \u001b[39m=\u001b[39m compute_matrix_stand(results_stand, search_spaces, optimizers, optimizer\u001b[39m=\u001b[39moptimizer)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=13'>14</a>\u001b[0m \u001b[39m#norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=14'>15</a>\u001b[0m norm_matrices\u001b[39m.\u001b[39mappend(norm)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=15'>16</a>\u001b[0m kt_matrices\u001b[39m.\u001b[39mappend(kt_mat)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/lars/Projects/NASLib_cleanup/examples/optimizers_HPO.ipynb#ch0000023?line=16'>17</a>\u001b[0m kt_matrix \u001b[39m=\u001b[39m scale_matrix(kt_mat, mean_div\u001b[39m=\u001b[39m\u001b[39m2.5\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'norm' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "#have to load different results\n",
     "norm_matrices = []\n",
     "kt_matrices = []\n",
-    "results_stand = results\n",
-    "root = '~/plotting_data/trans/'\n",
-    "results = get_hpo_results(optimizers, search_spaces, root=root)\n",
-    "\n",
-    "best_configs = [[0,0,0],\n",
-    "                [9, 85, 65],\n",
-    "                [59,60,28],\n",
-    "                [58,9,87]]\n",
     "for i, optimizer in enumerate(optimizers):\n",
-    "    kt_mat = compute_matrix_stand(results_stand, search_spaces, optimizers, optimizer=optimizer)\n",
+    "    div_mat, scaled_mat, kt_mat, scaled_seed_results = compute_matrix(results, search_spaces, optimizers, optimizer)\n",
     "    #norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\n",
-    "    norm_matrices.append(norm)\n",
+    "    norm_matrices.append(scaled_mat)\n",
     "    kt_matrices.append(kt_mat)\n",
-    "    kt_matrix = scale_matrix(kt_mat, mean_div=2.5)\n",
-    "    avg_scaled_matrix = plot_matrix(kt_matrix, search_spaces, cmap='viridis', title=optimizer) #, save='bbo_matrix_avg.pdf')\n",
+    "    scaled_matrix = scale_matrix(kt_mat, mean_div=-2.5)\n",
+    "    scaled_kt_matrix = scale_matrix(kt_mat, mean_div=-100000)\n",
+    "    scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis', title=optimizer) #, save='bbo_matrix_avg.pdf')\n",
+    "    scaled_kt_matrix = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r',hparam=True,  title=optimizer)\n",
     "\n",
     "avg_norm_matrix = np.mean(np.array(norm_matrices), axis=0)\n",
     "avg_kt_matrix = np.mean(np.array(kt_matrices), axis=0)\n",
     "\n",
-    "scaled_matrix = scale_matrix(avg_norm_matrix, mean_div=2.5)\n",
-    "scaled_kt_matrix = scale_matrix(avg_kt_matrix, mean_div=-1000000)\n",
-    "\n",
+    "scaled_avg_matrix = scale_matrix(avg_norm_matrix, mean_div=2.5)\n",
+    "scaled_avg_kt_matrix = scale_matrix(avg_kt_matrix, mean_div=-1000000)\n",
+    "#this the difference vs right search space and the wrong for the best seed of every search space \n",
     "avg_scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis') #, save='bbo_matrix_avg.pdf')\n",
+    "#run all config on all searchspaces and compare the rank\n",
     "_ = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r', hparam=True) #, save='bbo_corr_matrix.pdf')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kt_matrices"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.9 ('jupyter-venv')",
+   "display_name": "Python 3.7.13 ('naslib_exercises')",
    "language": "python",
    "name": "python3"
   },
@@ -1736,11 +1733,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.7.13"
   },
   "vscode": {
    "interpreter": {
-    "hash": "86605511fd47612766282e128cf9cb9d4e63c6452a0637c1eb09019680aeca68"
+    "hash": "a100edf3d46bc2e6c79c61e7e0e4c85fe6b13f4c7883d33d138476068eadc537"
    }
   }
  },

From 6aeeaa1269ddde2e2848335a9a789312e087784b Mon Sep 17 00:00:00 2001
From: Till Fetzer <till.fetzer@googlemail.com>
Date: Tue, 21 Jun 2022 10:31:03 +0200
Subject: [PATCH 127/130] plots

---
 examples/optimizers_HPO.ipynb | 60 ++++++++++++++++++++++++++++-------
 1 file changed, 49 insertions(+), 11 deletions(-)

diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index 27e877a01..b9cb13e68 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -1557,7 +1557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1572,7 +1572,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -1596,7 +1596,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -1620,7 +1620,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -1668,7 +1668,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -1680,7 +1680,7 @@
     },
     {
      "data": {
-      "image/png": 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kM4HJEXEd8BXgJ5JOJOulPCoiorN6nbTMzKwpDxdHxA3ADVXbvpn7PAPYqZ46nbTMzMyzvJuZWUkIGFCOrOWkZWZmnnvQzMys0dzSMjMz39MyM7PyKEv3oJOWmVm76+arRvoDJy0zszaXzT1YdBTd46RlZma+p2VmZuXhlpaZmZWD8D0tMzMrD7e0zMysPJy0zMysLKIk8yM5aZmZtbvmvLm4KZy0zMzM97TMzKxEPHrQzMzKoEwzYnT71pukkHRubv1kSRPS5wmS5kuaIukRSRdJWq5uSSMk3SlphqTpkk7Ila0l6VZJs9Kfa6btm0m6V9Kbkk7uJL4hkn4s6TFJD0i6S9IOqey19OdISf9KcVaWFVPZ+ekaBuTqPErS87nrOjFXtqukByUtkXRwVSxHpuuYJenI3PZtJD0kabak/5VK8quNmbU+9WApQD3jRd4EDpI0tIPy8yJiDDAa2BLYrcY+S4CvRMRoYEfgC5JGp7JTgNsjYhRwe1oHWAgcD/ygi/h+mvYdFRHbAJ8FasX6WESMyS2LUqI6EJhbI+5fpevaCThN0oi0/SngKODy/M6S1gLOAHYAtgfOqCRg4CLgc8CotIzr4prMzJpPWUur3qUI9SStJcBE4MQu9lsRGAS8VF0QEQsi4sH0+VXgYWCDVLw/cGn6fClwQNrvuYiYBCzu6ISSNiFLEqdHxNJ03BMR8YduXRnsDkwnSyrja+0QES8Cs4FhaX1OREwFllbtujdwa0QsjIiXgFuBcZKGAatFxH0REcBllWs0M7PuqXdk/gXAEZJWr1F2oqQpwAJgZkRM6awiSSOBrYD706Z1I2JB+vwMsG4dcW0OTImIt7qx7ya5rsEL0rbxwBXA74CPSlqhRrwbkiXjqV3UvwFZi61iXtq2QfpcvT1/jv0kTezGNZiZNVYLdg8SEa+QtRCOr1Fc6R5cBxgs6bCO6pE0BPgN8OVUZ/V5guzeYDPkuwe/kO5p7Qtck2K5n6y1VPFJSVPJWlkXRsQbTYqLiLg+Io5tVv1mZh1pxe7BivOBo4HBtQojYjFwE7BrGnhRadUcB5BaMb8BfhkRv80d+mzqQiP9+VwdMU0HPiBpYN1XkyWoNYCHJM0BdmbZLsJfRcT7gQ8B35e0Xhf1zQdG5NaHp23z0+fq7WZmxWvFlhZARCwEriJLXMtJI+J2ImvRzM21an6Uyn4GPBwRP6w69DqgMtLuSODaOmJ6DJgMfKsyIi+NFPxoNw4fDxwTESMjYiSwMbCnpFWqzjEZ+DlwwvJVLONmYC9Ja6YBGHsBN6euz1ck7Zhi/Ew912hm1kyt3NICOJflR+ZV7mlNAwYCF9Y4bifg08AeuRbYvqns+2TJYhYwNq0jaT1J84CTgNMlzZO0Wo26jyG7DzZb0jTgErporaXENA54e8BGRPwTuBvYr8YhZwOflbSqpO1SXIcAP5Y0PR2/EPg2MCktZ6ZtAJ8nG+U4G3gMuLGz+MzM+kRPWlkFJa1uP1wcEUNyn58FVsmtTwAmdKOOu+ngUtPovI/U2P4My3ardVT3K2TDyWuVDUl/zgG2yG1/HVirxv4H5VYvyW1/Gqh0D07qKK6IuBi4uMb2yfnzm5n1F1GSx0Y9I4aZmXnCXDMzK4cyTePkpGVmZm5pmZlZSfh9WmZmVibuHjQzs/Jw0jIzs7JwS8vMzMrDScvMzEqhwGmZ6uWkZWZmpWlp9XTuQTMzsz7nlpaZmZWmpeWkZWZmvqdlZmYl4qRlZmZlEGkpAyctM7N257kHzcysVJy0zMysLMoyEMPPaZmZ2TtdhPUsXVUpjZP0qKTZkk7pYJ9DJc2QNF3S5V3V6ZaWmZk1vKUlaSBwAbAnMA+YJOm6iJiR22cU8A1gp4h4SdI6XdXrlpaZWbvrSSur6yS3PTA7Ih6PiEXAlcD+Vft8DrggIl4CiIjnuqrUScvMzJqRtDYA5ubW56VteZsCm0q6R9J9ksZ1Vam7B/upmZ84o+gQmuK2uLroEBruicNPKzqEphg4bFbRITTFXu95pOgQ+qUedg8OlTQ5tz4xIibWcfy7gFHA7sBw4E+StoyIlzs7wPqhkb/4btEhNNycT53KxpefVXQYDfXE4acxVgcXHUbD3RZXM/2p9YsOo+E23/BpZs4dVnQYDbXpiAWNqahnSeuFiNi2g7L5wIjc+vC0LW8ecH9ELAaekDSTLIlN6uiE7h40M7NmdA9OAkZJ2ljSisBhwHVV+1xD1spC0lCy7sLHO6vULS0zs3bXhJdARsQSSV8EbgYGAhdHxHRJZwKTI+K6VLaXpBnAW8BXI+LFzup10jIzs6bMiBERNwA3VG37Zu5zACelpVvcPWhmZqXhlpaZWZsLyjONk5OWmZl5wlwzMysRJy0zMyuFJowebBYnLTMzK01Ly6MHzcysNNzSMjMzdw+amVmJOGmZmVlpOGmZmVkpePSgmZmVipOWmZmVhVtaZmZWHk5aZmZWGoqiI+gWJy0zszYXHohhZmalUpKk5WmczMysNLpMWpJC0rm59ZMlTUifJ0iaL2mKpEckXSRpuToljZB0p6QZkqZLOiFXtpakWyXNSn+umbZvJuleSW9KOrmT+OZIGlrndTeEpLskTc6tbyvpri6OGSnp8KYHZ2ZWD/VgKUB3WlpvAgd1khjOi4gxwGhgS2C3GvssAb4SEaOBHYEvSBqdyk4Bbo+IUcDtaR1gIXA88IPuXEiB1pG0Tx37jwSctMysX6nc16pnKUJ3ktYSYCJwYhf7rQgMAl6qLoiIBRHxYPr8KvAwsEEq3h+4NH2+FDgg7fdcREwCFncjxkoL5hFJl0iaKemXksZKuie14rZP+22fWnB/k/QXSe9L21eRdFVqDf5O0v2Stk1le6VjHpT0a0lDcqc+BzitRjwDJZ0jaZKkqZL+IxV9H9gltU67+js1M+sbLdTSArgAOELS6jXKTpQ0BVgAzIyIKZ1VJGkksBVwf9q0bkQsSJ+fAdbtZky1vBc4F9gsLYcDOwMnA6emfR4BdomIrYBvAt9N2z8PvJRag/8FbJPiHQqcDoyNiK2BycBJuXPeCyyS9OGqWI4G/hER2wHbAZ+TtDFZS/LPETEmIs7rxbWamTVGTxJWf05aEfEKcBlZd121SvfgOsBgSYd1VE9qofwG+HKqs/o8AfTmYYEnIuKhiFgKTCfrdgzgIbJuOYDVgV9LmgacB2yetu8MXJnimAZMTdt3JOv6vCcl5yOBjarO+x2yxJa3F/CZdMz9wLuBUZ0FL2k/SRO7e7FmZg2jqH8pQD2jB88naz0MrlUYEYuBm4Bd08CLKWk5DkDSCmQJ65cR8dvcoc9KGpb2GQY8V/9lvO3N3OelufWlvDO8/9vAnRGxBbAfWZdmZwTcmlpGYyJidEQcnd8hIu4AViZLcPnjvpQ7buOIuKWzE0XE9RFxbBfxmJk1XCvd0wIgIhYCV5ElruVIErAT8FhEzM19Wf8olf0MeDgiflh16HVkrRfSn9fWexF1Wh2Ynz4fldt+D3AoQBoksmXafh+wk6T3prLBkjatUe93gK/l1m8G/jMlayRtKmkw8CqwamMuxcysvdT7nNa5QPUowso9rWnAQODCGsftBHwa2CPXAts3lX0f2FPSLGBsWkfSepLmkd0/Ol3SPEmr1RlvLf8NfE/S31j24eoLgbUlzSBLQNPJ7kk9T5bcrpA0lewe1mbVlUbEDcDzuU0/BWYAD6auyB+n800F3pL0dw/EMLN+oyT3tLqcESMihuQ+PwusklufAEzoRh1308ElRsSLwEdqbH8GGN6Nukemjy8AW+S2H5X7PKdSFhH3AvmWUuVe1BvApyLiDUmbALcBT6Zj7iAbTFF97t2r1rfJfV5KNvjjVJa3R1fXZWbWlzyNU/msAtyZuvMEfD4iFhUck5lZ8wlPmFs26fmxbYuOw8ysEG5pmZlZaThpmZlZaThpmZlZORT3sHC9nLTMzNpdgUPY6+WkZWZmHvJuZmYlUpLuQb+52MzMSsMtLTMz8z0tMzMrDzlpmZlZKXgaJzMzKxW3tMzMrDSctMzMrDzcPWhmZmXhlpaZmZWCQB6IYWZmpVGSlpZnxDAza3uBVP/SFUnjJD0qabakUzrZ7xOSQlKXL+J10jIzs3dmeq9n6aw6aSBwAbAPMBoYL2l0jf1WBU4A7u9OmE5aZmaGVP/She2B2RHxeEQsAq4E9q+x37eBs4E3uhOnk5aZWburzIhR79K5DYC5ufV5ads7p5W2BkZExB+6G6oHYvRTcz51atEhNMUTh59WdAgNd1tcXXQITbH5hk8XHUJTbDpiQdEh9Duix6MHh0qanFufGBETu3VOaQDwQ+Coek7opNVPvfb0hkWH0HBD1n+KtxaMKjqMhho4bBbTn1q/6DAabvMNn2asDi46jIa7La7mq1Na67rOGdOYX5p6OGHuCxHR0eCJ+cCI3PrwtK1iVWAL4C5lJ18PuE7SxyMinwiX4aRlZmbNeE5rEjBK0sZkyeow4PBKYUT8Axj6zvl1F3ByZwkLfE/LzMyaICKWAF8EbgYeBq6KiOmSzpT08Z7W65aWmZk15eHiiLgBuKFq2zc72Hf37tTppGVm1u48jZOZmZVJSWZxctIyM2t3onvTMvUHTlpmZuakZWZm5dHD57T6nJOWmZkxwC0tMzMrA3n0oJmZlYmTlpmZlYbvaZmZWUmE72mZmVk59OLVJH3OScvMzBiAk5aZmZWBfE/LzMxKQvg5LTMzK5Gy3NPySyDNzKw03NIyM2t7HvJuZmYl0jbdg5JC0rm59ZMlTUifJ0iaL2mKpEckXSRpuXNKGiTpr5L+Lmm6pG/lyjaWdL+k2ZJ+JWnFtH1XSQ9KWiLp4E7iW0/SlZIek/SApBskbSpppKRpaZ/dJf0jxTlF0m2546+RdF9VnfnrmiFpfK7skHQNSyVtW3XcN9J1PCpp7zr+ms3MmkbKBmLUuxShEfe03gQOkjS0g/LzImIMMBrYEtitgzr2iIgPAGOAcZJ2TGVnpzreC7wEHJ22PwUcBVzeUWCSBPwOuCsiNomIbYBvAOvW2P3PETEmLWPT8WsA2wCrS3pPB9e1P/BjSSuk7dOAg4A/VcUyGjgM2BwYB1woaWBHsZuZ9aUBRN1LMXH23hJgInBiF/utCAwiSzzLiMxraXWFtERKOnsAV6eyS4ED0jFzImIqsLSTc34YWBwRP8qd6+8R8eeuLio5CLgeuJIs4SwnImYBrwNrpvWHI+LRGrvuD1wZEW9GxBPAbGD7bsZhZtZUUtS9FKFRowcvAI6QtHqNshMlTQEWADMjYkqtCiQNTPs9B9waEfcD7wZejoglabd5wAZ1xLUF8EA3990l1z14Wto2HrgiLeNrHSRpa2BWRDzXRf0bAHNz6/Vei5lZU4j6uwbL3D1IRLwCXAYcX6O40o22DjBYUkctlrfSfsOB7SVt0YjY6pDvHjxL0rrAKODuiJgJLK6K6URJ04H7gbMaEYCk/SRNbERdZmb1aKuklZxPdr9pcK3CiFgM3ATsKmlErlVzXNV+LwN3kt33eRFYQ1JllONwYH4dMU0nuyfVE4eSdfk9IWkOMJJlW1vnRcTmwCeAn0ka1EV984ERufXlriUiro+IY3sYr5lZj1RmxGirpBURC4GreGegxDLS/amdgMciYm6uVfMjSWunQQ9IWhnYE3gkIoIsgVVGBx4JXFtHWHcAK0l6OxFIer+kXbpx7HhgXESMjIiRZMlvuVZiRFwHTE6xdeY64DBJK0namKwV99fuXYaZWRO12ejBvHOB6lGElXta04CBwIU1jhsG3ClpKjCJ7J7W71PZ14GTJM0mu8f1MwBJ20maBxxCNnpvenWlKekdCIxNQ96nA98DnunsIiSNBDYC3h7qngZP/EPSDjUOOTPFOEDSgSmuDwJ/kHRzOn46WVKfQdbi/EJEvNVZHGZmfaUsowd7/XBxRAzJfX4WWCW3PgGY0I06pgJbdVD2ODVG2UXEJLIutq7qfpqsq6+WLdI+dwF35Y6ZQ41BEhGxdfp4f9X2B4D3pdXfpaVWLGfRoPtfZmaNIs+IYWZmZeKkZWZmpeBXk5iZWak4aZmZWSkIChtYUS8nLTOzdlfgEPZ6+SWQZmZWGm5pmZkZA9TZ3OP9h5OWmVmb8+hBMzMrFQ/EMDOzUvCMGGZmViq+p2VmZqUgwUC3tMzMrCx8T8vMzEoi3D1oZmbl4CHvZmZWKgPdPWhmZmUgdw+amVmZuHvQzMxKoUxD3j3Lu5mZMYCldS9dkTRO0qOSZks6pUb5SZJmSJoq6XZJG3Udp5mZtbXKNE71Lp3WKQ0ELgD2AUYD4yWNrtrtb8C2EfF+4Grgv7uK1UnLzMwYyNK6ly5sD8yOiMcjYhFwJbB/foeIuDMiXk+r9wHDu6rU97T6qSHrP1V0CE0xcNisokNouM03fLroEJritri66BCa4pwxrXldBRkqaXJufWJETEyfNwDm5srmATt0UtfRwI1dndBJq5964MkRRYfQcNtsNJdbHt+s6DAaaq/3PMLMucOKDqPhNh2xgK9OObjoMBrunDFXM1atdV2N+OWiFw8XvxAR2/b6/NKngG2B3bra10nLzKztBQMb/5zWfCD/2/fwtG0ZksYCpwG7RcSbXVXqpGVm1uZEUybMnQSMkrQxWbI6DDh8mfNKWwE/BsZFxHPdqdRJy8zMGt7Sioglkr4I3AwMBC6OiOmSzgQmR8R1wDnAEODXkgCeioiPd1avk5aZWZuTmjONU0TcANxQte2buc9j663TScvMzDxhrpmZlUM2etAT5pqZWSmEW1pmZlYObmmZmVmpdGNapn7BScvMrM1VJswtAyctMzNzS8vMzMpBNP7h4mZx0jIza3vRjGmcmsJJy8yszUluaZmZWYkM8D0tMzMrAxEMLMnowQFFB2BmZtZdbmmZmbU54SHvZmZWIp7GyczMSkGEW1pmZlYeZRmI4aRlZtbmRHmGvBc2elBSSDo3t36ypAnp8wRJ8yVNkfSIpIskdRirpPPT/gNy29aV9HtJf5c0Q9INaftISf9Kdc+QdJmkFarq2zKVT5G0UNIT6fNt6fhpab/d03Uckzt2TNp2clq/RNLBkn6X6pgt6R+5+j/UoL9SM7MeCgZqad1LEYoc8v4mcJCkoR2UnxcRY4DRwJbAbrV2SonqQGBu1T5nArdGxAciYjRwSq7ssVT3lsBw4NB8nRHxUESMSftcB3w1rY+tEcK0quPHA3+v3ikiDkz1HQP8uVJ/RPylg+s3M+sTldGD9S5FKDJpLQEmAid2sd+KwCDgpQ7KdwemAxeRJYyKYcC8ykpETK0+MCLeAv4KbNDdoGt4EhiUWnYCxgE39qI+M7M+NyDNP1jPUkycxboAOELS6jXKTpQ0BVgAzIyIKR3UMR64Avgd8NFcV98FwM8k3SnpNEnrVx8oaRCwA3BT7y6Dq4FDgA8BD5K1IusmaT9JE3sZi5lZXSR3D3ZLRLwCXAYcX6O40j24DjBY0mHVO0haEdgXuCbVdT+wd6r7ZuA9wE+AzYC/SVo7HbpJSojPAgtqtcLqdBVZ0qok0B6JiOsj4thexmJmVreBRN1LEYpuaQGcDxwNDK5VGBGLyVpCu0oakRvAcBxZgloDeEjSHGBncl2EEbEwIi6PiE8Dk4BdU1HlntYmwDaSPt6bC4iIZ4DFwJ7A7b2py8ysr1VGD9a7FKHwIe8RsVDSVWSJ6+Lq8nSfaCfgbxExFxiTK7scOCYirkjrg4EnJK0C7AjcFxGvS1qVLEE9VXXuFySdAnyDbMBFb3wTWCci3spCNjMriyjNq0n6Q0sL4FygehRh5Z7WNGAgcGG+MCWmccAfKtsi4p/A3cB+wDbAZElTgXuBn0bEpBrnvgZYRdIuvbmAiPhLRFzTmzrMzIqQjR4sR/dgYS2tiBiS+/wssEpufQIwoYvjXwfWqrH9oNzqOTXK5wBb5NYD+EAn5zmqo+Mj4i7grhrHTMh9rj6+5jFmZkUa4BkxzMysDDz3oJmZlUale7AM+ss9LTMzsy65pWVmZr6nZWZm5VCm7kEnLTMzc9IyM7NyEDCgJHMiOGmZmbW94h4WrpeTlplZm8vmHiwHJy0zs3YnGOjuQTMzKwMhBlKOrOWkZWZm7h40M7NyEDCwJK9UctIyMzMGuHvQzMzKIJsRw0nLzMxKwi0tMzMrBSHf0zIzs/IYUJLxg+WI0szMmiabEUN1L13WK42T9Kik2ZJOqVG+kqRfpfL7JY3sqk4nLTMzazhJA4ELgH2A0cB4SaOrdjsaeCki3gucB5zdVb1OWmZmbU8M1IC6ly5sD8yOiMcjYhFwJbB/1T77A5emz1cDH5E6v7nmpGVm1uay7sH6/+vCBsDc3Pq8tK3mPhGxBPgH8O5OY40ox3T07UTS88CTfXS61cn+obSaVryuVrwmaM3r6str2igi1u5NBZJuAob24NBBwBu59YkRMTHVeTAwLiKOSeufBnaIiC/mzjst7TMvrT+W9nmhoxN69GA/1Nt/gPWQNDEiju2r8/WVVryuVrwmaM3rKts1RcS4JlQ7HxiRWx+ettXaZ56kd5El+xc7q9Tdg3Z90QE0SSteVyteE7TmdbXiNdVrEjBK0saSVgQOA66r2uc64Mj0+WDgjuii+8/dg2Zm1hSS9gXOBwYCF0fEWZLOBCZHxHWSBgE/B7YCFgKHRcTjndbppGVmZmXh7kEzMysNJy2zfkrSCd3ZVibK7CDpoLTs0NVzOf2ZpB2LjqHduHuwjUhaHRjHO89KzAdujoiXCwuqASTtDRzAstd1bUTcVFhQDSDpwYjYumrb3yJiq6Ji6g1JewEXArN4ZxTZcOC9wOcj4paiYuupWj8jay4nrTYh6TPAGcAtLPuFsSfwrYi4rKjYekPS+cCmwGVkDy9Cdl2fAWZFROlaJpLGA4cDOwN/zhWtCiyNiI8UElgvSXoY2Cci5lRt3xi4ISL+rZDAesFJq+85abUJSY+SPbT3ctX2NYH7I2LTQgLrJUkza8WeupxmRsSoAsLqFUkbARsD3wPyk4y+CkxNMweUjqRZwL9Vx5+GQ89I88+ViqSXgT91VB4RH++7aNqDHy5uHwJq/YayNJWV1RuStouISVXbt2PZJ/VLIyKeJJsR5YMpgY2KiNskrQysTJa8yuhiYJKkK3lnep8RZM/v/KywqHrneeDcooNoJ05a7eMs4EFJt/DOF8aGZN2D3y4sqt47CrhI0qq80z04gmwKnaMKiqkhJH0OOBZYC9iErNvzR0Apuwcj4nuSriGbJPWDafN84IiImFFYYL3zWkT8segg2om7B9tI6grcm+UHYrxUXFSNIWk9ctcVEc8UGU8jSJpCNlP2/ZXBF5IeiogtCw3M3ibptxFxUNFxtBO3tNpISk5XFh1Ho6VRkbuRS1qSSj8qEngzIhZVRoSnudla8rdMSTdGxD5Fx9EDv5TUYdKKiN/2ZTDtwEnLSv3bewejIj8MfFdSaUdFJn+UdCqwsqQ9gc9T4jntJHU0yk7AmD4MpZE+1klZAE5aDebuwTbRyW+DAn7UlzPLN1KrjooEkDSA7M2ue5H9nG4GftrVhKL9laS3gD9Se+DPjhGxch+HZCXkllb7+BXwS2p3Lw3q41gaqVVHRRIRS4GfpKUVPAz8R0TMqi6QNLfG/v2epJM6K4+IH/ZVLO3CSat9TAV+EBHTqgskjS0gnkZpuVGRku6k43tXUdaHi4EJdDx13Jf6MI5G+gEwBbgReJOS/6JUBu4ebBOSdgGejIinapRtGxGTCwirIVptVKSkbWps3hH4GvBcRGzXxyFZByR9ABhPNj3aA8AVwO1l7cItAyctQ9LgiPhn0XHY8iTtBvwXWRfuWRFxY8Eh9YqkgcCaldepp9kwjgJOLOM0TnmSPkSWwMYCX4+I6hceWgN4lvc2ImkDSdumLwokrSPpu2QTmLYcSQ8VHUNPSdpb0p/JEtZZEbFzCySsw8he9DdV0h/TBLqPA/sARxQaXC9JWpvsRYZbkj3k/lyxEbUu39NqE5K+DJwGzAZWknQhcDbZRLO1uqNKoYtRkev1ZSyNImkSsDZwDnBv2vb2cPGIeLCg0HrrdGCbiJidrude4OCIKPMw/n8HDiVrCV8NHBoRTlhN5O7BNiFpBrBzRCyUtCEwE9gpIh4oOLRekbSYjkdFHhwRq/ZxSL0m6S46H4ixRx+G0zDVM6JLmhYRWxQZU29JWgpMI5srEqp+bp4wt/Hc0mofb0TEQoCIeErSo2VPWEnLjYqMiN2LjqFJ1qkaIr5Gfr2kw8M/XHQA7cZJq30Ml/S/ufVh+fWIOL6AmBrhy8ArHZQd2IdxNJykVYCTgA0j4lhJo4D3RcTvCw6tp35C9k6wWuul7PLxZLl9z92DbULSkZ2VR8SlfRVLXyn7qEhJvyIbRv2ZiNgiJbG/RMSYYiNrvA5eL9PvpcE+HX6JRsT7+zCctuCWVpvoKClJGgTs18fhNJSkDYBhZC9IXCRpHbIW2FHA+gWG1lubRMQn05uMiYjXVZk9twVIGk02RHw88DKwbaEB9Uxncw9aEzhptaH0rMzeZF8We5G90v3XhQbVQ606KjJZlF78GACSNiGbdaG0JI3knUS1GNgI2DYi5hQYVm/8JCL2KjqIduKk1UbSg6qHA/sCfwV2AjaOiNcLDax3jiW7z9NSoyKTM4CbgBGSfkn28zqq0Ih6QdK9wGpkr8f5RETMkvREiRMWZI8mWB9y0moTkuYBTwEXASdHxKvpC6PMCQtad1QkEXGrpAfJpnAScEJlJomSepZsqq11yb7sZ1HSARg5q/t9Wn3LSat9XA0cAHwSeEvStZT/CwNad1Rk/oHiBenPDdMLL5+MiCUFhdVjEXFAiv8gYEIaDbmGpO0j4q8Fh9dTq5Pd16p1r9Hv02oCjx5sI+km/u5k9xP2Jfsf7mjghoh4rcDQeqyVR0VKug/YmuxZNAFbANPJfm7/GRG3FBher6UBM4eS/XvcMCJGFBxS3aofmLbmc9JqE5Lelf/tXNIKvDMYY++IGFpYcE1QGRUZEaUcYAIg6bfAf0XE9LQ+GjiTbLb335Zt6LukWyqDFiR9IyK+lyvbKCKe7Pjo/knS3yJiq6LjaCeeMLd9vN39Iun/RMTiiPh9RBwBlO433FokDZS0r6Sfk02r88miY+qlTSsJCyAiZgCbRcTjBcbUG/lBC4fkC8qYsJJPFx1Au/E9rfaR73PfKV8QEf/q41gaqkVHRQJMl3QR2Wg7yJLwDEkrkQ0XL5uW69apNX2YNZe7B9tEvu+9lfrhq0ZFXpMbFblxwaH1WnpG6/PAzmnTPcCFwBvAKmW7DynpZeBPZL9A7ZI+v82Ty1p3OGm1CUmvkz2AK2CT9Jm0HmWdbkbS+WSjIqcBlwPXAg9FxHsKDMtqSC3iDrXaPH6SdoqIe4qOo9U4abUJSRt1Vl7iewotOSoSIA0J/x4wmux9TQA4IfcfaXaZQ8meP7spIqZJ+hhwKrCyB2k0npOWlVorj4qUdDfZrBjnkc0P+VlgQER8s9DAeqiTyWVL29qXdAnZQKa/AjsAT5PNoXhKRFxTXGSty0mrTUh6lc6/MFbr45Aaoupe3f+JiC/lylYu8yATSQ9ExDaSHoqILfPbio6tJ1qxtS9pGvD+iFiaHrN4hmyi4xcLDq1lefRgmyjjG3y7qWVHRQJvShoAzJL0RWA+MKTgmHpjBWDd6vs8knYi+7Ivo0URsRQgIt6Q9LgTVnP5Oa02IWk7SfvU2L6PpFL+5p60clfBCcAqwPFkM9Z/Guh0BpB+7nxqv7DzlVRWRptJmpqWh3LrD0maWnRwrcjdg21C0h3AZ6u7YFKXzf+LiD2Kiax3WnVUZCuSNCkituug7O0u0DJpxS7P/s7dg+1j1Vr/A0XEk5JKO1gB+LeiA2gWSduSvStsI3L/r5Y4Ea/RSdnKfRVEI9X4JfDdwK7AU63ytoH+xkmrfazZSdkqfRZFg7X4b7K/BL4KPAQsLTiWRpgs6XMR8ZP8RknHAKX8gpf0e7KRgtMkDQMeBCYDm0iaGBHnFxpgC3L3YJuQ9CPgReD0SD/09HzTt4D1IuLYIuPrqVYdFQnZkPeI2LnrPctB0rrA74BFvJOktgVWBA6MiNINxpA0PSI2T59PJZsb8jOSVgXuKXGruN9yS6t9fAX4KTBb0pS07QNkvxUeU1RQvdXCoyIBzpD0U+B24M3KxrK+WDAingU+JOnDZK9ZAfhDRNxRYFi9lZ8D8iPATwDSdGKt0Drud9zSajOS3gNsnlanl3jGcCAbFQkMjYgbq7bvAzxX5vsKkn4BbEb2Dq3KF2BExL8XF5XlSboeuAWYB1xMNlHzy2neyMmVVpg1jpOWlVqrjooEkPRoRLyv6DisY+lFlmcCw4ALKi/mTK3JbSLiB0XG14rcPWhl16qjIgH+Iml0eo+W9UMR8RxwXI2ie4Gy//vrl5y0rOxaclRksiMwRdITZPe0/OxZP5Ymz63Me7kX8GegtG/O7q/cPWhIGlLW2dBbdVQkdPzgaisO8y/rw8XQ4UtI39MCLyHtl9zSMoAZwIZFB9FDLTcqUtJa6eOrhQbSYJIO6qgIWK8vY2mUqpeQnpx7CakTVpM4abUJSSd1VESJJ2GNiH8C41tsVOQDZM+eqUZZAGV9n9avyB6YrtW9M6jGtjK4muwlpJ8E3pJ0La09H2bh3D3YJiS9AZwDLKlRfGJErNG3EVm7kfQAcGRETKtRNjciRhQQVq/VeAnpGsC/U/KXkPZXTlptQtJfgC/Vem6pzF8YVh6SdgGejIinapRtGxGTCwiroVrpJaT9lZNWm5D0PmBhRDxfo2zdNFuBmfWSpGMjYmLZX0LaXzlpWcsq86jIViTpXcDRwIHA+mnzfOBa4GcRsbijY8sk/zZtazwnrTYhaXXgG2Q3jdchu1n8HNkXxvcj4uXCgmsSSU9FRFlHRdZU5kQs6QrgZeBSsmmPAIaTvdhyrYj4ZEGhNZSkv0XEVkXH0ao8erB9XAXcAexemU1b0npkXxhXkT0MWTqtOiqyE2V+PGGbiNi0ats84D5JM4sIqEn2KzqAVuak1T5GRsTZ+Q0peZ0tqcwTsH6XjkdFDujjWBqihRPxQkmHAL+JiKUAkgYAhwAvFRpZL6SHi1+KiKmSDgV2lfQYcGFEvNnF4VYndw+2CUm3ALcBl1YGXaT3Gx0F7BkRYwsMr8dacVRkqz6eIGkkcDawB1mSEtnw8DvIXqT4RGHB9ZCkC4D3AysBM8l+qbiJbFaMARFxRIHhtSQnrTYhaU3gFGB/sntaAM8C1wFnR8TComLrjVYcFdmKibhaei09EfFi0bH0hqQZETFa0iCyQSXrRMRb6dmtqWWdmqo/c9Iy62daMRFXSNqebNLfSZJGA+OAh6vfh1YW+ZGC1aMGPYqwOXxPq41I2gzYALgvTX9U2T4uIm4qLrKea8VRkRHxaCdlZU5YZwD7AO+SdCuwA3An8A1JW0fEWYUG2DPrpHuQyn0mra9dXFityy2tNiHpeOALwMPAGOCEiLg2lZX2N0JJN5PdE7m0xqjIj0RE6UZFtmIihmwmd7J/eysBzwDDI+KV9Jbf+8v4ypWUiDsUEd/qq1jahZNWm0hfGB+MiNfSDfGrgZ9HxP+U+bmSzt7uW9Y3/7ZiIoZln1+q/jcnaUpEjCksOCsNdw+2jwGVh1IjYo6k3YGr0zubas0mXhZPSvoatUdFzi0ysF5o1ccTFklaJb22Y5vKxtSyXFpcWD0n6ZudFEdEfLvPgmkTpXyOxXrkWUljKispgX2M7JXgZR7h9Eng3cAfJS2UtBC4C1gLOLTIwHrhSUlfS8kXyBKxpK9T3kQMsGvlPVOV57SSFchakWX0zxoLZNNVfb2ooFqZuwfbhKThwJJKd1NV2U4RcU8BYVkNrfp4Qi2VyWWLjqMRJK0KnECWsK4Czo2I54qNqvU4abWxVvnCaMVRke2izIOAKtKbpk8CjiCbV/F/IqK0M3z0d+4ebG/HFR1Ab6VRkdcCXwKmS9o/V/zdYqLqPUmbSfqIpMFV28cVFVOTlPl+KpLOASYBrwJbRsQEJ6zmckurjZV51GBFK46KbNXHE2qRNDwi5nW9Z/8kaSnwJtmUW/kvU5ENxFitkMBamEcPtrdWmI26FUdFfo5sRvS3E7GkkRHxP5T3moDWm1w2Itxb1cectNpIq31hJM9KGhMRUyAbFSnpY8DFlHdUZCsm4mUml02vIslPLnsx2T0hs065e7BNtOps1K04KlLSHcBJlUSctr2L9MUeEQOLiq03PLmsNYKTVptopy+Mso+KbMVEDJ5c1hrD3YPt4w2AiHhD0pMR8VZaD0mLiw2t4Y4DSpu0ag1MqCTisiasxJPLWq85abWPdvrCKO19n06UOhEnPwFWrfEZ4Kd9H46VkbsH20Q7zUZd9mHUtZR1+L5ZozlpWenVGhUJlH1U5DJaIRF7cllrBCetNtGqXxgtPCqy5RKxpK/U2DyYbK6+d0fEkD4OyUrISatNtOoXRiuOimzVRJznyWWtpzwQo01ExLmVz7kvjM8CVwLndnRcCbTiqMgPd5CIfwxMLTi2XqkxuezWnqvP6uGk1UZa9AujFUdFtmIirkwuexDZKMgtK7N+mNXD3YNtouoL44JW+cJoxVGRkuYBPyRLvCemz6T1L0fEiKJi6w1PLmuN4KTVJvyFUR6tmIjNGsVJy0qtVUdFmlltTlpWaq04KtKJ2KxjTlrWMlplGHUrJmKzRnHSstKrMSryf1pgVCTQOonYrFE85N1KrVWHUbfo4wlmveaWlpVaK46KbNXHE8wawUnLrJ9pxURs1ihOWmZmVhoDig7AzMysu5y0zMysNJy0zMysNJy0zMysNJy0zMysNJy0zMysNP4/k2EsO4ga4JIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 432x360 with 2 Axes>"
       ]
@@ -1700,7 +1700,7 @@
     "    #norm = compute_matrix(results, best_configs[i], optimizer, search_spaces)\n",
     "    norm_matrices.append(scaled_mat)\n",
     "    kt_matrices.append(kt_mat)\n",
-    "    scaled_matrix = scale_matrix(kt_mat, mean_div=-2.5)\n",
+    "    scaled_matrix = scale_matrix(kt_mat, mean_div=2.5)\n",
     "    scaled_kt_matrix = scale_matrix(kt_mat, mean_div=-100000)\n",
     "    scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis', title=optimizer) #, save='bbo_matrix_avg.pdf')\n",
     "    scaled_kt_matrix = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r',hparam=True,  title=optimizer)\n",
@@ -1708,13 +1708,51 @@
     "avg_norm_matrix = np.mean(np.array(norm_matrices), axis=0)\n",
     "avg_kt_matrix = np.mean(np.array(kt_matrices), axis=0)\n",
     "\n",
-    "scaled_avg_matrix = scale_matrix(avg_norm_matrix, mean_div=2.5)\n",
-    "scaled_avg_kt_matrix = scale_matrix(avg_kt_matrix, mean_div=-1000000)\n",
+    "scaled_matrix = scale_matrix(avg_norm_matrix, mean_div=2.5)\n",
+    "scaled_kt_matrix = scale_matrix(avg_kt_matrix, mean_div=-1000000)\n",
     "#this the difference vs right search space and the wrong for the best seed of every search space \n",
-    "avg_scaled_matrix = plot_matrix(scaled_matrix, search_spaces, cmap='viridis') #, save='bbo_matrix_avg.pdf')\n",
+    "avg_scaled_matrix = plot_matrix(avg_norm_matrix, search_spaces, cmap='viridis') #, save='bbo_matrix_avg.pdf')\n",
     "#run all config on all searchspaces and compare the rank\n",
-    "_ = plot_matrix(scaled_kt_matrix, search_spaces, cmap='viridis_r', hparam=True) #, save='bbo_corr_matrix.pdf')"
+    "_ = plot_matrix(avg_kt_matrix, search_spaces, cmap='viridis_r', hparam=True) #, save='bbo_corr_matrix.pdf')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.38065873, 0.41468707, 0.0158923 , 0.07523479],\n",
+       "       [0.38065873, 1.        , 0.40940666, 0.        , 0.10527008],\n",
+       "       [0.41468707, 0.40940666, 1.        , 0.06412977, 0.0558042 ],\n",
+       "       [0.0158923 , 0.        , 0.06412977, 1.        , 0.20932858],\n",
+       "       [0.07523479, 0.10527008, 0.0558042 , 0.20932858, 1.        ]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_kt_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From a7cdffb8d2036c460280a48fb28b31c24d095c80 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Thu, 14 Jul 2022 09:31:34 +0200
Subject: [PATCH 128/130] optimize mf experimental setup

---
 examples/optimizers-plotly.ipynb            | 9534 +++++++++++++++++++
 examples/optimizers.ipynb                   |  113 +-
 naslib/benchmarks/mf/config.yaml            |   33 +-
 naslib/benchmarks/mf/create_configs.py      |  243 +-
 naslib/benchmarks/mf/local_schedular.py     |   85 -
 naslib/benchmarks/mf/make_configs_nbxx.py   |   78 -
 naslib/benchmarks/mf/run_config.sh          |    8 +
 naslib/benchmarks/mf/runner.py              |   42 +-
 naslib/benchmarks/mf/scheduler_local.sh     |   22 -
 naslib/benchmarks/mf/submit_array.sbatch    |   19 +
 naslib/benchmarks/mf/submit_configs.sh      |   47 -
 naslib/benchmarks/mf/submit_folder_local.sh |   21 -
 naslib/benchmarks/mf/submit_mf.sh           |   10 -
 naslib/benchmarks/mf/submit_test.sh         |   28 -
 14 files changed, 9732 insertions(+), 551 deletions(-)
 create mode 100644 examples/optimizers-plotly.ipynb
 delete mode 100644 naslib/benchmarks/mf/local_schedular.py
 delete mode 100644 naslib/benchmarks/mf/make_configs_nbxx.py
 create mode 100644 naslib/benchmarks/mf/run_config.sh
 delete mode 100755 naslib/benchmarks/mf/scheduler_local.sh
 create mode 100644 naslib/benchmarks/mf/submit_array.sbatch
 delete mode 100755 naslib/benchmarks/mf/submit_configs.sh
 delete mode 100755 naslib/benchmarks/mf/submit_folder_local.sh
 delete mode 100755 naslib/benchmarks/mf/submit_mf.sh
 delete mode 100755 naslib/benchmarks/mf/submit_test.sh

diff --git a/examples/optimizers-plotly.ipynb b/examples/optimizers-plotly.ipynb
new file mode 100644
index 000000000..d7a61df7c
--- /dev/null
+++ b/examples/optimizers-plotly.ipynb
@@ -0,0 +1,9534 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "import pickle\n",
+    "import pandas as pd\n",
+    "\n",
+    "from collections import defaultdict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this defines all of the default colors and markers for the plots\n",
+    "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
+    "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
+    "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
+    "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb']\n",
+    "\n",
+    "# predictors = ['bohb', 'hb']\n",
+    "benchmark_datasets = ['cifar10', ]\n",
+    "# benchmark_datasets = ['ImageNet16-120', ]\n",
+    "# TODO: What of this am I really needing?\n",
+    "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+    "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+    "            (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+    "            (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+    "            (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+    "            (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+    "            (0.09019607843137255, 0.7450980392156863, 0.8117647058823529),\n",
+    "            (0.0, 0.0, 0.0),\n",
+    "            (0.7058823529411765, 0.12156862745098039, 0.4666666666666667),\n",
+    "            (0.4666666666666667, 0.8901960784313725, 0.7607843137254902,),\n",
+    "           ]\n",
+    "\n",
+    "fmts = ['-', '--', ':']\n",
+    "markers = ['^', 'v', 'o']\n",
+    "defaults = [np.array(d) for d in defaults]\n",
+    "\n",
+    "# \n",
+    "pred_plot_dict={\n",
+    "    'ls':{'color':defaults[0], 'fmt':fmts[0]},\n",
+    "    # 'ls_lce':{'color':defaults[0], 'fmt':fmts[1]},\n",
+    "    # 'ls_svr':{'color':defaults[0], 'fmt':fmts[2]},\n",
+    "    're':{'color':defaults[1], 'fmt':fmts[2]},\n",
+    "    # 'rea_lce':{'color':defaults[1], 'fmt':fmts[1]},\n",
+    "    # 'rea_svr':{'color':defaults[1], 'fmt':fmts[2]},\n",
+    "    'bananas/bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    'bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
+    "    # 'bananas_lce/bananas':{'color':defaults[2], 'fmt':fmts[1]},\n",
+    "    # 'bananas_svr/bananas':{'color':defaults[2], 'fmt':fmts[2]},    \n",
+    "    'rs':{'color':defaults[3], 'fmt':fmts[0]},\n",
+    "    'sh': {'color': defaults[1], 'fmt':fmts[0]},\n",
+    "    'hb':{'color':defaults[4], 'fmt':fmts[0]},\n",
+    "    'bohb':{'color':defaults[5], 'fmt':fmts[0]},\n",
+    "    'dehb':{'color':defaults[6], 'fmt':fmts[2]},\n",
+    "    'npenas':{'color':defaults[0], 'fmt':fmts[1]},\n",
+    "}\n",
+    "\n",
+    "# define colors, formats, and markers\n",
+    "c_max = 10\n",
+    "colors = [*defaults[:c_max], *defaults[:c_max], *defaults[:c_max]]\n",
+    "fmts = [*['-']*c_max, *['--']*c_max, *[':']*c_max]\n",
+    "markers = [*['^']*c_max, *['v']*c_max, *['o']*c_max]\n",
+    "\n",
+    "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
+    "\n",
+    "# some of them are not used\n",
+    "pred_label_dict={\n",
+    "    'bananas/gcn':'BANANAS-GCN', 'bananas':'BANANAS', 'bananas/xgb':'BANANAS-XGB', \n",
+    "    'npenas/gcn':'NPENAS-GCN', 'npenas/bananas':'NPENAS-BANANAS', 'npenas/xgb':'NPENAS-XGB', \n",
+    "    'rs':'RS',\n",
+    "    're': 'RE',\n",
+    "    'ls': 'LS',\n",
+    "    'hb': 'HB',\n",
+    "    'sh': 'SH',\n",
+    "    'bohb': 'BOHB',\n",
+    "    'dehb': 'DEHB',\n",
+    "    'rea_svr': 'REA-SVR',\n",
+    "    'bananas_svr/bananas':'BANANAS-SVR',\n",
+    "    'rea_lce': 'REA-WPM',\n",
+    "    'bananas_lce/bananas':'BANANAS-WPM',\n",
+    "    'ls_svr': 'LS-SVR',\n",
+    "    'ls_lce': 'LS-WPM',\n",
+    "    'ls_svr_hp': 'LS-SVR-HP',\n",
+    "    'rea_svr_hp': 'REA-SVR-HP',\n",
+    "    'bananas_svr_hp/bananas': 'BANANAS-SVR-HP',\n",
+    "    'npenas': 'NPENAS',\n",
+    "}\n",
+    "\n",
+    "val_gts = {'cifar10': 97.61, \n",
+    "           'cifar100': 73.49, \n",
+    "           'ImageNet16-120': 50.0, \n",
+    "           'nasbench311': 95.8, \n",
+    "           'nasbench111':95.6, \n",
+    "           'nasbenchnlp':100.7,\n",
+    "           'asr': 1.00,\n",
+    "           }\n",
+    "\n",
+    "datasets = {'cifar10': 'cifar10',\n",
+    "           'cifar100': 'cifar100', \n",
+    "           'ImageNet16-120': 'ImageNet16-120', \n",
+    "           'nasbench311': 'cifar10', \n",
+    "           'nasbench111':'cifar10', \n",
+    "           'nasbenchnlp':'ptb',\n",
+    "           'asr':'asr'\n",
+    "           }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_and_fill_trajectories(pandas_data_frames, default_value=None):\n",
+    "    # merge all trajectories keeping all time steps\n",
+    "    df = pd.DataFrame().join(pandas_data_frames, how='outer')\n",
+    "\n",
+    "    # forward fill to make it a propper step function\n",
+    "    df = df.fillna(method='ffill')\n",
+    "    if default_value is None:\n",
+    "        # backward fill to replace the NaNs for the early times by the\n",
+    "        # performance of a random configuration\n",
+    "        df = df.fillna(method='bfill')\n",
+    "    else:\n",
+    "        df = df.fillna(default_value)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_trajectories(losses, iterations):\n",
+    "    dfs = []\n",
+    "    for i in range(len(losses)):\n",
+    "        loss = losses[i]\n",
+    "        iteration = iterations[i]\n",
+    "        # print('Run %d, Min: %f'%(i, loss))\n",
+    "        df = pd.DataFrame({str(i): loss}, index=iteration)\n",
+    "        dfs.append(df)\n",
+    "\n",
+    "    df = merge_and_fill_trajectories(dfs, default_value=None)\n",
+    "\n",
+    "    return np.array(df.T), np.array(df.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_results(predictor, path, metric='valid_acc', key_choice=None):\n",
+    "    output = defaultdict(list)\n",
+    "    time = defaultdict(list)\n",
+    "    nan_count = 0\n",
+    "    missing_results_count = 0\n",
+    "    file_count = 0\n",
+    "    dataset_paths = []\n",
+    "    for dataset_folder in os.listdir(path):\n",
+    "        dataset_path = os.path.join(path, dataset_folder)\n",
+    "        if not(dataset_folder == datasets[key_choice] and os.path.isdir(dataset_path)):\n",
+    "            continue\n",
+    "        dataset_paths.append(dataset_path)\n",
+    "    \n",
+    "    optimizer_paths = []\n",
+    "    for dataset_path in dataset_paths:\n",
+    "        optimizer_path = os.path.join(dataset_path, predictor)\n",
+    "        if not os.path.isdir(optimizer_path):\n",
+    "            continue\n",
+    "        optimizer_paths.append(optimizer_path)\n",
+    "        # for optimizer_folder in os.listdir(dataset_path):\n",
+    "        #     optimizer_path = os.path.join(dataset_path, optimizer_folder)\n",
+    "        #     if not os.path.isdir(optimizer_path):\n",
+    "        #         continue\n",
+    "        #     optimizer_paths.append(optimizer_path)\n",
+    "\n",
+    "    config_paths = []\n",
+    "    for optimizer_path in optimizer_paths:\n",
+    "        for config_folder in os.listdir(optimizer_path):\n",
+    "            if config_folder[:7] != \"config_\":\n",
+    "                continue\n",
+    "            config_path = os.path.join(optimizer_path, config_folder)\n",
+    "            if not os.path.isdir(config_path):\n",
+    "                continue\n",
+    "            config_paths.append(config_path)\n",
+    "    \n",
+    "    result_paths = []\n",
+    "    for config_path in config_paths:\n",
+    "        for seed_folder in os.listdir(config_path):\n",
+    "            seed_dir = os.path.join(config_path, seed_folder)\n",
+    "            if not os.path.isdir(seed_dir):\n",
+    "                continue\n",
+    "            result_file = os.path.join(config_path, seed_folder, 'errors.json')\n",
+    "            if not os.path.isfile(result_file):\n",
+    "                continue\n",
+    "            result_paths.append(result_file)\n",
+    "    \n",
+    "    for result_file in result_paths:\n",
+    "        try:\n",
+    "            result = json.load(open(result_file))\n",
+    "        except:\n",
+    "            print(\"An exception occurred\")\n",
+    "            continue\n",
+    "        # config = result[0]\n",
+    "        epochs = len(result[1]['train_time'])\n",
+    "\n",
+    "        val_acc = result[1]['valid_acc'][:epochs]\n",
+    "\n",
+    "        if key_choice == 'nasbench111':\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0] *  100.) / 100.]\n",
+    "            for  ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
+    "        elif key_choice == 'nasbenchnlp':\n",
+    "            val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
+    "        elif key_choice == 'asr':\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0])]\n",
+    "            for ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])))\n",
+    "        else:\n",
+    "            # TODO: is this scaling really necessary?\n",
+    "            val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
+    "            for ind in range(1, len(val_acc)):\n",
+    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])) / 100)\n",
+    "\n",
+    "\n",
+    "        runtime = result[1]['runtime']\n",
+    "        train_time = result[1]['train_time']\n",
+    "        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
+    "        train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
+    "        total_time = [i+j for i,j in zip(runtime, train_time)]\n",
+    "        if predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and not key_choice == 'asr':\n",
+    "            # continue\n",
+    "            total_time[:] = [x * 200.0 for x in total_time]\n",
+    "        elif predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and key_choice == 'asr':\n",
+    "            total_time = train_time\n",
+    "            total_time[:] = [x * -40 for x in total_time]\n",
+    "        # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
+    "        if metric == 'valid_acc':\n",
+    "            incumbent = val_incumbent\n",
+    "            #incumbent = val_acc\n",
+    "        elif metric == 'test_acc':\n",
+    "            test_err = [100 - x for x in result[1]['test_acc']]\n",
+    "            inc_idx, best, best_idx = [], np.inf, 0\n",
+    "            for i, err in enumerate(val_err):\n",
+    "                if err < best:\n",
+    "                    best, best_idx = err, i\n",
+    "                inc_idx.append(best_idx)\n",
+    "            incumbent = [test_err[idx] for idx in inc_idx]\n",
+    "\n",
+    "        if len(incumbent) == epochs:\n",
+    "            output[result[0]['config_id']].append(incumbent)\n",
+    "            time[result[0]['config_id']].append(total_time)\n",
+    "\n",
+    "            # output.append(incumbent)\n",
+    "            # time.append(total_time)\n",
+    "        else:\n",
+    "            nan_count += 1\n",
+    "\n",
+    "    output = {key: np.array(item) for key,item in output.items()}\n",
+    "    time = {key: np.array(item) for key,item in time.items()}\n",
+    "    # output = np.array(output)\n",
+    "    # time = np.array(time)\n",
+    "    \n",
+    "    # output, time = get_trajectories(output, time)\n",
+    "\n",
+    "    for config_id in output.keys():\n",
+    "        output[config_id], time[config_id] = get_trajectories(output[config_id], time[config_id])\n",
+    "    # output, time = get_trajectories(output, time)\n",
+    "    print(f\"predictor: {predictor}\")\n",
+    "    means = defaultdict(list)\n",
+    "    means = {key: np.mean(item, axis=0) for key,item in output.items()}\n",
+    "    incumbent_settings = min(means.items(), key=lambda item: item[1][-1])\n",
+    "    # incumbent_settings = [0, means[0]]\n",
+    "    print(f\"Best config: {incumbent_settings[0]}\")\n",
+    "    mean = incumbent_settings[1]\n",
+    "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
+    "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
+    "    std_error_2 = np.std(output[incumbent_settings[0]], axis=0, ddof=1) / np.sqrt(np.size(output[incumbent_settings[0]]))\n",
+    "    time = time[incumbent_settings[0]]\n",
+    "    # print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
+    "    # print(f\"predictor: {predictor}, best config: {incumbent_settings[0]}\")\n",
+    "    print('first mean', mean[0], 'last mean', mean[-1])\n",
+    "    print('first std', std[0], 'last std', std[-1])\n",
+    "    print('time ', time[-1])\n",
+    "    return mean, std, std_error_2, time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_results_to_dict(folder, predictors, key_choice):\n",
+    "    results_dict = {}\n",
+    "    print('\\n saving', key_choice)\n",
+    "    for _, predictor in enumerate(predictors):\n",
+    "        mean, std, std_error, runtime = get_results(predictor, folder, metric='valid_acc', key_choice=key_choice)\n",
+    "        key = predictor\n",
+    "        results_dict[key] = {'label':pred_label_dict[predictor], \n",
+    "                             'key':key, 'mean':mean, 'std':std, \n",
+    "                             'std_error': std_error, 'runtime': runtime}\n",
+    "    return results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_all_results():     \n",
+    "    # that was/is very very shity code!\n",
+    "    # key_choice = 'nasbench311'\n",
+    "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
+    "    all_results = {dataset:save_results_to_dict(folder, predictors, dataset) for dataset in benchmark_datasets}\n",
+    "    return all_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load results into dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " saving cifar10\n",
+      "predictor: ls\n",
+      "Best config: 22\n",
+      "first mean 0.10324999999999998 last mean 0.06087000000000005\n",
+      "first std 0.021849313490359377 last std 0.0009466255859630773\n",
+      "time  891204.295082887\n",
+      "predictor: bananas\n",
+      "Best config: 87\n",
+      "first mean 0.10324999999999998 last mean 0.06305000000000001\n",
+      "first std 0.021849313490359377 last std 0.0017024981644630077\n",
+      "time  876740.4151002569\n",
+      "predictor: re\n",
+      "Best config: 0\n",
+      "first mean 0.10324999999999998 last mean 0.06040000000000005\n",
+      "first std 0.021849313490359377 last std 1.3877787807814457e-17\n",
+      "time  968633.8430881497\n",
+      "predictor: rs\n",
+      "Best config: 36\n",
+      "first mean 0.10324999999999998 last mean 0.06581999999999996\n",
+      "first std 0.021849313490359377 last std 0.002371413080844401\n",
+      "time  761624.9727487558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_23010/3844687332.py:108: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  output = {key: np.array(item) for key,item in output.items()}\n",
+      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_23010/3844687332.py:109: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  time = {key: np.array(item) for key,item in time.items()}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predictor: bohb\n",
+      "Best config: 82\n",
+      "first mean 0.4176000000567627 last mean 0.06107600010742189\n",
+      "first std 0.14031822252285084 last std 0.0013472579703791069\n",
+      "time  1101040.2693293483\n",
+      "predictor: dehb\n",
+      "Best config: 58\n",
+      "first mean 0.4176000000567627 last mean 0.06031600016113279\n",
+      "first std 0.14031822252285084 last std 0.0007581451321007666\n",
+      "time  1102614.4726861517\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function print>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_path = '/Users/lars/Projects/results/all_experiments/'\n",
+    "\n",
+    "results = save_all_results()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'wb')\n",
+    "# pickle.dump(results, result_file)\n",
+    "# result_file.close()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
+    "# results = pickle.load(result_file)\n",
+    "print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false --no-raise-error\n",
+    "filepath='/Users/lars/Projects/results/plot_files/nb201.pickle'\n",
+    "result_file = open(filepath, 'wb')\n",
+    "pickle.dump(results, result_file)\n",
+    "result_file.close()\n",
+    "\n",
+    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
+    "# results = pickle.load(result_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_folder = 'plots_naslib/'\n",
+    "if not os.path.exists(result_folder):\n",
+    "    os.makedirs(result_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%script false --no-raise-error\n",
+    "# plot performance vs runtime\n",
+    "\n",
+    "for key_choice in results.keys():\n",
+    "    results_dict = results[key_choice]\n",
+    "\n",
+    "    keys = predictors\n",
+    "\n",
+    "\n",
+    "    keys = sorted(keys)\n",
+    "\n",
+    "    plt.rcParams['axes.grid'] = True\n",
+    "    plt.rcParams['grid.linestyle'] = 'dotted'\n",
+    "    add_sub_axes = False\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=[4.5, 3.5])\n",
+    "    if add_sub_axes:\n",
+    "        sub_axes = plt.axes([.6, .5, .3, .38]) \n",
+    "\n",
+    "\n",
+    "    for i, key in enumerate(keys):\n",
+    "        mean = results_dict[key]['mean']\n",
+    "        sem = results_dict[key]['std_error']\n",
+    "        label = results_dict[key]['label']\n",
+    "        x = results_dict[key]['runtime']\n",
+    "        #tag = '_'.join(key.split('_')[:-1])\n",
+    "        tag = key\n",
+    "        color = pred_plot_dict[tag]['color']\n",
+    "        fmt = pred_plot_dict[tag]['fmt']\n",
+    "        ax.plot(x, mean, label=label, color=color, linestyle=fmt, linewidth=2.5)\n",
+    "        ax.fill_between(x, mean-50*sem, mean+50*sem,\n",
+    "                        color=color, alpha=0.2)\n",
+    "\n",
+    "        if add_sub_axes:\n",
+    "            n = 450\n",
+    "            X_detail = x[n:]\n",
+    "            Y_detail = mean[n:]\n",
+    "            sem_detail = sem[n:]\n",
+    "            sub_axes.plot(X_detail, Y_detail, color=color, linestyle=fmt)\n",
+    "            sub_axes.tick_params(labelbottom=False) \n",
+    "\n",
+    "    ax.set_xlabel('Runtime (seconds)', fontsize=14)\n",
+    "\n",
+    "    if key_choice == 'nasbenchnlp':\n",
+    "        ax.set_ylabel('Perplexity regret', fontsize=14) #nas201\n",
+    "    else:\n",
+    "        ax.set_ylabel('Valid. regret', fontsize=14) #nas201\n",
+    "\n",
+    "    # ax.grid(True, which=\"both\",ls=\"-\", alpha=.5)\n",
+    "\n",
+    "    if key_choice == 'cifar10':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        # for nasbench201\n",
+    "        # ax.set_ylim([2e-4, 1e-2])\n",
+    "        # ax.set_xlim([2.0e5, 7.6*1e5])\n",
+    "\n",
+    "        # for nasbench311\n",
+    "        ax.set_ylim([2.5e-2, 4.2e-2])\n",
+    "        ax.set_xlim([1.9e4 *7 / 7, 1.8e6])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
+    "        # ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
+    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'cifar100':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([0.002, 0.03])\n",
+    "        ax.set_xlim([1e5, 1e6])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
+    "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'ImageNet16-120':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        # ax.set_ylim([0.0025, 0.3])\n",
+    "        ax.set_ylim([2.7e-2, 6.8e-2])\n",
+    "        ax.set_xlim([1.5e5, 3.2e6])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
+    "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
+    "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    elif key_choice == 'nasbench311':    \n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        # ax.set_ylim([.035, .066])\n",
+    "        # ax.set_xlim([400, 2100])\n",
+    "        # ax.set_ylim([2e-3, .028])\n",
+    "        # ax.set_xlim([5e4, 4*10e5])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas311_c10.pdf')\n",
+    "        plt.show()\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "\n",
+    "    elif key_choice == 'nasbench111':\n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([4e-4, 8e-2])\n",
+    "        ax.set_xlim([2e4, 5.8*10e4])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-111 CIFAR10', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nas111_c10.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "\n",
+    "    elif key_choice == 'nasbenchnlp':\n",
+    "        ax.set_xscale('log')\n",
+    "        ax.set_yscale('log')\n",
+    "        ax.set_ylim([.5, 4.2e1])\n",
+    "        ax.set_xlim([5e4, 1.1*10e6])\n",
+    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
+    "        ax.set_title('NAS-Bench-NLP11 PTB', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nasn11_ptb.pdf')\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
+    "    \n",
+    "    elif key_choice == 'asr':\n",
+    "        # ax.set_xscale('log')\n",
+    "        # ax.set_yscale('log')\n",
+    "        ax.set_ylim([0.026, 0.04])\n",
+    "        ax.set_xlim([700, 16000])\n",
+    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
+    "        ax.set_title('NAS-Bench-ASR', fontsize=14)\n",
+    "        save_path = os.path.join(result_folder, 'nbasr.pdf')\n",
+    "        ax.set_xlabel('Epochs', fontsize=14)\n",
+    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ploting results using plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "type": "log"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# trying to plot \n",
+    "mean = results['cifar10']['rs']['mean']\n",
+    "sem = results['cifar10']['rs']['std_error']\n",
+    "label = results['cifar10']['rs']['label']\n",
+    "runtime = results['cifar10']['rs']['runtime']\n",
+    "fig = go.Figure([\n",
+    "    go.Scatter(\n",
+    "        x=runtime, \n",
+    "        y=mean, \n",
+    "        name=label)\n",
+    "])\n",
+    "fig.update_xaxes(type=\"log\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e28e65abac2798f30acc960ffff31a2f506562b074c32d55b97f302318a49627"
+  },
+  "kernelspec": {
+   "display_name": "jupyter-venv",
+   "language": "python",
+   "name": "jupyter-venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index b17a78883..4b00e9fa9 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
     "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
     "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
     "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
-    "# predictors = ['bohb', 'dehb', 'hb', 'sh', ]\n",
+    "# predictors = ['bohb', 'hb']\n",
     "benchmark_datasets = ['asr', ]\n",
     "# benchmark_datasets = ['ImageNet16-120', ]\n",
     "# TODO: What of this am I really needing?\n",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,6 +301,7 @@
     "    means = {key: np.mean(item, axis=0) for key,item in output.items()}\n",
     "    incumbent_settings = min(means.items(), key=lambda item: item[1][-1])\n",
     "    # incumbent_settings = [0, means[0]]\n",
+    "    print(f\"Best config: {incumbent_settings[0]}\")\n",
     "    mean = incumbent_settings[1]\n",
     "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
     "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
@@ -316,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,14 +335,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "def save_all_results():     \n",
-    "    # that was/is very very shity code!\n",
     "    # key_choice = 'nasbench311'\n",
-    "    folder = os.path.expanduser(os.path.join(base_path, 'asr'))\n",
+    "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
     "    all_results = {dataset:save_results_to_dict(folder, predictors, dataset) for dataset in benchmark_datasets}\n",
     "    return all_results"
    ]
@@ -357,78 +357,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " saving asr\n",
-      "predictor: ls\n",
-      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
-      "first std 0.27409130929712305 last std 0.0\n",
-      "time  16000\n",
-      "predictor: bananas\n",
-      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
-      "first std 0.27409130929712305 last std 0.0\n",
-      "time  16000\n",
-      "predictor: re\n",
-      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
-      "first std 0.27409130929712305 last std 0.0\n",
-      "time  16000\n",
-      "predictor: rs\n",
-      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
-      "first std 0.27409130929712305 last std 0.0\n",
-      "time  16000\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3829/1081133750.py:108: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  output = {key: np.array(item) for key,item in output.items()}\n",
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_3829/1081133750.py:109: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  time = {key: np.array(item) for key,item in time.items()}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "predictor: bohb\n",
-      "first mean 0.5410241097211838 last mean 0.02777442932128906\n",
-      "first std 0.23560936207907718 last std 0.00033180661295487476\n",
-      "time  16011.08962893486\n",
-      "predictor: dehb\n",
-      "first mean 0.5410241097211838 last mean 0.02910938858985901\n",
-      "first std 0.23560936207907718 last std 0.0013909191444725138\n",
-      "time  16034.51386833191\n",
-      "predictor: hb\n",
-      "first mean 0.5410241097211838 last mean 0.027595114707946778\n",
-      "first std 0.23560936207907718 last std 0.00043575140860873865\n",
-      "time  16018.907649040222\n",
-      "predictor: sh\n",
-      "first mean 0.5410241097211838 last mean 0.02686458230018616\n",
-      "first std 0.23560936207907718 last std 0.0004565035557138738\n",
-      "time  16000.764096736908\n",
-      "predictor: npenas\n",
-      "first mean 0.642352394759655 last mean 0.031148314476013184\n",
-      "first std 0.27409130929712305 last std 0.0\n",
-      "time  16000\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function print>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "base_path = '/Users/lars/Projects/results/all_experiments/'\n",
     "\n",
@@ -449,13 +378,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filepath='/Users/lars/Projects/results/plot_files/nbasr.pickle'\n",
-    "result_file = open(filepath, 'wb')\n",
-    "pickle.dump(results, result_file)\n",
-    "result_file.close()\n",
+    "# filepath='/Users/lars/Projects/results/plot_files/nb201.pickle'\n",
+    "# result_file = open(filepath, 'wb')\n",
+    "# pickle.dump(results, result_file)\n",
+    "# result_file.close()\n",
     "\n",
-    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
-    "# results = pickle.load(result_file)"
+    "result_file = open('/Users/lars/Projects/results/plot_files/nbasr.pickle', 'rb')\n",
+    "results = pickle.load(result_file)"
    ]
   },
   {
@@ -478,12 +407,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 324x252 with 1 Axes>"
       ]
@@ -613,13 +542,13 @@
     "    \n",
     "    elif key_choice == 'asr':\n",
     "        # ax.set_xscale('log')\n",
-    "        # ax.set_yscale('log')\n",
+    "        ax.set_yscale('log')\n",
     "        ax.set_ylim([0.026, 0.04])\n",
     "        ax.set_xlim([700, 16000])\n",
     "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
     "        ax.set_title('NAS-Bench-ASR', fontsize=14)\n",
     "        save_path = os.path.join(result_folder, 'nbasr.pdf')\n",
-    "        ax.set_xlabel('Epochs', fontsize=14)\n",
+    "        ax.set_xlabel('Iterations', fontsize=14)\n",
     "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n"
    ]
   },
diff --git a/naslib/benchmarks/mf/config.yaml b/naslib/benchmarks/mf/config.yaml
index 6288a25b1..a55c8153b 100644
--- a/naslib/benchmarks/mf/config.yaml
+++ b/naslib/benchmarks/mf/config.yaml
@@ -1,21 +1,26 @@
 start_seed: 0
-trials: 1
+trials: 2
 out_dir: run
-out_dir_config: configs
-config_type: bbo-bs
-search_space: nasbench201
+out_dir_configs: configs
+
+search_space: 
+  - nasbench201
+  - nasbench311
+  - asr
 datasets: 
   - cifar10
+  - cifar100
+  - ImageNet16-120
+  - TIMIT
 optimizers: # sh, rs, ls, ...
+  - rs
+  - ls
+  - re
+  - bananas
+  - npenas
   - sh
-fidelity: 200
-
-HPO: True
-# only relevant if HPO is enabled
-num_config: 10
-
-# for SH: 4
-# for default BBOs: 200
-epochs: 200
+  - hb
+  - bohb
+  - dehb
 
-predictor: var_sparse_gp
+num_config: 2
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/create_configs.py b/naslib/benchmarks/mf/create_configs.py
index d2cd9fec1..acab9f532 100644
--- a/naslib/benchmarks/mf/create_configs.py
+++ b/naslib/benchmarks/mf/create_configs.py
@@ -1,33 +1,83 @@
-import argparse
 import os
-import random
-from venv import create
+
+
 import yaml
 from glob import glob
 import numpy as np
 from addict import Dict
 import itertools
 
-# pycodestyle --max-line-length=120 naslib/benchmarks/mf/create_configs.py
+def dump_configs(
+    configs: list,
+    optimizer: str,
+    dataset: str,
+    search_space: str,
+    out_dir = 'run'
+):
+    # setting default values specific to search space/dataset combination
+    if search_space == 'nasbench201':
+        fidelity = 200
+        max_budget = fidelity
+        enc_dim = 6
+        min_points_in_model = enc_dim + 1
+
+        if dataset == 'cifar10':
+            budgets = 1_100_000
+        elif dataset == 'cifar100':
+            budgets = 2_000_000
+        elif dataset == 'ImageNet16-120':
+            budgets = 6_500_5000
+        else:
+            return
+        
+    elif search_space == 'nasbench311':
+        fidelity = 97
+        max_budget = fidelity
+        enc_dim = 32
+        min_points_in_model = enc_dim + 1
+        if dataset == 'cifar10':
+            budgets = 2_500_000
+        else: 
+            return
+
+    elif search_space == 'asr':
+        fidelity = 39
+        max_budget = fidelity
+        enc_dim = 6
+        min_points_in_model = enc_dim + 1
+        if dataset != 'TIMIT':
+            return
+        budgets = 16_000
+    else:
+        return
+
+    os.makedirs(out_dir, exist_ok=True)
+    for config in configs:
+        config_id = config['config_id']
+        seed = config['seed']
+        filename = os.path.join(".", out_dir, f"{search_space}_{dataset}_{optimizer}_config_{config_id}_{seed}.yaml")
 
-# TODO: add max_budget
-# TODO: add fidelity
-# TODO: add budget
+        with open(filename, "w") as fh:
+            config['search_space'] = search_space
+            config['dataset'] = dataset
+            config['optimizer'] = optimizer
+
+            config['search']['max_budget'] = max_budget
+            config['search']['fidelity'] = fidelity
+            config['search']['budgets'] = budgets
+            config['search']['min_points_in_model'] = min_points_in_model
+            config['search']['enc_dim'] = enc_dim
+            yaml.dump(config, fh)
 
 def create_configs(
     start_seed: int = 0,
     trials: int = 100,
-    optimizer: str = "rs",
     predictor: str = "xgb",
     fidelity: int = 200,
     acq_fn_optimization: str = "mutation",
-    dataset: str = "cifar10",
     out_dir: str = "run",
     checkpoint_freq: int = 5000,
-    epochs: int = 150,
-    budget: int = 1000,
-    search_space: str = "nasbench201",
-    HPO: bool = False, 
+    epochs: int = 400,
     num_config: int = 100,
     **kwargs
 ):
@@ -36,40 +86,27 @@ def create_configs(
     Args:
         start_seed: starting seed.
         trials: Number of trials.
-        optimizer: which optimizer.
         predictor: which predictor.
         fidelity: Fidelity.
         acq_fn_optimization: acq_fn.
-        dataset: Which dataset.
         epochs: How many search epochs.
-        search_space: nasbench201 or darts?.
-        HPO: Hyperparameter Optimisation enabled/disabled.
         num_config: Number of configs explored by HPO (Random Search).
 
     Returns:
-        No return.
+        configs.
 
     """
     start_seed = int(start_seed)
     trials = int(trials)
-    
     # first generate the default config at config 0
     config_id = 0
-    # TODO: add config out dir to config
-    folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-    filename = f"./configs/{search_space}_{dataset}_{optimizer}_config_{config_id}"
-    # folder = f"naslib/benchmarks/bbo/configs_cpu/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-    os.makedirs(folder, exist_ok=True)       
-        
+    configs = []
     for seed in range(start_seed, start_seed + trials):
-        # np.random.seed(seed)
-        # random.seed(seed)
-
         config = {
             "seed": seed,
-            "search_space": search_space,
-            "dataset": dataset,
-            "optimizer": optimizer,
+            "search_space": None,
+            "dataset": None,
+            "optimizer": None,
             "out_dir": out_dir,
             "config_id": config_id,
             "search": {
@@ -80,18 +117,13 @@ def create_configs(
                 "num_ensemble": 3,
                 "acq_fn_type": "its",
                 "num_arches_to_mutate": 1,
-                "max_mutations": 1,
                 "num_candidates": 50,
                 "checkpoint_freq": checkpoint_freq,
                 "epochs": epochs,
-                "fidelity": fidelity,
-                "min_fidelity": 1,
                 "number_archs": 128,
-                "budget_type": "epoch",
-                "budget_max": 128,
                 "method": "random",
                 "eta": 2,
-                "max_budget": 200,
+                "max_budget": None,
                 "min_budget": 1,
                 "n_process": 1000,
                 "epsilon": 1e-6,
@@ -103,35 +135,32 @@ def create_configs(
                 "debug_predictor": False,
                 # config secton for successive halving/ hyperband,
                 "min_budget": 1,
-                "max_budget": 200,
-                "fidelity": 200,
+                "fidelity": fidelity,
                 "n_process": 1_000_000,
-                "budgets": 360_000, 
+                "budgets": None, 
                 # config section for bohb
                 "min_bandwith": 0.001,
                 "top_n_percent": 0.1,
-                "min_points_in_model": 7,
+                "min_points_in_model": None,
                 # config section for dehb
-                "enc_dim": 6,
+                "enc_dim": None,
                 "max_mutations": 1,
                 "crossover_prob": 0.5,
                 "mutate_prob": 0.5,
             },
         }
-        # path = os.path.join(filename, f"seed_{seed}.yaml")
-        path = filename + f"_{seed}.yaml"
-
-        with open(path, "w") as fh:
-            yaml.dump(config, fh)
-    num_config = num_config if HPO else 1
+        configs.append(config)
+        
     for config_id in range(1, num_config):
-        folder = f"./configs/{search_space}/{dataset}/{optimizer}/config_{config_id}"
-        filename = f"./configs/{search_space}_{dataset}_{optimizer}_config_{config_id}"
-
-        os.makedirs(folder, exist_ok=True)
+        sample_size = int(np.random.choice(range(5, 100)))
+        population_size = int(np.random.choice(range(5, 100)))
+        num_init = int(np.random.choice(range(5, 100)))
+        k = int(np.random.choice(range(10, 50)))
+        num_arches_to_mutate = int(np.random.choice(range(1, 20)))
+        max_mutations = int(np.random.choice(range(1, 20)))
+        num_candidates = int(np.random.choice(range(5, 50)))
 
         # SH/HB
-        max_fidelity = 200 # int(np.random.choice(range(100, 200)))
         min_budget = int(np.random.choice(range(1, 50)))
         eta = int(np.random.choice(range(2, 5)))
         # BOHB
@@ -143,130 +172,86 @@ def create_configs(
         mutate_prob = float(np.random.choice(np.arange(0.0, 1.10, 0.1)))
 
         for seed in range(start_seed, start_seed + trials):
-            # np.random.seed(seed)
-            # random.seed(seed)
-            # TODO: max_fidelity should be dependent on eta and min_fidelity
-            # fidelity_range = [2**i for i in range(0, 9)]
-            # max_fidelity = int(np.random.choice(fidelity_range))
-            # min fidelity has to be lower/equal to max_fidelity
-            # min_fidelity = int(np.random.choice(
-            #     list(
-            #         filter(
-            #             lambda n: n <= max_fidelity, fidelity_range))))
             config = {
                 "seed": seed,
-                "search_space": search_space,
-                "dataset": dataset,
-                "optimizer": optimizer,
+                "search_space": None,
+                "dataset": None,
+                "optimizer": None,
                 "out_dir": out_dir,
                 "config_id": config_id,
                 "search": {
                     "checkpoint_freq": checkpoint_freq,
                     "epochs": epochs,
                     "fidelity": fidelity,
-                    "sample_size": int(np.random.choice(range(5, 100))),
-                    "population_size": int(np.random.choice(range(5, 100))),
-                    "num_init": int(np.random.choice(range(5, 100))),
-                    "k":int(np.random.choice(range(10, 50))),
+                    "sample_size": sample_size,
+                    "population_size": population_size,
+                    "num_init": num_init,
+                    "k": k,
                     "num_ensemble": 3,
                     "acq_fn_type": "its",
                     "acq_fn_optimization": acq_fn_optimization,
                     "encoding_type": "path",
-                    "num_arches_to_mutate": int(np.random.choice(range(1, 20))),
-                    "max_mutations": int(np.random.choice(range(1, 20))),
-                    "num_candidates": int(np.random.choice(range(5, 50))),
+                    "num_arches_to_mutate": num_arches_to_mutate,
+                    "max_mutations": max_mutations,
+                    "num_candidates": num_candidates,
                     "predictor": predictor,
                     "debug_predictor": False,
                     # config section for successive halving,
                     # config secton for Hyperband,
                     "min_budget": min_budget,
-                    "max_budget": max_fidelity,
-                    "fidelity": 200,
+                    "max_budget": None,
+                    "fidelity": fidelity,
                     "n_process": 1_000_000,
-                    "budgets": 360_000, 
+                    "budgets": None, 
                     "eta": eta,
                     "epsilon": 1e-6,
                     # config section for BOHB
                     "min_bandwith": min_bandwith,
                     "top_n_percent": top_n_percent,
-                    "min_points_in_model": 7,
+                    "min_points_in_model": None,
                     # config section for DEHB
                     # config section for dehb
-                    "enc_dim": 6,
+                    "enc_dim": None,
                     "max_mutations": max_mutations,
                     "crossover_prob": crossover_prob,
                     "mutate_prob": mutate_prob,
                 },
             }
-            print(f"folder: {folder}")
-            # path = os.path.join(filename, f"seed_{seed}.yaml")
-            path = filename + f"_{seed}.yaml"
-            print(f"path: {path}")
-            with open(path, "w") as fh:
-                yaml.dump(config, fh)
+            configs.append(config)
+    return configs
 
-if __name__ == "__main__":
-    main()
 
-def check_config(search_space, dataset, optimizer, trials, HPO, num_config):
-    folder = os.path.join(".", "configs", search_space, dataset, optimizer)
-    test = os.path.join(folder, "**", "*.yaml")  
-    files = glob(test, recursive=True)
-    print(f"{len(files)} files")
-    num_config = num_config if HPO else 1
-    if len(files) == trials * num_config:
-        print("Created config file(s) successfully")
-        return
-    print("Config file(s) not successfully created")
-    exit(1)
+def check_config(out_dir):
+    folder = os.path.join(out_dir)
+    config_files_path = os.path.join(folder, "**", "*.yaml")  
+    files = glob(config_files_path, recursive=True)
+    print(f"{len(files)} config file(s) created")
 
 def main():
-    with open("config.yaml", 'r') as stream:
+    with open("/Users/lars/Projects/NASLib_cleanup/naslib/benchmarks/mf/config.yaml", 'r') as stream:
         config = yaml.safe_load(stream)
     # convert such that config elements are accessiable via attributes
     config = Dict(config)
     start_seed = config.start_seed if config.start_seed else 0
     trials = config.trials
-    end_seed = start_seed + trials - 1
 
     optimizers = config.optimizers
-    # TODO: Implement check for optimizers
-    # See lines 13 - 18 in make_configs_nb201.sh
 
     out_dir = config.out_dir
+    out_dir_configs = config.out_dir_configs
 
-    config_type = config.config_type
-    if config_type not in {'bbo-bs', 'predictor-bs'}:
-        print('Invalid config')
-        print('config_type either bbo-bs or predictor-bs')
-        exit(1)
-
-    search_space = config.search_space
-
+    search_spaces = config.search_space
     datasets = config.datasets
-    fidelity = config.fidelity
-    epochs = config.epochs
-    budget = config.budget
-    predictor = config.predictor
-
-    HPO = config.HPO
     num_config = config.num_config
-
-    for dataset, optimizer in itertools.product(datasets, optimizers):
-        print(f"Creating config for dataset: {dataset}: & optimizer: {optimizer}")
-        create_configs(
+    configs = create_configs(
             start_seed=start_seed,
             trials=trials,
             out_dir=out_dir,
-            dataset=dataset,
-            config_type=config_type,
-            search_space=search_space,
-            optimizer=optimizer,
-            predictor=predictor,
-            fidelity=fidelity,
-            epochs=epochs,
-            budget=budget,
-            HPO=HPO,
             num_config=num_config
         )
-        check_config(search_space, dataset, optimizer, trials, HPO, num_config)
\ No newline at end of file
+    for search_space, dataset, optimizer in itertools.product(search_spaces, datasets, optimizers):        
+        dump_configs(dataset=dataset, optimizer=optimizer, search_space=search_space, configs=configs, out_dir=out_dir_configs)
+    check_config(out_dir_configs)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/local_schedular.py b/naslib/benchmarks/mf/local_schedular.py
deleted file mode 100644
index a0ef0ff48..000000000
--- a/naslib/benchmarks/mf/local_schedular.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import os
-import subprocess
-import shutil
-
-
-def progressBar(iterable, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = "\r"):
-    """
-    Call in a loop to create terminal progress bar
-    @params:
-        iterable    - Required  : iterable object (Iterable)
-        prefix      - Optional  : prefix string (Str)
-        suffix      - Optional  : suffix string (Str)
-        decimals    - Optional  : positive number of decimals in percent complete (Int)
-        length      - Optional  : character length of bar (Int)
-        fill        - Optional  : bar fill character (Str)
-        printEnd    - Optional  : end character (e.g. "\r", "\r\n") (Str)
-    """
-    total = len(iterable)
-    # Progress Bar Printing Function
-    def printProgressBar (iteration):
-        percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
-        filledLength = int(length * iteration // total)
-        bar = fill * filledLength + '-' * (length - filledLength)
-        print(f'\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)
-    # Initial Call
-    printProgressBar(0)
-    # Update Progress Bar
-    for i, item in enumerate(iterable):
-        yield item
-        printProgressBar(i + 1)
-    # Print New Line on Complete
-    print()
-
-def clean_runs(search_space, optimizers_delete :list = [], delete_all=False):
-    dataset_dir = f"/Users/lars/Projects/NASLib/naslib/benchmarks/mf/run_m1/{search_space}"
-    datasets = os.listdir(dataset_dir)
-    for dataset in datasets:
-        optimizer_dir = os.path.join(dataset_dir, dataset)
-        if not os.path.isdir(optimizer_dir):
-            continue
-        optimizers = os.listdir(optimizer_dir)
-        for optimizer in optimizers:
-            if not os.path.isdir(os.path.join(optimizer_dir, optimizer)):
-                continue
-            if not delete_all and not optimizer in optimizers_delete:
-                continue
-            optimizer_path = os.path.join(optimizer_dir, optimizer)
-            shutil.rmtree(optimizer_path)
-            print(optimizer_path)
-
-
-def main():
-    clean_runs("nasbench201", optimizers_delete=['rs'])
-    config_files = []
-
-    # Obtaining config files
-    search_space = "nasbench201"
-    dataset_dir = f"/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/{search_space}"
-    datasets = os.listdir(dataset_dir)
-    for dataset in datasets:
-        optimizer_dir = os.path.join(dataset_dir, dataset)
-        if not os.path.isdir(optimizer_dir):
-            continue
-        optimizers = os.listdir(optimizer_dir)
-        for optimizer in optimizers:
-            if not os.path.isdir(os.path.join(optimizer_dir, optimizer)):
-                continue
-            config_path = os.path.join(optimizer_dir, optimizer, "config_0")
-            for seed in os.listdir(config_path):
-                config_file = os.path.join(config_path, seed)
-                if not os.path.isfile(config_file):
-                    continue
-                config_files.append(config_file)
-
-    # A Nicer, Single-Call Usage
-    for config in progressBar(config_files, prefix = 'Progress:', suffix = 'Complete', length = 50):
-        # Use a list of args instead of a string
-        my_cmd = ["/Users/lars/Projects/naslib-venv/bin/python", 
-            "/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/runner.py", 
-            "--config-file", config]
-        with open('bbo-exps-output.log', "w") as outfile:
-            subprocess.run(my_cmd, stdout=outfile, stderr=outfile)
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/make_configs_nbxx.py b/naslib/benchmarks/mf/make_configs_nbxx.py
deleted file mode 100644
index d6934f76b..000000000
--- a/naslib/benchmarks/mf/make_configs_nbxx.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# read yaml config as in nasbench for creating nice configs and easy access
-import yaml
-import os
-from glob import glob
-
-# Short workaround, such that I do not have to design a proper class for a small config file during development
-# Allows easier access to config by attributes instead of dictionary key.
-from addict import Dict
-
-from create_configs import create_configs
-# Read YAML file
-with open("config.yaml", 'r') as stream:
-    config = yaml.safe_load(stream)
-
-def check_config(search_space, dataset, optimizer, trials, HPO, num_config):
-    folder = os.path.join(".", "configs", search_space, dataset, optimizer)
-    test = os.path.join(folder, "**", "*.yaml")  
-    files = glob(test, recursive=True)
-    print(f"{len(files)} files")
-    num_config = num_config if HPO else 1
-    if len(files) == trials * num_config:
-        print("Created config file(s) successfully")
-        return
-    print("Config file(s) not successfully created")
-    exit(1)
-
-# convert such that config elements are accessiable via attributes
-config = Dict(config)
-start_seed = config.start_seed if config.start_seed else 0
-trials = config.trials
-end_seed = start_seed + trials - 1
-
-optimizers = config.optimizers
-# TODO: Implement check for optimizers
-# See lines 13 - 18 in make_configs_nb201.sh
-
-out_dir = config.out_dir
-
-config_type = config.config_type
-if config_type not in {'bbo-bs', 'predictor-bs'}:
-    print('Invalid config')
-    print('config_type either bbo-bs or predictor-bs')
-    exit(1)
-
-search_space = config.search_space
-
-datasets = config.datasets
-fidelity = config.fidelity
-epochs = config.epochs
-
-predictor = config.predictor
-
-HPO = config.HPO
-num_config = config.num_config
-
-for dataset in datasets:
-    for optimizer in optimizers:
-        print(f"Creating config for dataset: {dataset}: & optimizer: {optimizer}")
-        create_configs(
-            start_seed=start_seed,
-            trials=trials,
-            out_dir=out_dir,
-            dataset=dataset,
-            config_type=config_type,
-            search_space=search_space,
-            optimizer=optimizer,
-            predictor=predictor,
-            fidelity=fidelity,
-            epochs=epochs,
-            HPO=HPO,
-            num_config=num_config
-        )
-        check_config(search_space, dataset, optimizer, trials, HPO, num_config)
-
-""" 
-    --acq_fn_optimization $acq_fn_optimization --predictor $predictor
-    TODO: acq_fn_optimization -- this needs to be done
-"""
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/run_config.sh b/naslib/benchmarks/mf/run_config.sh
new file mode 100644
index 000000000..450d4c309
--- /dev/null
+++ b/naslib/benchmarks/mf/run_config.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+directory=/path/to/configs
+
+index=$1
+# selects config by index given from SLURM scheduler
+config_file_seed=$(ls -d $directory/* | sed "${index}q;d")
+
+/home/gernel/miniconda3/bin/python -u ~/work/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed
diff --git a/naslib/benchmarks/mf/runner.py b/naslib/benchmarks/mf/runner.py
index 51f77e2be..aacb9d759 100644
--- a/naslib/benchmarks/mf/runner.py
+++ b/naslib/benchmarks/mf/runner.py
@@ -1,19 +1,18 @@
 import logging
-import sys
+
 #from nasbench import api
 
 from naslib.defaults.trainer import Trainer
 from naslib.defaults.trainer_multifidelity import Trainer as Trainer_MF
 
 from naslib.optimizers import RandomSearch, Npenas, \
-RegularizedEvolution, LocalSearch, Bananas, BasePredictor, SuccessiveHalving, HB, BOHB, DEHB
+RegularizedEvolution, LocalSearch, Bananas, SuccessiveHalving, HB, BOHB, DEHB
 
 from naslib.search_spaces.core.query_metrics import Metric
-from naslib.search_spaces import NasBench101SearchSpace, NasBench201SearchSpace, \
-DartsSearchSpace, NasBenchNLPSearchSpace, TransBench101SearchSpace, NasBenchASRSearchSpace
+from naslib.search_spaces import NasBench201SearchSpace, NasBenchASRSearchSpace
 from naslib.utils import utils, setup_logger, get_dataset_api
 
-from torch.utils.tensorboard import SummaryWriter
+# from torch.utils.tensorboard import SummaryWriter
 
 config = utils.get_config_from_args(config_type='bbo-bs')
 
@@ -25,37 +24,30 @@
 # writer = SummaryWriter(config.save)
 
 supported_optimizers = {
-    'rs': RandomSearch(config),
-    're': RegularizedEvolution(config),
-    'bananas': Bananas(config),
-    'npenas': Npenas(config),
-    'ls': LocalSearch(config),
-    'sh': SuccessiveHalving(config),
-    'hb': HB(config),
-    'bohb': BOHB(config),
-    'dehb': DEHB(config),
+    'rs': RandomSearch,
+    're': RegularizedEvolution,
+    'bananas': Bananas,
+    'npenas': Npenas,
+    'ls': LocalSearch,
+    'sh': SuccessiveHalving,
+    'hb': HB,
+    'bohb': BOHB,
+    'dehb': DEHB,
 }
 
 supported_search_spaces = {
-    'nasbench101': NasBench101SearchSpace(),
-    'nasbench201': NasBench201SearchSpace(),
-    'darts': DartsSearchSpace(),
-    'nlp': NasBenchNLPSearchSpace(),
-    'transbench101_micro': TransBench101SearchSpace(),
-    'transbench101_macro': TransBench101SearchSpace(),
-    'asr': NasBenchASRSearchSpace(),
+    'nasbench201': NasBench201SearchSpace,    
+    'asr': NasBenchASRSearchSpace,
 }
 
 dataset_api = get_dataset_api(config.search_space, config.dataset)
 utils.set_seed(config.seed)
 
-search_space = supported_search_spaces[config.search_space]
-if config.search_space == 'transbench101_macro':
-    search_space.space = 'macro'
+search_space = supported_search_spaces[config.search_space]()
 
 metric = Metric.VAL_ACCURACY if config.search_space == 'darts' else None
 
-optimizer = supported_optimizers[config.optimizer]
+optimizer = supported_optimizers[config.optimizer](config)
 optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
 
 trainer = Trainer(optimizer, config, lightweight_output=True)
diff --git a/naslib/benchmarks/mf/scheduler_local.sh b/naslib/benchmarks/mf/scheduler_local.sh
deleted file mode 100755
index 42b935ae6..000000000
--- a/naslib/benchmarks/mf/scheduler_local.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-search_space=$1
-
-# dataset_dir="/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/naslib/benchmarks/bbo/configs_cpu/$search_space/*"
-dataset_dir="/Users/lars/Projects/NASLib/naslib/benchmarks/bbo/configs_m1/$search_space/*"
- 
-#for optimizer_dir in $dataset_dir/*
-# do 
-	# echo $config_dir	
-	# for config_dir in $optimizer_dir/*
-	# do
-		# echo starting to run ${config_dir} across 10 seeds ...
-		# sbatch submit_folder.sh $config_dir
-		
-	# done
-# done
-
-# for running default config files separately
-for optimizer_dir in $dataset_dir/*
-do 
-	echo starting to run $optimizer_dir/config_0 across x seeds ...
-	./submit_folder_local.sh $optimizer_dir/config_0 
-done
diff --git a/naslib/benchmarks/mf/submit_array.sbatch b/naslib/benchmarks/mf/submit_array.sbatch
new file mode 100644
index 000000000..874ab6901
--- /dev/null
+++ b/naslib/benchmarks/mf/submit_array.sbatch
@@ -0,0 +1,19 @@
+#!/bin/bash
+#SBATCH -p bosch_cpu-cascadelake #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
+#SBATCH -t 1-00:00           # time (D-HH:MM)
+#SBATCH --output=/path/to/log/%A_%a.%N.out
+#SBATCH --job-name=naslib-exps
+#SBATCH --chdir=/path/to/your/workdir
+#SBATCH --mem=40G
+#SBATCH --array=1-12000
+
+# array size needs to be set to amount of config files
+
+# Print some information about the job to STDOUT
+echo "Workingdir: $PWD";
+echo "Started at $(date)";
+echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
+/path/to/run_config.sh $SLURM_ARRAY_TASK_ID
+ 
+echo "DONE";
+echo "Finished at $(date)";
diff --git a/naslib/benchmarks/mf/submit_configs.sh b/naslib/benchmarks/mf/submit_configs.sh
deleted file mode 100755
index 9693dccb4..000000000
--- a/naslib/benchmarks/mf/submit_configs.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/bin/bash
-# define variable names
-workspace=/work/dlclarge2/gernel-naslib-experiments
-
-
-#SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
-#SBATCH -t 0-1:00           # time (D-HH:MM)
-#SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -J naslib-bbo-rs            # sets the job name. 
-#SBATCH --mem=40G
-#SBATCH -c=8
-#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
-# Print some information about the job to STDOUT
-
-echo "Workingdir: $PWD";
-echo "Started at $(date)";
-echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
-
-config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh
-if [[ $# -eq 0 ]] ; then
-    echo 'some message'
-    exit 0
-fi
-start=$1
-end=$2
-echo "Starting at config $start"
-# python -u runner.py --config-file $1
-i=-1
-for config in $config_dir/*
-    do
-    for config_file_seed in $config/*
-        do
-            ((i=i+1))
-            if [[ "$i" -ge "$start" ]] && [[ "$i" -lt "$end" ]]; then # &&  $i -gt $end ]] ; then
-		echo $i
-            	echo submitted ${config_file_seed}
-		echo "/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed"
-	    fi
-        done
-    done
- 
-# echo $COMMAND;
-# eval $COMMAND;
-
-echo "DONE";
-echo "Finished at $(date)";
diff --git a/naslib/benchmarks/mf/submit_folder_local.sh b/naslib/benchmarks/mf/submit_folder_local.sh
deleted file mode 100755
index 8488829de..000000000
--- a/naslib/benchmarks/mf/submit_folder_local.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-#SBATCH -o slurmlog/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -e slurmlog/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -J bbo-exps              # sets the job name. 
-#SBATCH --mem=7G  
-
-# Print some information about the job to STDOUT
-
-echo "Workingdir: $PWD";
-echo "Started at $(date)";
-
-# python -u runner.py --config-file $1
-
-for config_file_seed in $1/*
-	do
-		echo submitted ${config_file_seed}
-		/Users/lars/Projects/naslib-venv/bin/python runner.py --config-file $config_file_seed
-	done
-
-echo "DONE";
-echo "Finished at $(date)";
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/submit_mf.sh b/naslib/benchmarks/mf/submit_mf.sh
deleted file mode 100755
index d7cb9c9ce..000000000
--- a/naslib/benchmarks/mf/submit_mf.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-num_nodes=21
-num_configs=1000
-#testOne=$(( ($num_configs + ($stepsize - 1)) / $step_size))
-step_size=$(( ($num_configs + ($num_nodes - 1)) / $num_nodes))
-echo Stepsize: $step_size
-for ((i=0;i<=num_configs;i=i+step_size)); do
-    echo ./submit_configs.sh $i $(( $i + $step_size))
-    echo $i
-done
\ No newline at end of file
diff --git a/naslib/benchmarks/mf/submit_test.sh b/naslib/benchmarks/mf/submit_test.sh
deleted file mode 100755
index 0838baf9f..000000000
--- a/naslib/benchmarks/mf/submit_test.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-# define variable names
-workspace=/work/dlclarge2/gernel-naslib-experiments
-
-
-#SBATCH -p mlhiwidlc_gpu-rtx2080 #bosch_gpu-rtx2080    #bosch_cpu-cascadelake  # partition (queue)
-#SBATCH -t 0-15:00           # time (D-HH:MM)
-#SBATCH -o /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.out       # STDOUT  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -e /work/dlclarge2/gernel-naslib-experiments/log/%A.%N.err       # STDERR  (the folder log has to exist) %A will be replaced by the SLURM_ARRAY_JOB_ID value
-#SBATCH -J naslib-bbo-rs            # sets the job name. 
-#SBATCH --mem=7G  
-#SBATCH --chdir=/work/dlclarge2/gernel-naslib-experiments/NASLib
-# Print some information about the job to STDOUT
-
-echo "Workingdir: $PWD";
-echo "Started at $(date)";
-echo "Running job $SLURM_JOB_NAME using $SLURM_JOB_CPUS_PER_NODE cpus per node with given JID $SLURM_JOB_ID on queue $SLURM_JOB_PARTITION";
-
-config_dir=$workspace/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh
-
-config_file_seed=/work/dlclarge2/gernel-naslib-experiments/NASLib/naslib/benchmarks/mf/configs/nasbench201/cifar10/sh/config_0/seed_0.yaml
-/home/gernel/miniconda3/bin/python -u $workspace/NASLib/naslib/benchmarks/mf/runner.py --config-file $config_file_seed
- 
-# echo $COMMAND;
-# eval $COMMAND;
-
-echo "DONE";
-echo "Finished at $(date)";

From 4744dcf8916dac2ce05122b220ce94c64487a948 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 18 Jul 2022 13:03:58 +0200
Subject: [PATCH 129/130] cleanup

---
 examples/mf_demo.py   | 109 ------------------------------------------
 examples/mf_demo.yaml |   3 --
 examples/mf_plot.py   |  58 ----------------------
 3 files changed, 170 deletions(-)
 delete mode 100644 examples/mf_demo.py
 delete mode 100644 examples/mf_demo.yaml
 delete mode 100644 examples/mf_plot.py

diff --git a/examples/mf_demo.py b/examples/mf_demo.py
deleted file mode 100644
index 57e3fc47d..000000000
--- a/examples/mf_demo.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# purpose of this script:
-# debug random search optimizer to better understand function
-
-from naslib import search_spaces
-from naslib.optimizers.discrete.sh import optimizer
-from naslib.search_spaces import NasBench201SearchSpace as NB201
-from naslib.search_spaces import NasBenchASRSearchSpace as NBASR
-import logging
-#from naslib.utils import utils, setup_logger, get_dataset_api
-from naslib.utils import utils, utils_asr ,setup_logger, get_dataset_api
-from naslib.optimizers import RandomSearch as RS
-from naslib.optimizers import Npenas as npenas
-from naslib.optimizers import RegularizedEvolution as RE
-from naslib.optimizers import SuccessiveHalving as SH
-from naslib.optimizers import HB
-from naslib.optimizers import BOHB 
-from naslib.optimizers import DEHB
-#from naslib.search_spaces.nasbenchasr.conversions as h
-#from naslib.defaults.trainer_multifidelity import Trainer
-from naslib.defaults.trainer import Trainer
-# TODO: rethinking logging is rigth now, completly ingnored 
-import yaml
-from pathlib import Path
-import os
-
-import mf_plot
-
-from shutil import copyfile
-
-demo_config = None
-with open(os.path.join(str(Path(__file__).parent), 'mf_demo.yaml'), "r") as stream:
-    try:
-        demo_config = yaml.safe_load(stream)
-    except yaml.YAMLError as exc:
-        print(exc)
-
-config_optimizer = demo_config['optimizer']
-config_path_optimizer = os.path.join(
-    utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config"
-) + "_" + "SH" + ".yaml"
-default_config_path = os.path.join(
-    utils.get_project_root(), "benchmarks", "nas_predictors", "discrete_config.yaml"
-) 
-
-copyfile(config_path_optimizer, default_config_path)
-
-# init search space
-#search_space = NB201()
-#TODO: fix overflow error 
-search_space = NBASR()
-
-# read config
-config = utils.get_config_from_args(config_type="nas_predictor")
-utils.set_seed(config.seed)
-utils.log_args(config)
-
-logger = setup_logger(config.save + "/log.log")
-logger.setLevel(logging.INFO)
-
-# define optimizer
-
-if config_optimizer == 'SH':
-    predictor = "var_sparse_gp"
-    optimizer = SH(config)
-    plot_func = mf_plot.plot_sh
-elif config_optimizer == 'HB':
-    predictor = "var_sparse_gp"
-    optimizer = HB(config)
-    plot_func = mf_plot.plot_hb
-elif config_optimizer == 'RS':
-    optimizer = RS(config)
-elif config_optimizer == 'BOHB':
-    optimizer = BOHB(config)
-    plot_func = mf_plot.plot_hb
-    predictor = "tpe"
-elif config_optimizer == "NPENAS":
-    #i thin kby save dir is soometihng strange
-    
-    predictor = "var_sparse_gp"
-    optimizer = npenas(config)
-    plot_func = mf_plot.plot_hb
-elif config_optimizer == "DEHB":
-    #i thin kby save dir is soometihng strange
-    
-    predictor = "var_sparse_gp"
-    optimizer = DEHB(config)
-    plot_func = mf_plot.plot_hb
-else:
-    raise Exception('invalid config')
-
-# load nasbench data, there data seems to be generalised
-dataset_api = get_dataset_api(config.search_space, config.dataset)
-
-# adapt search space
-optimizer.adapt_search_space(search_space, dataset_api=dataset_api)
-
-## Running search with Trainer
-trainer = Trainer(optimizer, config, lightweight_output=True)
-
-# run search for number of iterations specified
-trainer.search()
-
-trainer.evaluate(dataset_api=dataset_api)
-
-if demo_config['plot'] == False:
-    exit()
-
-# TODO: Make this dependent on optimizer type, currently statistics are just for SH available
-#plot_func(predictor)
diff --git a/examples/mf_demo.yaml b/examples/mf_demo.yaml
deleted file mode 100644
index ec48d5c3b..000000000
--- a/examples/mf_demo.yaml
+++ /dev/null
@@ -1,3 +0,0 @@
-optimizer: DEHB
-# Activates/deactivates plotting
-plot: FAlSE
\ No newline at end of file
diff --git a/examples/mf_plot.py b/examples/mf_plot.py
deleted file mode 100644
index 277d17f0c..000000000
--- a/examples/mf_plot.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import json
-# from matplotlib.lines import _LineStyle
-import matplotlib.pyplot as plt
-
-from collections import defaultdict
-
-def get_results(predictor, path, filename, metric='valid_acc'):
-    """
-    Get statistics for successive halving
-    TODO: make metric selectable, currently 'val_acc' is fixed 
-    """
-    algo_path = os.path.join(path, predictor)
-    for seed_dir in os.listdir(algo_path):
-        if not os.path.isdir(os.path.join(algo_path, seed_dir)):
-            continue
-        result_file = os.path.join(algo_path, seed_dir, filename)
-        result = json.load(open(result_file))
-        return result
-
-def plot_sh(predictor):
-    """
-    Plots learning curves for successive halving
-    """
-    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
-    predictor = predictor
-    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
-    for arch, stats in results.items():
-        x = stats['fidelity']
-        values = stats['val_acc']
-        plt.plot(x, values, linestyle='-', label=arch)
-    plt.xscale('log', base=2)
-    plt.rcParams['grid.linestyle'] = 'dotted'
-    plt.show()
-    plt.savefig('plot_sh_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
-
-def plot_hb(predictor): #or an extra method
-    """
-    Plots learning curves for hyperband
-    """
-    folder = os.path.expanduser('./run/cifar10/nas_predictors/nasbench201')
-    predictor = predictor
-    results = get_results(predictor, folder, 'sh_stats.json', metric='test_acc')
-    s = len(results)
-    
-    figure, axis = plt.subplots(s, 1)
-    for sh, sh_stats in results.items():
-        for arch, stats in sh_stats.items():
-            x = stats['fidelity']
-            values = stats['val_acc']
-            axis[int(sh)].plot(x, values, linestyle='-', label=arch, marker='x')
-            axis[int(sh)].set_xscale('log', base=3)
-    plt.show()
-    plt.savefig('plot_hb_nb201.pdf', bbox_inches = 'tight', pad_inches = 0.1)
-
-
-if __name__ == '__main__':
-    plot_hb("tpe")
\ No newline at end of file

From 1cdd513f39176783214e77672ff471c84e8b4376 Mon Sep 17 00:00:00 2001
From: Lars Gerne <lage2104@gmail.com>
Date: Mon, 18 Jul 2022 13:32:26 +0200
Subject: [PATCH 130/130] preparing pull request

---
 examples/optimizers-plotly.ipynb             | 9534 ------------------
 examples/optimizers.ipynb                    |    7 +-
 examples/optimizers_HPO.ipynb                |    7 +-
 naslib/analysis/analyze_results.py           |   95 -
 naslib/optimizers/discrete/bohb/optimizer.py |    6 +
 naslib/optimizers/discrete/dehb/optimizer.py |    7 +
 naslib/optimizers/discrete/hb/optimizer.py   |    8 +
 7 files changed, 27 insertions(+), 9637 deletions(-)
 delete mode 100644 examples/optimizers-plotly.ipynb
 delete mode 100644 naslib/analysis/analyze_results.py

diff --git a/examples/optimizers-plotly.ipynb b/examples/optimizers-plotly.ipynb
deleted file mode 100644
index d7a61df7c..000000000
--- a/examples/optimizers-plotly.ipynb
+++ /dev/null
@@ -1,9534 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import json\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
-    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
-    "import pickle\n",
-    "import pandas as pd\n",
-    "\n",
-    "from collections import defaultdict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this defines all of the default colors and markers for the plots\n",
-    "# keys = ['bananas/bananas', 'hb', 'ls', 'rs', 're', 'ls_svr', 'rea_svr', 'bananas_svr/bananas']\n",
-    "# predictors = ['hb', 'bohb', 'ls', 'bananas', 're', 'npenas', 'dehb', 'rs', 'sh', ]\n",
-    "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb', 'hb', 'sh', 'npenas']\n",
-    "predictors = ['ls', 'bananas', 're', 'rs', 'bohb', 'dehb']\n",
-    "\n",
-    "# predictors = ['bohb', 'hb']\n",
-    "benchmark_datasets = ['cifar10', ]\n",
-    "# benchmark_datasets = ['ImageNet16-120', ]\n",
-    "# TODO: What of this am I really needing?\n",
-    "defaults = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
-    "            (1.0, 0.4980392156862745, 0.054901960784313725),\n",
-    "            (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
-    "            (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
-    "            (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
-    "            (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
-    "            (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
-    "            (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
-    "            (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
-    "            (0.09019607843137255, 0.7450980392156863, 0.8117647058823529),\n",
-    "            (0.0, 0.0, 0.0),\n",
-    "            (0.7058823529411765, 0.12156862745098039, 0.4666666666666667),\n",
-    "            (0.4666666666666667, 0.8901960784313725, 0.7607843137254902,),\n",
-    "           ]\n",
-    "\n",
-    "fmts = ['-', '--', ':']\n",
-    "markers = ['^', 'v', 'o']\n",
-    "defaults = [np.array(d) for d in defaults]\n",
-    "\n",
-    "# \n",
-    "pred_plot_dict={\n",
-    "    'ls':{'color':defaults[0], 'fmt':fmts[0]},\n",
-    "    # 'ls_lce':{'color':defaults[0], 'fmt':fmts[1]},\n",
-    "    # 'ls_svr':{'color':defaults[0], 'fmt':fmts[2]},\n",
-    "    're':{'color':defaults[1], 'fmt':fmts[2]},\n",
-    "    # 'rea_lce':{'color':defaults[1], 'fmt':fmts[1]},\n",
-    "    # 'rea_svr':{'color':defaults[1], 'fmt':fmts[2]},\n",
-    "    'bananas/bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
-    "    'bananas':{'color':defaults[2], 'fmt':fmts[0]},\n",
-    "    # 'bananas_lce/bananas':{'color':defaults[2], 'fmt':fmts[1]},\n",
-    "    # 'bananas_svr/bananas':{'color':defaults[2], 'fmt':fmts[2]},    \n",
-    "    'rs':{'color':defaults[3], 'fmt':fmts[0]},\n",
-    "    'sh': {'color': defaults[1], 'fmt':fmts[0]},\n",
-    "    'hb':{'color':defaults[4], 'fmt':fmts[0]},\n",
-    "    'bohb':{'color':defaults[5], 'fmt':fmts[0]},\n",
-    "    'dehb':{'color':defaults[6], 'fmt':fmts[2]},\n",
-    "    'npenas':{'color':defaults[0], 'fmt':fmts[1]},\n",
-    "}\n",
-    "\n",
-    "# define colors, formats, and markers\n",
-    "c_max = 10\n",
-    "colors = [*defaults[:c_max], *defaults[:c_max], *defaults[:c_max]]\n",
-    "fmts = [*['-']*c_max, *['--']*c_max, *[':']*c_max]\n",
-    "markers = [*['^']*c_max, *['v']*c_max, *['o']*c_max]\n",
-    "\n",
-    "# https://matplotlib.org/2.1.2/api/_as_gen/matplotlib.pyplot.plot.html\n",
-    "\n",
-    "# some of them are not used\n",
-    "pred_label_dict={\n",
-    "    'bananas/gcn':'BANANAS-GCN', 'bananas':'BANANAS', 'bananas/xgb':'BANANAS-XGB', \n",
-    "    'npenas/gcn':'NPENAS-GCN', 'npenas/bananas':'NPENAS-BANANAS', 'npenas/xgb':'NPENAS-XGB', \n",
-    "    'rs':'RS',\n",
-    "    're': 'RE',\n",
-    "    'ls': 'LS',\n",
-    "    'hb': 'HB',\n",
-    "    'sh': 'SH',\n",
-    "    'bohb': 'BOHB',\n",
-    "    'dehb': 'DEHB',\n",
-    "    'rea_svr': 'REA-SVR',\n",
-    "    'bananas_svr/bananas':'BANANAS-SVR',\n",
-    "    'rea_lce': 'REA-WPM',\n",
-    "    'bananas_lce/bananas':'BANANAS-WPM',\n",
-    "    'ls_svr': 'LS-SVR',\n",
-    "    'ls_lce': 'LS-WPM',\n",
-    "    'ls_svr_hp': 'LS-SVR-HP',\n",
-    "    'rea_svr_hp': 'REA-SVR-HP',\n",
-    "    'bananas_svr_hp/bananas': 'BANANAS-SVR-HP',\n",
-    "    'npenas': 'NPENAS',\n",
-    "}\n",
-    "\n",
-    "val_gts = {'cifar10': 97.61, \n",
-    "           'cifar100': 73.49, \n",
-    "           'ImageNet16-120': 50.0, \n",
-    "           'nasbench311': 95.8, \n",
-    "           'nasbench111':95.6, \n",
-    "           'nasbenchnlp':100.7,\n",
-    "           'asr': 1.00,\n",
-    "           }\n",
-    "\n",
-    "datasets = {'cifar10': 'cifar10',\n",
-    "           'cifar100': 'cifar100', \n",
-    "           'ImageNet16-120': 'ImageNet16-120', \n",
-    "           'nasbench311': 'cifar10', \n",
-    "           'nasbench111':'cifar10', \n",
-    "           'nasbenchnlp':'ptb',\n",
-    "           'asr':'asr'\n",
-    "           }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_and_fill_trajectories(pandas_data_frames, default_value=None):\n",
-    "    # merge all trajectories keeping all time steps\n",
-    "    df = pd.DataFrame().join(pandas_data_frames, how='outer')\n",
-    "\n",
-    "    # forward fill to make it a propper step function\n",
-    "    df = df.fillna(method='ffill')\n",
-    "    if default_value is None:\n",
-    "        # backward fill to replace the NaNs for the early times by the\n",
-    "        # performance of a random configuration\n",
-    "        df = df.fillna(method='bfill')\n",
-    "    else:\n",
-    "        df = df.fillna(default_value)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_trajectories(losses, iterations):\n",
-    "    dfs = []\n",
-    "    for i in range(len(losses)):\n",
-    "        loss = losses[i]\n",
-    "        iteration = iterations[i]\n",
-    "        # print('Run %d, Min: %f'%(i, loss))\n",
-    "        df = pd.DataFrame({str(i): loss}, index=iteration)\n",
-    "        dfs.append(df)\n",
-    "\n",
-    "    df = merge_and_fill_trajectories(dfs, default_value=None)\n",
-    "\n",
-    "    return np.array(df.T), np.array(df.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_results(predictor, path, metric='valid_acc', key_choice=None):\n",
-    "    output = defaultdict(list)\n",
-    "    time = defaultdict(list)\n",
-    "    nan_count = 0\n",
-    "    missing_results_count = 0\n",
-    "    file_count = 0\n",
-    "    dataset_paths = []\n",
-    "    for dataset_folder in os.listdir(path):\n",
-    "        dataset_path = os.path.join(path, dataset_folder)\n",
-    "        if not(dataset_folder == datasets[key_choice] and os.path.isdir(dataset_path)):\n",
-    "            continue\n",
-    "        dataset_paths.append(dataset_path)\n",
-    "    \n",
-    "    optimizer_paths = []\n",
-    "    for dataset_path in dataset_paths:\n",
-    "        optimizer_path = os.path.join(dataset_path, predictor)\n",
-    "        if not os.path.isdir(optimizer_path):\n",
-    "            continue\n",
-    "        optimizer_paths.append(optimizer_path)\n",
-    "        # for optimizer_folder in os.listdir(dataset_path):\n",
-    "        #     optimizer_path = os.path.join(dataset_path, optimizer_folder)\n",
-    "        #     if not os.path.isdir(optimizer_path):\n",
-    "        #         continue\n",
-    "        #     optimizer_paths.append(optimizer_path)\n",
-    "\n",
-    "    config_paths = []\n",
-    "    for optimizer_path in optimizer_paths:\n",
-    "        for config_folder in os.listdir(optimizer_path):\n",
-    "            if config_folder[:7] != \"config_\":\n",
-    "                continue\n",
-    "            config_path = os.path.join(optimizer_path, config_folder)\n",
-    "            if not os.path.isdir(config_path):\n",
-    "                continue\n",
-    "            config_paths.append(config_path)\n",
-    "    \n",
-    "    result_paths = []\n",
-    "    for config_path in config_paths:\n",
-    "        for seed_folder in os.listdir(config_path):\n",
-    "            seed_dir = os.path.join(config_path, seed_folder)\n",
-    "            if not os.path.isdir(seed_dir):\n",
-    "                continue\n",
-    "            result_file = os.path.join(config_path, seed_folder, 'errors.json')\n",
-    "            if not os.path.isfile(result_file):\n",
-    "                continue\n",
-    "            result_paths.append(result_file)\n",
-    "    \n",
-    "    for result_file in result_paths:\n",
-    "        try:\n",
-    "            result = json.load(open(result_file))\n",
-    "        except:\n",
-    "            print(\"An exception occurred\")\n",
-    "            continue\n",
-    "        # config = result[0]\n",
-    "        epochs = len(result[1]['train_time'])\n",
-    "\n",
-    "        val_acc = result[1]['valid_acc'][:epochs]\n",
-    "\n",
-    "        if key_choice == 'nasbench111':\n",
-    "            val_incumbent = [(val_gts[key_choice] - val_acc[0] *  100.) / 100.]\n",
-    "            for  ind in range(1, len(val_acc)):\n",
-    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind]) *100.)  / 100)\n",
-    "        elif key_choice == 'nasbenchnlp':\n",
-    "            val_incumbent = [np.exp(100 - val) - val_gts[key_choice] for val in val_acc]\n",
-    "        elif key_choice == 'asr':\n",
-    "            val_incumbent = [(val_gts[key_choice] - val_acc[0])]\n",
-    "            for ind in range(1, len(val_acc)):\n",
-    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])))\n",
-    "        else:\n",
-    "            # TODO: is this scaling really necessary?\n",
-    "            val_incumbent = [(val_gts[key_choice] - val_acc[0]) / 100]\n",
-    "            for ind in range(1, len(val_acc)):\n",
-    "                val_incumbent.append((val_gts[key_choice] - max(val_acc[:ind])) / 100)\n",
-    "\n",
-    "\n",
-    "        runtime = result[1]['runtime']\n",
-    "        train_time = result[1]['train_time']\n",
-    "        runtime = [sum(runtime[:epoch]) for epoch in range(1, len(runtime)+1)]\n",
-    "        train_time = [sum(train_time[:epoch]) for epoch in range(1, len(train_time)+1)]\n",
-    "        total_time = [i+j for i,j in zip(runtime, train_time)]\n",
-    "        if predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and not key_choice == 'asr':\n",
-    "            # continue\n",
-    "            total_time[:] = [x * 200.0 for x in total_time]\n",
-    "        elif predictor in {'rs', 'ls', 're', 'bananas', 'npenas'} and key_choice == 'asr':\n",
-    "            total_time = train_time\n",
-    "            total_time[:] = [x * -40 for x in total_time]\n",
-    "        # I think we should just plot validation accs, since that's what the nas algorithm uses\n",
-    "        if metric == 'valid_acc':\n",
-    "            incumbent = val_incumbent\n",
-    "            #incumbent = val_acc\n",
-    "        elif metric == 'test_acc':\n",
-    "            test_err = [100 - x for x in result[1]['test_acc']]\n",
-    "            inc_idx, best, best_idx = [], np.inf, 0\n",
-    "            for i, err in enumerate(val_err):\n",
-    "                if err < best:\n",
-    "                    best, best_idx = err, i\n",
-    "                inc_idx.append(best_idx)\n",
-    "            incumbent = [test_err[idx] for idx in inc_idx]\n",
-    "\n",
-    "        if len(incumbent) == epochs:\n",
-    "            output[result[0]['config_id']].append(incumbent)\n",
-    "            time[result[0]['config_id']].append(total_time)\n",
-    "\n",
-    "            # output.append(incumbent)\n",
-    "            # time.append(total_time)\n",
-    "        else:\n",
-    "            nan_count += 1\n",
-    "\n",
-    "    output = {key: np.array(item) for key,item in output.items()}\n",
-    "    time = {key: np.array(item) for key,item in time.items()}\n",
-    "    # output = np.array(output)\n",
-    "    # time = np.array(time)\n",
-    "    \n",
-    "    # output, time = get_trajectories(output, time)\n",
-    "\n",
-    "    for config_id in output.keys():\n",
-    "        output[config_id], time[config_id] = get_trajectories(output[config_id], time[config_id])\n",
-    "    # output, time = get_trajectories(output, time)\n",
-    "    print(f\"predictor: {predictor}\")\n",
-    "    means = defaultdict(list)\n",
-    "    means = {key: np.mean(item, axis=0) for key,item in output.items()}\n",
-    "    incumbent_settings = min(means.items(), key=lambda item: item[1][-1])\n",
-    "    # incumbent_settings = [0, means[0]]\n",
-    "    print(f\"Best config: {incumbent_settings[0]}\")\n",
-    "    mean = incumbent_settings[1]\n",
-    "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
-    "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
-    "    std_error_2 = np.std(output[incumbent_settings[0]], axis=0, ddof=1) / np.sqrt(np.size(output[incumbent_settings[0]]))\n",
-    "    time = time[incumbent_settings[0]]\n",
-    "    # print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
-    "    # print(f\"predictor: {predictor}, best config: {incumbent_settings[0]}\")\n",
-    "    print('first mean', mean[0], 'last mean', mean[-1])\n",
-    "    print('first std', std[0], 'last std', std[-1])\n",
-    "    print('time ', time[-1])\n",
-    "    return mean, std, std_error_2, time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def save_results_to_dict(folder, predictors, key_choice):\n",
-    "    results_dict = {}\n",
-    "    print('\\n saving', key_choice)\n",
-    "    for _, predictor in enumerate(predictors):\n",
-    "        mean, std, std_error, runtime = get_results(predictor, folder, metric='valid_acc', key_choice=key_choice)\n",
-    "        key = predictor\n",
-    "        results_dict[key] = {'label':pred_label_dict[predictor], \n",
-    "                             'key':key, 'mean':mean, 'std':std, \n",
-    "                             'std_error': std_error, 'runtime': runtime}\n",
-    "    return results_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def save_all_results():     \n",
-    "    # that was/is very very shity code!\n",
-    "    # key_choice = 'nasbench311'\n",
-    "    folder = os.path.expanduser(os.path.join(base_path, 'nasbench201'))\n",
-    "    all_results = {dataset:save_results_to_dict(folder, predictors, dataset) for dataset in benchmark_datasets}\n",
-    "    return all_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load results into dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " saving cifar10\n",
-      "predictor: ls\n",
-      "Best config: 22\n",
-      "first mean 0.10324999999999998 last mean 0.06087000000000005\n",
-      "first std 0.021849313490359377 last std 0.0009466255859630773\n",
-      "time  891204.295082887\n",
-      "predictor: bananas\n",
-      "Best config: 87\n",
-      "first mean 0.10324999999999998 last mean 0.06305000000000001\n",
-      "first std 0.021849313490359377 last std 0.0017024981644630077\n",
-      "time  876740.4151002569\n",
-      "predictor: re\n",
-      "Best config: 0\n",
-      "first mean 0.10324999999999998 last mean 0.06040000000000005\n",
-      "first std 0.021849313490359377 last std 1.3877787807814457e-17\n",
-      "time  968633.8430881497\n",
-      "predictor: rs\n",
-      "Best config: 36\n",
-      "first mean 0.10324999999999998 last mean 0.06581999999999996\n",
-      "first std 0.021849313490359377 last std 0.002371413080844401\n",
-      "time  761624.9727487558\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_23010/3844687332.py:108: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  output = {key: np.array(item) for key,item in output.items()}\n",
-      "/var/folders/z5/hfz39qr12191g12d3m66j4d80000gn/T/ipykernel_23010/3844687332.py:109: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  time = {key: np.array(item) for key,item in time.items()}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "predictor: bohb\n",
-      "Best config: 82\n",
-      "first mean 0.4176000000567627 last mean 0.06107600010742189\n",
-      "first std 0.14031822252285084 last std 0.0013472579703791069\n",
-      "time  1101040.2693293483\n",
-      "predictor: dehb\n",
-      "Best config: 58\n",
-      "first mean 0.4176000000567627 last mean 0.06031600016113279\n",
-      "first std 0.14031822252285084 last std 0.0007581451321007666\n",
-      "time  1102614.4726861517\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function print>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_path = '/Users/lars/Projects/results/all_experiments/'\n",
-    "\n",
-    "results = save_all_results()\n",
-    "\n",
-    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'wb')\n",
-    "# pickle.dump(results, result_file)\n",
-    "# result_file.close()\n",
-    "\n",
-    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
-    "# results = pickle.load(result_file)\n",
-    "print"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%script false --no-raise-error\n",
-    "filepath='/Users/lars/Projects/results/plot_files/nb201.pickle'\n",
-    "result_file = open(filepath, 'wb')\n",
-    "pickle.dump(results, result_file)\n",
-    "result_file.close()\n",
-    "\n",
-    "# result_file = open('/Users/lars/Downloads/results_1/results_ob', 'rb')\n",
-    "# results = pickle.load(result_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plotting results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result_folder = 'plots_naslib/'\n",
-    "if not os.path.exists(result_folder):\n",
-    "    os.makedirs(result_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%script false --no-raise-error\n",
-    "# plot performance vs runtime\n",
-    "\n",
-    "for key_choice in results.keys():\n",
-    "    results_dict = results[key_choice]\n",
-    "\n",
-    "    keys = predictors\n",
-    "\n",
-    "\n",
-    "    keys = sorted(keys)\n",
-    "\n",
-    "    plt.rcParams['axes.grid'] = True\n",
-    "    plt.rcParams['grid.linestyle'] = 'dotted'\n",
-    "    add_sub_axes = False\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=[4.5, 3.5])\n",
-    "    if add_sub_axes:\n",
-    "        sub_axes = plt.axes([.6, .5, .3, .38]) \n",
-    "\n",
-    "\n",
-    "    for i, key in enumerate(keys):\n",
-    "        mean = results_dict[key]['mean']\n",
-    "        sem = results_dict[key]['std_error']\n",
-    "        label = results_dict[key]['label']\n",
-    "        x = results_dict[key]['runtime']\n",
-    "        #tag = '_'.join(key.split('_')[:-1])\n",
-    "        tag = key\n",
-    "        color = pred_plot_dict[tag]['color']\n",
-    "        fmt = pred_plot_dict[tag]['fmt']\n",
-    "        ax.plot(x, mean, label=label, color=color, linestyle=fmt, linewidth=2.5)\n",
-    "        ax.fill_between(x, mean-50*sem, mean+50*sem,\n",
-    "                        color=color, alpha=0.2)\n",
-    "\n",
-    "        if add_sub_axes:\n",
-    "            n = 450\n",
-    "            X_detail = x[n:]\n",
-    "            Y_detail = mean[n:]\n",
-    "            sem_detail = sem[n:]\n",
-    "            sub_axes.plot(X_detail, Y_detail, color=color, linestyle=fmt)\n",
-    "            sub_axes.tick_params(labelbottom=False) \n",
-    "\n",
-    "    ax.set_xlabel('Runtime (seconds)', fontsize=14)\n",
-    "\n",
-    "    if key_choice == 'nasbenchnlp':\n",
-    "        ax.set_ylabel('Perplexity regret', fontsize=14) #nas201\n",
-    "    else:\n",
-    "        ax.set_ylabel('Valid. regret', fontsize=14) #nas201\n",
-    "\n",
-    "    # ax.grid(True, which=\"both\",ls=\"-\", alpha=.5)\n",
-    "\n",
-    "    if key_choice == 'cifar10':    \n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        # for nasbench201\n",
-    "        # ax.set_ylim([2e-4, 1e-2])\n",
-    "        # ax.set_xlim([2.0e5, 7.6*1e5])\n",
-    "\n",
-    "        # for nasbench311\n",
-    "        ax.set_ylim([2.5e-2, 4.2e-2])\n",
-    "        ax.set_xlim([1.9e4 *7 / 7, 1.8e6])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
-    "        # ax.set_title('NAS-Bench-201 CIFAR10', fontsize=14)\n",
-    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nas201_c10.pdf')\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "    elif key_choice == 'cifar100':    \n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([0.002, 0.03])\n",
-    "        ax.set_xlim([1e5, 1e6])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
-    "        ax.set_title('NAS-Bench-201 CIFAR100', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nas201_c100.pdf')\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "    elif key_choice == 'ImageNet16-120':    \n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        # ax.set_ylim([0.0025, 0.3])\n",
-    "        ax.set_ylim([2.7e-2, 6.8e-2])\n",
-    "        ax.set_xlim([1.5e5, 3.2e6])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
-    "        ax.set_title('NAS-Bench-201 ImageNet16-120', fontsize=14, loc='right')\n",
-    "        save_path = os.path.join(result_folder, 'nas201_im.pdf')\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "    elif key_choice == 'nasbench311':    \n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        # ax.set_ylim([.035, .066])\n",
-    "        # ax.set_xlim([400, 2100])\n",
-    "        # ax.set_ylim([2e-3, .028])\n",
-    "        # ax.set_xlim([5e4, 4*10e5])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
-    "        ax.set_title('NAS-Bench-311 CIFAR10', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nas311_c10.pdf')\n",
-    "        plt.show()\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "\n",
-    "    elif key_choice == 'nasbench111':\n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([4e-4, 8e-2])\n",
-    "        ax.set_xlim([2e4, 5.8*10e4])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
-    "        ax.set_title('NAS-Bench-111 CIFAR10', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nas111_c10.pdf')\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "\n",
-    "    elif key_choice == 'nasbenchnlp':\n",
-    "        ax.set_xscale('log')\n",
-    "        ax.set_yscale('log')\n",
-    "        ax.set_ylim([.5, 4.2e1])\n",
-    "        ax.set_xlim([5e4, 1.1*10e6])\n",
-    "        ax.legend(ncol=2, loc=3, fontsize=8)\n",
-    "        ax.set_title('NAS-Bench-NLP11 PTB', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nasn11_ptb.pdf')\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n",
-    "    \n",
-    "    elif key_choice == 'asr':\n",
-    "        # ax.set_xscale('log')\n",
-    "        # ax.set_yscale('log')\n",
-    "        ax.set_ylim([0.026, 0.04])\n",
-    "        ax.set_xlim([700, 16000])\n",
-    "        ax.legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\", borderaxespad=0)\n",
-    "        ax.set_title('NAS-Bench-ASR', fontsize=14)\n",
-    "        save_path = os.path.join(result_folder, 'nbasr.pdf')\n",
-    "        ax.set_xlabel('Epochs', fontsize=14)\n",
-    "        plt.savefig(os.path.expanduser(save_path), bbox_inches = 'tight', pad_inches = 0.1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ploting results using plotly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.graph_objects as go"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
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-            "type": "mesh3d"
-           }
-          ],
-          "parcoords": [
-           {
-            "line": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "parcoords"
-           }
-          ],
-          "pie": [
-           {
-            "automargin": true,
-            "type": "pie"
-           }
-          ],
-          "scatter": [
-           {
-            "fillpattern": {
-             "fillmode": "overlay",
-             "size": 10,
-             "solidity": 0.2
-            },
-            "type": "scatter"
-           }
-          ],
-          "scatter3d": [
-           {
-            "line": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatter3d"
-           }
-          ],
-          "scattercarpet": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattercarpet"
-           }
-          ],
-          "scattergeo": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattergeo"
-           }
-          ],
-          "scattergl": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattergl"
-           }
-          ],
-          "scattermapbox": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scattermapbox"
-           }
-          ],
-          "scatterpolar": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterpolar"
-           }
-          ],
-          "scatterpolargl": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterpolargl"
-           }
-          ],
-          "scatterternary": [
-           {
-            "marker": {
-             "colorbar": {
-              "outlinewidth": 0,
-              "ticks": ""
-             }
-            },
-            "type": "scatterternary"
-           }
-          ],
-          "surface": [
-           {
-            "colorbar": {
-             "outlinewidth": 0,
-             "ticks": ""
-            },
-            "colorscale": [
-             [
-              0,
-              "#0d0887"
-             ],
-             [
-              0.1111111111111111,
-              "#46039f"
-             ],
-             [
-              0.2222222222222222,
-              "#7201a8"
-             ],
-             [
-              0.3333333333333333,
-              "#9c179e"
-             ],
-             [
-              0.4444444444444444,
-              "#bd3786"
-             ],
-             [
-              0.5555555555555556,
-              "#d8576b"
-             ],
-             [
-              0.6666666666666666,
-              "#ed7953"
-             ],
-             [
-              0.7777777777777778,
-              "#fb9f3a"
-             ],
-             [
-              0.8888888888888888,
-              "#fdca26"
-             ],
-             [
-              1,
-              "#f0f921"
-             ]
-            ],
-            "type": "surface"
-           }
-          ],
-          "table": [
-           {
-            "cells": {
-             "fill": {
-              "color": "#EBF0F8"
-             },
-             "line": {
-              "color": "white"
-             }
-            },
-            "header": {
-             "fill": {
-              "color": "#C8D4E3"
-             },
-             "line": {
-              "color": "white"
-             }
-            },
-            "type": "table"
-           }
-          ]
-         },
-         "layout": {
-          "annotationdefaults": {
-           "arrowcolor": "#2a3f5f",
-           "arrowhead": 0,
-           "arrowwidth": 1
-          },
-          "autotypenumbers": "strict",
-          "coloraxis": {
-           "colorbar": {
-            "outlinewidth": 0,
-            "ticks": ""
-           }
-          },
-          "colorscale": {
-           "diverging": [
-            [
-             0,
-             "#8e0152"
-            ],
-            [
-             0.1,
-             "#c51b7d"
-            ],
-            [
-             0.2,
-             "#de77ae"
-            ],
-            [
-             0.3,
-             "#f1b6da"
-            ],
-            [
-             0.4,
-             "#fde0ef"
-            ],
-            [
-             0.5,
-             "#f7f7f7"
-            ],
-            [
-             0.6,
-             "#e6f5d0"
-            ],
-            [
-             0.7,
-             "#b8e186"
-            ],
-            [
-             0.8,
-             "#7fbc41"
-            ],
-            [
-             0.9,
-             "#4d9221"
-            ],
-            [
-             1,
-             "#276419"
-            ]
-           ],
-           "sequential": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ],
-           "sequentialminus": [
-            [
-             0,
-             "#0d0887"
-            ],
-            [
-             0.1111111111111111,
-             "#46039f"
-            ],
-            [
-             0.2222222222222222,
-             "#7201a8"
-            ],
-            [
-             0.3333333333333333,
-             "#9c179e"
-            ],
-            [
-             0.4444444444444444,
-             "#bd3786"
-            ],
-            [
-             0.5555555555555556,
-             "#d8576b"
-            ],
-            [
-             0.6666666666666666,
-             "#ed7953"
-            ],
-            [
-             0.7777777777777778,
-             "#fb9f3a"
-            ],
-            [
-             0.8888888888888888,
-             "#fdca26"
-            ],
-            [
-             1,
-             "#f0f921"
-            ]
-           ]
-          },
-          "colorway": [
-           "#636efa",
-           "#EF553B",
-           "#00cc96",
-           "#ab63fa",
-           "#FFA15A",
-           "#19d3f3",
-           "#FF6692",
-           "#B6E880",
-           "#FF97FF",
-           "#FECB52"
-          ],
-          "font": {
-           "color": "#2a3f5f"
-          },
-          "geo": {
-           "bgcolor": "white",
-           "lakecolor": "white",
-           "landcolor": "#E5ECF6",
-           "showlakes": true,
-           "showland": true,
-           "subunitcolor": "white"
-          },
-          "hoverlabel": {
-           "align": "left"
-          },
-          "hovermode": "closest",
-          "mapbox": {
-           "style": "light"
-          },
-          "paper_bgcolor": "white",
-          "plot_bgcolor": "#E5ECF6",
-          "polar": {
-           "angularaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "radialaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "scene": {
-           "xaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "yaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           },
-           "zaxis": {
-            "backgroundcolor": "#E5ECF6",
-            "gridcolor": "white",
-            "gridwidth": 2,
-            "linecolor": "white",
-            "showbackground": true,
-            "ticks": "",
-            "zerolinecolor": "white"
-           }
-          },
-          "shapedefaults": {
-           "line": {
-            "color": "#2a3f5f"
-           }
-          },
-          "ternary": {
-           "aaxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        },
-        "xaxis": {
-         "type": "log"
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# trying to plot \n",
-    "mean = results['cifar10']['rs']['mean']\n",
-    "sem = results['cifar10']['rs']['std_error']\n",
-    "label = results['cifar10']['rs']['label']\n",
-    "runtime = results['cifar10']['rs']['runtime']\n",
-    "fig = go.Figure([\n",
-    "    go.Scatter(\n",
-    "        x=runtime, \n",
-    "        y=mean, \n",
-    "        name=label)\n",
-    "])\n",
-    "fig.update_xaxes(type=\"log\")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "e28e65abac2798f30acc960ffff31a2f506562b074c32d55b97f302318a49627"
-  },
-  "kernelspec": {
-   "display_name": "jupyter-venv",
-   "language": "python",
-   "name": "jupyter-venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.9"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/optimizers.ipynb b/examples/optimizers.ipynb
index 4b00e9fa9..c166de60e 100644
--- a/examples/optimizers.ipynb
+++ b/examples/optimizers.ipynb
@@ -304,15 +304,14 @@
     "    print(f\"Best config: {incumbent_settings[0]}\")\n",
     "    mean = incumbent_settings[1]\n",
     "    std = np.std(output[incumbent_settings[0]], axis=0)\n",
-    "    std_error = np.sqrt(np.var(output[incumbent_settings[0]], axis=0, ddof=1)/np.asarray(output[incumbent_settings[0]]).shape[0])\n",
-    "    std_error_2 = np.std(output[incumbent_settings[0]], axis=0, ddof=1) / np.sqrt(np.size(output[incumbent_settings[0]]))\n",
+    "    std_error = np.std(output[incumbent_settings[0]], axis=0, ddof=1) / np.sqrt(np.size(output[incumbent_settings[0]]))\n",
     "    time = time[incumbent_settings[0]]\n",
     "    # print(predictor, 'output shape', output.shape, 'nans', nan_count, 'missing files', missing_results_count)\n",
     "    # print(f\"predictor: {predictor}, best config: {incumbent_settings[0]}\")\n",
     "    print('first mean', mean[0], 'last mean', mean[-1])\n",
     "    print('first std', std[0], 'last std', std[-1])\n",
     "    print('time ', time[-1])\n",
-    "    return mean, std, std_error_2, time"
+    "    return mean, std, std_error, time"
    ]
   },
   {
@@ -453,7 +452,7 @@
     "        color = pred_plot_dict[tag]['color']\n",
     "        fmt = pred_plot_dict[tag]['fmt']\n",
     "        ax.plot(x, mean, label=label, color=color, linestyle=fmt, linewidth=2.5)\n",
-    "        ax.fill_between(x, mean-50*sem, mean+50*sem,\n",
+    "        ax.fill_between(x, mean-2*sem, mean+2*sem,\n",
     "                        color=color, alpha=0.2)\n",
     "\n",
     "        if add_sub_axes:\n",
diff --git a/examples/optimizers_HPO.ipynb b/examples/optimizers_HPO.ipynb
index b9cb13e68..23e7e9876 100644
--- a/examples/optimizers_HPO.ipynb
+++ b/examples/optimizers_HPO.ipynb
@@ -83,7 +83,6 @@
     "                    result_path = optimizer_path + '/' + hpo_folder + '/' + seed + '/errors.json'\n",
     "                    # print(result_path)\n",
     "                    if not os.path.isfile(result_path):\n",
-    "                        print(\"what?\")\n",
     "                        continue\n",
     "                    result = json.load(open(result_path))\n",
     "                    valid_accs = result[1]['valid_acc']\n",
@@ -1757,7 +1756,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.13 ('naslib_exercises')",
+   "display_name": "Python 3.7.12 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -1771,11 +1770,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.7.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "a100edf3d46bc2e6c79c61e7e0e4c85fe6b13f4c7883d33d138476068eadc537"
+    "hash": "0cbe1a134e19725d24844610af4192e14b6927fb15622514c6c43df3d887b744"
    }
   }
  },
diff --git a/naslib/analysis/analyze_results.py b/naslib/analysis/analyze_results.py
deleted file mode 100644
index dce0e5c55..000000000
--- a/naslib/analysis/analyze_results.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import json
-import numpy as np
-
-base_path = '/home/till/plotting_data/bbo/nasbench201/'
-benchmark = 'ImageNet16-120/' #ImageNet16-120
-base_path += benchmark
-
-optimizers = {'dehb'}
-
-optimizer_paths = []
-for filename in os.listdir(base_path):
-    optimizer_path = os.path.join(base_path, filename)
-    if filename not in optimizers:
-        continue
-    if not os.path.isdir(optimizer_path):
-        continue
-    optimizer_paths.append(optimizer_path)
-
-config_paths = []
-for path in optimizer_paths:
-    for filename in os.listdir(path):
-        config_path = os.path.join(path, filename)
-        if not os.path.isdir(config_path):
-            continue
-        config_paths.append(config_path)
-
-result_files = []
-for path in config_paths:
-    for filename in os.listdir(path):
-        result_file = os.path.join(path, filename, 'errors.json')
-        if not os.path.isfile(result_file):
-            continue
-        result_files.append(result_file)
-
-results = []
-for result_file in result_files:
-    f = open(result_file)
-    results.append(json.load(f))
-
-metric = 'valid_acc'
-# metric = 'test_acc'
-
-config_ids = 100
-avgs = []
-for id in range(config_ids):
-    arch_seeds = [d for d in results if d[0]['config_id'] == id]
-    avg_acc_seed = [max(d[1][metric]) for d in arch_seeds]
-    avgs.append(np.mean(avg_acc_seed))
-best_arch_avg_index = avgs.index(max(avgs))
-best_arch_avg = max(avgs)
-
-
-print("best archiecteure in average is config:{}, with: {} acc".format(best_arch_avg_index, best_arch_avg))
-print("for the {} benchmark and the {} optimizer".format(benchmark, optimizers))
-
-best_arch = max(results, key=lambda x:(x[1][metric]))
-best_config_id = best_arch[0]['config_id']
-best_arch_seeds = [d for d in results if d[0]['config_id'] == best_config_id]
-
-
-worst_arch = min(results, key=lambda x:x[1][metric][-1])
-default_archs = [d for d in results if d[0]['config_id'] == 0]
-print(len(default_archs))
-best_default_seed = max(default_archs, key=lambda x:x[1][metric][-1])
-worst_default_seed = min(default_archs, key=lambda x:x[1][metric][-1])
-
-
-print("-- Best Architecture --")
-print("config_id: {}".format(best_arch[0]['config_id']))
-print("Valid accuracy: {}".format(max(best_arch[1][metric])))
-# [1][metric][-1]
-print("Worst seed within architecture: {}".format(min(best_arch_seeds, key=lambda x:x[1]['valid_acc'][-1])[1][metric][-1]))
-test = [d[1][metric][-1] for d in best_arch_seeds]
-print("mean:{}".format(np.mean(test)))
-
-print()
-print("Default Architecture (without HPO)")
-print("config_id: {}".format(best_default_seed[0]['config_id']))
-print("Best Seed: Valid accuracy: {}".format(best_default_seed[1][metric][-1]))
-print("Worst Seed: Valid accuracy: {}".format(worst_default_seed[1][metric][-1]))
-test = [d[1][metric][-1] for d in default_archs]
-print("mean:{}".format(np.mean(test)))
-
-print()
-print("-- Worst Architecture --")
-print("config_id: {}".format(worst_arch[0]['config_id']))
-print("Valid accuracy: {}".format(worst_arch[1][metric][-1]))
-
-
-#results:
-#sh for all try the config_0
-#hb cifar 100: 85 cifar 10:  9   image_net: 65 
-#bohb image net: 28   cifar 10: 59   cifar100: 60
-#dehb: cifar100: 9    cifar10: 58      image_net: 87 
diff --git a/naslib/optimizers/discrete/bohb/optimizer.py b/naslib/optimizers/discrete/bohb/optimizer.py
index c6c0d7c2a..966eb8134 100644
--- a/naslib/optimizers/discrete/bohb/optimizer.py
+++ b/naslib/optimizers/discrete/bohb/optimizer.py
@@ -16,6 +16,12 @@
 
 
 class BOHB(MetaOptimizer):
+    """
+    This implementation is mainly based on https://github.com/automl/nas-bench-x11. 
+    Falkner, Stefan, et al. 
+    BOHB: Robust and Efficient Hyperparameter Optimization at Scale. arXiv:1807.01774, 
+    arXiv, 4th July 2018. arXiv.org, http://arxiv.org/abs/1807.01774.
+    """
     # training the models is not implemented
     using_step_function = False
 
diff --git a/naslib/optimizers/discrete/dehb/optimizer.py b/naslib/optimizers/discrete/dehb/optimizer.py
index 5c19aea3d..62b4c8812 100644
--- a/naslib/optimizers/discrete/dehb/optimizer.py
+++ b/naslib/optimizers/discrete/dehb/optimizer.py
@@ -14,6 +14,13 @@
 
 
 class DEHB(MetaOptimizer):
+    """
+    This implementation is mainly based on https://github.com/automl/nas-bench-x11. 
+    Awad, Noor, et al. 
+    DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization. 
+    arXiv:2105.09821, arXiv, 21st October 2021. 
+    arXiv.org, http://arxiv.org/abs/2105.09821.
+    """
     # training the models is not implemented
     using_step_function = False
 
diff --git a/naslib/optimizers/discrete/hb/optimizer.py b/naslib/optimizers/discrete/hb/optimizer.py
index ccaed1c78..4483b9573 100644
--- a/naslib/optimizers/discrete/hb/optimizer.py
+++ b/naslib/optimizers/discrete/hb/optimizer.py
@@ -14,6 +14,14 @@
 
 
 class HB(MetaOptimizer):
+    """
+    This implementation is mainly based on https://github.com/automl/nas-bench-x11. 
+    
+    Li, Lisha, et al. 
+    Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. 
+    arXiv:1603.06560, arXiv, 18th June 2018. 
+    arXiv.org, http://arxiv.org/abs/1603.06560.
+    """
     # training the models is not implemented
     using_step_function = False