Reformat / lint repository with new dev dependency versions (#2248)

* update dev requirements with new pre commit hook lint dependency versions

* black reformatting

* fix flake8 checks

darts/ad/aggregators/or_aggregator.py

@@ -6,7 +6,6 @@
 is flagged as anomalous (logical OR).
 """
 
-
 from typing import Sequence
 
 from darts import TimeSeries

darts/ad/anomaly_model/forecasting_am.py

@@ -221,7 +221,8 @@ def _prepare_covariates(
         series: Sequence[TimeSeries],
         name_covariates: str,
     ) -> Sequence[TimeSeries]:
-        """Convert `covariates` into Sequence, if not already, and checks if their length is equal to the one of `series`.
+        """Convert `covariates` into Sequence, if not already, and checks if their length is equal to the one of
+        `series`.
 
         Parameters
         ----------
@@ -515,7 +516,6 @@ def _predict_with_forecasting(
         start: Union[pd.Timestamp, float, int] = None,
         num_samples: int = 1,
     ) -> TimeSeries:
-
         """Compute the historical forecasts that would have been obtained by this model on the `series`.
 
         `retrain` is set to False if possible (this is not supported by all models). If set to True, it will always

darts/ad/detectors/quantile_detector.py

@@ -69,9 +69,7 @@ def _prep_quantile(q):
             return (
                 q.tolist()
                 if isinstance(q, np.ndarray)
-                else [q]
-                if not isinstance(q, Sequence)
-                else q
+                else [q] if not isinstance(q, Sequence) else q
             )
 
         low = _prep_quantile(low_quantile)

darts/ad/detectors/threshold_detector.py

@@ -62,9 +62,7 @@ def _prep_thresholds(q):
             return (
                 q.tolist()
                 if isinstance(q, np.ndarray)
-                else [q]
-                if not isinstance(q, Sequence)
-                else q
+                else [q] if not isinstance(q, Sequence) else q
             )
 
         low = _prep_thresholds(low_threshold)

darts/ad/scorers/scorers.py

@@ -527,7 +527,7 @@ def score_from_prediction(
         _assert_same_length(list_actual_series, list_pred_series)
 
         anomaly_scores = []
-        for (s1, s2) in zip(list_actual_series, list_pred_series):
+        for s1, s2 in zip(list_actual_series, list_pred_series):
             _sanity_check_two_series(s1, s2)
             s1 = self._assert_deterministic(s1, "actual_series")
             s2 = self._assert_deterministic(s2, "pred_series")

darts/dataprocessing/pipeline.py

@@ -2,6 +2,7 @@
 Pipeline
 --------
 """
+
 from copy import deepcopy
 from typing import Iterator, Sequence, Union
 

darts/dataprocessing/transformers/midas.py

@@ -2,6 +2,7 @@
 Mixed-data sampling (MIDAS) Transformer
 ---------------------------------------
 """
+
 from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
 
 import numpy as np

darts/dataprocessing/transformers/reconciliation.py

@@ -9,7 +9,6 @@
 It can be added to a ``TimeSeries`` using e.g., the :meth:`TimeSeries.with_hierarchy` method.
 """
 
-
 from typing import Any, Mapping, Optional
 
 import numpy as np

darts/dataprocessing/transformers/static_covariates_transformer.py

@@ -2,6 +2,7 @@
 Static Covariates Transformer
 ------
 """
+
 from collections import OrderedDict
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 

darts/datasets/__init__.py

@@ -520,7 +520,7 @@ def __init__(self, multivariate: bool = True):
 
         def pre_proces_fn(extracted_dir, dataset_path):
             with open(Path(extracted_dir, "LD2011_2014.txt")) as fin:
-                with open(dataset_path, "wt", newline="\n") as fout:
+                with open(dataset_path, "w", newline="\n") as fout:
                     for line in fin:
                         fout.write(line.replace(",", ".").replace(";", ","))
 
@@ -622,9 +622,11 @@ def pre_proces_fn(extracted_dir, dataset_path):
                 uri="https://github.com/fivethirtyeight/uber-tlc-foil-response/raw/"
                 "63bb878b76f47f69b4527d50af57aac26dead983/"
                 "uber-trip-data/uber-raw-data-janjune-15.csv.zip",
-                hash="9ed84ebe0df4bc664748724b633b3fe6"
-                if sample_freq == "hourly"
-                else "24f9fd67e4b9e53f0214a90268cd9bee",
+                hash=(
+                    "9ed84ebe0df4bc664748724b633b3fe6"
+                    if sample_freq == "hourly"
+                    else "24f9fd67e4b9e53f0214a90268cd9bee"
+                ),
                 header_time="Pickup_date",
                 format_time="%Y-%m-%d %H:%M:%S",
                 pre_process_zipped_csv_fn=pre_proces_fn,

darts/explainability/explainability.py

@@ -3,6 +3,7 @@
 
 A `_ForecastingModelExplainer` takes a fitted forecasting model as input and generates explanations for it.
 """
+
 from abc import ABC, abstractmethod
 from typing import Optional, Sequence, Tuple, Union
 

darts/explainability/shap_explainer.py

@@ -624,7 +624,6 @@ def shap_explanations(
         horizons: Optional[Sequence[int]] = None,
         target_components: Optional[Sequence[str]] = None,
     ) -> Dict[int, Dict[str, shap.Explanation]]:
-
         """
         Return a dictionary of dictionaries of shap.Explanation instances:
         - the first dimension corresponds to the n forecasts ahead we want to explain (Horizon).
@@ -760,14 +759,14 @@ def _create_regression_model_shap_X(
         X, indexes = create_lagged_prediction_data(
             target_series=target_series if lags_list else None,
             past_covariates=past_covariates if lags_past_covariates_list else None,
-            future_covariates=future_covariates
-            if lags_future_covariates_list
-            else None,
+            future_covariates=(
+                future_covariates if lags_future_covariates_list else None
+            ),
             lags=lags_list,
             lags_past_covariates=lags_past_covariates_list if past_covariates else None,
-            lags_future_covariates=lags_future_covariates_list
-            if future_covariates
-            else None,
+            lags_future_covariates=(
+                lags_future_covariates_list if future_covariates else None
+            ),
             uses_static_covariates=self.model.uses_static_covariates,
             last_static_covariates_shape=self.model._static_covariates_shape,
         )

darts/explainability/utils.py

@@ -345,13 +345,18 @@ def _check_valid_input(
             all(
                 [
                     series[idx].columns.to_list() == target_components,
-                    past_covariates[idx].columns.to_list() == past_covariates_components
-                    if past_covariates is not None
-                    else True,
-                    future_covariates[idx].columns.to_list()
-                    == future_covariates_components
-                    if future_covariates is not None
-                    else True,
+                    (
+                        past_covariates[idx].columns.to_list()
+                        == past_covariates_components
+                        if past_covariates is not None
+                        else True
+                    ),
+                    (
+                        future_covariates[idx].columns.to_list()
+                        == future_covariates_components
+                        if future_covariates is not None
+                        else True
+                    ),
                 ]
             ),
             "Columns names must be identical between TimeSeries list components (multi-TimeSeries).",

darts/metrics/metrics.py

@@ -46,9 +46,7 @@ def wrapper_multi_ts_support(*args, **kwargs):
         pred_series = (
             kwargs["pred_series"]
             if "pred_series" in kwargs
-            else args[0]
-            if "actual_series" in kwargs
-            else args[1]
+            else args[0] if "actual_series" in kwargs else args[1]
         )
 
         n_jobs = kwargs.pop("n_jobs", signature(func).parameters["n_jobs"].default)
@@ -1134,7 +1132,6 @@ def rho_risk(
     n_jobs: int = 1,
     verbose: bool = False
 ) -> float:
-
     """:math:`\\rho`-risk (rho-risk or quantile risk).
 
     Given a time series of actual values :math:`y_t` of length :math:`T` and a time series of stochastic predictions

darts/models/forecasting/arima.py

@@ -193,17 +193,19 @@ def _predict(
         if series is not None:
             self.model = self.model.apply(
                 series.values(copy=False),
-                exog=historic_future_covariates.values(copy=False)
-                if historic_future_covariates
-                else None,
+                exog=(
+                    historic_future_covariates.values(copy=False)
+                    if historic_future_covariates
+                    else None
+                ),
             )
 
         if num_samples == 1:
             forecast = self.model.forecast(
                 steps=n,
-                exog=future_covariates.values(copy=False)
-                if future_covariates
-                else None,
+                exog=(
+                    future_covariates.values(copy=False) if future_covariates else None
+                ),
             )
         else:
             forecast = self.model.simulate(
@@ -212,18 +214,20 @@ def _predict(
                 initial_state=self.model.states.predicted[-1, :],
                 random_state=self._random_state,
                 anchor="end",
-                exog=future_covariates.values(copy=False)
-                if future_covariates
-                else None,
+                exog=(
+                    future_covariates.values(copy=False) if future_covariates else None
+                ),
             )
 
         # restoring statsmodels results object state
         if series is not None:
             self.model = self.model.apply(
                 self._orig_training_series.values(copy=False),
-                exog=self.training_historic_future_covariates.values(copy=False)
-                if self.training_historic_future_covariates
-                else None,
+                exog=(
+                    self.training_historic_future_covariates.values(copy=False)
+                    if self.training_historic_future_covariates
+                    else None
+                ),
             )
 
         return self._build_forecast_series(forecast)

darts/models/forecasting/baselines.py

@@ -336,12 +336,12 @@ def fit(
             for model in self.forecasting_models:
                 model._fit_wrapper(
                     series=series,
-                    past_covariates=past_covariates
-                    if model.supports_past_covariates
-                    else None,
-                    future_covariates=future_covariates
-                    if model.supports_future_covariates
-                    else None,
+                    past_covariates=(
+                        past_covariates if model.supports_past_covariates else None
+                    ),
+                    future_covariates=(
+                        future_covariates if model.supports_future_covariates else None
+                    ),
                 )
 
         return self
@@ -364,9 +364,11 @@ def ensemble(
 
         if isinstance(predictions, Sequence):
             return [
-                self._target_average(p, ts)
-                if not predict_likelihood_parameters
-                else self._params_average(p, ts)
+                (
+                    self._target_average(p, ts)
+                    if not predict_likelihood_parameters
+                    else self._params_average(p, ts)
+                )
                 for p, ts in zip(predictions, series)
             ]
         else:

darts/models/forecasting/block_rnn_model.py

@@ -106,7 +106,6 @@ def __init__(
         name: str,
         **kwargs,
     ):
-
         """PyTorch module implementing a block RNN to be used in `BlockRNNModel`.
 
         PyTorch module implementing a simple block RNN with the specified `name` layer.
@@ -196,7 +195,6 @@ def __init__(
         dropout: float = 0.0,
         **kwargs,
     ):
-
         """Block Recurrent Neural Network Model (RNNs).
 
         This is a neural network model that uses an RNN encoder to encode fixed-length input chunks, and

darts/models/forecasting/catboost_model.py

@@ -305,7 +305,9 @@ def min_train_series_length(self) -> int:
         # for other regression models
         return max(
             3,
-            -self.lags["target"][0] + self.output_chunk_length + 1
-            if "target" in self.lags
-            else self.output_chunk_length,
+            (
+                -self.lags["target"][0] + self.output_chunk_length + 1
+                if "target" in self.lags
+                else self.output_chunk_length
+            ),
         )

darts/models/forecasting/croston.py

@@ -130,9 +130,11 @@ def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = Non
 
         self.model.fit(
             y=series.values(copy=False).flatten(),
-            X=future_covariates.values(copy=False).flatten()
-            if future_covariates is not None
-            else None,
+            X=(
+                future_covariates.values(copy=False).flatten()
+                if future_covariates is not None
+                else None
+            ),
         )
 
         return self
@@ -147,9 +149,11 @@ def _predict(
         super()._predict(n, future_covariates, num_samples)
         values = self.model.predict(
             h=n,
-            X=future_covariates.values(copy=False).flatten()
-            if future_covariates is not None
-            else None,
+            X=(
+                future_covariates.values(copy=False).flatten()
+                if future_covariates is not None
+                else None
+            ),
         )["mean"]
         return self._build_forecast_series(values)
 

darts/models/forecasting/ensemble_model.py

@@ -255,12 +255,12 @@ def _make_multiple_predictions(
             model._predict_wrapper(
                 n=n,
                 series=series,
-                past_covariates=past_covariates
-                if model.supports_past_covariates
-                else None,
-                future_covariates=future_covariates
-                if model.supports_future_covariates
-                else None,
+                past_covariates=(
+                    past_covariates if model.supports_past_covariates else None
+                ),
+                future_covariates=(
+                    future_covariates if model.supports_future_covariates else None
+                ),
                 num_samples=num_samples if model._is_probabilistic else 1,
                 predict_likelihood_parameters=predict_likelihood_parameters,
             )

darts/models/forecasting/exponential_smoothing.py

@@ -27,7 +27,6 @@ def __init__(
         kwargs: Optional[Dict[str, Any]] = None,
         **fit_kwargs
     ):
-
         """Exponential Smoothing
 
         This is a wrapper around

darts/models/forecasting/fft.py

@@ -105,7 +105,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set:
         # check for yearly seasonality
         if _check_approximate_seasonality(series, 12, 1, 0):
             relevant_attributes.add("month")
-    elif type(series.freq) == pd.tseries.offsets.Day:
+    elif type(series.freq) is pd.tseries.offsets.Day:
         # check for yearly seasonality
         if _check_approximate_seasonality(series, 365, 5, 20):
             relevant_attributes.update({"month", "day"})
@@ -115,7 +115,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set:
         # check for weekly seasonality
         elif _check_approximate_seasonality(series, 7, 0, 0):
             relevant_attributes.add("weekday")
-    elif type(series.freq) == pd.tseries.offsets.Hour:
+    elif type(series.freq) is pd.tseries.offsets.Hour:
         # check for yearly seasonality
         if _check_approximate_seasonality(series, 8760, 100, 100):
             relevant_attributes.update({"month", "day", "hour"})
@@ -128,7 +128,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set:
         # check for daily seasonality
         elif _check_approximate_seasonality(series, 24, 1, 1):
             relevant_attributes.add("hour")
-    elif type(series.freq) == pd.tseries.offsets.Minute:
+    elif type(series.freq) is pd.tseries.offsets.Minute:
         # check for daily seasonality
         if _check_approximate_seasonality(series, 1440, 20, 50):
             relevant_attributes.update({"hour", "minute"})