From 6a3f0ce5a2d640db14cfd45ffdfd0eed537e17e4 Mon Sep 17 00:00:00 2001 From: RektPunk <110188257+RektPunk@users.noreply.github.com> Date: Wed, 18 Sep 2024 17:01:31 +0900 Subject: [PATCH] [Feature] introduce weighted loss (#8) * introduce weighted loss * remove optimize based on optuna * refactor objective --- experiments/basic.py | 22 ++- imlightgbm/__init__.py | 2 +- imlightgbm/base.py | 35 ++++ imlightgbm/docstring.py | 23 +-- imlightgbm/engine.py | 47 +----- imlightgbm/objective.py | 130 +++++++++------ imlightgbm/utils.py | 10 -- poetry.lock | 350 +--------------------------------------- pyproject.toml | 1 - 9 files changed, 133 insertions(+), 487 deletions(-) create mode 100644 imlightgbm/base.py delete mode 100644 imlightgbm/utils.py diff --git a/experiments/basic.py b/experiments/basic.py index a58cce9..72f1a06 100644 --- a/experiments/basic.py +++ b/experiments/basic.py @@ -31,21 +31,33 @@ "early_stopping_rounds": 10, } -# # Train standard LightGBM model +# Train standard LightGBM model bst_standard = lgb.train( params_standard, train_data, num_boost_round=100, valid_sets=[test_data] ) +# Parameters for Imbalanced LightGBM model +params_imbalanced = { + "objective": "weighted", # focal + "metric": "binary_logloss", # auc + "learning_rate": 0.05, + "num_leaves": 31, + "feature_fraction": 0.9, + "bagging_fraction": 0.8, + "bagging_freq": 5, + "seed": 42, + "early_stopping_rounds": 10, +} bst_focal = imlgb.train( - params_standard, train_data, num_boost_round=100, valid_sets=[test_data] + params_imbalanced, train_data, num_boost_round=100, valid_sets=[test_data] ) -# Predict using the standard LightGBM model +# Predict using standard LightGBM model y_pred_standard = bst_standard.predict(X_test) y_pred_standard_binary = (y_pred_standard > 0.5).astype(int) -# Predict using the focal loss model +# Predict using Imbalanced LightGBM model y_pred_focal = bst_focal.predict(X_test) y_pred_focal_binary = (y_pred_focal > 0.5).astype(int) @@ -64,5 +76,3 @@ print( f"LightGBM with Focal Loss - Accuracy: {accuracy_focal:.4f}, Log Loss: {logloss_focal:.4f}, rocauc: {rocauc_focal:.4f}" ) -# Standard LightGBM - Accuracy: 0.9737, Log Loss: 0.1029, rocauc: 0.9931 -# LightGBM with Focal Loss - Accuracy: 0.8158, Log Loss: 0.6955, rocauc: 0.9843 diff --git a/imlightgbm/__init__.py b/imlightgbm/__init__.py index d96061c..447fb82 100644 --- a/imlightgbm/__init__.py +++ b/imlightgbm/__init__.py @@ -1,2 +1,2 @@ # ruff: noqa -from imlightgbm.engine import cv, optimize, train +from imlightgbm.engine import cv, train diff --git a/imlightgbm/base.py b/imlightgbm/base.py new file mode 100644 index 0000000..cb88b70 --- /dev/null +++ b/imlightgbm/base.py @@ -0,0 +1,35 @@ +from enum import Enum + + +class BaseEnum(str, Enum): + @classmethod + def get(cls, text: str) -> Enum: + cls.__check_valid(text) + return cls[text] + + @classmethod + def __check_valid(cls, text: str) -> None: + if text not in cls._member_map_.keys(): + valid_members = ", ".join(list(cls._member_map_.keys())) + raise ValueError( + f"Invalid value: '{text}'. Expected one of: {valid_members}." + ) + + +class SupportedTask(BaseEnum): + binary: str = "binary" + multiclass: str = "multiclass" + + +class Metric(BaseEnum): + auc: str = "auc" + binary_logloss: str = "binary_logloss" + binary_error: str = "binary_error" + auc_mu: str = "auc_mu" + multi_logloss: str = "multi_logloss" + multi_error: str = "multi_error" + + +class Objective(BaseEnum): + focal: str = "focal" + weighted: str = "weighted" diff --git a/imlightgbm/docstring.py b/imlightgbm/docstring.py index aff6554..ab2caa0 100644 --- a/imlightgbm/docstring.py +++ b/imlightgbm/docstring.py @@ -11,16 +11,13 @@ "nfold": f"int, optional (default=5){_space}Number of folds in CV.", "stratified": f"bool, optional (default=True){_space}Whether to perform stratified sampling.", "shuffle": f"bool, optional (default=True){_space}Whether to shuffle before splitting data.", - "metrics": f"str, list of str, or None, optional (default=None){_space}Evaluation metrics to be monitored while CV.", "init_model": f"str, pathlib.Path, Booster or None, optional (default=None){_space}Filename of LightGBM model or Booster instance used for continue training.", "fpreproc": f"callable or None, optional (default=None){_space}Preprocessing function that takes (dtrain, dtest, params) and returns transformed versions of those.", "seed": f"int, optional (default=0){_space}Seed used to generate the folds (passed to numpy.random.seed).", + "keep_training_booster": f"bool, optional (default=False){_space}Whether the returned Booster will be used to keep training.{_space}If False, the returned value will be converted into _InnerPredictor before returning.{_space}This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.{_space}When your model is very large and cause the memory error,{_space}you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.{_space}You can still use _InnerPredictor as ``init_model`` for future continue training.", "callbacks": f"list of callable, or None, optional (default=None){_space}List of callback functions that are applied at each iteration.{_space}See Callbacks in Python API for more information.", "eval_train_metric": f"bool, optional (default=False){_space}Whether to display the train metric in progress.", "return_cvbooster": f"bool, optional (default=False){_space}Whether to return Booster models trained on each fold through ``CVBooster``.", - "keep_training_booster": f"bool, optional (default=False){_space}Whether the returned Booster will be used to keep training.{_space}If False, the returned value will be converted into _InnerPredictor before returning.{_space}This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.{_space}When your model is very large and cause the memory error,{_space}you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.{_space}You can still use _InnerPredictor as ``init_model`` for future continue training.", - "num_trials": f"int, optional (default=10){_space}Number of hyperparameter tuning trials.", - "get_params": 'callable, optional (default=get_params)\n Number of hyperparameter tuning trials.\n def get_params(trial: optuna.Trial):\n return {\n "alpha": trial.suggest_float("alpha", 0.25, 0.75),\n "gamma": trial.suggest_float("gamma", 0.0, 3.0),\n "num_leaves": trial.suggest_int("num_leaves", 20, 150),\n "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1),\n "feature_fraction": trial.suggest_float("feature_fraction", 0.5, 1.0),\n "bagging_fraction": trial.suggest_float("bagging_fraction", 0.5, 1.0),\n "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),\n }', } @@ -58,24 +55,6 @@ ], "return_description": "eval_results: dict\n History of evaluation results of each metric.\n The dictionary has the following format:\n {'valid metric1-mean': [values], 'valid metric1-stdv': [values],\n 'valid metric2-mean': [values], 'valid metric2-stdv': [values],\n ...}.\n If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.\n If ``eval_train_metric=True``, also returns the train metric history.\n In this case, the dictionary has the following format:\n {'train metric1-mean': [values], 'valid metric1-mean': [values],\n 'train metric2-mean': [values], 'valid metric2-mean': [values],\n ...}.", }, - "optimize": { - "description": "Perform the hyperparameter tuning with optuna.", - "selected_params": [ - "train_set", - "num_trials", - "num_boost_round", - "folds", - "nfold", - "stratified", - "shuffle", - "get_params", - "init_model", - "fpreproc", - "seed", - "callbacks", - ], - "return_description": f"study: optuna.Study{_space}study.best_params{_space}study.best_value", - }, } diff --git a/imlightgbm/engine.py b/imlightgbm/engine.py index 53180b4..c83bbdf 100644 --- a/imlightgbm/engine.py +++ b/imlightgbm/engine.py @@ -3,11 +3,10 @@ import lightgbm as lgb import numpy as np -import optuna from sklearn.model_selection import BaseCrossValidator from imlightgbm.docstring import add_docstring -from imlightgbm.objective import get_params, set_params +from imlightgbm.objective import set_params @add_docstring("train") @@ -70,47 +69,3 @@ def cv( eval_train_metric=eval_train_metric, return_cvbooster=return_cvbooster, ) - - -@add_docstring("optimize") -def optimize( - train_set: lgb.Dataset, - num_trials: int = 10, - num_boost_round: int = 100, - folds: Iterable[tuple[np.ndarray, np.ndarray]] | BaseCrossValidator | None = None, - nfold: int = 5, - stratified: bool = True, - shuffle: bool = True, - get_params: Callable[[optuna.Trial], dict[str, Any]] = get_params, - init_model: str | lgb.Path | lgb.Booster | None = None, - fpreproc: Callable[ - [lgb.Dataset, lgb.Dataset, dict[str, Any]], - tuple[lgb.Dataset, lgb.Dataset, dict[str, Any]], - ] - | None = None, - seed: int = 0, - callbacks: list[Callable] | None = None, -) -> optuna.Study: - def _objective(trial: optuna.Trial): - """Optuna objective function.""" - params = get_params(trial) - cv_results = cv( - params=params, - train_set=train_set, - num_boost_round=num_boost_round, - folds=folds, - nfold=nfold, - stratified=stratified, - shuffle=shuffle, - init_model=init_model, - fpreproc=fpreproc, - seed=seed, - callbacks=callbacks, - ) - _keys = [_ for _ in cv_results.keys() if _.endswith("mean")] - assert len(_keys) == 1 - return min(cv_results[_keys[0]]) - - study = optuna.create_study(direction="minimize") - study.optimize(_objective, n_trials=num_trials) - return study diff --git a/imlightgbm/objective.py b/imlightgbm/objective.py index 26b84d0..e750786 100644 --- a/imlightgbm/objective.py +++ b/imlightgbm/objective.py @@ -3,13 +3,11 @@ from typing import Any, Callable import numpy as np -import optuna from lightgbm import Dataset from sklearn.utils.multiclass import type_of_target -from imlightgbm.utils import logger +from imlightgbm.base import Metric, Objective, SupportedTask -EvalLike = Callable[[np.ndarray, Dataset], tuple[str, float, bool]] ObjLike = Callable[[np.ndarray, Dataset], tuple[np.ndarray, np.ndarray]] ALPHA_DEFAULT: float = 0.25 GAMMA_DEFAULT: float = 2.0 @@ -37,7 +35,7 @@ def _sigmoid(x: np.ndarray) -> np.ndarray: def binary_focal_objective( pred: np.ndarray, train_data: Dataset, gamma: float ) -> tuple[np.ndarray, np.ndarray]: - """Return binary focal objective.""" + """Return grad, hess for binary focal objective.""" label = train_data.get_label() pred_prob = _sigmoid(pred) @@ -58,17 +56,13 @@ def binary_focal_objective( return grad, hess -def binary_focal_eval( - pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float -) -> tuple[str, float, bool]: - """Return binary focal eval.""" +def binary_weighted_objective(pred: np.ndarray, train_data: Dataset, alpha: float): + """Return grad, hess for binary weighted objective.""" label = train_data.get_label() pred_prob = _sigmoid(pred) - p_t = np.where(label == 1, pred_prob, 1 - pred_prob) - loss = -alpha * ((1 - p_t) ** gamma) * _log(p_t, True) - - focal_loss = np.mean(loss) - return "focal", focal_loss, IS_HIGHER_BETTER + grad = -(alpha**label) * (label - pred_prob) + hess = (alpha**label) * pred_prob * (1.0 - pred_prob) + return grad, hess def multiclass_focal_objective( @@ -78,59 +72,91 @@ def multiclass_focal_objective( return -def multiclass_focal_eval( +def multiclass_weighted_objective( pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float ) -> tuple[str, float, bool]: # TODO return -def _set_fobj_feval( - train_set: Dataset, alpha: float, gamma: float -) -> tuple[ObjLike, EvalLike]: - """Return obj and eval with respect to task type.""" - inferred_task = type_of_target(train_set.get_label()) - if inferred_task not in {"binary", "multiclass"}: - raise ValueError( - f"Invalid target type: {inferred_task}. Supported types are 'binary' or 'multiclass'." - ) - objective_mapper: dict[str, ObjLike] = { - "binary": partial(binary_focal_objective, gamma=gamma), - "multiclass": partial(multiclass_focal_objective, alpha=alpha, gamma=gamma), +def _get_metric(task_enum: SupportedTask, metric: str | None) -> str: + """Retrieve the appropriate metric function based on task.""" + metric_mapper: dict[SupportedTask, list[Metric]] = { + SupportedTask.binary: [Metric.auc, Metric.binary_error, Metric.binary_logloss], + SupportedTask.multiclass: [ + Metric.auc_mu, + Metric.multi_logloss, + Metric.multi_error, + ], } - eval_mapper: dict[str, EvalLike] = { - "binary": "binary_logloss", - "multiclass": "multi_logloss", + if metric: + metric_enum = Metric.get(metric) + metric_enums = metric_mapper[task_enum] + if metric_enum not in metric_enums: + valid_metrics = ", ".join([m.value for m in metric_enums]) + raise ValueError(f"Invalid metric: Supported metrics are {valid_metrics}") + return metric_enum.value + + return metric_mapper[task_enum][0].value + + +def _get_objective( + task_enum: SupportedTask, objective: str | None, alpha: float, gamma: float +) -> ObjLike: + """Retrieve the appropriate objective function based on task and objective type.""" + objective_mapper: dict[SupportedTask, dict[Objective, ObjLike]] = { + SupportedTask.binary: { + Objective.focal: partial(binary_focal_objective, gamma=gamma), + Objective.weighted: partial(binary_weighted_objective, alpha=alpha), + }, + SupportedTask.multiclass: { + Objective.focal: partial( + multiclass_focal_objective, alpha=alpha, gamma=gamma + ), + Objective.weighted: partial( + multiclass_weighted_objective, alpha=alpha, gamma=gamma + ), + }, } - fobj = objective_mapper[inferred_task] - feval = eval_mapper[inferred_task] + if objective: + objective_enum = Objective.get(objective) + return objective_mapper[task_enum][objective_enum] + + return objective_mapper[task_enum][Objective.focal] + +def _get_fobj_feval( + train_set: Dataset, + alpha: float, + gamma: float, + objective: str | None, + metric: str | None, +) -> tuple[ObjLike, str]: + """Return obj and eval with respect to task type.""" + _task = type_of_target(train_set.get_label()) + task_enum = SupportedTask.get(_task) + feval = _get_metric(task_enum=task_enum, metric=metric) + fobj = _get_objective( + task_enum=task_enum, objective=objective, alpha=alpha, gamma=gamma + ) return fobj, feval def set_params(params: dict[str, Any], train_set: Dataset) -> dict[str, Any]: """Set params and eval finction, objective in params.""" _params = deepcopy(params) - if OBJECTIVE_STR in _params: - logger.warning(f"'{OBJECTIVE_STR}' exists in params will not used.") - del _params[OBJECTIVE_STR] - - _alpha = _params.pop("alpha", ALPHA_DEFAULT) - _gamma = _params.pop("gamma", GAMMA_DEFAULT) - - fobj, feval = _set_fobj_feval(train_set=train_set, alpha=_alpha, gamma=_gamma) + _objective = _params.pop(OBJECTIVE_STR, None) + _metric = _params.pop(METRIC_STR, None) + + if _metric and not isinstance(_metric, str): + raise ValueError("metric must be str") + + fobj, feval = _get_fobj_feval( + train_set=train_set, + alpha=_params.pop("alpha", ALPHA_DEFAULT), + gamma=_params.pop("gamma", GAMMA_DEFAULT), + objective=_objective, + metric=_metric, + ) _params.update({OBJECTIVE_STR: fobj, METRIC_STR: feval}) return _params - - -def get_params(trial: optuna.Trial) -> dict[str, Any]: - """Get default params.""" - return { - "alpha": trial.suggest_float("alpha", 0.25, 0.75), - "gamma": trial.suggest_float("gamma", 0.0, 3.0), - "num_leaves": trial.suggest_int("num_leaves", 20, 150), - "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1), - "feature_fraction": trial.suggest_float("feature_fraction", 0.5, 1.0), - "bagging_fraction": trial.suggest_float("bagging_fraction", 0.5, 1.0), - "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), - } diff --git a/imlightgbm/utils.py b/imlightgbm/utils.py deleted file mode 100644 index 0bc1aa7..0000000 --- a/imlightgbm/utils.py +++ /dev/null @@ -1,10 +0,0 @@ -import logging - - -def init_logger() -> logging.Logger: - logger = logging.getLogger("imlightgbm") - logger.setLevel(logging.INFO) - return logger - - -logger = init_logger() diff --git a/poetry.lock b/poetry.lock index 8ed8f60..e7b7649 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,24 +1,5 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. -[[package]] -name = "alembic" -version = "1.13.2" -description = "A database migration tool for SQLAlchemy." -optional = false -python-versions = ">=3.8" -files = [ - {file = "alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953"}, - {file = "alembic-1.13.2.tar.gz", hash = "sha256:1ff0ae32975f4fd96028c39ed9bb3c867fe3af956bd7bb37343b54c9fe7445ef"}, -] - -[package.dependencies] -Mako = "*" -SQLAlchemy = ">=1.3.0" -typing-extensions = ">=4" - -[package.extras] -tz = ["backports.zoneinfo"] - [[package]] name = "appnope" version = "0.1.4" @@ -149,23 +130,6 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -[[package]] -name = "colorlog" -version = "6.8.2" -description = "Add colours to the output of Python's logging module." -optional = false -python-versions = ">=3.6" -files = [ - {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"}, - {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"}, -] - -[package.dependencies] -colorama = {version = "*", markers = "sys_platform == \"win32\""} - -[package.extras] -development = ["black", "flake8", "mypy", "pytest", "types-colorama"] - [[package]] name = "comm" version = "0.2.2" @@ -266,85 +230,6 @@ docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2. testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.1.1)", "pytest (>=8.3.2)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.3)"] typing = ["typing-extensions (>=4.12.2)"] -[[package]] -name = "greenlet" -version = "3.1.0" -description = "Lightweight in-process concurrent programming" -optional = false -python-versions = ">=3.7" -files = [ - {file = "greenlet-3.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a814dc3100e8a046ff48faeaa909e80cdb358411a3d6dd5293158425c684eda8"}, - {file = "greenlet-3.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a771dc64fa44ebe58d65768d869fcfb9060169d203446c1d446e844b62bdfdca"}, - {file = "greenlet-3.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0e49a65d25d7350cca2da15aac31b6f67a43d867448babf997fe83c7505f57bc"}, - {file = "greenlet-3.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2cd8518eade968bc52262d8c46727cfc0826ff4d552cf0430b8d65aaf50bb91d"}, - {file = "greenlet-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:76dc19e660baea5c38e949455c1181bc018893f25372d10ffe24b3ed7341fb25"}, - {file = "greenlet-3.1.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c0a5b1c22c82831f56f2f7ad9bbe4948879762fe0d59833a4a71f16e5fa0f682"}, - {file = "greenlet-3.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:2651dfb006f391bcb240635079a68a261b227a10a08af6349cba834a2141efa1"}, - {file = "greenlet-3.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:3e7e6ef1737a819819b1163116ad4b48d06cfdd40352d813bb14436024fcda99"}, - {file = "greenlet-3.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:ffb08f2a1e59d38c7b8b9ac8083c9c8b9875f0955b1e9b9b9a965607a51f8e54"}, - {file = "greenlet-3.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9730929375021ec90f6447bff4f7f5508faef1c02f399a1953870cdb78e0c345"}, - {file = "greenlet-3.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:713d450cf8e61854de9420fb7eea8ad228df4e27e7d4ed465de98c955d2b3fa6"}, - {file = "greenlet-3.1.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4c3446937be153718250fe421da548f973124189f18fe4575a0510b5c928f0cc"}, - {file = "greenlet-3.1.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1ddc7bcedeb47187be74208bc652d63d6b20cb24f4e596bd356092d8000da6d6"}, - {file = "greenlet-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44151d7b81b9391ed759a2f2865bbe623ef00d648fed59363be2bbbd5154656f"}, - {file = "greenlet-3.1.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6cea1cca3be76c9483282dc7760ea1cc08a6ecec1f0b6ca0a94ea0d17432da19"}, - {file = "greenlet-3.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:619935a44f414274a2c08c9e74611965650b730eb4efe4b2270f91df5e4adf9a"}, - {file = "greenlet-3.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:221169d31cada333a0c7fd087b957c8f431c1dba202c3a58cf5a3583ed973e9b"}, - {file = "greenlet-3.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:01059afb9b178606b4b6e92c3e710ea1635597c3537e44da69f4531e111dd5e9"}, - {file = "greenlet-3.1.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:24fc216ec7c8be9becba8b64a98a78f9cd057fd2dc75ae952ca94ed8a893bf27"}, - {file = "greenlet-3.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d07c28b85b350564bdff9f51c1c5007dfb2f389385d1bc23288de51134ca303"}, - {file = "greenlet-3.1.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:243a223c96a4246f8a30ea470c440fe9db1f5e444941ee3c3cd79df119b8eebf"}, - {file = "greenlet-3.1.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:26811df4dc81271033a7836bc20d12cd30938e6bd2e9437f56fa03da81b0f8fc"}, - {file = "greenlet-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d86401550b09a55410f32ceb5fe7efcd998bd2dad9e82521713cb148a4a15f"}, - {file = "greenlet-3.1.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:26d9c1c4f1748ccac0bae1dbb465fb1a795a75aba8af8ca871503019f4285e2a"}, - {file = "greenlet-3.1.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:cd468ec62257bb4544989402b19d795d2305eccb06cde5da0eb739b63dc04665"}, - {file = "greenlet-3.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a53dfe8f82b715319e9953330fa5c8708b610d48b5c59f1316337302af5c0811"}, - {file = "greenlet-3.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:28fe80a3eb673b2d5cc3b12eea468a5e5f4603c26aa34d88bf61bba82ceb2f9b"}, - {file = "greenlet-3.1.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:76b3e3976d2a452cba7aa9e453498ac72240d43030fdc6d538a72b87eaff52fd"}, - {file = "greenlet-3.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:655b21ffd37a96b1e78cc48bf254f5ea4b5b85efaf9e9e2a526b3c9309d660ca"}, - {file = "greenlet-3.1.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c6f4c2027689093775fd58ca2388d58789009116844432d920e9147f91acbe64"}, - {file = "greenlet-3.1.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:76e5064fd8e94c3f74d9fd69b02d99e3cdb8fc286ed49a1f10b256e59d0d3a0b"}, - {file = "greenlet-3.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a4bf607f690f7987ab3291406e012cd8591a4f77aa54f29b890f9c331e84989"}, - {file = "greenlet-3.1.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:037d9ac99540ace9424cb9ea89f0accfaff4316f149520b4ae293eebc5bded17"}, - {file = "greenlet-3.1.0-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:90b5bbf05fe3d3ef697103850c2ce3374558f6fe40fd57c9fac1bf14903f50a5"}, - {file = "greenlet-3.1.0-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:726377bd60081172685c0ff46afbc600d064f01053190e4450857483c4d44484"}, - {file = "greenlet-3.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:d46d5069e2eeda111d6f71970e341f4bd9aeeee92074e649ae263b834286ecc0"}, - {file = "greenlet-3.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:81eeec4403a7d7684b5812a8aaa626fa23b7d0848edb3a28d2eb3220daddcbd0"}, - {file = "greenlet-3.1.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4a3dae7492d16e85ea6045fd11cb8e782b63eac8c8d520c3a92c02ac4573b0a6"}, - {file = "greenlet-3.1.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4b5ea3664eed571779403858d7cd0a9b0ebf50d57d2cdeafc7748e09ef8cd81a"}, - {file = "greenlet-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a22f4e26400f7f48faef2d69c20dc055a1f3043d330923f9abe08ea0aecc44df"}, - {file = "greenlet-3.1.0-cp37-cp37m-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13ff8c8e54a10472ce3b2a2da007f915175192f18e6495bad50486e87c7f6637"}, - {file = "greenlet-3.1.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:f9671e7282d8c6fcabc32c0fb8d7c0ea8894ae85cee89c9aadc2d7129e1a9954"}, - {file = "greenlet-3.1.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:184258372ae9e1e9bddce6f187967f2e08ecd16906557c4320e3ba88a93438c3"}, - {file = "greenlet-3.1.0-cp37-cp37m-win32.whl", hash = "sha256:a0409bc18a9f85321399c29baf93545152d74a49d92f2f55302f122007cfda00"}, - {file = "greenlet-3.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:9eb4a1d7399b9f3c7ac68ae6baa6be5f9195d1d08c9ddc45ad559aa6b556bce6"}, - {file = "greenlet-3.1.0-cp38-cp38-macosx_11_0_universal2.whl", hash = "sha256:a8870983af660798dc1b529e1fd6f1cefd94e45135a32e58bd70edd694540f33"}, - {file = "greenlet-3.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cfcfb73aed40f550a57ea904629bdaf2e562c68fa1164fa4588e752af6efdc3f"}, - {file = "greenlet-3.1.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f9482c2ed414781c0af0b35d9d575226da6b728bd1a720668fa05837184965b7"}, - {file = "greenlet-3.1.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d58ec349e0c2c0bc6669bf2cd4982d2f93bf067860d23a0ea1fe677b0f0b1e09"}, - {file = "greenlet-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd65695a8df1233309b701dec2539cc4b11e97d4fcc0f4185b4a12ce54db0491"}, - {file = "greenlet-3.1.0-cp38-cp38-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:665b21e95bc0fce5cab03b2e1d90ba9c66c510f1bb5fdc864f3a377d0f553f6b"}, - {file = "greenlet-3.1.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:d3c59a06c2c28a81a026ff11fbf012081ea34fb9b7052f2ed0366e14896f0a1d"}, - {file = "greenlet-3.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5415b9494ff6240b09af06b91a375731febe0090218e2898d2b85f9b92abcda0"}, - {file = "greenlet-3.1.0-cp38-cp38-win32.whl", hash = "sha256:1544b8dd090b494c55e60c4ff46e238be44fdc472d2589e943c241e0169bcea2"}, - {file = "greenlet-3.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:7f346d24d74c00b6730440f5eb8ec3fe5774ca8d1c9574e8e57c8671bb51b910"}, - {file = "greenlet-3.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:db1b3ccb93488328c74e97ff888604a8b95ae4f35f4f56677ca57a4fc3a4220b"}, - {file = "greenlet-3.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:44cd313629ded43bb3b98737bba2f3e2c2c8679b55ea29ed73daea6b755fe8e7"}, - {file = "greenlet-3.1.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fad7a051e07f64e297e6e8399b4d6a3bdcad3d7297409e9a06ef8cbccff4f501"}, - {file = "greenlet-3.1.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c3967dcc1cd2ea61b08b0b276659242cbce5caca39e7cbc02408222fb9e6ff39"}, - {file = "greenlet-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d45b75b0f3fd8d99f62eb7908cfa6d727b7ed190737dec7fe46d993da550b81a"}, - {file = "greenlet-3.1.0-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2d004db911ed7b6218ec5c5bfe4cf70ae8aa2223dffbb5b3c69e342bb253cb28"}, - {file = "greenlet-3.1.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b9505a0c8579899057cbefd4ec34d865ab99852baf1ff33a9481eb3924e2da0b"}, - {file = "greenlet-3.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fd6e94593f6f9714dbad1aaba734b5ec04593374fa6638df61592055868f8b8"}, - {file = "greenlet-3.1.0-cp39-cp39-win32.whl", hash = "sha256:d0dd943282231480aad5f50f89bdf26690c995e8ff555f26d8a5b9887b559bcc"}, - {file = "greenlet-3.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:ac0adfdb3a21dc2a24ed728b61e72440d297d0fd3a577389df566651fcd08f97"}, - {file = "greenlet-3.1.0.tar.gz", hash = "sha256:b395121e9bbe8d02a750886f108d540abe66075e61e22f7353d9acb0b81be0f0"}, -] - -[package.extras] -docs = ["Sphinx", "furo"] -test = ["objgraph", "psutil"] - [[package]] name = "identify" version = "2.6.0" @@ -525,94 +410,6 @@ dask = ["dask[array,dataframe,distributed] (>=2.0.0)", "pandas (>=0.24.0)"] pandas = ["pandas (>=0.24.0)"] scikit-learn = ["scikit-learn (!=0.22.0)"] -[[package]] -name = "mako" -version = "1.3.5" -description = "A super-fast templating language that borrows the best ideas from the existing templating languages." -optional = false -python-versions = ">=3.8" -files = [ - {file = "Mako-1.3.5-py3-none-any.whl", hash = "sha256:260f1dbc3a519453a9c856dedfe4beb4e50bd5a26d96386cb6c80856556bb91a"}, - {file = "Mako-1.3.5.tar.gz", hash = "sha256:48dbc20568c1d276a2698b36d968fa76161bf127194907ea6fc594fa81f943bc"}, -] - -[package.dependencies] -MarkupSafe = ">=0.9.2" - -[package.extras] -babel = ["Babel"] -lingua = ["lingua"] -testing = ["pytest"] - -[[package]] -name = "markupsafe" -version = "2.1.5" -description = "Safely add untrusted strings to HTML/XML markup." -optional = false -python-versions = ">=3.7" -files = [ - {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e61659ba32cf2cf1481e575d0462554625196a1f2fc06a1c777d3f48e8865d46"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2174c595a0d73a3080ca3257b40096db99799265e1c27cc5a610743acd86d62f"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae2ad8ae6ebee9d2d94b17fb62763125f3f374c25618198f40cbb8b525411900"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:075202fa5b72c86ad32dc7d0b56024ebdbcf2048c0ba09f1cde31bfdd57bcfff"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:598e3276b64aff0e7b3451b72e94fa3c238d452e7ddcd893c3ab324717456bad"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fce659a462a1be54d2ffcacea5e3ba2d74daa74f30f5f143fe0c58636e355fdd"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-win32.whl", hash = "sha256:d9fad5155d72433c921b782e58892377c44bd6252b5af2f67f16b194987338a4"}, - {file = "MarkupSafe-2.1.5-cp310-cp310-win_amd64.whl", hash = "sha256:bf50cd79a75d181c9181df03572cdce0fbb75cc353bc350712073108cba98de5"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:629ddd2ca402ae6dbedfceeba9c46d5f7b2a61d9749597d4307f943ef198fc1f"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5b7b716f97b52c5a14bffdf688f971b2d5ef4029127f1ad7a513973cfd818df2"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ec585f69cec0aa07d945b20805be741395e28ac1627333b1c5b0105962ffced"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7502934a33b54030eaf1194c21c692a534196063db72176b0c4028e140f8f32c"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0e397ac966fdf721b2c528cf028494e86172b4feba51d65f81ffd65c63798f3f"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c061bb86a71b42465156a3ee7bd58c8c2ceacdbeb95d05a99893e08b8467359a"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3a57fdd7ce31c7ff06cdfbf31dafa96cc533c21e443d57f5b1ecc6cdc668ec7f"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-win32.whl", hash = "sha256:397081c1a0bfb5124355710fe79478cdbeb39626492b15d399526ae53422b906"}, - {file = "MarkupSafe-2.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:2b7c57a4dfc4f16f7142221afe5ba4e093e09e728ca65c51f5620c9aaeb9a617"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:8dec4936e9c3100156f8a2dc89c4b88d5c435175ff03413b443469c7c8c5f4d1"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3c6b973f22eb18a789b1460b4b91bf04ae3f0c4234a0a6aa6b0a92f6f7b951d4"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac07bad82163452a6884fe8fa0963fb98c2346ba78d779ec06bd7a6262132aee"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ea3d8a3d18833cf4304cd2fc9cbb1efe188ca9b5efef2bdac7adc20594a0e46b"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d050b3361367a06d752db6ead6e7edeb0009be66bc3bae0ee9d97fb326badc2a"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bec0a414d016ac1a18862a519e54b2fd0fc8bbfd6890376898a6c0891dd82e9f"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:58c98fee265677f63a4385256a6d7683ab1832f3ddd1e66fe948d5880c21a169"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-win32.whl", hash = "sha256:8590b4ae07a35970728874632fed7bd57b26b0102df2d2b233b6d9d82f6c62ad"}, - {file = "MarkupSafe-2.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:823b65d8706e32ad2df51ed89496147a42a2a6e01c13cfb6ffb8b1e92bc910bb"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c8b29db45f8fe46ad280a7294f5c3ec36dbac9491f2d1c17345be8e69cc5928f"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec6a563cff360b50eed26f13adc43e61bc0c04d94b8be985e6fb24b81f6dcfdf"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a549b9c31bec33820e885335b451286e2969a2d9e24879f83fe904a5ce59d70a"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4f11aa001c540f62c6166c7726f71f7573b52c68c31f014c25cc7901deea0b52"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:7b2e5a267c855eea6b4283940daa6e88a285f5f2a67f2220203786dfa59b37e9"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:2d2d793e36e230fd32babe143b04cec8a8b3eb8a3122d2aceb4a371e6b09b8df"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ce409136744f6521e39fd8e2a24c53fa18ad67aa5bc7c2cf83645cce5b5c4e50"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-win32.whl", hash = "sha256:4096e9de5c6fdf43fb4f04c26fb114f61ef0bf2e5604b6ee3019d51b69e8c371"}, - {file = "MarkupSafe-2.1.5-cp37-cp37m-win_amd64.whl", hash = "sha256:4275d846e41ecefa46e2015117a9f491e57a71ddd59bbead77e904dc02b1bed2"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:656f7526c69fac7f600bd1f400991cc282b417d17539a1b228617081106feb4a"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:97cafb1f3cbcd3fd2b6fbfb99ae11cdb14deea0736fc2b0952ee177f2b813a46"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f3fbcb7ef1f16e48246f704ab79d79da8a46891e2da03f8783a5b6fa41a9532"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa9db3f79de01457b03d4f01b34cf91bc0048eb2c3846ff26f66687c2f6d16ab"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffee1f21e5ef0d712f9033568f8344d5da8cc2869dbd08d87c84656e6a2d2f68"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5dedb4db619ba5a2787a94d877bc8ffc0566f92a01c0ef214865e54ecc9ee5e0"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:30b600cf0a7ac9234b2638fbc0fb6158ba5bdcdf46aeb631ead21248b9affbc4"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8dd717634f5a044f860435c1d8c16a270ddf0ef8588d4887037c5028b859b0c3"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-win32.whl", hash = "sha256:daa4ee5a243f0f20d528d939d06670a298dd39b1ad5f8a72a4275124a7819eff"}, - {file = "MarkupSafe-2.1.5-cp38-cp38-win_amd64.whl", hash = "sha256:619bc166c4f2de5caa5a633b8b7326fbe98e0ccbfacabd87268a2b15ff73a029"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7a68b554d356a91cce1236aa7682dc01df0edba8d043fd1ce607c49dd3c1edcf"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:db0b55e0f3cc0be60c1f19efdde9a637c32740486004f20d1cff53c3c0ece4d2"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e53af139f8579a6d5f7b76549125f0d94d7e630761a2111bc431fd820e163b8"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c31f53cdae6ecfa91a77820e8b151dba54ab528ba65dfd235c80b086d68a465"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bff1b4290a66b490a2f4719358c0cdcd9bafb6b8f061e45c7a2460866bf50c2e"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bc1667f8b83f48511b94671e0e441401371dfd0f0a795c7daa4a3cd1dde55bea"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5049256f536511ee3f7e1b3f87d1d1209d327e818e6ae1365e8653d7e3abb6a6"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-win32.whl", hash = "sha256:00e046b6dd71aa03a41079792f8473dc494d564611a8f89bbbd7cb93295ebdcf"}, - {file = "MarkupSafe-2.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:fa173ec60341d6bb97a89f5ea19c85c5643c1e7dedebc22f5181eb73573142c5"}, - {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"}, -] - [[package]] name = "matplotlib-inline" version = "0.1.7" @@ -711,33 +508,6 @@ files = [ {file = "numpy-2.1.1.tar.gz", hash = "sha256:d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd"}, ] -[[package]] -name = "optuna" -version = "4.0.0" -description = "A hyperparameter optimization framework" -optional = false -python-versions = ">=3.7" -files = [ - {file = "optuna-4.0.0-py3-none-any.whl", hash = "sha256:a825c32d13f6085bcb2229b2724a5078f2e0f61a7533e800e580ce41a8c6c10d"}, - {file = "optuna-4.0.0.tar.gz", hash = "sha256:844949f09e2a7353ab414e9cfd783cf0a647a65fc32a7236212ed6a37fe08973"}, -] - -[package.dependencies] -alembic = ">=1.5.0" -colorlog = "*" -numpy = "*" -packaging = ">=20.0" -PyYAML = "*" -sqlalchemy = ">=1.3.0" -tqdm = "*" - -[package.extras] -benchmark = ["asv (>=0.5.0)", "botorch", "cma", "virtualenv"] -checking = ["black", "blackdoc", "flake8", "isort", "mypy", "mypy-boto3-s3", "types-PyYAML", "types-redis", "types-setuptools", "types-tqdm", "typing-extensions (>=3.10.0.0)"] -document = ["ase", "cmaes (>=0.10.0)", "fvcore", "kaleido", "lightgbm", "matplotlib (!=3.6.0)", "pandas", "pillow", "plotly (>=4.9.0)", "scikit-learn", "sphinx", "sphinx-copybutton", "sphinx-gallery", "sphinx-rtd-theme (>=1.2.0)", "torch", "torchvision"] -optional = ["boto3", "cmaes (>=0.10.0)", "google-cloud-storage", "matplotlib (!=3.6.0)", "pandas", "plotly (>=4.9.0)", "redis", "scikit-learn (>=0.24.2)", "scipy", "torch"] -test = ["coverage", "fakeredis[lua]", "kaleido", "moto", "pytest", "scipy (>=1.9.2)", "torch"] - [[package]] name = "packaging" version = "24.1" @@ -1231,93 +1001,6 @@ files = [ {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, ] -[[package]] -name = "sqlalchemy" -version = "2.0.35" -description = "Database Abstraction Library" -optional = false -python-versions = ">=3.7" -files = [ - {file = "SQLAlchemy-2.0.35-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:67219632be22f14750f0d1c70e62f204ba69d28f62fd6432ba05ab295853de9b"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4668bd8faf7e5b71c0319407b608f278f279668f358857dbfd10ef1954ac9f90"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb8bea573863762bbf45d1e13f87c2d2fd32cee2dbd50d050f83f87429c9e1ea"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f552023710d4b93d8fb29a91fadf97de89c5926c6bd758897875435f2a939f33"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:016b2e665f778f13d3c438651dd4de244214b527a275e0acf1d44c05bc6026a9"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:7befc148de64b6060937231cbff8d01ccf0bfd75aa26383ffdf8d82b12ec04ff"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-win32.whl", hash = "sha256:22b83aed390e3099584b839b93f80a0f4a95ee7f48270c97c90acd40ee646f0b"}, - {file = "SQLAlchemy-2.0.35-cp310-cp310-win_amd64.whl", hash = "sha256:a29762cd3d116585278ffb2e5b8cc311fb095ea278b96feef28d0b423154858e"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e21f66748ab725ade40fa7af8ec8b5019c68ab00b929f6643e1b1af461eddb60"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8a6219108a15fc6d24de499d0d515c7235c617b2540d97116b663dade1a54d62"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:042622a5306c23b972192283f4e22372da3b8ddf5f7aac1cc5d9c9b222ab3ff6"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:627dee0c280eea91aed87b20a1f849e9ae2fe719d52cbf847c0e0ea34464b3f7"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:4fdcd72a789c1c31ed242fd8c1bcd9ea186a98ee8e5408a50e610edfef980d71"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:89b64cd8898a3a6f642db4eb7b26d1b28a497d4022eccd7717ca066823e9fb01"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-win32.whl", hash = "sha256:6a93c5a0dfe8d34951e8a6f499a9479ffb9258123551fa007fc708ae2ac2bc5e"}, - {file = "SQLAlchemy-2.0.35-cp311-cp311-win_amd64.whl", hash = "sha256:c68fe3fcde03920c46697585620135b4ecfdfc1ed23e75cc2c2ae9f8502c10b8"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:eb60b026d8ad0c97917cb81d3662d0b39b8ff1335e3fabb24984c6acd0c900a2"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6921ee01caf375363be5e9ae70d08ce7ca9d7e0e8983183080211a062d299468"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8cdf1a0dbe5ced887a9b127da4ffd7354e9c1a3b9bb330dce84df6b70ccb3a8d"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:93a71c8601e823236ac0e5d087e4f397874a421017b3318fd92c0b14acf2b6db"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e04b622bb8a88f10e439084486f2f6349bf4d50605ac3e445869c7ea5cf0fa8c"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1b56961e2d31389aaadf4906d453859f35302b4eb818d34a26fab72596076bb8"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-win32.whl", hash = "sha256:0f9f3f9a3763b9c4deb8c5d09c4cc52ffe49f9876af41cc1b2ad0138878453cf"}, - {file = "SQLAlchemy-2.0.35-cp312-cp312-win_amd64.whl", hash = "sha256:25b0f63e7fcc2a6290cb5f7f5b4fc4047843504983a28856ce9b35d8f7de03cc"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f021d334f2ca692523aaf7bbf7592ceff70c8594fad853416a81d66b35e3abf9"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05c3f58cf91683102f2f0265c0db3bd3892e9eedabe059720492dbaa4f922da1"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:032d979ce77a6c2432653322ba4cbeabf5a6837f704d16fa38b5a05d8e21fa00"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:2e795c2f7d7249b75bb5f479b432a51b59041580d20599d4e112b5f2046437a3"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:cc32b2990fc34380ec2f6195f33a76b6cdaa9eecf09f0c9404b74fc120aef36f"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-win32.whl", hash = "sha256:9509c4123491d0e63fb5e16199e09f8e262066e58903e84615c301dde8fa2e87"}, - {file = "SQLAlchemy-2.0.35-cp37-cp37m-win_amd64.whl", hash = "sha256:3655af10ebcc0f1e4e06c5900bb33e080d6a1fa4228f502121f28a3b1753cde5"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:4c31943b61ed8fdd63dfd12ccc919f2bf95eefca133767db6fbbd15da62078ec"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a62dd5d7cc8626a3634208df458c5fe4f21200d96a74d122c83bc2015b333bc1"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0630774b0977804fba4b6bbea6852ab56c14965a2b0c7fc7282c5f7d90a1ae72"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d625eddf7efeba2abfd9c014a22c0f6b3796e0ffb48f5d5ab106568ef01ff5a"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:ada603db10bb865bbe591939de854faf2c60f43c9b763e90f653224138f910d9"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:c41411e192f8d3ea39ea70e0fae48762cd11a2244e03751a98bd3c0ca9a4e936"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-win32.whl", hash = "sha256:d299797d75cd747e7797b1b41817111406b8b10a4f88b6e8fe5b5e59598b43b0"}, - {file = "SQLAlchemy-2.0.35-cp38-cp38-win_amd64.whl", hash = "sha256:0375a141e1c0878103eb3d719eb6d5aa444b490c96f3fedab8471c7f6ffe70ee"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ccae5de2a0140d8be6838c331604f91d6fafd0735dbdcee1ac78fc8fbaba76b4"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2a275a806f73e849e1c309ac11108ea1a14cd7058577aba962cd7190e27c9e3c"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:732e026240cdd1c1b2e3ac515c7a23820430ed94292ce33806a95869c46bd139"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:890da8cd1941fa3dab28c5bac3b9da8502e7e366f895b3b8e500896f12f94d11"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:c0d8326269dbf944b9201911b0d9f3dc524d64779a07518199a58384c3d37a44"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:b76d63495b0508ab9fc23f8152bac63205d2a704cd009a2b0722f4c8e0cba8e0"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-win32.whl", hash = "sha256:69683e02e8a9de37f17985905a5eca18ad651bf592314b4d3d799029797d0eb3"}, - {file = "SQLAlchemy-2.0.35-cp39-cp39-win_amd64.whl", hash = "sha256:aee110e4ef3c528f3abbc3c2018c121e708938adeeff9006428dd7c8555e9b3f"}, - {file = "SQLAlchemy-2.0.35-py3-none-any.whl", hash = "sha256:2ab3f0336c0387662ce6221ad30ab3a5e6499aab01b9790879b6578fd9b8faa1"}, - {file = "sqlalchemy-2.0.35.tar.gz", hash = "sha256:e11d7ea4d24f0a262bccf9a7cd6284c976c5369dac21db237cff59586045ab9f"}, -] - -[package.dependencies] -greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"} -typing-extensions = ">=4.6.0" - -[package.extras] -aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"] -aioodbc = ["aioodbc", "greenlet (!=0.4.17)"] -aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"] -asyncio = ["greenlet (!=0.4.17)"] -asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"] -mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"] -mssql = ["pyodbc"] -mssql-pymssql = ["pymssql"] -mssql-pyodbc = ["pyodbc"] -mypy = ["mypy (>=0.910)"] -mysql = ["mysqlclient (>=1.4.0)"] -mysql-connector = ["mysql-connector-python"] -oracle = ["cx_oracle (>=8)"] -oracle-oracledb = ["oracledb (>=1.0.1)"] -postgresql = ["psycopg2 (>=2.7)"] -postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"] -postgresql-pg8000 = ["pg8000 (>=1.29.1)"] -postgresql-psycopg = ["psycopg (>=3.0.7)"] -postgresql-psycopg2binary = ["psycopg2-binary"] -postgresql-psycopg2cffi = ["psycopg2cffi"] -postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"] -pymysql = ["pymysql"] -sqlcipher = ["sqlcipher3_binary"] - [[package]] name = "stack-data" version = "0.6.3" @@ -1368,26 +1051,6 @@ files = [ {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"}, ] -[[package]] -name = "tqdm" -version = "4.66.5" -description = "Fast, Extensible Progress Meter" -optional = false -python-versions = ">=3.7" -files = [ - {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"}, - {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"}, -] - -[package.dependencies] -colorama = {version = "*", markers = "platform_system == \"Windows\""} - -[package.extras] -dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] -notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] - [[package]] name = "traitlets" version = "5.14.3" @@ -1403,17 +1066,6 @@ files = [ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"] -[[package]] -name = "typing-extensions" -version = "4.12.2" -description = "Backported and Experimental Type Hints for Python 3.8+" -optional = false -python-versions = ">=3.8" -files = [ - {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"}, - {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"}, -] - [[package]] name = "virtualenv" version = "20.26.4" @@ -1448,4 +1100,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.12" -content-hash = "03a6b1fc5fcc4c0398a0e0220ff31e8461f2205dd90fb74266126501c64b1ec6" +content-hash = "45de7e7704e73a96f7275c655db7c2b1953b06ba4825796739a92e69179e65ee" diff --git a/pyproject.toml b/pyproject.toml index fc37ae3..a7a972a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,6 @@ readme = "README.md" python = "^3.12" lightgbm = "^4.5.0" scikit-learn = "^1.5.2" -optuna = "^4.0.0" [tool.poetry.group.dev.dependencies]