# coding: utf-8 """Compatibility library.""" import inspect from typing import TYPE_CHECKING, Any, List # scikit-learn is intentionally imported first here, # see https://github.com/lightgbm-org/LightGBM/issues/6509 """sklearn""" try: from sklearn import __version__ as _sklearn_version from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin from sklearn.exceptions import NotFittedError from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold from sklearn.preprocessing import LabelEncoder from sklearn.utils.class_weight import compute_sample_weight from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import _check_sample_weight, assert_all_finite, check_array, check_X_y # As of https://github.com/scikit-learn/scikit-learn/pull/32212, scikit-learn started raising an error # when sample weights are all 0. This argument allow_all_zero_weights can be used switch back # to the old behavior of allowing them. # # This can be removed when the minimum scikit-learn version supported here is v1.9. SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = ( "allow_all_zero_weights" in inspect.signature(_check_sample_weight).parameters ) try: from sklearn.utils.validation import validate_data except ImportError: # validate_data() was added in scikit-learn 1.6, this function roughly imitates it for older versions. # It can be removed when lightgbm's minimum scikit-learn version is at least 1.6. def validate_data( _estimator: Any, X: Any, y: Any = "no_validation", accept_sparse: bool = True, # 'force_all_finite' was renamed to 'ensure_all_finite' in scikit-learn 1.6 ensure_all_finite: bool = False, ensure_min_samples: int = 1, # trap other keyword arguments that only work on scikit-learn >=1.6, like 'reset' **ignored_kwargs: Any, ) -> Any: # it's safe to import _num_features unconditionally because: # # * it was first added in scikit-learn 0.24.2 # * lightgbm cannot be used with scikit-learn versions older than that # * this validate_data() re-implementation will not be called in scikit-learn>=1.6 # from sklearn.utils.validation import _num_features # noqa: PLC0415 # _num_features() raises a TypeError on 1-dimensional input. That's a problem # because scikit-learn's 'check_fit1d' estimator check sets that expectation that # estimators must raise a ValueError when a 1-dimensional input is passed to fit(). # # So here, lightgbm avoids calling _num_features() on 1-dimensional inputs. if hasattr(X, "shape") and len(X.shape) == 1: n_features_in_ = 1 else: n_features_in_ = _num_features(X) no_val_y = isinstance(y, str) and y == "no_validation" # NOTE: check_X_y() calls check_array() internally, so only need to call one or the other of them here if no_val_y: X = check_array( X, accept_sparse=accept_sparse, force_all_finite=ensure_all_finite, ensure_min_samples=ensure_min_samples, ) else: X, y = check_X_y( X, y, accept_sparse=accept_sparse, force_all_finite=ensure_all_finite, ensure_min_samples=ensure_min_samples, ) # this only needs to be updated at fit() time _estimator.n_features_in_ = n_features_in_ # raise the same error that scikit-learn's `validate_data()` does on scikit-learn>=1.6 if _estimator.__sklearn_is_fitted__() and _estimator._n_features != n_features_in_: raise ValueError( f"X has {n_features_in_} features, but {_estimator.__class__.__name__} " f"is expecting {_estimator._n_features} features as input." ) if no_val_y: return X else: return X, y SKLEARN_INSTALLED = True _LGBMBaseCrossValidator = BaseCrossValidator _LGBMModelBase = BaseEstimator _LGBMRegressorBase = RegressorMixin _LGBMClassifierBase = ClassifierMixin _LGBMLabelEncoder = LabelEncoder LGBMNotFittedError = NotFittedError _LGBMStratifiedKFold = StratifiedKFold _LGBMGroupKFold = GroupKFold _LGBMCheckSampleWeight = _check_sample_weight _LGBMAssertAllFinite = assert_all_finite _LGBMCheckClassificationTargets = check_classification_targets _LGBMComputeSampleWeight = compute_sample_weight _LGBMValidateData = validate_data except ImportError: SKLEARN_INSTALLED = False SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False class _LGBMModelBase: # type: ignore """Dummy class for sklearn.base.BaseEstimator.""" pass class _LGBMClassifierBase: # type: ignore """Dummy class for sklearn.base.ClassifierMixin.""" pass class _LGBMRegressorBase: # type: ignore """Dummy class for sklearn.base.RegressorMixin.""" pass _LGBMBaseCrossValidator = None _LGBMLabelEncoder = None LGBMNotFittedError = ValueError _LGBMStratifiedKFold = None _LGBMGroupKFold = None _LGBMCheckSampleWeight = None _LGBMAssertAllFinite = None _LGBMCheckClassificationTargets = None _LGBMComputeSampleWeight = None _LGBMValidateData = None _sklearn_version = None # additional scikit-learn imports only for type hints if TYPE_CHECKING: # sklearn.utils.Tags can be imported unconditionally once # lightgbm's minimum scikit-learn version is 1.6 or higher try: from sklearn.utils import Tags as _sklearn_Tags except ImportError: _sklearn_Tags = None """pandas""" try: from pandas import CategoricalDtype as pd_CategoricalDtype from pandas import DataFrame as pd_DataFrame from pandas import Series as pd_Series from pandas import concat PANDAS_INSTALLED = True except ImportError: PANDAS_INSTALLED = False class pd_Series: # type: ignore """Dummy class for pandas.Series.""" def __init__(self, *args: Any, **kwargs: Any): pass class pd_DataFrame: # type: ignore """Dummy class for pandas.DataFrame.""" def __init__(self, *args: Any, **kwargs: Any): pass class pd_CategoricalDtype: # type: ignore """Dummy class for pandas.CategoricalDtype.""" def __init__(self, *args: Any, **kwargs: Any): pass concat = None """cpu_count()""" def _LGBMCpuCount(only_physical_cores: bool = True) -> int: ret: int try: from joblib import cpu_count # noqa: I001,PLC0415 ret = cpu_count(only_physical_cores=only_physical_cores) except ImportError: try: from psutil import cpu_count # noqa: I001,PLC0415 ret = cpu_count(logical=not only_physical_cores) or 1 except ImportError: from multiprocessing import cpu_count # noqa: I001,PLC0415 ret = cpu_count() return ret __all__: List[str] = []