MAINT use sklearn_compat for multi-version scikit-learn compatibilities

python-package/lightgbm/compat.py

@@ -12,91 +12,11 @@
     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 assert_all_finite, check_array, check_X_y
-
-    try:
-        from sklearn.exceptions import NotFittedError
-        from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold
-    except ImportError:
-        from sklearn.cross_validation import BaseCrossValidator, GroupKFold, StratifiedKFold
-        from sklearn.utils.validation import NotFittedError
-    try:
-        from sklearn.utils.validation import _check_sample_weight
-    except ImportError:
-        from sklearn.utils.validation import check_consistent_length
-
-        # dummy function to support older version of scikit-learn
-        def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any:
-            check_consistent_length(sample_weight, X)
-            return sample_weight
-
-    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,
-            X,
-            y="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,
-        ):
-            # 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
-
-            # _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
+    from sklearn.utils.validation import assert_all_finite
+    from sklearn.exceptions import NotFittedError
+    from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold
+    from sklearn.utils.validation import _check_sample_weight
+    from .sklearn_compat.utils.validation import validate_data
 
     SKLEARN_INSTALLED = True
     _LGBMBaseCrossValidator = BaseCrossValidator
@@ -144,13 +64,7 @@ class _LGBMRegressorBase:  # type: ignore
 
 # 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
-
+    from .sklearn_compat.utils import Tags as _sklearn_Tags
 
 """pandas"""
 try: