[python-package] require scikit-learn>=0.24.2, make scikit-learn estimators compatible with scikit-learn>=1.6.0dev

.ci/test-python-latest.sh

@@ -22,7 +22,7 @@ python -m pip install \
         'numpy>=2.0.0.dev0' \
         'matplotlib>=3.10.0.dev0' \
         'pandas>=3.0.0.dev0' \
-        'scikit-learn==1.5.*' \
+        'scikit-learn>=1.6.dev0' \
         'scipy>=1.15.0.dev0'
 
 python -m pip install \

.ci/test-python-oldest.sh

@@ -15,7 +15,7 @@ pip install \
   'numpy==1.19.0' \
   'pandas==1.1.3' \
   'pyarrow==6.0.1' \
-  'scikit-learn==0.24.0' \
+  'scikit-learn==0.24.2' \
   'scipy==1.6.0' \
 || exit 1
 echo "done installing lightgbm's dependencies"

.ci/test.sh

@@ -103,6 +103,7 @@ if [[ $TASK == "lint" ]]; then
         'mypy>=1.11.1' \
         'pre-commit>=3.8.0' \
         'pyarrow-core>=17.0' \
+        'scikit-learn>=1.5.2' \
         'r-lintr>=3.1.2'
     source activate $CONDA_ENV
     echo "Linting Python code"

python-package/lightgbm/compat.py

@@ -1,12 +1,13 @@
 # coding: utf-8
 """Compatibility library."""
 
-from typing import Any, List
+from typing import TYPE_CHECKING, Any, List
 
 # scikit-learn is intentionally imported first here,
 # see https://github.com/microsoft/LightGBM/issues/6509
 """sklearn"""
 try:
+    from sklearn import __version__ as _sklearn_version
     from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
     from sklearn.preprocessing import LabelEncoder
     from sklearn.utils.class_weight import compute_sample_weight
@@ -29,6 +30,69 @@ 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)
+            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
@@ -38,12 +102,11 @@ def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any:
     LGBMNotFittedError = NotFittedError
     _LGBMStratifiedKFold = StratifiedKFold
     _LGBMGroupKFold = GroupKFold
-    _LGBMCheckXY = check_X_y
-    _LGBMCheckArray = check_array
     _LGBMCheckSampleWeight = _check_sample_weight
     _LGBMAssertAllFinite = assert_all_finite
     _LGBMCheckClassificationTargets = check_classification_targets
     _LGBMComputeSampleWeight = compute_sample_weight
+    _LGBMValidateData = validate_data
 except ImportError:
     SKLEARN_INSTALLED = False
 
@@ -67,12 +130,22 @@ class _LGBMRegressorBase:  # type: ignore
     LGBMNotFittedError = ValueError
     _LGBMStratifiedKFold = None
     _LGBMGroupKFold = None
-    _LGBMCheckXY = None
-    _LGBMCheckArray = 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: