[python-package] simplify scikit-learn 1.6+ tags support (#6735)

python-package/lightgbm/compat.py

@@ -14,14 +14,6 @@ try:
     from sklearn.utils.multiclass import check_classification_targets
     from sklearn.utils.validation import assert_all_finite, check_array, check_X_y
 
-    # sklearn.utils Tags types can be imported unconditionally once
-    # lightgbm's minimum scikit-learn version is 1.6 or higher
-    try:
-        from sklearn.utils import ClassifierTags as _sklearn_ClassifierTags
-        from sklearn.utils import RegressorTags as _sklearn_RegressorTags
-    except ImportError:
-        _sklearn_ClassifierTags = None
-        _sklearn_RegressorTags = None
     try:
         from sklearn.exceptions import NotFittedError
         from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold
@@ -148,8 +140,6 @@ except ImportError:
     _LGBMCheckClassificationTargets = None
     _LGBMComputeSampleWeight = None
     _LGBMValidateData = None
-    _sklearn_ClassifierTags = None
-    _sklearn_RegressorTags = None
     _sklearn_version = None
 
 # additional scikit-learn imports only for type hints

python-package/lightgbm/sklearn.py

@@ -40,8 +40,6 @@ from .compat import (
     _LGBMModelBase,
     _LGBMRegressorBase,
     _LGBMValidateData,
-    _sklearn_ClassifierTags,
-    _sklearn_RegressorTags,
     _sklearn_version,
     dt_DataTable,
     pd_DataFrame,
@@ -726,7 +724,7 @@ class LGBMModel(_LGBMModelBase):
         # take whatever tags are provided by BaseEstimator, then modify
         # them with LightGBM-specific values
         return self._update_sklearn_tags_from_dict(
-            tags=_LGBMModelBase.__sklearn_tags__(self),
+            tags=super().__sklearn_tags__(),
             tags_dict=self._more_tags(),
         )
 
@@ -1298,10 +1296,7 @@ class LGBMRegressor(_LGBMRegressorBase, LGBMModel):
         return tags
 
     def __sklearn_tags__(self) -> "_sklearn_Tags":
-        tags = LGBMModel.__sklearn_tags__(self)
-        tags.estimator_type = "regressor"
-        tags.regressor_tags = _sklearn_RegressorTags(multi_label=False)
-        return tags
+        return super().__sklearn_tags__()
 
     def fit(  # type: ignore[override]
         self,
@@ -1360,9 +1355,9 @@ class LGBMClassifier(_LGBMClassifierBase, LGBMModel):
         return tags
 
     def __sklearn_tags__(self) -> "_sklearn_Tags":
-        tags = LGBMModel.__sklearn_tags__(self)
-        tags.estimator_type = "classifier"
-        tags.classifier_tags = _sklearn_ClassifierTags(multi_class=True, multi_label=False)
+        tags = super().__sklearn_tags__()
+        tags.classifier_tags.multi_class = True
+        tags.classifier_tags.multi_label = False
         return tags
 
     def fit(  # type: ignore[override]

tests/python_package_test/test_sklearn.py

@@ -1488,6 +1488,12 @@ def test_sklearn_tags_should_correctly_reflect_lightgbm_specific_values(estimato
         assert sklearn_tags.input_tags.allow_nan is True
         assert sklearn_tags.input_tags.sparse is True
         assert sklearn_tags.target_tags.one_d_labels is True
+        if estimator_class is lgb.LGBMClassifier:
+            assert sklearn_tags.estimator_type == "classifier"
+            assert sklearn_tags.classifier_tags.multi_class is True
+            assert sklearn_tags.classifier_tags.multi_label is False
+        elif estimator_class is lgb.LGBMRegressor:
+            assert sklearn_tags.estimator_type == "regressor"
 
 
 @pytest.mark.parametrize("task", all_tasks)