scikit-learn · OmarManzoor · Sep 6, 2024 · Aug 21, 2024 · Aug 22, 2024 · Aug 22, 2024
diff --git a/doc/developers/develop.rst b/doc/developers/develop.rst
@@ -562,15 +562,6 @@ for your estimator's tags. For example::
 You can create a new subclass of :class:`~sklearn.utils.Tags` if you wish
 to add new tags to the existing set.
 
-In addition to the tags, estimators also need to declare any non-optional
-parameters to ``__init__`` in the ``_required_parameters`` class attribute,
-which is a list or tuple.  If ``_required_parameters`` is only
-``["estimator"]`` or ``["base_estimator"]``, then the estimator will be
-instantiated with an instance of ``LogisticRegression`` (or
-``RidgeRegression`` if the estimator is a regressor) in the tests. The choice
-of these two models is somewhat idiosyncratic but both should provide robust
-closed-form solutions.
-
 .. _developer_api_set_output:
 
 Developer API for `set_output`

diff --git a/sklearn/base.py b/sklearn/base.py
@@ -1037,9 +1037,12 @@ def fit_predict(self, X, y=None, **kwargs):
 class MetaEstimatorMixin:
     """Mixin class for all meta estimators in scikit-learn.
 
-    This mixin defines the following functionality:
+    This mixin is empty, and only exists to indicate that the estimator is a
+    meta-estimator.
 
-    - define `_required_parameters` that specify the mandatory `estimator` parameter.
+    .. versionchanged:: 1.6
+        The `_required_parameters` is now removed and is unnecessary since tests are
+        refactored and don't use this anymore.
 
     Examples
     --------
@@ -1061,8 +1064,6 @@ class MetaEstimatorMixin:
     LogisticRegression()
     """
 
-    _required_parameters = ["estimator"]
-
 
 class MultiOutputMixin:
     """Mixin to mark estimators that support multioutput."""

diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
@@ -287,8 +287,6 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
     :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`.
     """
 
-    _required_parameters = ["transformers"]
-
     _parameter_constraints: dict = {
         "transformers": [list, Hidden(tuple)],
         "remainder": [
@@ -1322,6 +1320,21 @@ def get_metadata_routing(self):
 
         return router
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags._xfail_checks = {
+            "check_estimators_empty_data_messages": "FIXME",
+            "check_estimators_nan_inf": "FIXME",
+            "check_estimator_sparse_array": "FIXME",
+            "check_estimator_sparse_matrix": "FIXME",
+            "check_transformer_data_not_an_array": "FIXME",
+            "check_fit1d": "FIXME",
+            "check_fit2d_predict1d": "FIXME",
+            "check_complex_data": "FIXME",
+            "check_fit2d_1feature": "FIXME",
+        }
+        return tags
+
 
 def _check_X(X):
     """Use check_array only when necessary, e.g. on lists and other non-array-likes."""

diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py
@@ -1279,8 +1279,6 @@ class SparseCoder(_BaseSparseCoding, BaseEstimator):
            [ 0.,  1.,  1.,  0.,  0.]])
     """
 
-    _required_parameters = ["dictionary"]
-
     def __init__(
         self,
         dictionary,

diff --git a/sklearn/ensemble/_base.py b/sklearn/ensemble/_base.py
@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from abc import ABCMeta, abstractmethod
-from typing import List
 
 import numpy as np
 from joblib import effective_n_jobs
@@ -106,9 +105,6 @@ class BaseEnsemble(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
         The collection of fitted base estimators.
     """
 
-    # overwrite _required_parameters from MetaEstimatorMixin
-    _required_parameters: List[str] = []
-
     @abstractmethod
     def __init__(
         self,
@@ -200,8 +196,6 @@ class _BaseHeterogeneousEnsemble(
         appear in `estimators_`.
     """
 
-    _required_parameters = ["estimators"]
-
     @property
     def named_estimators(self):
         """Dictionary to access any fitted sub-estimators by name.

diff --git a/sklearn/model_selection/_classification_threshold.py b/sklearn/model_selection/_classification_threshold.py
@@ -87,7 +87,6 @@ class BaseThresholdClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator
           error.
     """
 
-    _required_parameters = ["estimator"]
     _parameter_constraints: dict = {
         "estimator": [
             HasMethods(["fit", "predict_proba"]),

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -1532,8 +1532,6 @@ class GridSearchCV(BaseSearchCV):
      'std_fit_time', 'std_score_time', 'std_test_score']
     """
 
-    _required_parameters = ["estimator", "param_grid"]
-
     _parameter_constraints: dict = {
         **BaseSearchCV._parameter_constraints,
         "param_grid": [dict, list],
@@ -1913,8 +1911,6 @@ class RandomizedSearchCV(BaseSearchCV):
     {'C': np.float64(2...), 'penalty': 'l1'}
     """
 
-    _required_parameters = ["estimator", "param_distributions"]
-
     _parameter_constraints: dict = {
         **BaseSearchCV._parameter_constraints,
         "param_distributions": [dict, list],

diff --git a/sklearn/model_selection/_search_successive_halving.py b/sklearn/model_selection/_search_successive_halving.py
@@ -378,6 +378,9 @@ def __sklearn_tags__(self):
                     "Fail during parameter check since min/max resources requires"
                     " more samples"
                 ),
+                "check_estimators_nan_inf": "FIXME",
+                "check_classifiers_one_label_sample_weights": "FIXME",
+                "check_fit2d_1feature": "FIXME",
             }
         )
         return tags
@@ -668,8 +671,6 @@ class HalvingGridSearchCV(BaseSuccessiveHalving):
     {'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
     """
 
-    _required_parameters = ["estimator", "param_grid"]
-
     _parameter_constraints: dict = {
         **BaseSuccessiveHalving._parameter_constraints,
         "param_grid": [dict, list],
@@ -1018,8 +1019,6 @@ class HalvingRandomSearchCV(BaseSuccessiveHalving):
     {'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
     """
 
-    _required_parameters = ["estimator", "param_distributions"]
-
     _parameter_constraints: dict = {
         **BaseSuccessiveHalving._parameter_constraints,
         "param_distributions": [dict, list],

diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
@@ -152,8 +152,6 @@ class Pipeline(_BaseComposition):
     """
 
     # BaseEstimator interface
-    _required_parameters = ["steps"]
-
     _parameter_constraints: dict = {
         "steps": [list, Hidden(tuple)],
         "memory": [None, str, HasMethods(["cache"])],
@@ -1427,8 +1425,6 @@ class FeatureUnion(TransformerMixin, _BaseComposition):
     :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`.
     """
 
-    _required_parameters = ["transformer_list"]
-
     def __init__(
         self,
         transformer_list,
@@ -1882,6 +1878,15 @@ def get_metadata_routing(self):
 
         return router
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags._xfail_checks = {
+            "check_estimators_overwrite_params": "FIXME",
+            "check_estimators_nan_inf": "FIXME",
+            "check_dont_overwrite_parameters": "FIXME",
+        }
+        return tags
+
 
 def make_union(*transformers, n_jobs=None, verbose=False):
     """Construct a :class:`FeatureUnion` from the given transformers.

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -26,14 +26,15 @@
     MeanShift,
     SpectralClustering,
 )
+from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_blobs
 from sklearn.exceptions import ConvergenceWarning, FitFailedWarning
+
+# make it possible to discover experimental estimators when calling `all_estimators`
 from sklearn.experimental import (
     enable_halving_search_cv,  # noqa
     enable_iterative_imputer,  # noqa
 )
-
-# make it possible to discover experimental estimators when calling `all_estimators`
 from sklearn.linear_model import LogisticRegression
 from sklearn.manifold import TSNE, Isomap, LocallyLinearEmbedding
 from sklearn.neighbors import (
@@ -43,7 +44,7 @@
     RadiusNeighborsClassifier,
     RadiusNeighborsRegressor,
 )
-from sklearn.pipeline import make_pipeline
+from sklearn.pipeline import FeatureUnion, make_pipeline
 from sklearn.preprocessing import (
     FunctionTransformer,
     MinMaxScaler,
@@ -54,11 +55,9 @@
 from sklearn.utils import all_estimators
 from sklearn.utils._tags import get_tags
 from sklearn.utils._test_common.instance_generator import (
-    _generate_column_transformer_instances,
     _generate_pipeline,
     _generate_search_cv_instances,
     _get_check_estimator_ids,
-    _set_checking_parameters,
     _tested_estimators,
 )
 from sklearn.utils._testing import (
@@ -139,7 +138,6 @@ def test_estimators(estimator, check, request):
     with ignore_warnings(
         category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
     ):
-        _set_checking_parameters(estimator)
         check(estimator)
 
 
@@ -285,7 +283,6 @@ def check_field_types(tags, defaults):
     "estimator", _tested_estimators(), ids=_get_check_estimator_ids
 )
 def test_check_n_features_in_after_fitting(estimator):
-    _set_checking_parameters(estimator)
     check_n_features_in_after_fitting(estimator.__class__.__name__, estimator)
 
 
@@ -324,7 +321,8 @@ def _estimators_that_predict_in_fit():
     "estimator", column_name_estimators, ids=_get_check_estimator_ids
 )
 def test_pandas_column_name_consistency(estimator):
-    _set_checking_parameters(estimator)
+    if isinstance(estimator, ColumnTransformer):
+        pytest.skip("ColumnTransformer is not tested here")
     with ignore_warnings(category=(FutureWarning)):
         with warnings.catch_warnings(record=True) as record:
             check_dataframe_column_names_consistency(
@@ -360,7 +358,6 @@ def _include_in_get_feature_names_out_check(transformer):
     "transformer", GET_FEATURES_OUT_ESTIMATORS, ids=_get_check_estimator_ids
 )
 def test_transformers_get_feature_names_out(transformer):
-    _set_checking_parameters(transformer)
 
     with ignore_warnings(category=(FutureWarning)):
         check_transformer_get_feature_names_out(
@@ -381,7 +378,6 @@ def test_transformers_get_feature_names_out(transformer):
 )
 def test_estimators_get_feature_names_out_error(estimator):
     estimator_name = estimator.__class__.__name__
-    _set_checking_parameters(estimator)
     check_get_feature_names_out_error(estimator_name, estimator)
 
 
@@ -409,14 +405,14 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
     chain(
         _tested_estimators(),
         _generate_pipeline(),
-        _generate_column_transformer_instances(),
         _generate_search_cv_instances(),
     ),
     ids=_get_check_estimator_ids,
 )
 def test_check_param_validation(estimator):
+    if isinstance(estimator, FeatureUnion):
+        pytest.skip("FeatureUnion is not tested here")
     name = estimator.__class__.__name__
-    _set_checking_parameters(estimator)
     check_param_validation(name, estimator)
 
 
@@ -481,7 +477,6 @@ def test_set_output_transform(estimator):
             f"Skipping check_set_output_transform for {name}: Does not support"
             " set_output API"
         )
-    _set_checking_parameters(estimator)
     with ignore_warnings(category=(FutureWarning)):
         check_set_output_transform(estimator.__class__.__name__, estimator)
 
@@ -505,7 +500,6 @@ def test_set_output_transform_configured(estimator, check_func):
             f"Skipping {check_func.__name__} for {name}: Does not support"
             " set_output API yet"
         )
-    _set_checking_parameters(estimator)
     with ignore_warnings(category=(FutureWarning)):
         check_func(estimator.__class__.__name__, estimator)
 
@@ -523,8 +517,6 @@ def test_check_inplace_ensure_writeable(estimator):
     else:
         raise SkipTest(f"{name} doesn't require writeable input.")
 
-    _set_checking_parameters(estimator)
-
     # The following estimators can work inplace only with certain settings
     if name == "HDBSCAN":
         estimator.set_params(metric="precomputed", algorithm="brute")