scikit-learn · rth · Apr 27, 2020 · Apr 24, 2020 · Apr 24, 2020 · Apr 24, 2020
diff --git a/doc/developers/develop.rst b/doc/developers/develop.rst
@@ -246,13 +246,13 @@ whether it is just for you or for contributing it to scikit-learn, there are
 several internals of scikit-learn that you should be aware of in addition to
 the scikit-learn API outlined above. You can check whether your estimator
 adheres to the scikit-learn interface and standards by running
-:func:`utils.estimator_checks.check_estimator` on the class or using
-:func:`~sklearn.utils.parametrize_with_checks` pytest decorator (see its
-docstring for details and possible interactions with `pytest`)::
+:func:`~sklearn.utils.estimator_checks.check_estimator` on an instance. The
+:func:`~sklearn.utils.parametrize_with_checks` pytest decorator can also be
+used (see its docstring for details and possible interactions with `pytest`)::
 
   >>> from sklearn.utils.estimator_checks import check_estimator
   >>> from sklearn.svm import LinearSVC
-  >>> check_estimator(LinearSVC)  # passes
+  >>> check_estimator(LinearSVC())  # passes
 
 The main motivation to make a class compatible to the scikit-learn estimator
 interface might be that you want to use it together with model evaluation and

diff --git a/doc/whats_new/v0.23.rst b/doc/whats_new/v0.23.rst
@@ -511,6 +511,11 @@ Changelog
   matrix from a pandas DataFrame that contains only `SparseArray`s.
   :pr:`16728` by `Thomas Fan`_.
 
+- |API| Passing classes to :func:`utils.estimator_checks.check_estimator` and
+  :func:`utils.estimator_checks.parametrize_with_checks` is now deprecated,
+  and support for classes will be removed in 0.24. Pass instances instead.
+  :pr:`17032` by `Nicolas Hug`_.
+
 :mod:`sklearn.cluster`
 ......................
 

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
@@ -48,7 +48,9 @@ def test_all_estimator_no_base_class():
         assert not name.lower().startswith('base'), msg
 
 
+@ignore_warnings("Passing a class is depr", category=FutureWarning)  # 0.24
 def test_estimator_cls_parameterize_with_checks():
+    # TODO: remove test in 0.24
     # Non-regression test for #16707 to ensure that parametrize_with_checks
     # works with estimator classes
     param_checks = parametrize_with_checks([LogisticRegression])
@@ -105,7 +107,7 @@ def _tested_estimators():
         yield estimator
 
 
-@parametrize_with_checks(_tested_estimators())
+@parametrize_with_checks(list(_tested_estimators()))
 def test_estimators(estimator, check, request):
     # Common tests for estimator instances
     with ignore_warnings(category=(FutureWarning,
@@ -115,7 +117,9 @@ def test_estimators(estimator, check, request):
         check(estimator)
 
 
+@ignore_warnings("Passing a class is depr", category=FutureWarning)  # 0.24
 def test_check_estimator_generate_only():
+    # TODO in 0.24: remove checks on passing a class
     estimator_cls_gen_checks = check_estimator(LogisticRegression,
                                                generate_only=True)
     all_instance_gen_checks = check_estimator(LogisticRegression(),
@@ -238,3 +242,19 @@ def test_all_tests_are_importable():
                                  '__init__.py or an add_subpackage directive '
                                  'in the parent '
                                  'setup.py'.format(missing_tests))
+
+
+# TODO: remove in 0.24
+def test_class_support_deprecated():
+    # Make sure passing classes to check_estimator or parametrize_with_checks
+    # is deprecated
+
+    msg = "Passing a class is deprecated"
+    with pytest.warns(FutureWarning, match=msg):
+        check_estimator(LogisticRegression)
+
+    with pytest.warns(FutureWarning, match=msg):
+        parametrize_with_checks([LogisticRegression])
+
+    # Make sure check_parameters_default_constructible accepts instances now
+    check_parameters_default_constructible('name', LogisticRegression())
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
@@ -33,7 +33,7 @@
 from ..linear_model import Ridge
 
 from ..base import (clone, ClusterMixin, is_classifier, is_regressor,
-                    RegressorMixin, is_outlier_detector)
+                    RegressorMixin, is_outlier_detector, BaseEstimator)
 
 from ..metrics import accuracy_score, adjusted_rand_score, f1_score
 from ..random_projection import BaseRandomProjection
@@ -333,12 +333,15 @@ def _construct_instance(Estimator):
     return estimator
 
 
+# TODO: probably not needed anymore in 0.24 since _generate_class_checks should
+# be removed too. Just put this in check_estimator()
 def _generate_instance_checks(name, estimator):
     """Generate instance checks."""
     yield from ((estimator, partial(check, name))
                 for check in _yield_all_checks(name, estimator))
 
 
+# TODO: remove this in 0.24
 def _generate_class_checks(Estimator):
     """Generate class checks."""
     name = Estimator.__name__
@@ -353,6 +356,8 @@ def _mark_xfail_checks(estimator, check, pytest):
     if isinstance(estimator, type):
         # try to construct estimator instance, if it is unable to then
         # return the estimator class, ignoring the tag
+        # TODO: remove this if block in 0.24 since passing instances isn't
+        # supported anymore
         try:
             estimator = _construct_instance(estimator)
         except Exception:
@@ -385,6 +390,10 @@ def parametrize_with_checks(estimators):
     estimators : list of estimators objects or classes
         Estimators to generated checks for.
 
+        .. deprecated:: 0.23
+           Passing a class is deprecated from version 0.23, and won't be
+           supported in 0.24. Pass an instance instead.
+
     Returns
     -------
     decorator : `pytest.mark.parametrize`
@@ -395,13 +404,21 @@ def parametrize_with_checks(estimators):
     >>> from sklearn.linear_model import LogisticRegression
     >>> from sklearn.tree import DecisionTreeRegressor
 
-    >>> @parametrize_with_checks([LogisticRegression, DecisionTreeRegressor])
+    >>> @parametrize_with_checks([LogisticRegression(),
+    ...                           DecisionTreeRegressor()])
     ... def test_sklearn_compatible_estimator(estimator, check):
     ...     check(estimator)
 
     """
     import pytest
 
+    if any(isinstance(est, type) for est in estimators):
+        # TODO: remove class support in 0.24 and update docstrings
+        msg = ("Passing a class is deprecated since version 0.23 "
+               "and won't be supported in 0.24."
+               "Please pass an instance instead.")
+        warnings.warn(msg, FutureWarning)
+
     checks_generator = chain.from_iterable(
         check_estimator(estimator, generate_only=True)
         for estimator in estimators)
@@ -418,15 +435,17 @@ def check_estimator(Estimator, generate_only=False):
     """Check if estimator adheres to scikit-learn conventions.
 
     This estimator will run an extensive test-suite for input validation,
-    shapes, etc, making sure that the estimator complies with `scikit-leanrn`
+    shapes, etc, making sure that the estimator complies with `scikit-learn`
     conventions as detailed in :ref:`rolling_your_own_estimator`.
     Additional tests for classifiers, regressors, clustering or transformers
     will be run if the Estimator class inherits from the corresponding mixin
     from sklearn.base.
 
     This test can be applied to classes or instances.
     Classes currently have some additional tests that related to construction,
-    while passing instances allows the testing of multiple options.
+    while passing instances allows the testing of multiple options. However,
+    support for classes is deprecated since version 0.23 and will be removed
+    in version 0.24 (class checks will still be run on the instances).
 
     Setting `generate_only=True` returns a generator that yields (estimator,
     check) tuples where the check can be called independently from each
@@ -439,9 +458,13 @@ def check_estimator(Estimator, generate_only=False):
 
     Parameters
     ----------
-    estimator : estimator object or class
+    estimator : estimator object
         Estimator to check. Estimator is a class object or instance.
 
+        .. deprecated:: 0.23
+           Passing a class is deprecated from version 0.23, and won't be
+           supported in 0.24. Pass an instance instead.
+
     generate_only : bool, optional (default=False)
         When `False`, checks are evaluated when `check_estimator` is called.
         When `True`, `check_estimator` returns a generator that yields
@@ -456,8 +479,14 @@ def check_estimator(Estimator, generate_only=False):
         Generator that yields (estimator, check) tuples. Returned when
         `generate_only=True`.
     """
+    # TODO: remove class support in 0.24 and update docstrings
     if isinstance(Estimator, type):
         # got a class
+        msg = ("Passing a class is deprecated since version 0.23 "
+               "and won't be supported in 0.24."
+               "Please pass an instance instead.")
+        warnings.warn(msg, FutureWarning)
+
         checks_generator = _generate_class_checks(Estimator)
     else:
         # got an instance
@@ -2570,6 +2599,12 @@ def check_parameters_default_constructible(name, Estimator):
     # this check works on classes, not instances
     # test default-constructibility
     # get rid of deprecation warnings
+    if isinstance(Estimator, BaseEstimator):
+        # Convert estimator instance to its class
+        # TODO: Always convert to class in 0.24, because check_estimator() will
+        # only accept instances, not classes
+        Estimator = Estimator.__class__
+
     with ignore_warnings(category=FutureWarning):
         estimator = _construct_instance(Estimator)
         # test cloning

diff --git a/sklearn/utils/tests/test_estimator_checks.py b/sklearn/utils/tests/test_estimator_checks.py
@@ -356,6 +356,7 @@ def fit(self, X, y):
     check_fit_score_takes_y("test", TestEstimatorWithDeprecatedFitMethod())
 
 
+@ignore_warnings("Passing a class is depr", category=FutureWarning)  # 0.24
 def test_check_estimator():
     # tests that the estimator actually fails on "bad" estimators.
     # not a complete test of all checks, which are very extensive.
@@ -579,7 +580,10 @@ def test_check_regressor_data_not_an_array():
                         EstimatorInconsistentForPandas())
 
 
+@ignore_warnings("Passing a class is depr", category=FutureWarning)  # 0.24
 def test_check_estimator_required_parameters_skip():
+    # TODO: remove whole test in 0.24 since passes classes to check_estimator()
+    # isn't supported anymore
     class MyEstimator(BaseEstimator):
         _required_parameters = ["special_parameter"]