scikit-learn
diff --git a/‎sklearn/utils/tests/test_estimator_checks.py
Lines changed: 107 additions & 44 deletions b/‎sklearn/utils/tests/test_estimator_checks.py
Lines changed: 107 additions & 44 deletions
@@ -48,12 +48,19 @@
     check_classifiers_multilabel_output_format_decision_function,
     check_classifiers_multilabel_output_format_predict,
     check_classifiers_multilabel_output_format_predict_proba,
+    check_classifiers_one_label_sample_weights,
     check_dataframe_column_names_consistency,
     check_decision_proba_consistency,
+    check_dict_unchanged,
+    check_dont_overwrite_parameters,
     check_estimator,
     check_estimator_cloneable,
     check_estimator_repr,
+    check_estimator_sparse_array,
+    check_estimator_sparse_matrix,
     check_estimator_tags_renamed,
+    check_estimators_nan_inf,
+    check_estimators_overwrite_params,
     check_estimators_unfitted,
     check_fit_check_is_fitted,
     check_fit_score_takes_y,
@@ -62,8 +69,10 @@
     check_no_attributes_set_in_init,
     check_outlier_contamination,
     check_outlier_corruption,
+    check_parameters_default_constructible,
     check_regressor_data_not_an_array,
     check_requires_y_none,
+    check_sample_weights_pandas_series,
     check_set_params,
     set_random_state,
 )
@@ -573,40 +582,58 @@ def fit(self, X, y):
     check_fit_score_takes_y("test", TestEstimatorWithDeprecatedFitMethod())
 
 
-def test_check_estimator():
-    # tests that the estimator actually fails on "bad" estimators.
-    # not a complete test of all checks, which are very extensive.
-
-    # check that we have a set_params and can clone
+def test_check_estimator_with_class_removed():
+    """Test that passing a class instead of an instance fails."""
     msg = "Passing a class was deprecated"
     with raises(TypeError, match=msg):
-        check_estimator(object)
+        check_estimator(LogisticRegression)
+
+
+def test_mutable_default_params():
+    """Test that constructor cannot have mutable default parameters."""
     msg = (
         "Parameter 'p' of estimator 'HasMutableParameters' is of type "
         "object which is not allowed"
     )
     # check that the "default_constructible" test checks for mutable parameters
-    check_estimator(HasImmutableParameters())  # should pass
+    check_parameters_default_constructible(
+        "Immutable", HasImmutableParameters()
+    )  # should pass
     with raises(AssertionError, match=msg):
-        check_estimator(HasMutableParameters())
+        check_parameters_default_constructible("Mutable", HasMutableParameters())
+
+
+def test_check_set_params():
+    """Check set_params doesn't fail and sets the right values."""
     # check that values returned by get_params match set_params
     msg = "get_params result does not match what was passed to set_params"
     with raises(AssertionError, match=msg):
         check_set_params("test", ModifiesValueInsteadOfRaisingError())
+
     with warnings.catch_warnings(record=True) as records:
         check_set_params("test", RaisesErrorInSetParams())
     assert UserWarning in [rec.category for rec in records]
 
     with raises(AssertionError, match=msg):
-        check_estimator(ModifiesAnotherValue())
-    # check that we have a fit method
-    msg = "object has no attribute 'fit'"
-    with raises(AttributeError, match=msg):
-        check_estimator(BaseEstimator())
-    # check that fit does input validation
-    msg = "Did not raise"
+        check_set_params("test", ModifiesAnotherValue())
+
+
+def test_check_estimators_nan_inf():
+    # check that predict does input validation (doesn't accept dicts in input)
+    msg = "Estimator NoCheckinPredict doesn't check for NaN and inf in predict"
     with raises(AssertionError, match=msg):
-        check_estimator(BaseBadClassifier())
+        check_estimators_nan_inf("NoCheckinPredict", NoCheckinPredict())
+
+
+def test_check_dict_unchanged():
+    # check that estimator state does not change
+    # at transform/predict/predict_proba time
+    msg = "Estimator changes __dict__ during predict"
+    with raises(AssertionError, match=msg):
+        check_dict_unchanged("test", ChangesDict())
+
+
+def test_check_sample_weights_pandas_series():
     # check that sample_weights in fit accepts pandas.Series type
     try:
         from pandas import Series  # noqa
@@ -616,27 +643,28 @@ def test_check_estimator():
             "'sample_weight' parameter is of type pandas.Series"
         )
         with raises(ValueError, match=msg):
-            check_estimator(NoSampleWeightPandasSeriesType())
+            check_sample_weights_pandas_series(
+                "NoSampleWeightPandasSeriesType", NoSampleWeightPandasSeriesType()
+            )
     except ImportError:
         pass
-    # check that predict does input validation (doesn't accept dicts in input)
-    msg = "Estimator NoCheckinPredict doesn't check for NaN and inf in predict"
-    with raises(AssertionError, match=msg):
-        check_estimator(NoCheckinPredict())
-    # check that estimator state does not change
-    # at transform/predict/predict_proba time
-    msg = "Estimator changes __dict__ during predict"
-    with raises(AssertionError, match=msg):
-        check_estimator(ChangesDict())
+
+
+def test_check_estimators_overwrite_params():
     # check that `fit` only changes attributes that
     # are private (start with an _ or end with a _).
     msg = (
         "Estimator ChangesWrongAttribute should not change or mutate  "
         "the parameter wrong_attribute from 0 to 1 during fit."
     )
     with raises(AssertionError, match=msg):
-        check_estimator(ChangesWrongAttribute())
-    check_estimator(ChangesUnderscoreAttribute())
+        check_estimators_overwrite_params(
+            "ChangesWrongAttribute", ChangesWrongAttribute()
+        )
+    check_estimators_overwrite_params("test", ChangesUnderscoreAttribute())
+
+
+def test_check_dont_overwrite_parameters():
     # check that `fit` doesn't add any public attribute
     msg = (
         r"Estimator adds public attribute\(s\) during the fit method."
@@ -645,7 +673,10 @@ def test_check_estimator():
         " with _ but wrong_attribute added"
     )
     with raises(AssertionError, match=msg):
-        check_estimator(SetsWrongAttribute())
+        check_dont_overwrite_parameters("test", SetsWrongAttribute())
+
+
+def test_check_methods_sample_order_invariance():
     # check for sample order invariance
     name = NotInvariantSampleOrder.__name__
     method = "predict"
@@ -654,25 +685,53 @@ def test_check_estimator():
         "with different sample order."
     ).format(method=method, name=name)
     with raises(AssertionError, match=msg):
-        check_estimator(NotInvariantSampleOrder())
+        check_methods_sample_order_invariance(
+            "NotInvariantSampleOrder", NotInvariantSampleOrder()
+        )
+
+
+def test_check_methods_subset_invariance():
     # check for invariant method
     name = NotInvariantPredict.__name__
     method = "predict"
     msg = ("{method} of {name} is not invariant when applied to a subset.").format(
         method=method, name=name
     )
     with raises(AssertionError, match=msg):
-        check_estimator(NotInvariantPredict())
+        check_methods_subset_invariance("NotInvariantPredict", NotInvariantPredict())
+
+
+def test_check_estimator_sparse_data():
     # check for sparse data input handling
     name = NoSparseClassifier.__name__
     msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
     with raises(AssertionError, match=msg):
-        check_estimator(NoSparseClassifier("sparse_matrix"))
+        check_estimator_sparse_matrix(name, NoSparseClassifier("sparse_matrix"))
 
     if SPARRAY_PRESENT:
         with raises(AssertionError, match=msg):
-            check_estimator(NoSparseClassifier("sparse_array"))
+            check_estimator_sparse_array(name, NoSparseClassifier("sparse_array"))
 
+    # Large indices test on bad estimator
+    msg = (
+        "Estimator LargeSparseNotSupportedClassifier doesn't seem to "
+        r"support \S{3}_64 matrix, and is not failing gracefully.*"
+    )
+    with raises(AssertionError, match=msg):
+        check_estimator_sparse_matrix(
+            "LargeSparseNotSupportedClassifier",
+            LargeSparseNotSupportedClassifier("sparse_matrix"),
+        )
+
+    if SPARRAY_PRESENT:
+        with raises(AssertionError, match=msg):
+            check_estimator_sparse_array(
+                "LargeSparseNotSupportedClassifier",
+                LargeSparseNotSupportedClassifier("sparse_array"),
+            )
+
+
+def test_check_classifiers_one_label_sample_weights():
     # check for classifiers reducing to less than two classes via sample weights
     name = OneClassSampleErrorClassifier.__name__
     msg = (
@@ -681,19 +740,23 @@ def test_check_estimator():
         "'class'."
     )
     with raises(AssertionError, match=msg):
-        check_estimator(OneClassSampleErrorClassifier())
+        check_classifiers_one_label_sample_weights(
+        )
 
-    # Large indices test on bad estimator
-    msg = (
-        "Estimator LargeSparseNotSupportedClassifier doesn't seem to "
-        r"support \S{3}_64 matrix, and is not failing gracefully.*"
-    )
-    with raises(AssertionError, match=msg):
-        check_estimator(LargeSparseNotSupportedClassifier("sparse_matrix"))
 
-    if SPARRAY_PRESENT:
-        with raises(AssertionError, match=msg):
-            check_estimator(LargeSparseNotSupportedClassifier("sparse_array"))
+def test_check_estimator():
+    # tests that the estimator actually fails on "bad" estimators.
+    # not a complete test of all checks, which are very extensive.
+
+    # check that we have a fit method
+    msg = "object has no attribute 'fit'"
+    with raises(AttributeError, match=msg):
+        check_estimator(BaseEstimator())
+    # check that fit does input validation
+    msg = "Did not raise"
+    with raises(AssertionError, match=msg):
+        check_estimator(BaseBadClassifier())
 
     # does error on binary_only untagged estimator
     msg = "Only 2 classes are supported"