TST create sparse and dataframe tags for X_types

imblearn/base.py

@@ -130,6 +130,9 @@ def _check_X_y(self, X, y, accept_sparse=None):
         X, y = self._validate_data(X, y, reset=True, accept_sparse=accept_sparse)
         return X, y, binarize_y
 
+    def _more_tags(self):
+        return {"X_types": ["2darray", "sparse", "dataframe"]}
+
 
 def _identity(X, y):
     return X, y

imblearn/over_sampling/_random_over_sampler.py

@@ -241,7 +241,7 @@ def _fit_resample(self, X, y):
 
     def _more_tags(self):
         return {
-            "X_types": ["2darray", "string"],
+            "X_types": ["2darray", "string", "sparse", "dataframe"],
             "sample_indices": True,
             "allow_nan": True,
         }

imblearn/under_sampling/_prototype_selection/_random_under_sampler.py

@@ -108,7 +108,10 @@ def _fit_resample(self, X, y):
                 index_target_class = slice(None)
 
             idx_under = np.concatenate(
-                (idx_under, np.flatnonzero(y == target_class)[index_target_class],),
+                (
+                    idx_under,
+                    np.flatnonzero(y == target_class)[index_target_class],
+                ),
                 axis=0,
             )
 
@@ -118,7 +121,7 @@ def _fit_resample(self, X, y):
 
     def _more_tags(self):
         return {
-            "X_types": ["2darray", "string"],
+            "X_types": ["2darray", "string", "sparse", "dataframe"],
             "sample_indices": True,
             "allow_nan": True,
         }

imblearn/utils/estimator_checks.py

@@ -51,16 +51,22 @@ def _set_checking_parameters(estimator):
 
 
 def _yield_sampler_checks(sampler):
+    tags = sampler._get_tags()
     yield check_target_type
     yield check_samplers_one_label
     yield check_samplers_fit
     yield check_samplers_fit_resample
     yield check_samplers_sampling_strategy_fit_resample
-    yield check_samplers_sparse
-    yield check_samplers_pandas
+    if "sparse" in tags["X_types"]:
+        yield check_samplers_sparse
+    if "dataframe" in tags["X_types"]:
+        yield check_samplers_pandas
     yield check_samplers_list
     yield check_samplers_multiclass_ova
     yield check_samplers_preserve_dtype
+    # we don't filter samplers based on their tag here because we want to make
+    # sure that the fitted attribute does not exist if the tag is not
+    # stipulated
     yield check_samplers_sample_indices
     yield check_samplers_2d_target
 
@@ -75,7 +81,8 @@ def _yield_all_checks(estimator):
     tags = estimator._get_tags()
     if tags["_skip_test"]:
         warnings.warn(
-            f"Explicit SKIP via _skip_test tag for estimator {name}.", SkipTestWarning,
+            f"Explicit SKIP via _skip_test tag for estimator {name}.",
+            SkipTestWarning,
         )
         return
     # trigger our checks if this is a SamplerMixin
@@ -116,6 +123,7 @@ def parametrize_with_checks(estimators):
     ... def test_sklearn_compatible_estimator(estimator, check):
     ...     check(estimator)
     """
+
     def checks_generator():
         for estimator in estimators:
             name = type(estimator).__name__
@@ -124,9 +132,7 @@ def checks_generator():
                 yield _maybe_mark_xfail(estimator, check, pytest)
 
     return pytest.mark.parametrize(
-        "estimator, check",
-        checks_generator(),
-        ids=_get_check_estimator_ids
+        "estimator, check", checks_generator(), ids=_get_check_estimator_ids
     )
 
 
@@ -137,14 +143,22 @@ def check_target_type(name, estimator_orig):
     y = np.linspace(0, 1, 20)
     msg = "Unknown label type: 'continuous'"
     assert_raises_regex(
-        ValueError, msg, estimator.fit_resample, X, y,
+        ValueError,
+        msg,
+        estimator.fit_resample,
+        X,
+        y,
     )
     # if the target is multilabel then we should raise an error
     rng = np.random.RandomState(42)
     y = rng.randint(2, size=(20, 3))
     msg = "Multilabel and multioutput targets are not supported."
     assert_raises_regex(
-        ValueError, msg, estimator.fit_resample, X, y,
+        ValueError,
+        msg,
+        estimator.fit_resample,
+        X,
+        y,
     )
 
 
@@ -385,9 +399,7 @@ def check_samplers_sample_indices(name, sampler_orig):
         assert not hasattr(sampler, "sample_indices_")
 
 
-def check_classifier_on_multilabel_or_multioutput_targets(
-    name, estimator_orig
-):
+def check_classifier_on_multilabel_or_multioutput_targets(name, estimator_orig):
     estimator = clone(estimator_orig)
     X, y = make_multilabel_classification(n_samples=30)
     msg = "Multilabel and multioutput targets are not supported."