be more explicit about which sklearn checks to run

kmodes/tests/test_common.py

@@ -1,25 +1,66 @@
 """
 General sklearn tests for the estimators in kmodes.
 """
-from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.estimator_checks import (
     _yield_all_checks,
-    check_parameters_default_constructible)
+    check_parameters_default_constructible
+)
 
 from kmodes.kmodes import KModes
 from kmodes.kprototypes import KPrototypes
 from kmodes.util.testing import _named_check
 
 all_estimators = lambda: (('kmodes', KModes), ('kprototypes', KPrototypes))
 
+KMODES_INCLUDE_CHECKS = (
+    'check_estimators_dtypes',
+    'check_fit_score_takes_y',
+    'check_sample_weights_pandas_series',
+    'check_sample_weights_list',
+    'check_sample_weights_invariance',
+    'check_estimators_fit_returns_self',
+    'check_complex_data',
+    'check_estimators_empty_data_messages',
+    'check_pipeline_consistency',
+    'check_estimators_nan_inf',
+    'check_estimators_overwrite_params',
+    'check_estimator_sparse_data',
+    'check_estimators_overwrite_params',
+    'check_estimators_pickle',
+    'check_fit2d_predict1d',
+    'check_methods_subset_invariance',
+    'check_fit2d_1sample',
+    'check_fit2d_1feature',
+    'check_fit1d',
+    'check_get_params_invariance',
+    'check_set_params',
+    'check_dict_unchanged',
+    'check_dont_overwrite_parameters',
+    'check_fit_idempotent',
+    'check_clusterer_compute_labels_predict',
+    'check_estimators_partial_fit_n_features',
+    'check_non_transformer_estimators_n_iter',
+)
+
+KPROTOTYPES_INCLUDE_CHECKS = (
+    'check_sample_weights_pandas_series',
+    'check_sample_weights_list',
+    'check_sample_weights_invariance',
+    'check_estimator_sparse_data',
+    'check_get_params_invariance',
+    'check_set_params',
+    'check_clusterer_compute_labels_predict',
+    'check_estimators_partial_fit_n_features',
+)
+
 
 def test_all_estimator_no_base_class():
     # test that all_estimators doesn't find abstract classes.
     for name, Estimator in all_estimators():
         msg = ("Base estimators such as {0} should not be included"
                " in all_estimators").format(name)
-        assert_false(name.lower().startswith('base'), msg=msg)
+        assert not name.lower().startswith('base'), msg
 
 
 def test_all_estimators():
@@ -37,22 +78,13 @@ def test_all_estimators():
 
 def test_non_meta_estimators():
     for name, Estimator in all_estimators():
-        estimator = Estimator()
         if name == 'kmodes':
-            for check in _yield_all_checks(name, Estimator):
-                # Skip these
-                if hasattr(check, '__name__'):
-                    if check.__name__ not in ('check_clustering',
-                                              'check_dtype_object'):
-                        yield _named_check(check, name), name, estimator
-                else:
-                    yield check, name, estimator
+            relevant_checks = KMODES_INCLUDE_CHECKS
         elif name == 'kprototypes':
-            for check in _yield_all_checks(name, Estimator):
-                # Only do these
-                if hasattr(check, '__name__') and check.__name__ in (
-                        'check_estimator_sparse_data',
-                        'check_clusterer_compute_labels_predict',
-                        'check_estimators_partial_fit_n_features'
-                ):
-                    yield _named_check(check, name), name, estimator
+            relevant_checks = KPROTOTYPES_INCLUDE_CHECKS
+        else:
+            raise NotImplementedError
+        estimator = Estimator()
+        for check in _yield_all_checks(name, estimator):
+            if hasattr(check, '__name__') and check.__name__ in relevant_checks:
+                yield _named_check(check, name), name, estimator