make testing function private

imblearn/over_sampling/tests/test_common.py

@@ -12,7 +12,7 @@
     SMOTENC,
     SVMSMOTE,
 )
-from imblearn.utils.testing import CustomNearestNeighbors
+from imblearn.utils.testing import _CustomNearestNeighbors
 
 
 @pytest.fixture
@@ -76,7 +76,7 @@ def test_smote_m_neighbors(numerical_data, smote):
 def test_numerical_smote_custom_nn(numerical_data, smote, neighbor_estimator_name):
     X, y = numerical_data
     params = {
-        neighbor_estimator_name: CustomNearestNeighbors(n_neighbors=5),
+        neighbor_estimator_name: _CustomNearestNeighbors(n_neighbors=5),
     }
     smote.set_params(**params)
     X_res, _ = smote.fit_resample(X, y)
@@ -86,7 +86,7 @@ def test_numerical_smote_custom_nn(numerical_data, smote, neighbor_estimator_nam
 
 def test_categorical_smote_k_custom_nn(categorical_data):
     X, y = categorical_data
-    smote = SMOTEN(k_neighbors=CustomNearestNeighbors(n_neighbors=5))
+    smote = SMOTEN(k_neighbors=_CustomNearestNeighbors(n_neighbors=5))
     X_res, y_res = smote.fit_resample(X, y)
 
     assert X_res.shape == (80, 3)
@@ -96,7 +96,7 @@ def test_categorical_smote_k_custom_nn(categorical_data):
 def test_heterogeneous_smote_k_custom_nn(heterogeneous_data):
     X, y, categorical_features = heterogeneous_data
     smote = SMOTENC(
-        categorical_features, k_neighbors=CustomNearestNeighbors(n_neighbors=5)
+        categorical_features, k_neighbors=_CustomNearestNeighbors(n_neighbors=5)
     )
     X_res, y_res = smote.fit_resample(X, y)
 
@@ -111,7 +111,7 @@ def test_heterogeneous_smote_k_custom_nn(heterogeneous_data):
 )
 def test_numerical_smote_extra_custom_nn(numerical_data, smote):
     X, y = numerical_data
-    smote.set_params(m_neighbors=CustomNearestNeighbors(n_neighbors=5))
+    smote.set_params(m_neighbors=_CustomNearestNeighbors(n_neighbors=5))
     X_res, y_res = smote.fit_resample(X, y)
 
     assert X_res.shape == (120, 2)

imblearn/under_sampling/_prototype_generation/tests/test_cluster_centroids.py

@@ -10,7 +10,7 @@
 from sklearn.datasets import make_classification
 
 from imblearn.under_sampling import ClusterCentroids
-from imblearn.utils.testing import CustomClusterer
+from imblearn.utils.testing import _CustomClusterer
 
 RND_SEED = 0
 X = np.array(
@@ -170,4 +170,4 @@ def test_cluster_centroids_error_estimator():
         "`cluster_centers_`."
     )
     with pytest.raises(RuntimeError, match=err_msg):
-        ClusterCentroids(estimator=CustomClusterer()).fit_resample(X, Y)
+        ClusterCentroids(estimator=_CustomClusterer()).fit_resample(X, Y)

imblearn/utils/testing.py

@@ -168,7 +168,7 @@ def warns(expected_warning, match=None):
         pass
 
 
-class CustomNearestNeighbors(BaseEstimator):
+class _CustomNearestNeighbors(BaseEstimator):
     """Basic implementation of nearest neighbors not relying on scikit-learn.
 
     `kneighbors_graph` is ignored and `metric` does not have any impact.
@@ -197,7 +197,7 @@ def kneighbors_graph(X=None, n_neighbors=None, mode="connectivity"):
         pass
 
 
-class CustomClusterer(BaseEstimator):
+class _CustomClusterer(BaseEstimator):
     """Class that mimics a cluster that does not expose `cluster_centers_`."""
 
     def __init__(self, n_clusters=1):

imblearn/utils/tests/test_testing.py

@@ -10,7 +10,7 @@
 from sklearn.neighbors._base import KNeighborsMixin
 
 from imblearn.base import SamplerMixin
-from imblearn.utils.testing import all_estimators, CustomNearestNeighbors
+from imblearn.utils.testing import all_estimators, _CustomNearestNeighbors
 
 from imblearn.utils.testing import warns
 
@@ -69,7 +69,7 @@ def test_custom_nearest_neighbors():
     """Check that our custom nearest neighbors can be used for our internal
     duck-typing."""
 
-    neareat_neighbors = CustomNearestNeighbors(n_neighbors=3)
+    neareat_neighbors = _CustomNearestNeighbors(n_neighbors=3)
 
     assert not isinstance(neareat_neighbors, KNeighborsMixin)
     assert hasattr(neareat_neighbors, "kneighbors")