iter

Author: glemaitre

imblearn/over_sampling/_smote/cluster.py

@@ -220,8 +220,6 @@ def _fit_resample(self, X, y):
 
                 # the cluster is already considered balanced
                 if cluster_class_mean < balance_threshold:
-                    print(cluster_class_mean)
-                    print(balance_threshold)
                     continue
 
                 # not enough samples to apply SMOTE

imblearn/over_sampling/_smote/tests/test_kmeans_smote.py

@@ -90,20 +90,16 @@ def test_sample_kmeans_custom(data, k_neighbors, kmeans_estimator):
 
 def test_sample_kmeans_not_enough_clusters(data):
     X, y = data
-    smote = KMeansSMOTE(
-        random_state=42,
-        kmeans_estimator=10,
-        k_neighbors=2,
-    )
+    smote = KMeansSMOTE(cluster_balance_threshold=10, random_state=42)
     with pytest.raises(RuntimeError):
         smote.fit_resample(X, y)
 
 
-@pytest.mark.parametrize("density_exponent", ["auto", 18])
-@pytest.mark.parametrize("cluster_balance_threshold", ["auto", 0.2])
+@pytest.mark.parametrize("density_exponent", ["auto", 10])
+@pytest.mark.parametrize("cluster_balance_threshold", ["auto", 0.1])
 def test_sample_kmeans_density_estimation(density_exponent, cluster_balance_threshold):
     X, y = make_classification(
-        n_samples=10_000, n_classes=2, weights=[0.1, 0.9], random_state=42
+        n_samples=10_000, n_classes=2, weights=[0.3, 0.7], random_state=42
     )
     smote = KMeansSMOTE(
         random_state=0,