MAINT: split test for different SMOTE variants (#485)

Author: glemaitre

imblearn/over_sampling/tests/test_borderline_smote.py

@@ -0,0 +1,44 @@
+import pytest
+import numpy as np
+
+from sklearn.neighbors import NearestNeighbors
+from sklearn.utils.testing import assert_allclose
+from sklearn.utils.testing import assert_array_equal
+
+from imblearn.over_sampling import BorderlineSMOTE
+
+
+@pytest.fixture
+def data():
+    X = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
+                  [1.25192108, -0.22367336], [0.53366841, -0.30312976],
+                  [1.52091956, -0.49283504], [-0.28162401, -2.10400981],
+                  [0.83680821, 1.72827342], [0.3084254, 0.33299982],
+                  [0.70472253, -0.73309052], [0.28893132, -0.38761769],
+                  [1.15514042, 0.0129463], [0.88407872, 0.35454207],
+                  [1.31301027, -0.92648734], [-1.11515198, -0.93689695],
+                  [-0.18410027, -0.45194484], [0.9281014, 0.53085498],
+                  [-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
+                  [0.08711622, 0.93259929], [1.70580611, -0.11219234]])
+    y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
+    return X, y
+
+
+def test_borderline_smote_wrong_kind(data):
+    bsmote = BorderlineSMOTE(kind='rand')
+    with pytest.raises(ValueError, match='The possible "kind" of algorithm'):
+        bsmote.fit_resample(*data)
+
+
+@pytest.mark.parametrize('kind', ['borderline-1', 'borderline-2'])
+def test_borderline_smote(kind, data):
+    bsmote = BorderlineSMOTE(kind=kind, random_state=42)
+    bsmote_nn = BorderlineSMOTE(kind=kind, random_state=42,
+                                k_neighbors=NearestNeighbors(n_neighbors=6),
+                                m_neighbors=NearestNeighbors(n_neighbors=11))
+
+    X_res_1, y_res_1 = bsmote.fit_resample(*data)
+    X_res_2, y_res_2 = bsmote_nn.fit_resample(*data)
+
+    assert_allclose(X_res_1, X_res_2)
+    assert_array_equal(y_res_1, y_res_2)

imblearn/over_sampling/tests/test_smote.py

@@ -11,8 +11,7 @@
 from sklearn.svm import SVC
 
 from imblearn.over_sampling import SMOTE
-from imblearn.over_sampling import BorderlineSMOTE
-from imblearn.over_sampling import SVMSMOTE
+
 
 RND_SEED = 0
 X = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
@@ -287,37 +286,3 @@ def test_sample_with_nn_svm():
                      1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0])
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
     assert_array_equal(y_resampled, y_gt)
-
-
-def test_borderline_smote_wrong_kind():
-    bsmote = BorderlineSMOTE(kind='rand')
-    with pytest.raises(ValueError, match='The possible "kind" of algorithm'):
-        bsmote.fit_resample(X, Y)
-
-
-@pytest.mark.parametrize('kind', ['borderline-1', 'borderline-2'])
-def test_borderline_smote(kind):
-    bsmote = BorderlineSMOTE(kind=kind, random_state=42)
-    bsmote_nn = BorderlineSMOTE(kind=kind, random_state=42,
-                                k_neighbors=NearestNeighbors(n_neighbors=6),
-                                m_neighbors=NearestNeighbors(n_neighbors=11))
-
-    X_res_1, y_res_1 = bsmote.fit_resample(X, Y)
-    X_res_2, y_res_2 = bsmote_nn.fit_resample(X, Y)
-
-    assert_allclose(X_res_1, X_res_2)
-    assert_array_equal(y_res_1, y_res_2)
-
-
-def test_svm_smote():
-    svm_smote = SVMSMOTE(random_state=42)
-    svm_smote_nn = SVMSMOTE(random_state=42,
-                            k_neighbors=NearestNeighbors(n_neighbors=6),
-                            m_neighbors=NearestNeighbors(n_neighbors=11),
-                            svm_estimator=SVC(gamma='scale', random_state=42))
-
-    X_res_1, y_res_1 = svm_smote.fit_resample(X, Y)
-    X_res_2, y_res_2 = svm_smote_nn.fit_resample(X, Y)
-
-    assert_allclose(X_res_1, X_res_2)
-    assert_array_equal(y_res_1, y_res_2)

imblearn/over_sampling/tests/test_svm_smote.py

@@ -0,0 +1,40 @@
+import pytest
+import numpy as np
+
+from sklearn.neighbors import NearestNeighbors
+from sklearn.svm import SVC
+
+from sklearn.utils.testing import assert_allclose
+from sklearn.utils.testing import assert_array_equal
+
+from imblearn.over_sampling import SVMSMOTE
+
+
+@pytest.fixture
+def data():
+    X = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
+                  [1.25192108, -0.22367336], [0.53366841, -0.30312976],
+                  [1.52091956, -0.49283504], [-0.28162401, -2.10400981],
+                  [0.83680821, 1.72827342], [0.3084254, 0.33299982],
+                  [0.70472253, -0.73309052], [0.28893132, -0.38761769],
+                  [1.15514042, 0.0129463], [0.88407872, 0.35454207],
+                  [1.31301027, -0.92648734], [-1.11515198, -0.93689695],
+                  [-0.18410027, -0.45194484], [0.9281014, 0.53085498],
+                  [-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
+                  [0.08711622, 0.93259929], [1.70580611, -0.11219234]])
+    y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
+    return X, y
+
+
+def test_svm_smote(data):
+    svm_smote = SVMSMOTE(random_state=42)
+    svm_smote_nn = SVMSMOTE(random_state=42,
+                            k_neighbors=NearestNeighbors(n_neighbors=6),
+                            m_neighbors=NearestNeighbors(n_neighbors=11),
+                            svm_estimator=SVC(gamma='scale', random_state=42))
+
+    X_res_1, y_res_1 = svm_smote.fit_resample(*data)
+    X_res_2, y_res_2 = svm_smote_nn.fit_resample(*data)
+
+    assert_allclose(X_res_1, X_res_2)
+    assert_array_equal(y_res_1, y_res_2)