Remove supervised.py unit test for imbalanced-learn

UnitTests/test_supervised.py

@@ -241,21 +241,21 @@ def test_class2mineral_nn(self):
         np.testing.assert_array_equal(pred_mineral, expected_pred_mineral)
 
 
-class mineralML_supervised_balancing(unittest.TestCase):
-    def setUp(self):
-        # Create a small, imbalanced dataset for testing
-        self.train_x = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
-        self.train_y = np.array([0, 0, 0, 1, 1])
-
-    def test_balance_function(self):
-        train_x_balanced, train_y_balanced = mm.balance(self.train_x, self.train_y, n=3)
-        # Check the shape of the output
-        self.assertEqual(train_x_balanced.shape[0], train_y_balanced.shape[0])
-        self.assertEqual(train_x_balanced.shape[1], self.train_x.shape[1])
-
-        # Check that each class has the correct number of samples
-        unique, counts = np.unique(train_y_balanced, return_counts=True)
-        self.assertTrue((counts == 3).all())
+# class mineralML_supervised_balancing(unittest.TestCase):
+#     def setUp(self):
+#         # Create a small, imbalanced dataset for testing
+#         self.train_x = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
+#         self.train_y = np.array([0, 0, 0, 1, 1])
+
+#     def test_balance_function(self):
+#         train_x_balanced, train_y_balanced = mm.balance(self.train_x, self.train_y, n=3)
+#         # Check the shape of the output
+#         self.assertEqual(train_x_balanced.shape[0], train_y_balanced.shape[0])
+#         self.assertEqual(train_x_balanced.shape[1], self.train_x.shape[1])
+
+#         # Check that each class has the correct number of samples
+#         unique, counts = np.unique(train_y_balanced, return_counts=True)
+#         self.assertTrue((counts == 3).all())
 
 
 class test_variational_layer(unittest.TestCase):