FIX remove spurious warning raised when over-sampling the minority class

doc/whats_new/v0.11.rst

@@ -17,6 +17,10 @@ Bug fixes
   since it requires a conversion to dense matrices.
   :pr:`1003` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- Remove spurious warning raised when minority class get over-sampled more than the
+  number of sample in the majority class.
+  :pr:`1007` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Compatibility
 .............
 

imblearn/utils/_validation.py

@@ -307,8 +307,8 @@ def _sampling_strategy_dict(sampling_strategy, y, sampling_type):
         )
     sampling_strategy_ = {}
     if sampling_type == "over-sampling":
-        n_samples_majority = max(target_stats.values())
-        class_majority = max(target_stats, key=target_stats.get)
+        max(target_stats.values())
+        max(target_stats, key=target_stats.get)
         for class_sample, n_samples in sampling_strategy.items():
             if n_samples < target_stats[class_sample]:
                 raise ValueError(
@@ -318,13 +318,6 @@ def _sampling_strategy_dict(sampling_strategy, y, sampling_type):
                     f" Originally, there is {target_stats[class_sample]} "
                     f"samples and {n_samples} samples are asked."
                 )
-            if n_samples > n_samples_majority:
-                warnings.warn(
-                    f"After over-sampling, the number of samples ({n_samples})"
-                    f" in class {class_sample} will be larger than the number of"
-                    f" samples in the majority class (class #{class_majority} ->"
-                    f" {n_samples_majority})"
-                )
             sampling_strategy_[class_sample] = n_samples - target_stats[class_sample]
     elif sampling_type == "under-sampling":
         for class_sample, n_samples in sampling_strategy.items():

imblearn/utils/tests/test_validation.py

@@ -256,14 +256,6 @@ def test_check_sampling_strategy(
     assert sampling_strategy_ == expected_sampling_strategy
 
 
-def test_sampling_strategy_dict_over_sampling():
-    y = np.array([1] * 50 + [2] * 100 + [3] * 25)
-    sampling_strategy = {1: 70, 2: 140, 3: 70}
-    expected_msg = "After over-sampling, the number of samples "
-    with pytest.warns(UserWarning, match=expected_msg):
-        check_sampling_strategy(sampling_strategy, y, "over-sampling")
-
-
 def test_sampling_strategy_callable_args():
     y = np.array([1] * 50 + [2] * 100 + [3] * 25)
     multiplier = {1: 1.5, 2: 1, 3: 3}