MAINT be more inclusive regarding dict

imblearn/over_sampling/base.py

@@ -6,6 +6,7 @@
 # License: MIT
 
 import numbers
+from collections.abc import Mapping
 
 from ..base import BaseSampler
 from ..utils._param_validation import Interval, StrOptions
@@ -61,7 +62,7 @@ class BaseOverSampler(BaseSampler):
         "sampling_strategy": [
             Interval(numbers.Real, 0, 1, closed="right"),
             StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
-            dict,
+            Mapping,
             callable,
         ],
         "random_state": ["random_state"],

imblearn/tests/test_common.py

@@ -3,6 +3,9 @@
 #          Christos Aridas
 # License: MIT
 
+from collections import OrderedDict
+
+import numpy as np
 import pytest
 from sklearn.base import clone
 from sklearn.exceptions import ConvergenceWarning
@@ -12,7 +15,8 @@
     parametrize_with_checks as parametrize_with_checks_sklearn,
 )
 
-from imblearn.under_sampling import NearMiss
+from imblearn.over_sampling import RandomOverSampler
+from imblearn.under_sampling import NearMiss, RandomUnderSampler
 from imblearn.utils.estimator_checks import (
     _set_checking_parameters,
     check_param_validation,
@@ -73,3 +77,19 @@ def test_check_param_validation(estimator):
     print(name)
     _set_checking_parameters(estimator)
     check_param_validation(name, estimator)
+
+
+@pytest.mark.parametrize("Sampler", [RandomOverSampler, RandomUnderSampler])
+def test_strategy_as_ordered_dict(Sampler):
+    """Check that it is possible to pass an `OrderedDict` as strategy."""
+    rng = np.random.RandomState(42)
+    X, y = rng.randn(30, 2), np.array([0] * 10 + [1] * 20)
+    sampler = Sampler(random_state=42)
+    if isinstance(sampler, RandomOverSampler):
+        strategy = OrderedDict({0: 20, 1: 20})
+    else:
+        strategy = OrderedDict({0: 10, 1: 10})
+    sampler.set_params(sampling_strategy=strategy)
+    X_res, y_res = sampler.fit_resample(X, y)
+    assert X_res.shape[0] == sum(strategy.values())
+    assert y_res.shape[0] == sum(strategy.values())

imblearn/under_sampling/base.py

@@ -5,6 +5,7 @@
 # License: MIT
 
 import numbers
+from collections.abc import Mapping
 
 from ..base import BaseSampler
 from ..utils._param_validation import Interval, StrOptions
@@ -61,7 +62,7 @@ class BaseUnderSampler(BaseSampler):
         "sampling_strategy": [
             Interval(numbers.Real, 0, 1, closed="right"),
             StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
-            dict,
+            Mapping,
             callable,
         ],
     }