MAINT compatibility sklearn 1.5 (#1074)

Author: glemaitre

imblearn/ensemble/_bagging.py

@@ -386,7 +386,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
             self.sampler_ = clone(self.sampler)
         # RandomUnderSampler is not supporting sample_weight. We need to pass
         # None.
-        return super()._fit(X, y, self.max_samples, sample_weight=None)
+        return super()._fit(X, y, self.max_samples)
 
     # TODO: remove when minimum supported version of scikit-learn is 1.1
     @available_if(_estimator_has("decision_function"))

imblearn/ensemble/_easy_ensemble.py

@@ -300,7 +300,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         check_target_type(y)
         # RandomUnderSampler is not supporting sample_weight. We need to pass
         # None.
-        return super()._fit(X, y, self.max_samples, sample_weight=None)
+        return super()._fit(X, y, self.max_samples)
 
     # TODO: remove when minimum supported version of scikit-learn is 1.1
     @available_if(_estimator_has("decision_function"))
@@ -365,9 +365,11 @@ def base_estimator_(self):
                 raise error
         raise error
 
-    def _more_tags(self):
+    def _get_estimator(self):
         if self.estimator is None:
-            estimator = AdaBoostClassifier(algorithm="SAMME")
-        else:
-            estimator = self.estimator
-        return {"allow_nan": _safe_tags(estimator, "allow_nan")}
+            return AdaBoostClassifier(algorithm="SAMME")
+        return self.estimator
+
+    # TODO: remove when minimum supported version of scikit-learn is 1.5
+    def _more_tags(self):
+        return {"allow_nan": _safe_tags(self._get_estimator(), "allow_nan")}

imblearn/over_sampling/_smote/base.py

@@ -11,16 +11,17 @@
 import warnings
 
 import numpy as np
+import sklearn
 from scipy import sparse
 from sklearn.base import clone
 from sklearn.exceptions import DataConversionWarning
 from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
 from sklearn.utils import (
-    _get_column_indices,
     _safe_indexing,
     check_array,
     check_random_state,
 )
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.sparsefuncs_fast import (
     csr_mean_variance_axis0,
 )
@@ -34,6 +35,12 @@
 from ...utils.fixes import _is_pandas_df, _mode
 from ..base import BaseOverSampler
 
+sklearn_version = parse_version(sklearn.__version__).base_version
+if parse_version(sklearn_version) < parse_version("1.5"):
+    from sklearn.utils import _get_column_indices
+else:
+    from sklearn.utils._indexing import _get_column_indices
+
 
 class BaseSMOTE(BaseOverSampler):
     """Base class for the different SMOTE algorithms."""

imblearn/pipeline.py

@@ -12,9 +12,11 @@
 #         Christos Aridas
 #         Guillaume Lemaitre <g.lemaitre58@gmail.com>
 # License: BSD
+import sklearn
 from sklearn import pipeline
 from sklearn.base import clone
-from sklearn.utils import Bunch, _print_elapsed_time
+from sklearn.utils import Bunch
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.metaestimators import available_if
 from sklearn.utils.validation import check_memory
 
@@ -34,6 +36,12 @@
 
 __all__ = ["Pipeline", "make_pipeline"]
 
+sklearn_version = parse_version(sklearn.__version__).base_version
+if parse_version(sklearn_version) < parse_version("1.5"):
+    from sklearn.utils import _print_elapsed_time
+else:
+    from sklearn.utils._user_interface import _print_elapsed_time
+
 
 class Pipeline(_ParamsValidationMixin, pipeline.Pipeline):
     """Pipeline of transforms and resamples with a final estimator.

imblearn/utils/_metadata_requests.py

@@ -1086,9 +1086,12 @@ def _serialize(self):
 
         def __iter__(self):
             if self._self_request:
-                yield "$self_request", RouterMappingPair(
-                    mapping=MethodMapping.from_str("one-to-one"),
-                    router=self._self_request,
+                yield (
+                    "$self_request",
+                    RouterMappingPair(
+                        mapping=MethodMapping.from_str("one-to-one"),
+                        router=self._self_request,
+                    ),
                 )
             for name, route_mapping in self._route_mappings.items():
                 yield (name, route_mapping)
@@ -1234,7 +1237,7 @@ def __init__(self, name, keys, validate_keys=True):
 
         def __get__(self, instance, owner):
             # we would want to have a method which accepts only the expected args
-            def func(**kw):
+            def func(*args, **kw):
                 """Updates the request for provided parameters
 
                 This docstring is overwritten below.
@@ -1253,15 +1256,32 @@ def func(**kw):
                         f"arguments are: {set(self.keys)}"
                     )
 
-                requests = instance._get_metadata_request()
+                # This makes it possible to use the decorated method as an unbound
+                # method, for instance when monkeypatching.
+                # https://github.com/scikit-learn/scikit-learn/issues/28632
+                if instance is None:
+                    _instance = args[0]
+                    args = args[1:]
+                else:
+                    _instance = instance
+
+                # Replicating python's behavior when positional args are given other
+                # than `self`, and `self` is only allowed if this method is unbound.
+                if args:
+                    raise TypeError(
+                        f"set_{self.name}_request() takes 0 positional argument but"
+                        f" {len(args)} were given"
+                    )
+
+                requests = _instance._get_metadata_request()
                 method_metadata_request = getattr(requests, self.name)
 
                 for prop, alias in kw.items():
                     if alias is not UNCHANGED:
                         method_metadata_request.add_request(param=prop, alias=alias)
-                instance._metadata_request = requests
+                _instance._metadata_request = requests
 
-                return instance
+                return _instance
 
             # Now we set the relevant attributes of the function so that it seems
             # like a normal method to the end user, with known expected arguments.
@@ -1525,13 +1545,13 @@ def process_routing(_obj, _method, /, **kwargs):
             metadata to corresponding methods or corresponding child objects. The object
             names are those defined in `obj.get_metadata_routing()`.
         """
-        if not _routing_enabled() and not kwargs:
+        if not kwargs:
             # If routing is not enabled and kwargs are empty, then we don't have to
             # try doing any routing, we can simply return a structure which returns
             # an empty dict on routed_params.ANYTHING.ANY_METHOD.
             class EmptyRequest:
                 def get(self, name, default=None):
-                    return default if default else {}
+                    return Bunch(**{method: dict() for method in METHODS})
 
                 def __getitem__(self, name):
                     return Bunch(**{method: dict() for method in METHODS})