From 6caa659cd27d4291a55b1ee95e350c7df00d1b77 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 6 Jul 2023 23:11:54 +0200
Subject: [PATCH 01/38] MAINT compatibility scikit-learn 1.3

---
 build_tools/azure/linting.sh                  |   8 -
 imblearn/datasets/_imbalance.py               |   3 +-
 imblearn/datasets/_zenodo.py                  |   3 +-
 imblearn/ensemble/_bagging.py                 |   9 +-
 imblearn/ensemble/_easy_ensemble.py           |  17 +-
 imblearn/ensemble/_forest.py                  |   9 +-
 imblearn/metrics/_classification.py           |  23 +-
 imblearn/over_sampling/_smote/cluster.py      |   5 +
 .../_smote/tests/test_smote_nc.py             |   2 +-
 imblearn/pipeline.py                          |   5 +-
 imblearn/utils/_param_validation.py           | 313 +++++++++---------
 imblearn/utils/estimator_checks.py            |   4 +-
 imblearn/utils/tests/test_param_validation.py | 264 ++++++++++-----
 13 files changed, 413 insertions(+), 252 deletions(-)

diff --git a/build_tools/azure/linting.sh b/build_tools/azure/linting.sh
index 21ef53c80..0e34d5c1c 100755
--- a/build_tools/azure/linting.sh
+++ b/build_tools/azure/linting.sh
@@ -33,11 +33,3 @@ then
     echo "$doctest_directive"
     exit 1
 fi
-
-joblib_import="$(git grep -l -A 10 -E "joblib import.+delayed" -- "*.py" ":!sklearn/utils/_joblib.py" ":!sklearn/utils/fixes.py")"
-
-if [ ! -z "$joblib_import" ]; then
-    echo "Use from sklearn.utils.fixes import delayed instead of joblib delayed. The following files contains imports to joblib.delayed:"
-    echo "$joblib_import"
-    exit 1
-fi
diff --git a/imblearn/datasets/_imbalance.py b/imblearn/datasets/_imbalance.py
index 8c1c15aec..9e6e51290 100644
--- a/imblearn/datasets/_imbalance.py
+++ b/imblearn/datasets/_imbalance.py
@@ -20,7 +20,8 @@
         "sampling_strategy": [Mapping, callable, None],
         "random_state": ["random_state"],
         "verbose": ["boolean"],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def make_imbalance(
     X, y, *, sampling_strategy=None, random_state=None, verbose=False, **kwargs
diff --git a/imblearn/datasets/_zenodo.py b/imblearn/datasets/_zenodo.py
index 3a2c679a0..a73ef37b1 100644
--- a/imblearn/datasets/_zenodo.py
+++ b/imblearn/datasets/_zenodo.py
@@ -105,7 +105,8 @@
         "random_state": ["random_state"],
         "shuffle": ["boolean"],
         "verbose": ["boolean"],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def fetch_datasets(
     *,
diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py
index e35343f27..934f310d4 100644
--- a/imblearn/ensemble/_bagging.py
+++ b/imblearn/ensemble/_bagging.py
@@ -10,15 +10,20 @@
 import warnings
 
 import numpy as np
-from joblib import Parallel
 from sklearn.base import clone
 from sklearn.ensemble import BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils.fixes import delayed
 from sklearn.utils.validation import check_is_fitted
 
+try:
+    # scikit-learn >= 1.2
+    from sklearn.utils.parallel import Parallel, delayed
+except (ImportError, ModuleNotFoundError):
+    from sklearn.utils.fixes import delayed
+    from joblib import Parallel
+
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 2ec31e55d..2d5417fe6 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -10,14 +10,20 @@
 import warnings
 
 import numpy as np
-from joblib import Parallel
 from sklearn.base import clone
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
-from sklearn.utils.fixes import delayed
+from sklearn.utils._tags import _safe_tags
 from sklearn.utils.validation import check_is_fitted
 
+try:
+    # scikit-learn >= 1.2
+    from sklearn.utils.parallel import Parallel, delayed
+except (ImportError, ModuleNotFoundError):
+    from sklearn.utils.fixes import delayed
+    from joblib import Parallel
+
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
@@ -388,3 +394,10 @@ def decision_function(self, X):
         decisions = sum(all_decisions) / self.n_estimators
 
         return decisions
+
+    def _more_tags(self):
+        if self.estimator is None:
+            estimator = AdaBoostClassifier()
+        else:
+            estimator = self.estimator
+        return {"allow_nan": _safe_tags(estimator, "allow_nan")}
diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
index 4714b703a..ce557a103 100644
--- a/imblearn/ensemble/_forest.py
+++ b/imblearn/ensemble/_forest.py
@@ -9,7 +9,6 @@
 
 import numpy as np
 import sklearn
-from joblib import Parallel
 from numpy import float32 as DTYPE
 from numpy import float64 as DOUBLE
 from scipy.sparse import issparse
@@ -24,10 +23,16 @@
 from sklearn.exceptions import DataConversionWarning
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils import _safe_indexing, check_random_state, parse_version
-from sklearn.utils.fixes import delayed
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import _check_sample_weight
 
+try:
+    # scikit-learn >= 1.2
+    from sklearn.utils.parallel import Parallel, delayed
+except (ImportError, ModuleNotFoundError):
+    from sklearn.utils.fixes import delayed
+    from joblib import Parallel
+
 from ..base import _ParamsValidationMixin
 from ..pipeline import make_pipeline
 from ..under_sampling import RandomUnderSampler
diff --git a/imblearn/metrics/_classification.py b/imblearn/metrics/_classification.py
index b377db592..a5f896316 100644
--- a/imblearn/metrics/_classification.py
+++ b/imblearn/metrics/_classification.py
@@ -42,7 +42,8 @@
         ],
         "warn_for": ["array-like"],
         "sample_weight": ["array-like", None],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def sensitivity_specificity_support(
     y_true,
@@ -305,7 +306,8 @@ def sensitivity_specificity_support(
             StrOptions({"binary", "micro", "macro", "weighted", "samples"}),
         ],
         "sample_weight": ["array-like", None],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def sensitivity_score(
     y_true,
@@ -420,7 +422,8 @@ def sensitivity_score(
             StrOptions({"binary", "micro", "macro", "weighted", "samples"}),
         ],
         "sample_weight": ["array-like", None],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def specificity_score(
     y_true,
@@ -538,7 +541,8 @@ def specificity_score(
         ],
         "sample_weight": ["array-like", None],
         "correction": [Interval(numbers.Real, 0, None, closed="left")],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def geometric_mean_score(
     y_true,
@@ -729,7 +733,10 @@ class is unrecognized by the classifier, G-mean resolves to zero. To
         return gmean
 
 
-@validate_params({"alpha": [numbers.Real], "squared": ["boolean"]})
+@validate_params(
+    {"alpha": [numbers.Real], "squared": ["boolean"]},
+    prefer_skip_nested_validation=True,
+)
 def make_index_balanced_accuracy(*, alpha=0.1, squared=True):
     """Balance any scoring function using the index balanced accuracy.
 
@@ -849,7 +856,8 @@ def compute_score(*args, **kwargs):
             StrOptions({"warn"}),
             Interval(numbers.Integral, 0, 1, closed="both"),
         ],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def classification_report_imbalanced(
     y_true,
@@ -1063,7 +1071,8 @@ class 2       1.00      0.67      1.00      0.80      0.82      0.64\
         "y_true": ["array-like"],
         "y_pred": ["array-like"],
         "sample_weight": ["array-like", None],
-    }
+    },
+    prefer_skip_nested_validation=True,
 )
 def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
     """Compute Macro-Averaged MAE for imbalanced ordinal classification.
diff --git a/imblearn/over_sampling/_smote/cluster.py b/imblearn/over_sampling/_smote/cluster.py
index 4ca87e9a0..aa2cce434 100644
--- a/imblearn/over_sampling/_smote/cluster.py
+++ b/imblearn/over_sampling/_smote/cluster.py
@@ -232,6 +232,11 @@ def _fit_resample(self, X, y):
             for cluster_idx in range(self.kmeans_estimator_.n_clusters):
 
                 cluster_mask = np.flatnonzero(X_clusters == cluster_idx)
+
+                if cluster_mask.size == 0:
+                    # empty cluster
+                    continue
+
                 X_cluster = _safe_indexing(X, cluster_mask)
                 y_cluster = _safe_indexing(y, cluster_mask)
 
diff --git a/imblearn/over_sampling/_smote/tests/test_smote_nc.py b/imblearn/over_sampling/_smote/tests/test_smote_nc.py
index b59a54c87..63f36b62c 100644
--- a/imblearn/over_sampling/_smote/tests/test_smote_nc.py
+++ b/imblearn/over_sampling/_smote/tests/test_smote_nc.py
@@ -128,7 +128,7 @@ def test_smotenc_check_target_type():
     X, _, categorical_features = data_heterogneous_unordered()
     y = np.linspace(0, 1, 30)
     smote = SMOTENC(categorical_features=categorical_features, random_state=0)
-    with pytest.raises(ValueError, match="Unknown label type: 'continuous'"):
+    with pytest.raises(ValueError, match="Unknown label type"):
         smote.fit_resample(X, y)
     rng = np.random.RandomState(42)
     y = rng.randint(2, size=(20, 3))
diff --git a/imblearn/pipeline.py b/imblearn/pipeline.py
index 738f89b49..d584aa4f1 100644
--- a/imblearn/pipeline.py
+++ b/imblearn/pipeline.py
@@ -424,7 +424,10 @@ def _fit_resample_one(sampler, X, y, message_clsname="", message=None, **fit_par
         return X_res, y_res, sampler
 
 
-@validate_params({"memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"]})
+@validate_params(
+    {"memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"]},
+    prefer_skip_nested_validation=True,
+)
 def make_pipeline(*steps, memory=None, verbose=False):
     """Construct a Pipeline from the given estimators.
 
diff --git a/imblearn/utils/_param_validation.py b/imblearn/utils/_param_validation.py
index ae3855945..2f5443262 100644
--- a/imblearn/utils/_param_validation.py
+++ b/imblearn/utils/_param_validation.py
@@ -5,6 +5,7 @@
 import functools
 import math
 import operator
+import re
 import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
@@ -14,16 +15,24 @@
 import numpy as np
 import sklearn
 from scipy.sparse import csr_matrix, issparse
+from sklearn import config_context, get_config
 from sklearn.utils.fixes import parse_version
 
 from ..utils.fixes import _is_arraylike_not_scalar
 
 sklearn_version = parse_version(sklearn.__version__)
 
-if sklearn_version < parse_version("1.2"):
+if sklearn_version < parse_version("1.3"):
     # TODO: remove `if True` when we have clear support for:
     # - ignoring `*args` and `**kwargs` in the signature
 
+    class InvalidParameterError(ValueError, TypeError):
+        """Custom exception to be raised when the parameter of a
+        class/method/function does not have a valid type or value.
+        """
+
+    # Inherits from ValueError and TypeError to keep backward compatibility.
+
     def validate_parameter_constraints(parameter_constraints, params, caller_name):
         """Validate types and values of given parameters.
 
@@ -47,14 +56,14 @@ def validate_parameter_constraints(parameter_constraints, params, caller_name):
             - the string "boolean"
             - the string "verbose"
             - the string "cv_object"
-            - the string "missing_values"
+            - a MissingValues object representing markers for missing values
             - a HasMethods object, representing method(s) an object must have
             - a Hidden object, representing a constraint not meant to be exposed to the
               user
 
         params : dict
-            A dictionary `param_name: param_value`. The parameters to validate against
-            the constraints.
+            A dictionary `param_name: param_value`. The parameters to validate
+            against the constraints.
 
         caller_name : str
             The name of the estimator or function or method that called this function.
@@ -80,9 +89,9 @@ def validate_parameter_constraints(parameter_constraints, params, caller_name):
             else:
                 # No constraint is satisfied, raise with an informative message.
 
-                # Ignore constraints that we don't want to expose in the error message,
-                # i.e. options that are for internal purpose or not officially
-                # supported.
+                # Ignore constraints that we don't want to expose in the error
+                # message, i.e. options that are for internal purpose or not
+                # officially supported.
                 constraints = [
                     constraint for constraint in constraints if not constraint.hidden
                 ]
@@ -95,7 +104,7 @@ def validate_parameter_constraints(parameter_constraints, params, caller_name):
                         f" {constraints[-1]}"
                     )
 
-                raise ValueError(
+                raise InvalidParameterError(
                     f"The {param_name!r} parameter of {caller_name} must be"
                     f" {constraints_str}. Got {param_val!r} instead."
                 )
@@ -125,14 +134,14 @@ def make_constraint(constraint):
             return _NoneConstraint()
         if isinstance(constraint, type):
             return _InstancesOf(constraint)
-        if isinstance(constraint, (Interval, StrOptions, Options, HasMethods)):
+        if isinstance(
+            constraint, (Interval, StrOptions, Options, HasMethods, MissingValues)
+        ):
             return constraint
         if isinstance(constraint, str) and constraint == "boolean":
             return _Booleans()
         if isinstance(constraint, str) and constraint == "verbose":
             return _VerboseHelper()
-        if isinstance(constraint, str) and constraint == "missing_values":
-            return _MissingValues()
         if isinstance(constraint, str) and constraint == "cv_object":
             return _CVObjects()
         if isinstance(constraint, Hidden):
@@ -141,18 +150,31 @@ def make_constraint(constraint):
             return constraint
         raise ValueError(f"Unknown constraint type: {constraint}")
 
-    def validate_params(parameter_constraints):
+    def validate_params(parameter_constraints, *, prefer_skip_nested_validation):
         """Decorator to validate types and values of functions and methods.
 
         Parameters
         ----------
         parameter_constraints : dict
-            A dictionary `param_name: list of constraints`. See the docstring of
-            `validate_parameter_constraints` for a description of the accepted
-            constraints.
+            A dictionary `param_name: list of constraints`. See the docstring
+            of `validate_parameter_constraints` for a description of the
+            accepted constraints.
 
-            Note that the *args and **kwargs parameters are not validated and must not
-            be present in the parameter_constraints dictionary.
+            Note that the *args and **kwargs parameters are not validated and
+            must not be present in the parameter_constraints dictionary.
+
+        prefer_skip_nested_validation : bool
+            If True, the validation of parameters of inner estimators or functions
+            called by the decorated function will be skipped.
+
+            This is useful to avoid validating many times the parameters passed by the
+            user from the public facing API. It's also useful to avoid validating
+            parameters that we pass internally to inner functions that are guaranteed to
+            be valid by the test suite.
+
+            It should be set to True for most functions, except for those that receive
+            non-validated objects as parameters or that are just wrappers around classes
+            because they only perform a partial validation.
 
         Returns
         -------
@@ -168,6 +190,9 @@ def decorator(func):
 
             @functools.wraps(func)
             def wrapper(*args, **kwargs):
+                global_skip_validation = get_config()["skip_parameter_validation"]
+                if global_skip_validation:
+                    return func(*args, **kwargs)
 
                 func_sig = signature(func)
 
@@ -189,12 +214,41 @@ def wrapper(*args, **kwargs):
                 validate_parameter_constraints(
                     parameter_constraints, params, caller_name=func.__qualname__
                 )
-                return func(*args, **kwargs)
+
+                try:
+                    with config_context(
+                        skip_parameter_validation=(
+                            prefer_skip_nested_validation or global_skip_validation
+                        )
+                    ):
+                        return func(*args, **kwargs)
+                except InvalidParameterError as e:
+                    # When the function is just a wrapper around an estimator,
+                    # we allow the function to delegate validation to the
+                    # estimator, but we replace the name of the estimator by
+                    # the name of the function in the error message to avoid
+                    # confusion.
+                    msg = re.sub(
+                        r"parameter of \w+ must be",
+                        f"parameter of {func.__qualname__} must be",
+                        str(e),
+                    )
+                    raise InvalidParameterError(msg) from e
 
             return wrapper
 
         return decorator
 
+    class RealNotInt(Real):
+        """A type that represents reals that are not instances of int.
+
+        Behaves like float, but also works with values extracted from numpy arrays.
+        isintance(1, RealNotInt) -> False
+        isinstance(1.0, RealNotInt) -> True
+        """
+
+    RealNotInt.register(float)
+
     def _type_name(t):
         """Convert type into human readable string."""
         module = t.__module__
@@ -346,9 +400,12 @@ class Interval(_Constraint):
 
         Parameters
         ----------
-        type : {numbers.Integral, numbers.Real}
+        type : {numbers.Integral, numbers.Real, RealNotInt}
             The set of numbers in which to set the interval.
 
+            If RealNotInt, only reals that don't have the integer type
+            are allowed. For example 1.0 is allowed but 1 is not.
+
         left : float or int or None
             The left bound of the interval. None means left bound is -∞.
 
@@ -374,14 +431,6 @@ class Interval(_Constraint):
         corresponds to `[0, +∞) U {+∞}`.
         """
 
-        @validate_params(
-            {
-                "type": [type],
-                "left": [Integral, Real, None],
-                "right": [Integral, Real, None],
-                "closed": [StrOptions({"left", "right", "both", "neither"})],
-            }
-        )
         def __init__(self, type, left, right, *, closed):
             super().__init__()
             self.type = type
@@ -392,6 +441,18 @@ def __init__(self, type, left, right, *, closed):
             self._check_params()
 
         def _check_params(self):
+            if self.type not in (Integral, Real, RealNotInt):
+                raise ValueError(
+                    "type must be either numbers.Integral, numbers.Real or RealNotInt."
+                    f" Got {self.type} instead."
+                )
+
+            if self.closed not in ("left", "right", "both", "neither"):
+                raise ValueError(
+                    "closed must be either 'left', 'right', 'both' or 'neither'. "
+                    f"Got {self.closed} instead."
+                )
+
             if self.type is Integral:
                 suffix = "for an interval over the integers."
                 if self.left is not None and not isinstance(self.left, Integral):
@@ -406,6 +467,11 @@ def _check_params(self):
                     raise ValueError(
                         f"right can't be None when closed == {self.closed} {suffix}"
                     )
+            else:
+                if self.left is not None and not isinstance(self.left, Real):
+                    raise TypeError("Expecting left to be a real number.")
+                if self.right is not None and not isinstance(self.right, Real):
+                    raise TypeError("Expecting right to be a real number.")
 
             if (
                 self.right is not None
@@ -445,6 +511,13 @@ def __str__(self):
             left_bound = "-inf" if self.left is None else self.left
             right_bound = "inf" if self.right is None else self.right
             right_bracket = "]" if self.closed in ("right", "both") else ")"
+
+            # better repr if the bounds were given as integers
+            if not self.type == Integral and isinstance(self.left, Real):
+                left_bound = float(left_bound)
+            if not self.type == Integral and isinstance(self.right, Real):
+                right_bound = float(right_bound)
+
             return (
                 f"{type_str} in the range "
                 f"{left_bracket}{left_bound}, {right_bound}{right_bracket}"
@@ -520,8 +593,10 @@ def is_satisfied_by(self, val):
             # TODO(1.4) remove support for Integral.
             if isinstance(val, Integral) and not isinstance(val, bool):
                 warnings.warn(
-                    "Passing an int for a boolean parameter is deprecated in version"
-                    " 1.2 and won't be supported anymore in version 1.4.",
+                    (
+                        "Passing an int for a boolean parameter is deprecated in "
+                        " version 1.2 and won't be supported anymore in version 1.4."
+                    ),
                     FutureWarning,
                 )
 
@@ -557,32 +632,41 @@ def __str__(self):
                 f" {self._constraints[-1]}"
             )
 
-    class _MissingValues(_Constraint):
+    class MissingValues(_Constraint):
         """Helper constraint for the `missing_values` parameters.
 
         Convenience for
         [
             Integral,
             Interval(Real, None, None, closed="both"),
-            str,
-            None,
+            str,   # when numeric_only is False
+            None,  # when numeric_only is False
             _NanConstraint(),
             _PandasNAConstraint(),
         ]
+
+        Parameters
+        ----------
+        numeric_only : bool, default=False
+            Whether to consider only numeric missing value markers.
+
         """
 
-        def __init__(self):
+        def __init__(self, numeric_only=False):
             super().__init__()
+
+            self.numeric_only = numeric_only
+
             self._constraints = [
                 _InstancesOf(Integral),
                 # we use an interval of Real to ignore np.nan that has its own
                 # constraint
                 Interval(Real, None, None, closed="both"),
-                _InstancesOf(str),
-                _NoneConstraint(),
                 _NanConstraint(),
                 _PandasNAConstraint(),
             ]
+            if not self.numeric_only:
+                self._constraints.extend([_InstancesOf(str), _NoneConstraint()])
 
         def is_satisfied_by(self, val):
             return any(c.is_satisfied_by(val) for c in self._constraints)
@@ -596,8 +680,8 @@ def __str__(self):
     class HasMethods(_Constraint):
         """Constraint representing objects that expose specific methods.
 
-        It is useful for parameters following a protocol and where we don't want to
-        impose an affiliation to a specific module or class.
+        It is useful for parameters following a protocol and where we don't
+        want to impose an affiliation to a specific module or class.
 
         Parameters
         ----------
@@ -605,7 +689,10 @@ class HasMethods(_Constraint):
             The method(s) that the object is expected to expose.
         """
 
-        @validate_params({"methods": [str, list]})
+        @validate_params(
+            {"methods": [str, list]},
+            prefer_skip_nested_validation=True,
+        )
         def __init__(self, methods):
             super().__init__()
             if isinstance(methods, str):
@@ -676,7 +763,7 @@ class Hidden:
         def __init__(self, constraint):
             self.constraint = constraint
 
-    def generate_invalid_param_val(constraint, constraints=None):
+    def generate_invalid_param_val(constraint):
         """Return a value that does not satisfy the constraint.
 
         Raises a NotImplementedError if there exists no invalid value for this
@@ -689,10 +776,6 @@ def generate_invalid_param_val(constraint, constraints=None):
         constraint : _Constraint instance
             The constraint to generate a value for.
 
-        constraints : list of _Constraint instances or None, default=None
-            The list of all constraints for this parameter. If None, the list only
-            containing `constraint` is used.
-
         Returns
         -------
         val : object
@@ -701,7 +784,7 @@ def generate_invalid_param_val(constraint, constraints=None):
         if isinstance(constraint, StrOptions):
             return f"not {' or '.join(constraint.options)}"
 
-        if isinstance(constraint, _MissingValues):
+        if isinstance(constraint, MissingValues):
             return np.array([1, 2, 3])
 
         if isinstance(constraint, _VerboseHelper):
@@ -716,115 +799,31 @@ def generate_invalid_param_val(constraint, constraints=None):
         if isinstance(constraint, _CVObjects):
             return "not a cv object"
 
-        if not isinstance(constraint, Interval):
-            raise NotImplementedError
-
-        # constraint is an interval
-        constraints = [constraint] if constraints is None else constraints
-        return _generate_invalid_param_val_interval(constraint, constraints)
-
-    def _generate_invalid_param_val_interval(interval, constraints):
-        """Return a value that does not satisfy an interval constraint.
-
-        Generating an invalid value for an integer interval depends on the other
-        constraints since an int is a real, meaning that it can be valid for a real
-        interval. Assumes that there can be at most 2 interval constraints: one integer
-        interval and/or one real interval.
-
-        This is only useful for testing purpose.
-
-        Parameters
-        ----------
-        interval : Interval instance
-            The interval to generate a value for.
+        if isinstance(constraint, Interval) and constraint.type is Integral:
+            if constraint.left is not None:
+                return constraint.left - 1
+            if constraint.right is not None:
+                return constraint.right + 1
 
-        constraints : list of _Constraint instances
-            The list of all constraints for this parameter.
+            # There's no integer outside (-inf, +inf)
+            raise NotImplementedError
 
-        Returns
-        -------
-        val : object
-            A value that does not satisfy the interval constraint.
-        """
-        if interval.type is Real:
-            # generate a non-integer value such that it can't be valid even if there's
-            # also an integer interval constraint.
-            if interval.left is None and interval.right is None:
-                if interval.closed in ("left", "neither"):
-                    return np.inf
-                elif interval.closed in ("right", "neither"):
-                    return -np.inf
-                else:
-                    raise NotImplementedError
+        if isinstance(constraint, Interval) and constraint.type in (Real, RealNotInt):
+            if constraint.left is not None:
+                return constraint.left - 1e-6
+            if constraint.right is not None:
+                return constraint.right + 1e-6
 
-            if interval.left is not None:
-                return np.floor(interval.left) - 0.5
-            else:  # right is not None
-                return np.ceil(interval.right) + 0.5
+            # bounds are -inf, +inf
+            if constraint.closed in ("right", "neither"):
+                return -np.inf
+            if constraint.closed in ("left", "neither"):
+                return np.inf
 
-        else:  # interval.type is Integral
-            if interval.left is None and interval.right is None:
-                raise NotImplementedError
+            # interval is [-inf, +inf]
+            return np.nan
 
-            # We need to check if there's also a real interval constraint to generate a
-            # value that is not valid for any of the 2 interval constraints.
-            real_intervals = [
-                i for i in constraints if isinstance(i, Interval) and i.type is Real
-            ]
-            real_interval = real_intervals[0] if real_intervals else None
-
-            if real_interval is None:
-                # Only the integer interval constraint -> easy
-                if interval.left is not None:
-                    return interval.left - 1
-                else:  # interval.right is not None
-                    return interval.right + 1
-
-            # There's also a real interval constraint. Try to find a value left to both
-            # or right to both or in between them.
-
-            # redefine left and right bounds to be smallest and largest valid integers
-            # in both intervals.
-            int_left = interval.left
-            if int_left is not None and interval.closed in ("right", "neither"):
-                int_left = int_left + 1
-
-            int_right = interval.right
-            if int_right is not None and interval.closed in ("left", "neither"):
-                int_right = int_right - 1
-
-            real_left = real_interval.left
-            if real_interval.left is not None:
-                real_left = int(np.ceil(real_interval.left))
-                if real_interval.closed in ("right", "neither"):
-                    real_left = real_left + 1
-
-            real_right = real_interval.right
-            if real_interval.right is not None:
-                real_right = int(np.floor(real_interval.right))
-                if real_interval.closed in ("left", "neither"):
-                    real_right = real_right - 1
-
-            if int_left is not None and real_left is not None:
-                # there exists an int left to both intervals
-                return min(int_left, real_left) - 1
-
-            if int_right is not None and real_right is not None:
-                # there exists an int right to both intervals
-                return max(int_right, real_right) + 1
-
-            if int_left is not None:
-                if real_right is not None and int_left - real_right >= 2:
-                    # there exists an int between the 2 intervals
-                    return int_left - 1
-                else:
-                    raise NotImplementedError
-            else:  # int_right is not None
-                if real_left is not None and real_left - int_right >= 2:
-                    # there exists an int between the 2 intervals
-                    return int_right + 1
-                else:
-                    raise NotImplementedError
+        raise NotImplementedError
 
     def generate_valid_param(constraint):
         """Return a value that does satisfy a constraint.
@@ -857,6 +856,15 @@ def generate_valid_param(constraint):
             return None
 
         if isinstance(constraint, _InstancesOf):
+            if constraint.type is np.ndarray:
+                # special case for ndarray since it can't be instantiated without
+                # arguments
+                return np.array([1, 2, 3])
+
+            if constraint.type in (Integral, Real):
+                # special case for Integral and Real since they are abstract classes
+                return 1
+
             return constraint.type()
 
         if isinstance(constraint, _Booleans):
@@ -865,9 +873,12 @@ def generate_valid_param(constraint):
         if isinstance(constraint, _VerboseHelper):
             return 1
 
-        if isinstance(constraint, _MissingValues):
+        if isinstance(constraint, MissingValues) and constraint.numeric_only:
             return np.nan
 
+        if isinstance(constraint, MissingValues) and not constraint.numeric_only:
+            return "missing"
+
         if isinstance(constraint, HasMethods):
             return type(
                 "ValidHasMethods",
@@ -909,7 +920,10 @@ def generate_valid_param(constraint):
         HasMethods,
         Hidden,
         Interval,
+        InvalidParameterError,
+        MissingValues,
         Options,
+        RealNotInt,
         StrOptions,
         _ArrayLikes,
         _Booleans,
@@ -917,7 +931,6 @@ def generate_valid_param(constraint):
         _CVObjects,
         _InstancesOf,
         _IterablesNotString,
-        _MissingValues,
         _NoneConstraint,
         _PandasNAConstraint,
         _RandomStates,
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
index e5f50a668..0a7915a44 100644
--- a/imblearn/utils/estimator_checks.py
+++ b/imblearn/utils/estimator_checks.py
@@ -174,7 +174,7 @@ def check_target_type(name, estimator_orig):
     # should raise warning if the target is continuous (we cannot raise error)
     X = np.random.random((20, 2))
     y = np.linspace(0, 1, 20)
-    msg = "Unknown label type: 'continuous'"
+    msg = "Unknown label type:"
     assert_raises_regex(
         ValueError,
         msg,
@@ -562,7 +562,7 @@ def check_param_validation(name, estimator_orig):
 
         for constraint in constraints:
             try:
-                bad_value = generate_invalid_param_val(constraint, constraints)
+                bad_value = generate_invalid_param_val(constraint)
             except NotImplementedError:
                 continue
 
diff --git a/imblearn/utils/tests/test_param_validation.py b/imblearn/utils/tests/test_param_validation.py
index dae58a790..8a356f42a 100644
--- a/imblearn/utils/tests/test_param_validation.py
+++ b/imblearn/utils/tests/test_param_validation.py
@@ -6,16 +6,19 @@
 import numpy as np
 import pytest
 from scipy.sparse import csr_matrix
-from sklearn.base import BaseEstimator
+from sklearn._config import config_context, get_config
+from sklearn.base import BaseEstimator, _fit_context
 from sklearn.model_selection import LeaveOneOut
 from sklearn.utils import deprecated
 
-from imblearn.base import _ParamsValidationMixin
 from imblearn.utils._param_validation import (
     HasMethods,
     Hidden,
     Interval,
+    InvalidParameterError,
+    MissingValues,
     Options,
+    RealNotInt,
     StrOptions,
     _ArrayLikes,
     _Booleans,
@@ -23,7 +26,6 @@
     _CVObjects,
     _InstancesOf,
     _IterablesNotString,
-    _MissingValues,
     _NoneConstraint,
     _PandasNAConstraint,
     _RandomStates,
@@ -37,7 +39,10 @@
 
 
 # Some helpers for the tests
-@validate_params({"a": [Real], "b": [Real], "c": [Real], "d": [Real]})
+@validate_params(
+    {"a": [Real], "b": [Real], "c": [Real], "d": [Real]},
+    prefer_skip_nested_validation=True,
+)
 def _func(a, b=0, *args, c, d=0, **kwargs):
     """A function to test the validation of functions."""
 
@@ -45,17 +50,17 @@ def _func(a, b=0, *args, c, d=0, **kwargs):
 class _Class:
     """A class to test the _InstancesOf constraint and the validation of methods."""
 
-    @validate_params({"a": [Real]})
+    @validate_params({"a": [Real]}, prefer_skip_nested_validation=True)
     def _method(self, a):
         """A validated method"""
 
     @deprecated()
-    @validate_params({"a": [Real]})
+    @validate_params({"a": [Real]}, prefer_skip_nested_validation=True)
     def _deprecated_method(self, a):
         """A deprecated validated method"""
 
 
-class _Estimator(BaseEstimator, _ParamsValidationMixin):
+class _Estimator(BaseEstimator):
     """An estimator to test the validation of estimator parameters."""
 
     _parameter_constraints: dict = {"a": [Real]}
@@ -63,8 +68,9 @@ class _Estimator(BaseEstimator, _ParamsValidationMixin):
     def __init__(self, a):
         self.a = a
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X=None, y=None):
-        self._validate_params()
+        pass
 
 
 @pytest.mark.parametrize("interval_type", [Integral, Real])
@@ -206,7 +212,8 @@ def a(self):
         Interval(Real, 0, None, closed="left"),
         Interval(Real, None, None, closed="neither"),
         StrOptions({"a", "b", "c"}),
-        _MissingValues(),
+        MissingValues(),
+        MissingValues(numeric_only=True),
         _VerboseHelper(),
         HasMethods("fit"),
         _IterablesNotString(),
@@ -224,75 +231,75 @@ def test_generate_invalid_param_val(constraint):
     [
         (
             Interval(Integral, None, 3, closed="right"),
-            Interval(Real, -5, 5, closed="both"),
+            Interval(RealNotInt, -5, 5, closed="both"),
         ),
         (
             Interval(Integral, None, 3, closed="right"),
-            Interval(Real, -5, 5, closed="neither"),
+            Interval(RealNotInt, -5, 5, closed="neither"),
         ),
         (
             Interval(Integral, None, 3, closed="right"),
-            Interval(Real, 4, 5, closed="both"),
+            Interval(RealNotInt, 4, 5, closed="both"),
         ),
         (
             Interval(Integral, None, 3, closed="right"),
-            Interval(Real, 5, None, closed="left"),
+            Interval(RealNotInt, 5, None, closed="left"),
         ),
         (
             Interval(Integral, None, 3, closed="right"),
-            Interval(Real, 4, None, closed="neither"),
+            Interval(RealNotInt, 4, None, closed="neither"),
         ),
         (
             Interval(Integral, 3, None, closed="left"),
-            Interval(Real, -5, 5, closed="both"),
+            Interval(RealNotInt, -5, 5, closed="both"),
         ),
         (
             Interval(Integral, 3, None, closed="left"),
-            Interval(Real, -5, 5, closed="neither"),
+            Interval(RealNotInt, -5, 5, closed="neither"),
         ),
         (
             Interval(Integral, 3, None, closed="left"),
-            Interval(Real, 1, 2, closed="both"),
+            Interval(RealNotInt, 1, 2, closed="both"),
         ),
         (
             Interval(Integral, 3, None, closed="left"),
-            Interval(Real, None, -5, closed="left"),
+            Interval(RealNotInt, None, -5, closed="left"),
         ),
         (
             Interval(Integral, 3, None, closed="left"),
-            Interval(Real, None, -4, closed="neither"),
+            Interval(RealNotInt, None, -4, closed="neither"),
         ),
         (
             Interval(Integral, -5, 5, closed="both"),
-            Interval(Real, None, 1, closed="right"),
+            Interval(RealNotInt, None, 1, closed="right"),
         ),
         (
             Interval(Integral, -5, 5, closed="both"),
-            Interval(Real, 1, None, closed="left"),
+            Interval(RealNotInt, 1, None, closed="left"),
         ),
         (
             Interval(Integral, -5, 5, closed="both"),
-            Interval(Real, -10, -4, closed="neither"),
+            Interval(RealNotInt, -10, -4, closed="neither"),
         ),
         (
             Interval(Integral, -5, 5, closed="both"),
-            Interval(Real, -10, -4, closed="right"),
+            Interval(RealNotInt, -10, -4, closed="right"),
         ),
         (
             Interval(Integral, -5, 5, closed="neither"),
-            Interval(Real, 6, 10, closed="neither"),
+            Interval(RealNotInt, 6, 10, closed="neither"),
         ),
         (
             Interval(Integral, -5, 5, closed="neither"),
-            Interval(Real, 6, 10, closed="left"),
+            Interval(RealNotInt, 6, 10, closed="left"),
         ),
         (
             Interval(Integral, 2, None, closed="left"),
-            Interval(Real, 0, 1, closed="both"),
+            Interval(RealNotInt, 0, 1, closed="both"),
         ),
         (
             Interval(Integral, 1, None, closed="left"),
-            Interval(Real, 0, 1, closed="both"),
+            Interval(RealNotInt, 0, 1, closed="both"),
         ),
     ],
 )
@@ -300,42 +307,34 @@ def test_generate_invalid_param_val_2_intervals(integer_interval, real_interval)
     """Check that the value generated for an interval constraint does not satisfy any of
     the interval constraints.
     """
-    bad_value = generate_invalid_param_val(
-        real_interval, constraints=[real_interval, integer_interval]
-    )
+    bad_value = generate_invalid_param_val(constraint=real_interval)
     assert not real_interval.is_satisfied_by(bad_value)
     assert not integer_interval.is_satisfied_by(bad_value)
 
-    bad_value = generate_invalid_param_val(
-        integer_interval, constraints=[real_interval, integer_interval]
-    )
+    bad_value = generate_invalid_param_val(constraint=integer_interval)
     assert not real_interval.is_satisfied_by(bad_value)
     assert not integer_interval.is_satisfied_by(bad_value)
 
 
 @pytest.mark.parametrize(
-    "constraints",
+    "constraint",
     [
-        [_ArrayLikes()],
-        [_InstancesOf(list)],
-        [_Callables()],
-        [_NoneConstraint()],
-        [_RandomStates()],
-        [_SparseMatrices()],
-        [_Booleans()],
-        [Interval(Real, None, None, closed="both")],
-        [
-            Interval(Integral, 0, None, closed="left"),
-            Interval(Real, None, 0, closed="neither"),
-        ],
+        _ArrayLikes(),
+        _InstancesOf(list),
+        _Callables(),
+        _NoneConstraint(),
+        _RandomStates(),
+        _SparseMatrices(),
+        _Booleans(),
+        Interval(Integral, None, None, closed="neither"),
     ],
 )
-def test_generate_invalid_param_val_all_valid(constraints):
+def test_generate_invalid_param_val_all_valid(constraint):
     """Check that the function raises NotImplementedError when there's no invalid value
     for the constraint.
     """
     with pytest.raises(NotImplementedError):
-        generate_invalid_param_val(constraints[0], constraints=constraints)
+        generate_invalid_param_val(constraint)
 
 
 @pytest.mark.parametrize(
@@ -349,7 +348,8 @@ def test_generate_invalid_param_val_all_valid(constraints):
         _SparseMatrices(),
         _Booleans(),
         _VerboseHelper(),
-        _MissingValues(),
+        MissingValues(),
+        MissingValues(numeric_only=True),
         StrOptions({"a", "b", "c"}),
         Options(Integral, {1, 2, 3}),
         Interval(Integral, None, None, closed="neither"),
@@ -390,12 +390,12 @@ def test_generate_valid_param(constraint):
         (Real, 0.5),
         ("boolean", False),
         ("verbose", 1),
-        ("missing_values", -1),
-        ("missing_values", -1.0),
-        ("missing_values", None),
-        ("missing_values", float("nan")),
-        ("missing_values", np.nan),
-        ("missing_values", "missing"),
+        (MissingValues(), -1),
+        (MissingValues(), -1.0),
+        (MissingValues(), None),
+        (MissingValues(), float("nan")),
+        (MissingValues(), np.nan),
+        (MissingValues(), "missing"),
         (HasMethods("fit"), _Estimator(a=0)),
         ("cv_object", 5),
     ],
@@ -420,7 +420,7 @@ def test_is_satisfied_by(constraint_declaration, value):
         (int, _InstancesOf),
         ("boolean", _Booleans),
         ("verbose", _VerboseHelper),
-        ("missing_values", _MissingValues),
+        (MissingValues(numeric_only=True), MissingValues),
         (HasMethods("fit"), HasMethods),
         ("cv_object", _CVObjects),
     ],
@@ -439,23 +439,35 @@ def test_make_constraint_unknown():
 
 def test_validate_params():
     """Check that validate_params works no matter how the arguments are passed"""
-    with pytest.raises(ValueError, match="The 'a' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _func must be"
+    ):
         _func("wrong", c=1)
 
-    with pytest.raises(ValueError, match="The 'b' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'b' parameter of _func must be"
+    ):
         _func(*[1, "wrong"], c=1)
 
-    with pytest.raises(ValueError, match="The 'c' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'c' parameter of _func must be"
+    ):
         _func(1, **{"c": "wrong"})
 
-    with pytest.raises(ValueError, match="The 'd' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'd' parameter of _func must be"
+    ):
         _func(1, c=1, d="wrong")
 
     # check in the presence of extra positional and keyword args
-    with pytest.raises(ValueError, match="The 'b' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'b' parameter of _func must be"
+    ):
         _func(0, *["wrong", 2, 3], c=4, **{"e": 5})
 
-    with pytest.raises(ValueError, match="The 'c' parameter of _func must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'c' parameter of _func must be"
+    ):
         _func(0, *[1, 2, 3], c="four", **{"e": 5})
 
 
@@ -464,7 +476,7 @@ def test_validate_params_missing_params():
     constraints
     """
 
-    @validate_params({"a": [int]})
+    @validate_params({"a": [int]}, prefer_skip_nested_validation=True)
     def func(a, b):
         pass
 
@@ -478,21 +490,26 @@ def test_decorate_validated_function():
     with pytest.warns(FutureWarning, match="Function _func is deprecated"):
         decorated_function(1, 2, c=3)
 
-    # outer decorator does not interfer with validation
+    # outer decorator does not interfere with validation
     with pytest.warns(FutureWarning, match="Function _func is deprecated"):
-        with pytest.raises(ValueError, match=r"The 'c' parameter of _func must be"):
+        with pytest.raises(
+            InvalidParameterError, match=r"The 'c' parameter of _func must be"
+        ):
             decorated_function(1, 2, c="wrong")
 
 
 def test_validate_params_method():
     """Check that validate_params works with methods"""
-    with pytest.raises(ValueError, match="The 'a' parameter of _Class._method must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _Class._method must be"
+    ):
         _Class()._method("wrong")
 
     # validated method can be decorated
     with pytest.warns(FutureWarning, match="Function _deprecated_method is deprecated"):
         with pytest.raises(
-            ValueError, match="The 'a' parameter of _Class._deprecated_method must be"
+            InvalidParameterError,
+            match="The 'a' parameter of _Class._deprecated_method must be",
         ):
             _Class()._deprecated_method("wrong")
 
@@ -502,7 +519,9 @@ def test_validate_params_estimator():
     # no validation in init
     est = _Estimator("wrong")
 
-    with pytest.raises(ValueError, match="The 'a' parameter of _Estimator must be"):
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _Estimator must be"
+    ):
         est.fit()
 
 
@@ -515,7 +534,9 @@ def test_stroptions_deprecated_subset():
 def test_hidden_constraint():
     """Check that internal constraints are not exposed in the error message."""
 
-    @validate_params({"param": [Hidden(list), dict]})
+    @validate_params(
+        {"param": [Hidden(list), dict]}, prefer_skip_nested_validation=True
+    )
     def f(param):
         pass
 
@@ -523,7 +544,9 @@ def f(param):
     f({"a": 1, "b": 2, "c": 3})
     f([1, 2, 3])
 
-    with pytest.raises(ValueError, match="The 'param' parameter") as exc_info:
+    with pytest.raises(
+        InvalidParameterError, match="The 'param' parameter"
+    ) as exc_info:
         f(param="bad")
 
     # the list option is not exposed in the error message
@@ -535,7 +558,10 @@ def f(param):
 def test_hidden_stroptions():
     """Check that we can have 2 StrOptions constraints, one being hidden."""
 
-    @validate_params({"param": [StrOptions({"auto"}), Hidden(StrOptions({"warn"}))]})
+    @validate_params(
+        {"param": [StrOptions({"auto"}), Hidden(StrOptions({"warn"}))]},
+        prefer_skip_nested_validation=True,
+    )
     def f(param):
         pass
 
@@ -543,7 +569,9 @@ def f(param):
     f("auto")
     f("warn")
 
-    with pytest.raises(ValueError, match="The 'param' parameter") as exc_info:
+    with pytest.raises(
+        InvalidParameterError, match="The 'param' parameter"
+    ) as exc_info:
         f(param="bad")
 
     # the "warn" option is not exposed in the error message
@@ -565,7 +593,7 @@ def test_boolean_constraint_deprecated_int():
     validation when using an int for a parameter accepting a boolean.
     """
 
-    @validate_params({"param": ["boolean"]})
+    @validate_params({"param": ["boolean"]}, prefer_skip_nested_validation=True)
     def f(param):
         pass
 
@@ -583,12 +611,15 @@ def f(param):
 def test_no_validation():
     """Check that validation can be skipped for a parameter."""
 
-    @validate_params({"param1": [int, None], "param2": "no_validation"})
+    @validate_params(
+        {"param1": [int, None], "param2": "no_validation"},
+        prefer_skip_nested_validation=True,
+    )
     def f(param1=None, param2=None):
         pass
 
     # param1 is validated
-    with pytest.raises(ValueError, match="The 'param1' parameter"):
+    with pytest.raises(InvalidParameterError, match="The 'param1' parameter"):
         f(param1="wrong")
 
     # param2 is not validated: any type is valid.
@@ -644,3 +675,86 @@ def fit(self, X=None, y=None):
     # does not raise, even though "b" is not in the constraints dict and "a" is not
     # a parameter of the estimator.
     ThirdPartyEstimator(b=0).fit()
+
+
+def test_interval_real_not_int():
+    """Check for the type RealNotInt in the Interval constraint."""
+    constraint = Interval(RealNotInt, 0, 1, closed="both")
+    assert constraint.is_satisfied_by(1.0)
+    assert not constraint.is_satisfied_by(1)
+
+
+def test_real_not_int():
+    """Check for the RealNotInt type."""
+    assert isinstance(1.0, RealNotInt)
+    assert not isinstance(1, RealNotInt)
+    assert isinstance(np.float64(1), RealNotInt)
+    assert not isinstance(np.int64(1), RealNotInt)
+
+
+def test_skip_param_validation():
+    """Check that param validation can be skipped using config_context."""
+
+    @validate_params({"a": [int]}, prefer_skip_nested_validation=True)
+    def f(a):
+        pass
+
+    with pytest.raises(InvalidParameterError, match="The 'a' parameter"):
+        f(a="1")
+
+    # does not raise
+    with config_context(skip_parameter_validation=True):
+        f(a="1")
+
+
+@pytest.mark.parametrize("prefer_skip_nested_validation", [True, False])
+def test_skip_nested_validation(prefer_skip_nested_validation):
+    """Check that nested validation can be skipped."""
+
+    @validate_params({"a": [int]}, prefer_skip_nested_validation=True)
+    def f(a):
+        pass
+
+    @validate_params(
+        {"b": [int]},
+        prefer_skip_nested_validation=prefer_skip_nested_validation,
+    )
+    def g(b):
+        # calls f with a bad parameter type
+        return f(a="invalid_param_value")
+
+    # Validation for g is never skipped.
+    with pytest.raises(InvalidParameterError, match="The 'b' parameter"):
+        g(b="invalid_param_value")
+
+    if prefer_skip_nested_validation:
+        g(b=1)  # does not raise because inner f is not validated
+    else:
+        with pytest.raises(InvalidParameterError, match="The 'a' parameter"):
+            g(b=1)
+
+
+@pytest.mark.parametrize(
+    "skip_parameter_validation, prefer_skip_nested_validation, expected_skipped",
+    [
+        (True, True, True),
+        (True, False, True),
+        (False, True, True),
+        (False, False, False),
+    ],
+)
+def test_skip_nested_validation_and_config_context(
+    skip_parameter_validation, prefer_skip_nested_validation, expected_skipped
+):
+    """Check interaction between global skip and local skip."""
+
+    @validate_params(
+        {"a": [int]}, prefer_skip_nested_validation=prefer_skip_nested_validation
+    )
+    def g(a):
+        return get_config()["skip_parameter_validation"]
+
+    with config_context(skip_parameter_validation=skip_parameter_validation):
+        actual_skipped = g(1)
+
+    assert actual_skipped == expected_skipped

From 8fbf75ca716f1695cec8af85a9e1c44ab9153410 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 6 Jul 2023 23:17:20 +0200
Subject: [PATCH 02/38] iter

---
 .circleci/config.yml            | 2 +-
 build_tools/circle/build_doc.sh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 80d55554b..b5f679af6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -3,7 +3,7 @@ version: 2
 jobs:
   doc:
     docker:
-      - image: circleci/python:3.7.7-buster
+      - image: cimg/python:3.8.12
     environment:
       - USERNAME: "glemaitre"
       - ORGANIZATION: "imbalanced-learn"
diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh
index c963a8966..969c22e70 100755
--- a/build_tools/circle/build_doc.sh
+++ b/build_tools/circle/build_doc.sh
@@ -94,7 +94,7 @@ wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforg
     -O miniconda.sh
 chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
 export PATH="$MINICONDA_PATH/bin:$PATH"
-conda update --yes --quiet conda
+mamba update --yes --quiet conda
 
 # imports get_dep
 source build_tools/shared.sh

From 204c0942b3af23ac1fc6eae888d21f111b853d4b Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 6 Jul 2023 23:21:40 +0200
Subject: [PATCH 03/38] iter

---
 build_tools/circle/build_doc.sh | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh
index 969c22e70..dbdc7b2ff 100755
--- a/build_tools/circle/build_doc.sh
+++ b/build_tools/circle/build_doc.sh
@@ -89,11 +89,12 @@ if [[ `type -t deactivate` ]]; then
     deactivate
 fi
 
-# Install dependencies with miniconda
-wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh \
-    -O miniconda.sh
-chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
-export PATH="$MINICONDA_PATH/bin:$PATH"
+MAMBAFORGE_PATH=$HOME/mambaforge
+# Install dependencies with mamba
+wget -q https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh \
+    -O mambaforge.sh
+chmod +x mambaforge.sh && ./mambaforge.sh -b -p $MAMBAFORGE_PATH
+export PATH="$MAMBAFORGE_PATH/bin:$PATH"
 mamba update --yes --quiet conda
 
 # imports get_dep

From 9595d6a691d5d71dc3c74c14870e707984b7620f Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 08:52:29 +0200
Subject: [PATCH 04/38] iter

---
 .pre-commit-config.yaml             | 25 ++++++++++++++-----------
 imblearn/_min_dependencies.py       |  2 +-
 imblearn/utils/_param_validation.py |  5 ++++-
 3 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0f1caa607..c740290d1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,26 +1,29 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.3.0
+    rev: v4.3.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
 -   repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 23.3.0
     hooks:
     -   id: black
--   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.9.2
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.0.272
     hooks:
-    -   id: flake8
-        types: [file, python]
+    -   id: ruff
+        args: ["--fix", "--show-source"]
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.782
+    rev: v1.3.0
     hooks:
      -  id: mypy
-        files: sklearn/
+        files: imblearn/
         additional_dependencies: [pytest==6.2.4]
--   repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+-   repo: https://github.com/MarcoGorelli/cython-lint
+    rev: v0.15.0
     hooks:
-    -   id: isort
+    # TODO: add the double-quote-cython-strings hook when it's usability has improved:
+    # possibility to pass a directory and use it as a check instead of auto-formatter.
+    -   id: cython-lint
diff --git a/imblearn/_min_dependencies.py b/imblearn/_min_dependencies.py
index aaa5ce9ae..9bb1f917c 100644
--- a/imblearn/_min_dependencies.py
+++ b/imblearn/_min_dependencies.py
@@ -2,7 +2,7 @@
 import argparse
 
 NUMPY_MIN_VERSION = "1.17.3"
-SCIPY_MIN_VERSION = "1.3.2"
+SCIPY_MIN_VERSION = "1.5.0"
 PANDAS_MIN_VERSION = "1.0.5"
 SKLEARN_MIN_VERSION = "1.0.2"
 TENSORFLOW_MIN_VERSION = "2.4.3"
diff --git a/imblearn/utils/_param_validation.py b/imblearn/utils/_param_validation.py
index 2f5443262..ae2b8e16a 100644
--- a/imblearn/utils/_param_validation.py
+++ b/imblearn/utils/_param_validation.py
@@ -190,7 +190,10 @@ def decorator(func):
 
             @functools.wraps(func)
             def wrapper(*args, **kwargs):
-                global_skip_validation = get_config()["skip_parameter_validation"]
+                # This line is changed to be compatible with scikit-learn <=1.2
+                global_skip_validation = get_config().get(
+                    "skip_parameter_validation", False
+                )
                 if global_skip_validation:
                     return func(*args, **kwargs)
 

From 1895749001ded48f643a8afe5c9bf1d48643ef52 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 08:57:45 +0200
Subject: [PATCH 05/38] iter

---
 .pre-commit-config.yaml                |  6 ------
 imblearn/ensemble/_bagging.py          |  2 +-
 imblearn/ensemble/_easy_ensemble.py    |  2 +-
 imblearn/ensemble/_forest.py           |  2 +-
 imblearn/keras/tests/test_generator.py | 12 ++++++++----
 imblearn/over_sampling/_smote/base.py  |  3 ++-
 imblearn/tests/test_pipeline.py        |  3 +--
 pyproject.toml                         | 19 +++++++++++++++++++
 8 files changed, 33 insertions(+), 16 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c740290d1..d76915517 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -21,9 +21,3 @@ repos:
      -  id: mypy
         files: imblearn/
         additional_dependencies: [pytest==6.2.4]
--   repo: https://github.com/MarcoGorelli/cython-lint
-    rev: v0.15.0
-    hooks:
-    # TODO: add the double-quote-cython-strings hook when it's usability has improved:
-    # possibility to pass a directory and use it as a check instead of auto-formatter.
-    -   id: cython-lint
diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py
index 934f310d4..83b85ec13 100644
--- a/imblearn/ensemble/_bagging.py
+++ b/imblearn/ensemble/_bagging.py
@@ -21,8 +21,8 @@
     # scikit-learn >= 1.2
     from sklearn.utils.parallel import Parallel, delayed
 except (ImportError, ModuleNotFoundError):
-    from sklearn.utils.fixes import delayed
     from joblib import Parallel
+    from sklearn.utils.fixes import delayed
 
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 2d5417fe6..822516f2f 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -21,8 +21,8 @@
     # scikit-learn >= 1.2
     from sklearn.utils.parallel import Parallel, delayed
 except (ImportError, ModuleNotFoundError):
-    from sklearn.utils.fixes import delayed
     from joblib import Parallel
+    from sklearn.utils.fixes import delayed
 
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
index ce557a103..d77199481 100644
--- a/imblearn/ensemble/_forest.py
+++ b/imblearn/ensemble/_forest.py
@@ -30,8 +30,8 @@
     # scikit-learn >= 1.2
     from sklearn.utils.parallel import Parallel, delayed
 except (ImportError, ModuleNotFoundError):
-    from sklearn.utils.fixes import delayed
     from joblib import Parallel
+    from sklearn.utils.fixes import delayed
 
 from ..base import _ParamsValidationMixin
 from ..pipeline import make_pipeline
diff --git a/imblearn/keras/tests/test_generator.py b/imblearn/keras/tests/test_generator.py
index 3be157a4a..032571644 100644
--- a/imblearn/keras/tests/test_generator.py
+++ b/imblearn/keras/tests/test_generator.py
@@ -10,11 +10,15 @@
 from keras.utils.np_utils import to_categorical  # noqa: E402
 
 from imblearn.datasets import make_imbalance  # noqa: E402
-from imblearn.keras import BalancedBatchGenerator  # noqa: E402
-from imblearn.keras import balanced_batch_generator  # noqa: E402
+from imblearn.keras import (
+    BalancedBatchGenerator,  # noqa: E402
+    balanced_batch_generator,  # noqa: E402
+)
 from imblearn.over_sampling import RandomOverSampler  # noqa: E402
-from imblearn.under_sampling import ClusterCentroids  # noqa: E402
-from imblearn.under_sampling import NearMiss  # noqa: E402
+from imblearn.under_sampling import (
+    ClusterCentroids,  # noqa: E402
+    NearMiss,  # noqa: E402
+)
 
 
 @pytest.fixture
diff --git a/imblearn/over_sampling/_smote/base.py b/imblearn/over_sampling/_smote/base.py
index 266349ee5..967f59d6f 100644
--- a/imblearn/over_sampling/_smote/base.py
+++ b/imblearn/over_sampling/_smote/base.py
@@ -479,7 +479,8 @@ class SMOTENC(SMOTE):
 
     See
     :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`,
-    and :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py`.  # noqa
+    and
+    :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py`.
 
     References
     ----------
diff --git a/imblearn/tests/test_pipeline.py b/imblearn/tests/test_pipeline.py
index 7efa89326..6eca5dbc1 100644
--- a/imblearn/tests/test_pipeline.py
+++ b/imblearn/tests/test_pipeline.py
@@ -246,7 +246,7 @@ def test_pipeline_init():
 
     # Test clone
     pipe2 = clone(pipe)
-    assert not pipe.named_steps["svc"] is pipe2.named_steps["svc"]
+    assert pipe.named_steps["svc"] is not pipe2.named_steps["svc"]
 
     # Check that apart from estimators, the parameters are the same
     params = pipe.get_params(deep=True)
@@ -1163,7 +1163,6 @@ def test_predict_with_predict_params():
 
 
 def test_resampler_last_stage_passthrough():
-
     X, y = make_classification(
         n_classes=2,
         class_sep=2,
diff --git a/pyproject.toml b/pyproject.toml
index 664c56a61..b41338e5c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,3 +4,22 @@ include = '\.pyi?$'
 
 [tool.isort]
 profile = "black"
+
+[tool.ruff]
+# all rules can be found here: https://beta.ruff.rs/docs/rules/
+select = ["E", "F", "W", "I"]
+
+# max line length for black
+line-length = 88
+target-version = "py38"
+
+ignore=[
+    # space before : (needed for how black formats slicing)
+    "E203",
+    # do not assign a lambda expression, use a def
+    "E731",
+    # do not use variables named 'l', 'O', or 'I'
+    "E741",
+    # Import not on the top of the file
+    "E402",
+]

From 50ad67faeba4b4be673b5aadb9296b923944a848 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:08:17 +0200
Subject: [PATCH 06/38] iter

---
 doc/over_sampling.rst                                  | 10 +++++-----
 .../applications/plot_multi_class_under_sampling.py    |  4 +---
 examples/combine/plot_comparison_combine.py            |  4 ++--
 examples/evaluation/plot_classification_report.py      |  4 ++--
 examples/evaluation/plot_metrics.py                    |  7 ++++---
 imblearn/metrics/tests/test_score_objects.py           |  6 +++---
 6 files changed, 17 insertions(+), 18 deletions(-)

diff --git a/doc/over_sampling.rst b/doc/over_sampling.rst
index a7728fa51..581f395d7 100644
--- a/doc/over_sampling.rst
+++ b/doc/over_sampling.rst
@@ -38,10 +38,10 @@ randomly sampling with replacement the current available samples. The
 The augmented data set should be used instead of the original data set to train
 a classifier::
 
-  >>> from sklearn.svm import LinearSVC
-  >>> clf = LinearSVC()
+  >>> from sklearn.linear_model import LogisticRegression
+  >>> clf = LogisticRegression()
   >>> clf.fit(X_resampled, y_resampled)
-  LinearSVC(...)
+  LogisticRegression(...)
 
 In the figure below, we compare the decision functions of a classifier trained
 using the over-sampled data set and the original data set.
@@ -108,11 +108,11 @@ the same manner::
   >>> X_resampled, y_resampled = SMOTE().fit_resample(X, y)
   >>> print(sorted(Counter(y_resampled).items()))
   [(0, 4674), (1, 4674), (2, 4674)]
-  >>> clf_smote = LinearSVC().fit(X_resampled, y_resampled)
+  >>> clf_smote = LogisticRegression().fit(X_resampled, y_resampled)
   >>> X_resampled, y_resampled = ADASYN().fit_resample(X, y)
   >>> print(sorted(Counter(y_resampled).items()))
   [(0, 4673), (1, 4662), (2, 4674)]
-  >>> clf_adasyn = LinearSVC().fit(X_resampled, y_resampled)
+  >>> clf_adasyn = LogisticRegression().fit(X_resampled, y_resampled)
 
 The figure below illustrates the major difference of the different
 over-sampling methods.
diff --git a/examples/applications/plot_multi_class_under_sampling.py b/examples/applications/plot_multi_class_under_sampling.py
index 7dc2d8465..fbf265abc 100644
--- a/examples/applications/plot_multi_class_under_sampling.py
+++ b/examples/applications/plot_multi_class_under_sampling.py
@@ -43,9 +43,7 @@
 print(f"Testing target statistics: {Counter(y_test)}")
 
 # Create a pipeline
-pipeline = make_pipeline(
-    NearMiss(version=2), StandardScaler(), LogisticRegression(random_state=RANDOM_STATE)
-)
+pipeline = make_pipeline(NearMiss(version=2), StandardScaler(), LogisticRegression())
 pipeline.fit(X_train, y_train)
 
 # Classify and report the results
diff --git a/examples/combine/plot_comparison_combine.py b/examples/combine/plot_comparison_combine.py
index 48f35aa57..57fd06f32 100644
--- a/examples/combine/plot_comparison_combine.py
+++ b/examples/combine/plot_comparison_combine.py
@@ -102,7 +102,7 @@ def plot_decision_function(X, y, clf, ax):
 # :class:`~imblearn.combine.SMOTEENN` cleans more noisy data than
 # :class:`~imblearn.combine.SMOTETomek`.
 
-from sklearn.svm import LinearSVC
+from sklearn.linear_model import LogisticRegression
 
 from imblearn.combine import SMOTEENN, SMOTETomek
 
@@ -114,7 +114,7 @@ def plot_decision_function(X, y, clf, ax):
 
 fig, axs = plt.subplots(3, 2, figsize=(15, 25))
 for ax, sampler in zip(axs, samplers):
-    clf = make_pipeline(sampler, LinearSVC()).fit(X, y)
+    clf = make_pipeline(sampler, LogisticRegression()).fit(X, y)
     plot_decision_function(X, y, clf, ax[0])
     plot_resampling(X, y, sampler, ax[1])
 fig.tight_layout()
diff --git a/examples/evaluation/plot_classification_report.py b/examples/evaluation/plot_classification_report.py
index 3171b234c..7d21371b4 100644
--- a/examples/evaluation/plot_classification_report.py
+++ b/examples/evaluation/plot_classification_report.py
@@ -14,9 +14,9 @@
 
 
 from sklearn import datasets
+from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
-from sklearn.svm import LinearSVC
 
 from imblearn import over_sampling as os
 from imblearn import pipeline as pl
@@ -43,7 +43,7 @@
 pipeline = pl.make_pipeline(
     StandardScaler(),
     os.SMOTE(random_state=RANDOM_STATE),
-    LinearSVC(max_iter=10_000, random_state=RANDOM_STATE),
+    LogisticRegression(max_iter=10_000),
 )
 
 # Split the data
diff --git a/examples/evaluation/plot_metrics.py b/examples/evaluation/plot_metrics.py
index 9b319465f..f9f17aa1d 100644
--- a/examples/evaluation/plot_metrics.py
+++ b/examples/evaluation/plot_metrics.py
@@ -48,10 +48,11 @@
 
 # %% [markdown]
 # We will create a pipeline made of a :class:`~imblearn.over_sampling.SMOTE`
-# over-sampler followed by a :class:`~sklearn.svm.LinearSVC` classifier.
+# over-sampler followed by a :class:`~sklearn.linear_model.LogisticRegression`
+# classifier.
 
+from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
-from sklearn.svm import LinearSVC
 
 from imblearn.over_sampling import SMOTE
 
@@ -61,7 +62,7 @@
 model = make_pipeline(
     StandardScaler(),
     SMOTE(random_state=RANDOM_STATE),
-    LinearSVC(max_iter=10_000, random_state=RANDOM_STATE),
+    LogisticRegression(max_iter=10_000, random_state=RANDOM_STATE),
 )
 
 # %% [markdown]
diff --git a/imblearn/metrics/tests/test_score_objects.py b/imblearn/metrics/tests/test_score_objects.py
index c77a46169..d834766e5 100644
--- a/imblearn/metrics/tests/test_score_objects.py
+++ b/imblearn/metrics/tests/test_score_objects.py
@@ -5,9 +5,9 @@
 
 import pytest
 from sklearn.datasets import make_blobs
+from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import make_scorer
 from sklearn.model_selection import GridSearchCV, train_test_split
-from sklearn.svm import LinearSVC
 
 from imblearn.metrics import (
     geometric_mean_score,
@@ -41,7 +41,7 @@ def test_scorer_common_average(data, score, expected_score, average):
 
     scorer = make_scorer(score, pos_label=None, average=average)
     grid = GridSearchCV(
-        LinearSVC(random_state=0),
+        LogisticRegression(),
         param_grid={"C": [1, 10]},
         scoring=scorer,
         cv=3,
@@ -70,7 +70,7 @@ def test_scorer_default_average(data, score, average, expected_score):
 
     scorer = make_scorer(score, pos_label=1, average=average)
     grid = GridSearchCV(
-        LinearSVC(random_state=0),
+        LogisticRegression(),
         param_grid={"C": [1, 10]},
         scoring=scorer,
         cv=3,

From f7c8bf8892c8aa3ef8013bae849ca1a105a48d52 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:23:19 +0200
Subject: [PATCH 07/38] iter

---
 azure-pipelines.yml                                   |  6 +++---
 doc/common_pitfalls.rst                               | 11 +++++------
 .../applications/plot_impact_imbalanced_classes.py    |  1 -
 imblearn/keras/tests/test_generator.py                |  2 ++
 4 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 6971cdf2d..b7d7e69d8 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -45,14 +45,14 @@ jobs:
         versionSpec: '3.9'
     - bash: |
         # Include pytest compatibility with mypy
-        pip install pytest flake8 mypy==0.782 black==22.3 isort
+        pip install flake8 pytest mypy==1.3.0 black==23.3 ruff==0.0.272
       displayName: Install linters
     - bash: |
         black --check --diff .
       displayName: Run black
     - bash: |
-        isort --check --diff .
-      displayName: Run isort
+        ruff check --show-source .
+      displayName: Run ruff
     - bash: |
         ./build_tools/azure/linting.sh
       displayName: Run linting
diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
index a08999a14..249aabcc6 100644
--- a/doc/common_pitfalls.rst
+++ b/doc/common_pitfalls.rst
@@ -56,7 +56,7 @@ Let's first check the balancing ratio on this dataset::
   >>> y.value_counts(normalize=True)
   <=50K    0.98801
   >50K     0.01199
-  Name: class, dtype: float64
+  Name: proportion, dtype: float64
 
 To later highlight some of the issue, we will keep aside a left-out set that we
 will not use for the evaluation of the model::
@@ -72,7 +72,6 @@ classifier, without any preprocessing to alleviate the bias toward the majority
 class. We evaluate the generalization performance of the classifier via
 cross-validation::
 
-  >>> from sklearn.experimental import enable_hist_gradient_boosting
   >>> from sklearn.ensemble import HistGradientBoostingClassifier
   >>> from sklearn.model_selection import cross_validate
   >>> model = HistGradientBoostingClassifier(random_state=0)
@@ -130,9 +129,9 @@ cross-validation::
   ...     f"{cv_results['test_score'].std():.3f}"
   ... )
   Balanced accuracy mean +/- std. dev.: 0.724 +/- 0.042
-  
-The cross-validation performance looks good, but evaluating the classifiers 
-on the left-out data shows a different picture:: 
+
+The cross-validation performance looks good, but evaluating the classifiers
+on the left-out data shows a different picture::
 
   >>> scores = []
   >>> for fold_id, cv_model in enumerate(cv_results["estimator"]):
@@ -147,7 +146,7 @@ on the left-out data shows a different picture::
   ... )
   Balanced accuracy mean +/- std. dev.: 0.698 +/- 0.014
 
-We see that the performance is now worse than the cross-validated performance. 
+We see that the performance is now worse than the cross-validated performance.
 Indeed, the data leakage gave us too optimistic results due to the reason
 stated earlier in this section.
 
diff --git a/examples/applications/plot_impact_imbalanced_classes.py b/examples/applications/plot_impact_imbalanced_classes.py
index 0e710a725..278033ebb 100644
--- a/examples/applications/plot_impact_imbalanced_classes.py
+++ b/examples/applications/plot_impact_imbalanced_classes.py
@@ -338,7 +338,6 @@
 # classifier within a :class:`~imblearn.ensemble.BalancedBaggingClassifier`.
 
 from sklearn.ensemble import HistGradientBoostingClassifier
-from sklearn.experimental import enable_hist_gradient_boosting  # noqa
 
 from imblearn.ensemble import BalancedBaggingClassifier
 
diff --git a/imblearn/keras/tests/test_generator.py b/imblearn/keras/tests/test_generator.py
index 032571644..5c0538169 100644
--- a/imblearn/keras/tests/test_generator.py
+++ b/imblearn/keras/tests/test_generator.py
@@ -20,6 +20,8 @@
     NearMiss,  # noqa: E402
 )
 
+3
+
 
 @pytest.fixture
 def data():

From c66c1d1e223ff6117dce79483f0414472bc76e21 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:33:56 +0200
Subject: [PATCH 08/38] blackify

---
 imblearn/base.py                                 |  1 -
 imblearn/datasets/tests/test_zenodo.py           |  1 -
 imblearn/over_sampling/_smote/cluster.py         |  1 -
 imblearn/pipeline.py                             |  2 +-
 imblearn/tests/test_docstring_parameters.py      |  1 -
 .../_condensed_nearest_neighbour.py              |  1 -
 .../_edited_nearest_neighbours.py                | 16 ++++++++++++----
 imblearn/utils/_show_versions.py                 |  2 --
 8 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/imblearn/base.py b/imblearn/base.py
index dd4e1b3a8..408ab5525 100644
--- a/imblearn/base.py
+++ b/imblearn/base.py
@@ -398,7 +398,6 @@ def fit_resample(self, X, y):
         output = self._fit_resample(X, y)
 
         if self.validate:
-
             y_ = (
                 label_binarize(output[1], classes=np.unique(y))
                 if binarize_y
diff --git a/imblearn/datasets/tests/test_zenodo.py b/imblearn/datasets/tests/test_zenodo.py
index cfd7007a0..3854fd282 100644
--- a/imblearn/datasets/tests/test_zenodo.py
+++ b/imblearn/datasets/tests/test_zenodo.py
@@ -56,7 +56,6 @@ def test_fetch():
     datasets2 = fetch(shuffle=True, random_state=37)
 
     for k in DATASET_SHAPE.keys():
-
         X1, X2 = datasets1[k].data, datasets2[k].data
         assert DATASET_SHAPE[k] == X1.shape
         assert X1.shape == X2.shape
diff --git a/imblearn/over_sampling/_smote/cluster.py b/imblearn/over_sampling/_smote/cluster.py
index aa2cce434..2852cfd13 100644
--- a/imblearn/over_sampling/_smote/cluster.py
+++ b/imblearn/over_sampling/_smote/cluster.py
@@ -230,7 +230,6 @@ def _fit_resample(self, X, y):
 
             # identify cluster which are answering the requirements
             for cluster_idx in range(self.kmeans_estimator_.n_clusters):
-
                 cluster_mask = np.flatnonzero(X_clusters == cluster_idx)
 
                 if cluster_mask.size == 0:
diff --git a/imblearn/pipeline.py b/imblearn/pipeline.py
index d584aa4f1..ff013b17f 100644
--- a/imblearn/pipeline.py
+++ b/imblearn/pipeline.py
@@ -218,7 +218,7 @@ def _fit(self, X, y=None, **fit_params_steps):
         fit_transform_one_cached = memory.cache(pipeline._fit_transform_one)
         fit_resample_one_cached = memory.cache(_fit_resample_one)
 
-        for (step_idx, name, transformer) in self._iter(
+        for step_idx, name, transformer in self._iter(
             with_final=False, filter_passthrough=False, filter_resample=False
         ):
             if transformer is None or transformer == "passthrough":
diff --git a/imblearn/tests/test_docstring_parameters.py b/imblearn/tests/test_docstring_parameters.py
index 3663d1bc8..d273f04e4 100644
--- a/imblearn/tests/test_docstring_parameters.py
+++ b/imblearn/tests/test_docstring_parameters.py
@@ -154,7 +154,6 @@ def test_tabs():
     for importer, modname, ispkg in walk_packages(
         imblearn.__path__, prefix="imblearn."
     ):
-
         if IS_PYPY:
             continue
 
diff --git a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
index 2f03ca8a8..c80e40fdc 100644
--- a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
+++ b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
@@ -189,7 +189,6 @@ def _fit_resample(self, X, y):
                 good_classif_label = idx_maj_sample.copy()
                 # Check each sample in S if we keep it or drop it
                 for idx_sam, (x_sam, y_sam) in enumerate(zip(S_x, S_y)):
-
                     # Do not select sample which are already well classified
                     if idx_sam in good_classif_label:
                         continue
diff --git a/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py b/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py
index 64419ccdf..e8bdda71b 100644
--- a/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py
+++ b/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py
@@ -348,7 +348,6 @@ def _fit_resample(self, X, y):
         class_minority = min(target_stats, key=target_stats.get)
 
         for n_iter in range(self.max_iter):
-
             prev_len = y_.shape[0]
             X_enn, y_enn = self.enn_.fit_resample(X_, y_)
 
@@ -375,7 +374,10 @@ def _fit_resample(self, X, y):
             # Case 3
             b_remove_maj_class = len(stats_enn) < len(target_stats)
 
-            X_, y_, = (
+            (
+                X_,
+                y_,
+            ) = (
                 X_enn,
                 y_enn,
             )
@@ -383,7 +385,10 @@ def _fit_resample(self, X, y):
 
             if b_conv or b_min_bec_maj or b_remove_maj_class:
                 if b_conv:
-                    X_, y_, = (
+                    (
+                        X_,
+                        y_,
+                    ) = (
                         X_enn,
                         y_enn,
                     )
@@ -584,7 +589,10 @@ def _fit_resample(self, X, y):
             # Case 2
             b_remove_maj_class = len(stats_enn) < len(target_stats)
 
-            X_, y_, = (
+            (
+                X_,
+                y_,
+            ) = (
                 X_enn,
                 y_enn,
             )
diff --git a/imblearn/utils/_show_versions.py b/imblearn/utils/_show_versions.py
index 945ca24e1..4912e3ab7 100644
--- a/imblearn/utils/_show_versions.py
+++ b/imblearn/utils/_show_versions.py
@@ -72,7 +72,6 @@ def show_versions(github=False):
     )
 
     if github:
-
         _sys_markup = ""
         _deps_markup = ""
 
@@ -84,7 +83,6 @@ def show_versions(github=False):
         print(_github_markup.format(_sys_markup, _deps_markup))
 
     else:
-
         print("\nSystem:")
         for k, stat in _sys_info.items():
             print(f"{k:>11}: {stat}")

From 9393bdedab52038e70cbf89b261f904168831897 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:37:58 +0200
Subject: [PATCH 09/38] iter

---
 doc/sphinxext/sphinx_issues.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/doc/sphinxext/sphinx_issues.py b/doc/sphinxext/sphinx_issues.py
index 9ad5941c2..047fcb6ac 100644
--- a/doc/sphinxext/sphinx_issues.py
+++ b/doc/sphinxext/sphinx_issues.py
@@ -76,7 +76,6 @@ def cve_role(name, rawtext, text, lineno, inliner, options=None, content=None):
 
 
 class IssueRole(object):
-
     EXTERNAL_REPO_REGEX = re.compile(r"^(\w+)/(.+)([#@])([\w]+)$")
 
     def __init__(

From 1611716ed87ee841f95cd6e8dad2b74e51530db4 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:41:07 +0200
Subject: [PATCH 10/38] bump version doc

---
 imblearn/_min_dependencies.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/imblearn/_min_dependencies.py b/imblearn/_min_dependencies.py
index 9bb1f917c..eaf5bbcf1 100644
--- a/imblearn/_min_dependencies.py
+++ b/imblearn/_min_dependencies.py
@@ -29,13 +29,13 @@
     "pytest": (PYTEST_MIN_VERSION, "tests"),
     "pytest-cov": ("2.9.0", "tests"),
     "flake8": ("3.8.2", "tests"),
-    "black": ("22.3.0", "tests"),
-    "mypy": ("0.770", "tests"),
-    "sphinx": ("4.2.0", "docs"),
-    "sphinx-gallery": ("0.7.0", "docs"),
-    "numpydoc": ("1.0.0", "docs"),
+    "black": ("23.3.0", "tests"),
+    "mypy": ("1.3.0", "tests"),
+    "sphinx": ("6.0.0", "docs"),
+    "sphinx-gallery": ("0.13.0", "docs"),
+    "numpydoc": ("1.5.0", "docs"),
     "sphinxcontrib-bibtex": ("2.4.1", "docs"),
-    "pydata-sphinx-theme": ("0.7.2", "docs"),
+    "pydata-sphinx-theme": ("0.13.3", "docs"),
 }
 
 

From 25c11330245f32966c45e6debc44ced448750854 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:46:15 +0200
Subject: [PATCH 11/38] iter

---
 build_tools/azure/linting.sh | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/build_tools/azure/linting.sh b/build_tools/azure/linting.sh
index 0e34d5c1c..890874614 100755
--- a/build_tools/azure/linting.sh
+++ b/build_tools/azure/linting.sh
@@ -4,9 +4,6 @@ set -e
 # pipefail is necessary to propagate exit codes
 set -o pipefail
 
-flake8 --show-source .
-echo -e "No problem detected by flake8\n"
-
 # For docstrings and warnings of deprecated attributes to be rendered
 # properly, the property decorator must come before the deprecated decorator
 # (else they are treated as functions)

From bbab55c0fc11aa803623ba1880f1279b9b124dc2 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:52:05 +0200
Subject: [PATCH 12/38] iter

---
 README.rst                                   |  2 +-
 imblearn/metrics/tests/test_score_objects.py | 22 +++++++++-----------
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/README.rst b/README.rst
index bfebdd8d6..cbf3a7da6 100644
--- a/README.rst
+++ b/README.rst
@@ -29,7 +29,7 @@
 
 .. |PythonMinVersion| replace:: 3.8
 .. |NumPyMinVersion| replace:: 1.17.3
-.. |SciPyMinVersion| replace:: 1.3.2
+.. |SciPyMinVersion| replace:: 1.5.0
 .. |ScikitLearnMinVersion| replace:: 1.0.2
 .. |MatplotlibMinVersion| replace:: 3.1.2
 .. |PandasMinVersion| replace:: 1.0.5
diff --git a/imblearn/metrics/tests/test_score_objects.py b/imblearn/metrics/tests/test_score_objects.py
index d834766e5..10a1ced61 100644
--- a/imblearn/metrics/tests/test_score_objects.py
+++ b/imblearn/metrics/tests/test_score_objects.py
@@ -25,14 +25,13 @@ def data():
     return train_test_split(X, y, random_state=0)
 
 
-@pytest.mark.filterwarnings("ignore:Liblinear failed to converge")
 @pytest.mark.parametrize(
     "score, expected_score",
     [
-        (sensitivity_score, 0.92),
-        (specificity_score, 0.92),
-        (geometric_mean_score, 0.92),
-        (make_index_balanced_accuracy()(geometric_mean_score), 0.85),
+        (sensitivity_score, 0.90),
+        (specificity_score, 0.90),
+        (geometric_mean_score, 0.90),
+        (make_index_balanced_accuracy()(geometric_mean_score), 0.82),
     ],
 )
 @pytest.mark.parametrize("average", ["macro", "weighted", "micro"])
@@ -48,20 +47,19 @@ def test_scorer_common_average(data, score, expected_score, average):
     )
     grid.fit(X_train, y_train).predict(X_test)
 
-    assert grid.best_score_ == pytest.approx(expected_score, rel=R_TOL)
+    assert grid.best_score_ >= expected_score
 
 
-@pytest.mark.filterwarnings("ignore:Liblinear failed to converge")
 @pytest.mark.parametrize(
     "score, average, expected_score",
     [
-        (sensitivity_score, "binary", 0.92),
-        (specificity_score, "binary", 0.95),
-        (geometric_mean_score, "multiclass", 0.92),
+        (sensitivity_score, "binary", 0.94),
+        (specificity_score, "binary", 0.89),
+        (geometric_mean_score, "multiclass", 0.90),
         (
             make_index_balanced_accuracy()(geometric_mean_score),
             "multiclass",
-            0.84,
+            0.82,
         ),
     ],
 )
@@ -77,4 +75,4 @@ def test_scorer_default_average(data, score, average, expected_score):
     )
     grid.fit(X_train, y_train).predict(X_test)
 
-    assert grid.best_score_ == pytest.approx(expected_score, rel=R_TOL)
+    assert grid.best_score_ >= expected_score

From a3b6fb337e973c21ab82386b65a7fd7961e0b4f9 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 09:57:18 +0200
Subject: [PATCH 13/38] iter

---
 doc/_templates/breadcrumbs.html | 4 ----
 1 file changed, 4 deletions(-)
 delete mode 100644 doc/_templates/breadcrumbs.html

diff --git a/doc/_templates/breadcrumbs.html b/doc/_templates/breadcrumbs.html
deleted file mode 100644
index 339f008b9..000000000
--- a/doc/_templates/breadcrumbs.html
+++ /dev/null
@@ -1,4 +0,0 @@
-{%- extends "sphinx_rtd_theme/breadcrumbs.html" %}
-
-{% block breadcrumbs_aside %}
-{% endblock %}
\ No newline at end of file

From e8fa9d7270ef3c28de30b005328b617d464eded8 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:03:45 +0200
Subject: [PATCH 14/38] iter

---
 doc/Makefile                                  |   2 +-
 imblearn/base.py                              |  47 ++++----
 imblearn/ensemble/_bagging.py                 |  11 +-
 imblearn/ensemble/_common.py                  |  20 ++--
 imblearn/ensemble/_easy_ensemble.py           |  10 +-
 imblearn/ensemble/_forest.py                  |   9 +-
 imblearn/ensemble/_weight_boosting.py         |  12 ++-
 imblearn/metrics/pairwise.py                  |   2 +-
 imblearn/pipeline.py                          |   2 +-
 imblearn/utils/_param_validation.py           |   7 +-
 imblearn/utils/fixes.py                       | 101 ++++++++++++++++++
 imblearn/utils/tests/test_param_validation.py |   8 +-
 12 files changed, 178 insertions(+), 53 deletions(-)

diff --git a/doc/Makefile b/doc/Makefile
index e0ea9bedf..56d6d289f 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -2,7 +2,7 @@
 #
 
 # You can set these variables from the command line.
-SPHINXOPTS    =
+SPHINXOPTS    = -v
 SPHINXBUILD   = sphinx-build
 PAPER         =
 BUILDDIR      = _build
diff --git a/imblearn/base.py b/imblearn/base.py
index 408ab5525..e529fead6 100644
--- a/imblearn/base.py
+++ b/imblearn/base.py
@@ -7,6 +7,7 @@
 from abc import ABCMeta, abstractmethod
 
 import numpy as np
+import sklearn
 from sklearn.base import BaseEstimator
 
 try:
@@ -14,15 +15,38 @@
     from sklearn.base import OneToOneFeatureMixin
 except ImportError:
     from sklearn.base import _OneToOneFeatureMixin as OneToOneFeatureMixin
+
 from sklearn.preprocessing import label_binarize
+from sklearn.utils import parse_version
 from sklearn.utils.multiclass import check_classification_targets
 
 from .utils import check_sampling_strategy, check_target_type
 from .utils._param_validation import validate_parameter_constraints
 from .utils._validation import ArraysTransformer
 
+sklearn_version = parse_version(sklearn.__version__)
+
+
+class _ParamsValidationMixin:
+    """Mixin class to validate parameters."""
+
+    def _validate_params(self):
+        """Validate types and values of constructor parameters.
+
+        The expected type and values must be defined in the `_parameter_constraints`
+        class attribute, which is a dictionary `param_name: list of constraints`. See
+        the docstring of `validate_parameter_constraints` for a description of the
+        accepted constraints.
+        """
+        if hasattr(self, "_parameter_constraints"):
+            validate_parameter_constraints(
+                self._parameter_constraints,
+                self.get_params(deep=False),
+                caller_name=self.__class__.__name__,
+            )
+
 
-class SamplerMixin(BaseEstimator, metaclass=ABCMeta):
+class SamplerMixin(_ParamsValidationMixin, BaseEstimator, metaclass=ABCMeta):
     """Mixin class for samplers with abstract method.
 
     Warning: This class should not be used directly. Use the derive classes
@@ -120,26 +144,7 @@ def _fit_resample(self, X, y):
         pass
 
 
-class _ParamsValidationMixin:
-    """Mixin class to validate parameters."""
-
-    def _validate_params(self):
-        """Validate types and values of constructor parameters.
-
-        The expected type and values must be defined in the `_parameter_constraints`
-        class attribute, which is a dictionary `param_name: list of constraints`. See
-        the docstring of `validate_parameter_constraints` for a description of the
-        accepted constraints.
-        """
-        if hasattr(self, "_parameter_constraints"):
-            validate_parameter_constraints(
-                self._parameter_constraints,
-                self.get_params(deep=False),
-                caller_name=self.__class__.__name__,
-            )
-
-
-class BaseSampler(SamplerMixin, OneToOneFeatureMixin, _ParamsValidationMixin):
+class BaseSampler(SamplerMixin, OneToOneFeatureMixin):
     """Base class for sampling algorithms.
 
     Warning: This class should not be used directly. Use the derive classes
diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py
index 83b85ec13..04d2217b9 100644
--- a/imblearn/ensemble/_bagging.py
+++ b/imblearn/ensemble/_bagging.py
@@ -10,11 +10,13 @@
 import warnings
 
 import numpy as np
+import sklearn
 from sklearn.base import clone
 from sklearn.ensemble import BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.utils import parse_version
 from sklearn.utils.validation import check_is_fitted
 
 try:
@@ -32,15 +34,18 @@
 from ..utils._available_if import available_if
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import HasMethods, Interval, StrOptions
+from ..utils.fixes import _fit_context
 from ._common import _bagging_parameter_constraints, _estimator_has
 
+sklearn_version = parse_version(sklearn.__version__)
+
 
 @Substitution(
     sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
     n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
-class BalancedBaggingClassifier(BaggingClassifier, _ParamsValidationMixin):
+class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier):
     """A Bagging classifier with additional balancing.
 
     This implementation of Bagging is similar to the scikit-learn
@@ -261,8 +266,7 @@ class BalancedBaggingClassifier(BaggingClassifier, _ParamsValidationMixin):
     """
 
     # make a deepcopy to not modify the original dictionary
-    if hasattr(BaggingClassifier, "_parameter_constraints"):
-        # scikit-learn >= 1.2
+    if sklearn_version >= parse_version("1.3"):
         _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
     else:
         _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)
@@ -394,6 +398,7 @@ def n_features_(self):
         )
         return self.n_features_in_
 
+    @_fit_context(prefer_skip_nested_validation=False)
     def fit(self, X, y):
         """Build a Bagging ensemble of estimators from the training set (X, y).
 
diff --git a/imblearn/ensemble/_common.py b/imblearn/ensemble/_common.py
index 32f5fb1cc..abc242c4a 100644
--- a/imblearn/ensemble/_common.py
+++ b/imblearn/ensemble/_common.py
@@ -2,7 +2,13 @@
 
 from sklearn.tree._criterion import Criterion
 
-from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions
+from ..utils._param_validation import (
+    HasMethods,
+    Hidden,
+    Interval,
+    RealNotInt,
+    StrOptions,
+)
 
 
 def _estimator_has(attr):
@@ -27,11 +33,11 @@ def check(self):
     "n_estimators": [Interval(Integral, 1, None, closed="left")],
     "max_samples": [
         Interval(Integral, 1, None, closed="left"),
-        Interval(Real, 0, 1, closed="right"),
+        Interval(RealNotInt, 0, 1, closed="right"),
     ],
     "max_features": [
         Interval(Integral, 1, None, closed="left"),
-        Interval(Real, 0, 1, closed="right"),
+        Interval(RealNotInt, 0, 1, closed="right"),
     ],
     "bootstrap": ["boolean"],
     "bootstrap_features": ["boolean"],
@@ -73,17 +79,17 @@ def check(self):
     "max_depth": [Interval(Integral, 1, None, closed="left"), None],
     "min_samples_split": [
         Interval(Integral, 2, None, closed="left"),
-        Interval(Real, 0.0, 1.0, closed="right"),
+        Interval(RealNotInt, 0.0, 1.0, closed="right"),
     ],
     "min_samples_leaf": [
         Interval(Integral, 1, None, closed="left"),
-        Interval(Real, 0.0, 1.0, closed="neither"),
+        Interval(RealNotInt, 0.0, 1.0, closed="neither"),
     ],
     "min_weight_fraction_leaf": [Interval(Real, 0.0, 0.5, closed="both")],
     "max_features": [
         Interval(Integral, 1, None, closed="left"),
-        Interval(Real, 0.0, 1.0, closed="right"),
-        StrOptions({"auto", "sqrt", "log2"}, deprecated={"auto"}),
+        Interval(RealNotInt, 0.0, 1.0, closed="right"),
+        StrOptions({"sqrt", "log2"}),
         None,
     ],
     "max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None],
diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 822516f2f..e2f5575cb 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -10,10 +10,12 @@
 import warnings
 
 import numpy as np
+import sklearn
 from sklearn.base import clone
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
+from sklearn.utils import parse_version
 from sklearn.utils._tags import _safe_tags
 from sklearn.utils.validation import check_is_fitted
 
@@ -32,9 +34,11 @@
 from ..utils._available_if import available_if
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import Interval, StrOptions
+from ..utils.fixes import _fit_context
 from ._common import _bagging_parameter_constraints, _estimator_has
 
 MAX_INT = np.iinfo(np.int32).max
+sklearn_version = parse_version(sklearn.__version__)
 
 
 @Substitution(
@@ -42,7 +46,7 @@
     n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
-class EasyEnsembleClassifier(BaggingClassifier, _ParamsValidationMixin):
+class EasyEnsembleClassifier(_ParamsValidationMixin, BaggingClassifier):
     """Bag of balanced boosted learners also known as EasyEnsemble.
 
     This algorithm is known as EasyEnsemble [1]_. The classifier is an
@@ -187,8 +191,7 @@ class EasyEnsembleClassifier(BaggingClassifier, _ParamsValidationMixin):
     """
 
     # make a deepcopy to not modify the original dictionary
-    if hasattr(BaggingClassifier, "_parameter_constraints"):
-        # scikit-learn >= 1.2
+    if sklearn_version >= parse_version("1.3"):
         _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
     else:
         _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)
@@ -320,6 +323,7 @@ def n_features_(self):
         )
         return self.n_features_in_
 
+    @_fit_context(prefer_skip_nested_validation=False)
     def fit(self, X, y):
         """Build a Bagging ensemble of estimators from the training set (X, y).
 
diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
index d77199481..6e96908bf 100644
--- a/imblearn/ensemble/_forest.py
+++ b/imblearn/ensemble/_forest.py
@@ -41,6 +41,7 @@
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
 from ..utils._param_validation import Interval, StrOptions
 from ..utils._validation import check_sampling_strategy
+from ..utils.fixes import _fit_context
 from ._common import _random_forest_classifier_parameter_constraints
 
 MAX_INT = np.iinfo(np.int32).max
@@ -103,10 +104,10 @@ def _local_parallel_build_trees(
     n_jobs=_n_jobs_docstring,
     random_state=_random_state_docstring,
 )
-class BalancedRandomForestClassifier(RandomForestClassifier, _ParamsValidationMixin):
+class BalancedRandomForestClassifier(_ParamsValidationMixin, RandomForestClassifier):
     """A balanced random forest classifier.
 
-    A balanced random forest randomly under-samples each boostrap sample to
+    A balanced random forest randomly under-samples each bootstrap sample to
     balance it.
 
     Read more in the :ref:`User Guide <forest>`.
@@ -361,8 +362,7 @@ class labels (multi-output problem).
     """
 
     # make a deepcopy to not modify the original dictionary
-    if hasattr(RandomForestClassifier, "_parameter_constraints"):
-        # scikit-learn >= 1.2
+    if sklearn_version >= parse_version("1.3"):
         _parameter_constraints = deepcopy(RandomForestClassifier._parameter_constraints)
     else:
         _parameter_constraints = deepcopy(
@@ -468,6 +468,7 @@ def _make_sampler_estimator(self, random_state=None):
 
         return estimator, sampler
 
+    @_fit_context(prefer_skip_nested_validation=True)
     def fit(self, X, y, sample_weight=None):
         """Build a forest of trees from the training set (X, y).
 
diff --git a/imblearn/ensemble/_weight_boosting.py b/imblearn/ensemble/_weight_boosting.py
index 7ebc4ae7c..7f7965d5e 100644
--- a/imblearn/ensemble/_weight_boosting.py
+++ b/imblearn/ensemble/_weight_boosting.py
@@ -5,11 +5,12 @@
 from copy import deepcopy
 
 import numpy as np
+import sklearn
 from sklearn.base import clone
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.ensemble._base import _set_random_states
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils import _safe_indexing
+from sklearn.utils import _safe_indexing, parse_version
 from sklearn.utils.validation import has_fit_parameter
 
 from ..base import _ParamsValidationMixin
@@ -19,14 +20,17 @@
 from ..utils import Substitution, check_target_type
 from ..utils._docstring import _random_state_docstring
 from ..utils._param_validation import Interval, StrOptions
+from ..utils.fixes import _fit_context
 from ._common import _adaboost_classifier_parameter_constraints
 
+sklearn_version = parse_version(sklearn.__version__)
+
 
 @Substitution(
     sampling_strategy=BaseUnderSampler._sampling_strategy_docstring,
     random_state=_random_state_docstring,
 )
-class RUSBoostClassifier(AdaBoostClassifier, _ParamsValidationMixin):
+class RUSBoostClassifier(_ParamsValidationMixin, AdaBoostClassifier):
     """Random under-sampling integrated in the learning of AdaBoost.
 
     During learning, the problem of class balancing is alleviated by random
@@ -168,8 +172,7 @@ class RUSBoostClassifier(AdaBoostClassifier, _ParamsValidationMixin):
     """
 
     # make a deepcopy to not modify the original dictionary
-    if hasattr(AdaBoostClassifier, "_parameter_constraints"):
-        # scikit-learn >= 1.2
+    if sklearn_version >= parse_version("1.3"):
         _parameter_constraints = copy.deepcopy(
             AdaBoostClassifier._parameter_constraints
         )
@@ -220,6 +223,7 @@ def __init__(
         self.sampling_strategy = sampling_strategy
         self.replacement = replacement
 
+    @_fit_context(prefer_skip_nested_validation=False)
     def fit(self, X, y, sample_weight=None):
         """Build a boosted classifier from the training set (X, y).
 
diff --git a/imblearn/metrics/pairwise.py b/imblearn/metrics/pairwise.py
index 4aa7977ef..11f654f02 100644
--- a/imblearn/metrics/pairwise.py
+++ b/imblearn/metrics/pairwise.py
@@ -16,7 +16,7 @@
 from ..utils._param_validation import StrOptions
 
 
-class ValueDifferenceMetric(BaseEstimator, _ParamsValidationMixin):
+class ValueDifferenceMetric(_ParamsValidationMixin, BaseEstimator):
     r"""Class implementing the Value Difference Metric.
 
     This metric computes the distance between samples containing only
diff --git a/imblearn/pipeline.py b/imblearn/pipeline.py
index ff013b17f..2324d7cbd 100644
--- a/imblearn/pipeline.py
+++ b/imblearn/pipeline.py
@@ -24,7 +24,7 @@
 __all__ = ["Pipeline", "make_pipeline"]
 
 
-class Pipeline(pipeline.Pipeline, _ParamsValidationMixin):
+class Pipeline(_ParamsValidationMixin, pipeline.Pipeline):
     """Pipeline of transforms and resamples with a final estimator.
 
     Sequentially apply a list of transforms, sampling, and a final estimator.
diff --git a/imblearn/utils/_param_validation.py b/imblearn/utils/_param_validation.py
index ae2b8e16a..2bd5e8014 100644
--- a/imblearn/utils/_param_validation.py
+++ b/imblearn/utils/_param_validation.py
@@ -15,9 +15,9 @@
 import numpy as np
 import sklearn
 from scipy.sparse import csr_matrix, issparse
-from sklearn import config_context, get_config
 from sklearn.utils.fixes import parse_version
 
+from .._config import config_context, get_config
 from ..utils.fixes import _is_arraylike_not_scalar
 
 sklearn_version = parse_version(sklearn.__version__)
@@ -190,10 +190,7 @@ def decorator(func):
 
             @functools.wraps(func)
             def wrapper(*args, **kwargs):
-                # This line is changed to be compatible with scikit-learn <=1.2
-                global_skip_validation = get_config().get(
-                    "skip_parameter_validation", False
-                )
+                global_skip_validation = get_config()["skip_parameter_validation"]
                 if global_skip_validation:
                     return func(*args, **kwargs)
 
diff --git a/imblearn/utils/fixes.py b/imblearn/utils/fixes.py
index c2db6adc4..9ade4ea72 100644
--- a/imblearn/utils/fixes.py
+++ b/imblearn/utils/fixes.py
@@ -4,6 +4,7 @@
 If you add content to this file, please give the version of the package at
 which the fix is no longer needed.
 """
+import functools
 
 import numpy as np
 import scipy
@@ -11,6 +12,8 @@
 import sklearn
 from sklearn.utils.fixes import parse_version
 
+from .._config import config_context, get_config
+
 sp_version = parse_version(scipy.__version__)
 sklearn_version = parse_version(sklearn.__version__)
 
@@ -31,3 +34,101 @@ def _mode(a, axis=0):
     def _is_arraylike_not_scalar(array):
         """Return True if array is array-like and not a scalar"""
         return _is_arraylike(array) and not np.isscalar(array)
+
+
+# TODO: remove when scikit-learn minimum version is 1.3
+if sklearn_version < parse_version("1.3"):
+
+    def _fit_context(*, prefer_skip_nested_validation):
+        """Decorator to run the fit methods of estimators within context managers.
+
+        Parameters
+        ----------
+        prefer_skip_nested_validation : bool
+            If True, the validation of parameters of inner estimators or functions
+            called during fit will be skipped.
+
+            This is useful to avoid validating many times the parameters passed by the
+            user from the public facing API. It's also useful to avoid validating
+            parameters that we pass internally to inner functions that are guaranteed to
+            be valid by the test suite.
+
+            It should be set to True for most estimators, except for those that receive
+            non-validated objects as parameters, such as meta-estimators that are given
+            estimator objects.
+
+        Returns
+        -------
+        decorated_fit : method
+            The decorated fit method.
+        """
+
+        def decorator(fit_method):
+            @functools.wraps(fit_method)
+            def wrapper(estimator, *args, **kwargs):
+                global_skip_validation = get_config()["skip_parameter_validation"]
+
+                # we don't want to validate again for each call to partial_fit
+                partial_fit_and_fitted = (
+                    fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
+                )
+
+                if not global_skip_validation and not partial_fit_and_fitted:
+                    estimator._validate_params()
+
+                with config_context(
+                    skip_parameter_validation=(
+                        prefer_skip_nested_validation or global_skip_validation
+                    )
+                ):
+                    return fit_method(estimator, *args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+else:
+    pass  # type: ignore[no-redef]
+
+# TODO: remove when scikit-learn minimum version is 1.3
+if sklearn_version < parse_version("1.3"):
+
+    def _is_fitted(estimator, attributes=None, all_or_any=all):
+        """Determine if an estimator is fitted
+
+        Parameters
+        ----------
+        estimator : estimator instance
+            Estimator instance for which the check is performed.
+
+        attributes : str, list or tuple of str, default=None
+            Attribute name(s) given as string or a list/tuple of strings
+            Eg.: ``["coef_", "estimator_", ...], "coef_"``
+
+            If `None`, `estimator` is considered fitted if there exist an
+            attribute that ends with a underscore and does not start with double
+            underscore.
+
+        all_or_any : callable, {all, any}, default=all
+            Specify whether all or any of the given attributes must exist.
+
+        Returns
+        -------
+        fitted : bool
+            Whether the estimator is fitted.
+        """
+        if attributes is not None:
+            if not isinstance(attributes, (list, tuple)):
+                attributes = [attributes]
+            return all_or_any([hasattr(estimator, attr) for attr in attributes])
+
+        if hasattr(estimator, "__sklearn_is_fitted__"):
+            return estimator.__sklearn_is_fitted__()
+
+        fitted_attrs = [
+            v for v in vars(estimator) if v.endswith("_") and not v.startswith("__")
+        ]
+        return len(fitted_attrs) > 0
+
+else:
+    from sklearn.utils.validation import _is_fitted  # type: ignore[no-redef]
diff --git a/imblearn/utils/tests/test_param_validation.py b/imblearn/utils/tests/test_param_validation.py
index 8a356f42a..3a7ab65a3 100644
--- a/imblearn/utils/tests/test_param_validation.py
+++ b/imblearn/utils/tests/test_param_validation.py
@@ -6,11 +6,12 @@
 import numpy as np
 import pytest
 from scipy.sparse import csr_matrix
-from sklearn._config import config_context, get_config
-from sklearn.base import BaseEstimator, _fit_context
+from sklearn.base import BaseEstimator
 from sklearn.model_selection import LeaveOneOut
 from sklearn.utils import deprecated
 
+from imblearn._config import config_context, get_config
+from imblearn.base import _ParamsValidationMixin
 from imblearn.utils._param_validation import (
     HasMethods,
     Hidden,
@@ -36,6 +37,7 @@
     make_constraint,
     validate_params,
 )
+from imblearn.utils.fixes import _fit_context
 
 
 # Some helpers for the tests
@@ -60,7 +62,7 @@ def _deprecated_method(self, a):
         """A deprecated validated method"""
 
 
-class _Estimator(BaseEstimator):
+class _Estimator(_ParamsValidationMixin, BaseEstimator):
     """An estimator to test the validation of estimator parameters."""
 
     _parameter_constraints: dict = {"a": [Real]}

From 9d8a8b065dbfedb2859e7191f9eb5afdc630b346 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:11:27 +0200
Subject: [PATCH 15/38] iter

---
 examples/over-sampling/plot_comparison_over_sampling.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/over-sampling/plot_comparison_over_sampling.py b/examples/over-sampling/plot_comparison_over_sampling.py
index bddd10e89..43517cf3b 100644
--- a/examples/over-sampling/plot_comparison_over_sampling.py
+++ b/examples/over-sampling/plot_comparison_over_sampling.py
@@ -275,7 +275,8 @@ def plot_decision_function(X, y, clf, ax, title=None):
     BorderlineSMOTE(random_state=0, kind="borderline-1"),
     BorderlineSMOTE(random_state=0, kind="borderline-2"),
     KMeansSMOTE(
-        kmeans_estimator=MiniBatchKMeans(n_init=1, random_state=0), random_state=0
+        kmeans_estimator=MiniBatchKMeans(n_clusters=10, n_init=1, random_state=0),
+        random_state=0,
     ),
     SVMSMOTE(random_state=0),
 ]

From f953aab7ad1af7da77a8f5095eb64e04185fcfea Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:15:21 +0200
Subject: [PATCH 16/38] iter

---
 imblearn/_config.py | 343 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 343 insertions(+)
 create mode 100644 imblearn/_config.py

diff --git a/imblearn/_config.py b/imblearn/_config.py
new file mode 100644
index 000000000..5b26dc423
--- /dev/null
+++ b/imblearn/_config.py
@@ -0,0 +1,343 @@
+"""This is copy of sklearn/_config.py
+# TODO: remove this file when scikit-learn minimum version is 1.3
+We remove the array_api_dispatch for the moment.
+"""
+import os
+import threading
+from contextlib import contextmanager as contextmanager
+
+import sklearn
+from sklearn.utils import parse_version
+
+sklearn_version = parse_version(sklearn.__version__)
+
+if sklearn_version < parse_version("1.3"):
+    _global_config = {
+        "assume_finite": bool(os.environ.get("SKLEARN_ASSUME_FINITE", False)),
+        "working_memory": int(os.environ.get("SKLEARN_WORKING_MEMORY", 1024)),
+        "print_changed_only": True,
+        "display": "diagram",
+        "pairwise_dist_chunk_size": int(
+            os.environ.get("SKLEARN_PAIRWISE_DIST_CHUNK_SIZE", 256)
+        ),
+        "enable_cython_pairwise_dist": True,
+        "transform_output": "default",
+        "enable_metadata_routing": False,
+        "skip_parameter_validation": False,
+    }
+    _threadlocal = threading.local()
+
+    def _get_threadlocal_config():
+        """Get a threadlocal **mutable** configuration. If the configuration
+        does not exist, copy the default global configuration."""
+        if not hasattr(_threadlocal, "global_config"):
+            _threadlocal.global_config = _global_config.copy()
+        return _threadlocal.global_config
+
+    def get_config():
+        """Retrieve current values for configuration set by :func:`set_config`.
+
+        Returns
+        -------
+        config : dict
+            Keys are parameter names that can be passed to :func:`set_config`.
+
+        See Also
+        --------
+        config_context : Context manager for global scikit-learn configuration.
+        set_config : Set global scikit-learn configuration.
+        """
+        # Return a copy of the threadlocal configuration so that users will
+        # not be able to modify the configuration with the returned dict.
+        return _get_threadlocal_config().copy()
+
+    def set_config(
+        assume_finite=None,
+        working_memory=None,
+        print_changed_only=None,
+        display=None,
+        pairwise_dist_chunk_size=None,
+        enable_cython_pairwise_dist=None,
+        transform_output=None,
+        enable_metadata_routing=None,
+        skip_parameter_validation=None,
+    ):
+        """Set global scikit-learn configuration
+
+        .. versionadded:: 0.19
+
+        Parameters
+        ----------
+        assume_finite : bool, default=None
+            If True, validation for finiteness will be skipped,
+            saving time, but leading to potential crashes. If
+            False, validation for finiteness will be performed,
+            avoiding error.  Global default: False.
+
+            .. versionadded:: 0.19
+
+        working_memory : int, default=None
+            If set, scikit-learn will attempt to limit the size of temporary arrays
+            to this number of MiB (per job when parallelised), often saving both
+            computation time and memory on expensive operations that can be
+            performed in chunks. Global default: 1024.
+
+            .. versionadded:: 0.20
+
+        print_changed_only : bool, default=None
+            If True, only the parameters that were set to non-default
+            values will be printed when printing an estimator. For example,
+            ``print(SVC())`` while True will only print 'SVC()' while the default
+            behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with
+            all the non-changed parameters.
+
+            .. versionadded:: 0.21
+
+        display : {'text', 'diagram'}, default=None
+            If 'diagram', estimators will be displayed as a diagram in a Jupyter
+            lab or notebook context. If 'text', estimators will be displayed as
+            text. Default is 'diagram'.
+
+            .. versionadded:: 0.23
+
+        pairwise_dist_chunk_size : int, default=None
+            The number of row vectors per chunk for the accelerated pairwise-
+            distances reduction backend. Default is 256 (suitable for most of
+            modern laptops' caches and architectures).
+
+            Intended for easier benchmarking and testing of scikit-learn internals.
+            End users are not expected to benefit from customizing this configuration
+            setting.
+
+            .. versionadded:: 1.1
+
+        enable_cython_pairwise_dist : bool, default=None
+            Use the accelerated pairwise-distances reduction backend when
+            possible. Global default: True.
+
+            Intended for easier benchmarking and testing of scikit-learn internals.
+            End users are not expected to benefit from customizing this configuration
+            setting.
+
+            .. versionadded:: 1.1
+
+        transform_output : str, default=None
+            Configure output of `transform` and `fit_transform`.
+
+            See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
+            for an example on how to use the API.
+
+            - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
+            - `None`: Transform configuration is unchanged
+
+            .. versionadded:: 1.2
+
+        enable_metadata_routing : bool, default=None
+            Enable metadata routing. By default this feature is disabled.
+
+            Refer to :ref:`metadata routing user guide <metadata_routing>` for more
+            details.
+
+            - `True`: Metadata routing is enabled
+            - `False`: Metadata routing is disabled, use the old syntax.
+            - `None`: Configuration is unchanged
+
+            .. versionadded:: 1.3
+
+        skip_parameter_validation : bool, default=None
+            If `True`, disable the validation of the hyper-parameters' types
+            and values in the fit method of estimators and for arguments passed
+            to public helper functions. It can save time in some situations but
+            can lead to low level crashes and exceptions with confusing error
+            messages.
+
+            Note that for data parameters, such as `X` and `y`, only type validation is
+            skipped but validation with `check_array` will continue to run.
+
+            .. versionadded:: 1.3
+
+        See Also
+        --------
+        config_context : Context manager for global scikit-learn configuration.
+        get_config : Retrieve current values of the global configuration.
+        """
+        local_config = _get_threadlocal_config()
+
+        if assume_finite is not None:
+            local_config["assume_finite"] = assume_finite
+        if working_memory is not None:
+            local_config["working_memory"] = working_memory
+        if print_changed_only is not None:
+            local_config["print_changed_only"] = print_changed_only
+        if display is not None:
+            local_config["display"] = display
+        if pairwise_dist_chunk_size is not None:
+            local_config["pairwise_dist_chunk_size"] = pairwise_dist_chunk_size
+        if enable_cython_pairwise_dist is not None:
+            local_config["enable_cython_pairwise_dist"] = enable_cython_pairwise_dist
+        if transform_output is not None:
+            local_config["transform_output"] = transform_output
+        if enable_metadata_routing is not None:
+            local_config["enable_metadata_routing"] = enable_metadata_routing
+        if skip_parameter_validation is not None:
+            local_config["skip_parameter_validation"] = skip_parameter_validation
+
+    @contextmanager
+    def config_context(
+        *,
+        assume_finite=None,
+        working_memory=None,
+        print_changed_only=None,
+        display=None,
+        pairwise_dist_chunk_size=None,
+        enable_cython_pairwise_dist=None,
+        transform_output=None,
+        enable_metadata_routing=None,
+        skip_parameter_validation=None,
+    ):
+        """Context manager for global scikit-learn configuration.
+
+        Parameters
+        ----------
+        assume_finite : bool, default=None
+            If True, validation for finiteness will be skipped,
+            saving time, but leading to potential crashes. If
+            False, validation for finiteness will be performed,
+            avoiding error. If None, the existing value won't change.
+            The default value is False.
+
+        working_memory : int, default=None
+            If set, scikit-learn will attempt to limit the size of temporary arrays
+            to this number of MiB (per job when parallelised), often saving both
+            computation time and memory on expensive operations that can be
+            performed in chunks. If None, the existing value won't change.
+            The default value is 1024.
+
+        print_changed_only : bool, default=None
+            If True, only the parameters that were set to non-default
+            values will be printed when printing an estimator. For example,
+            ``print(SVC())`` while True will only print 'SVC()', but would print
+            'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
+            when False. If None, the existing value won't change.
+            The default value is True.
+
+            .. versionchanged:: 0.23
+            Default changed from False to True.
+
+        display : {'text', 'diagram'}, default=None
+            If 'diagram', estimators will be displayed as a diagram in a Jupyter
+            lab or notebook context. If 'text', estimators will be displayed as
+            text. If None, the existing value won't change.
+            The default value is 'diagram'.
+
+            .. versionadded:: 0.23
+
+        pairwise_dist_chunk_size : int, default=None
+            The number of row vectors per chunk for the accelerated pairwise-
+            distances reduction backend. Default is 256 (suitable for most of
+            modern laptops' caches and architectures).
+
+            Intended for easier benchmarking and testing of scikit-learn internals.
+            End users are not expected to benefit from customizing this configuration
+            setting.
+
+            .. versionadded:: 1.1
+
+        enable_cython_pairwise_dist : bool, default=None
+            Use the accelerated pairwise-distances reduction backend when
+            possible. Global default: True.
+
+            Intended for easier benchmarking and testing of scikit-learn internals.
+            End users are not expected to benefit from customizing this configuration
+            setting.
+
+            .. versionadded:: 1.1
+
+        transform_output : str, default=None
+            Configure output of `transform` and `fit_transform`.
+
+            See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
+            for an example on how to use the API.
+
+            - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
+            - `None`: Transform configuration is unchanged
+
+            .. versionadded:: 1.2
+
+        enable_metadata_routing : bool, default=None
+            Enable metadata routing. By default this feature is disabled.
+
+            Refer to :ref:`metadata routing user guide <metadata_routing>` for more
+            details.
+
+            - `True`: Metadata routing is enabled
+            - `False`: Metadata routing is disabled, use the old syntax.
+            - `None`: Configuration is unchanged
+
+            .. versionadded:: 1.3
+
+        skip_parameter_validation : bool, default=None
+            If `True`, disable the validation of the hyper-parameters' types
+            and values in the fit method of estimators and for arguments passed
+            to public helper functions. It can save time in some situations but
+            can lead to low level crashes and exceptions with confusing error
+            messages.
+
+            Note that for data parameters, such as `X` and `y`, only type validation is
+            skipped but validation with `check_array` will continue to run.
+
+            .. versionadded:: 1.3
+
+        Yields
+        ------
+        None.
+
+        See Also
+        --------
+        set_config : Set global scikit-learn configuration.
+        get_config : Retrieve current values of the global configuration.
+
+        Notes
+        -----
+        All settings, not just those presently modified, will be returned to
+        their previous values when the context manager is exited.
+
+        Examples
+        --------
+        >>> import sklearn
+        >>> from sklearn.utils.validation import assert_all_finite
+        >>> with sklearn.config_context(assume_finite=True):
+        ...     assert_all_finite([float('nan')])
+        >>> with sklearn.config_context(assume_finite=True):
+        ...     with sklearn.config_context(assume_finite=False):
+        ...         assert_all_finite([float('nan')])
+        Traceback (most recent call last):
+        ...
+        ValueError: Input contains NaN...
+        """
+        old_config = get_config()
+        set_config(
+            assume_finite=assume_finite,
+            working_memory=working_memory,
+            print_changed_only=print_changed_only,
+            display=display,
+            pairwise_dist_chunk_size=pairwise_dist_chunk_size,
+            enable_cython_pairwise_dist=enable_cython_pairwise_dist,
+            transform_output=transform_output,
+            enable_metadata_routing=enable_metadata_routing,
+            skip_parameter_validation=skip_parameter_validation,
+        )
+
+        try:
+            yield
+        finally:
+            set_config(**old_config)
+
+else:
+    from sklearn._config import (  # type: ignore[no-redef]
+        _get_threadlocal_config,
+        _global_config,
+        get_config,
+    )

From c8912ec03cc120e4433a9ff58317d9d738aa4356 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:19:21 +0200
Subject: [PATCH 17/38] iter

---
 imblearn/_config.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/imblearn/_config.py b/imblearn/_config.py
index 5b26dc423..4c093db09 100644
--- a/imblearn/_config.py
+++ b/imblearn/_config.py
@@ -339,5 +339,6 @@ def config_context(
     from sklearn._config import (  # type: ignore[no-redef]
         _get_threadlocal_config,
         _global_config,
+        config_context,  # noqa
         get_config,
     )

From 50b4bf81ec65540696f8737134ae7650b1c1f96d Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:20:06 +0200
Subject: [PATCH 18/38] iter

---
 imblearn/utils/fixes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/imblearn/utils/fixes.py b/imblearn/utils/fixes.py
index 9ade4ea72..1868cb1fd 100644
--- a/imblearn/utils/fixes.py
+++ b/imblearn/utils/fixes.py
@@ -88,7 +88,7 @@ def wrapper(estimator, *args, **kwargs):
         return decorator
 
 else:
-    pass  # type: ignore[no-redef]
+    from sklearn.base import _fit_context  # type: ignore[no-redef] # noqa
 
 # TODO: remove when scikit-learn minimum version is 1.3
 if sklearn_version < parse_version("1.3"):

From 1eccb319e976195ed011450ada54756556407f31 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:33:53 +0200
Subject: [PATCH 19/38] oter

---
 doc/common_pitfalls.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
index 249aabcc6..907667466 100644
--- a/doc/common_pitfalls.rst
+++ b/doc/common_pitfalls.rst
@@ -54,6 +54,7 @@ increase the effect of the wrongdoings::
 Let's first check the balancing ratio on this dataset::
 
   >>> y.value_counts(normalize=True)
+  class
   <=50K    0.98801
   >50K     0.01199
   Name: proportion, dtype: float64

From a8cc055c32ba5ad42960ba27f438336af3c7ef5a Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 12:44:09 +0200
Subject: [PATCH 20/38] iter

---
 doc/common_pitfalls.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
index 907667466..c74210358 100644
--- a/doc/common_pitfalls.rst
+++ b/doc/common_pitfalls.rst
@@ -54,10 +54,10 @@ increase the effect of the wrongdoings::
 Let's first check the balancing ratio on this dataset::
 
   >>> y.value_counts(normalize=True)
-  class
+  ...
   <=50K    0.98801
   >50K     0.01199
-  Name: proportion, dtype: float64
+  ...
 
 To later highlight some of the issue, we will keep aside a left-out set that we
 will not use for the evaluation of the model::

From eec32b322f82b7aa4403357a3cf956d9716a8f90 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 13:08:24 +0200
Subject: [PATCH 21/38] iter

---
 azure-pipelines.yml                    | 10 +++++-----
 imblearn/keras/tests/test_generator.py |  4 ++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index b7d7e69d8..3df642aeb 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -11,7 +11,7 @@ jobs:
 - job: git_commit
   displayName: Get Git Commit
   pool:
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-22.04
   steps:
     - bash: |
         set -ex
@@ -38,7 +38,7 @@ jobs:
     )
   displayName: Linting
   pool:
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-22.04
   steps:
     - task: UsePythonVersion@0
       inputs:
@@ -63,7 +63,7 @@ jobs:
 - template: build_tools/azure/posix.yml
   parameters:
     name: Linux_Nightly
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-22.04
     dependsOn: [git_commit, linting]
     condition: |
       and(
@@ -86,7 +86,7 @@ jobs:
 - template: build_tools/azure/posix.yml
   parameters:
     name: Linux_Runs
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-22.04
     dependsOn: [git_commit]
     condition: |
       and(
@@ -125,7 +125,7 @@ jobs:
 - template: build_tools/azure/posix.yml
   parameters:
     name: Linux
-    vmImage: ubuntu-20.04
+    vmImage: ubuntu-22.04
     dependsOn: [linting, git_commit]
     condition: |
       and(
diff --git a/imblearn/keras/tests/test_generator.py b/imblearn/keras/tests/test_generator.py
index 5c0538169..e806ee537 100644
--- a/imblearn/keras/tests/test_generator.py
+++ b/imblearn/keras/tests/test_generator.py
@@ -3,11 +3,11 @@
 from scipy import sparse
 from sklearn.cluster import KMeans
 from sklearn.datasets import load_iris
+from sklearn.preprocessing import LabelBinarizer
 
 keras = pytest.importorskip("keras")
 from keras.layers import Dense  # noqa: E402
 from keras.models import Sequential  # noqa: E402
-from keras.utils.np_utils import to_categorical  # noqa: E402
 
 from imblearn.datasets import make_imbalance  # noqa: E402
 from imblearn.keras import (
@@ -29,7 +29,7 @@ def data():
     X, y = make_imbalance(
         iris.data, iris.target, sampling_strategy={0: 30, 1: 50, 2: 40}
     )
-    y = to_categorical(y, 3)
+    y = LabelBinarizer().fit_transform(y, 3)
     return X, y
 
 

From 5055d8a7c3bbc1f023294ead3669007e1506c437 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 13:10:33 +0200
Subject: [PATCH 22/38] iter

---
 azure-pipelines.yml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 3df642aeb..de47ea840 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -170,6 +170,13 @@ jobs:
         TEST_DOCSTRINGS: 'true'
         CHECK_WARNINGS: 'false'
         SKLEARN_VERSION: '1.1.3'
+      pylatest_pip_openblas_sklearn_intermediate_bis:
+        DISTRIB: 'conda-pip-latest'
+        PYTHON_VERSION: '3.10'
+        TEST_DOCS: 'true'
+        TEST_DOCSTRINGS: 'true'
+        CHECK_WARNINGS: 'false'
+        SKLEARN_VERSION: '1.2.2'
       pylatest_pip_tensorflow:
         DISTRIB: 'conda-pip-latest-tensorflow'
         CONDA_CHANNEL: 'conda-forge'

From 254607ba48f3f085ce5b17d542a1d93dfd8c47f2 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 13:33:03 +0200
Subject: [PATCH 23/38] iter

---
 doc/common_pitfalls.rst | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
index c74210358..45fdbe9fa 100644
--- a/doc/common_pitfalls.rst
+++ b/doc/common_pitfalls.rst
@@ -53,11 +53,9 @@ increase the effect of the wrongdoings::
 
 Let's first check the balancing ratio on this dataset::
 
-  >>> y.value_counts(normalize=True)
-  ...
-  <=50K    0.98801
-  >50K     0.01199
-  ...
+  >>> from collections import Counter
+  >>> {key: value / len(y) for key, value in Counter(y).items()}
+  {'<=50K': 0.988..., '>50K': 0.011...}
 
 To later highlight some of the issue, we will keep aside a left-out set that we
 will not use for the evaluation of the model::

From cc63ccd0bcf6d1f1f1549f5bfeafea77bed94434 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 13:57:53 +0200
Subject: [PATCH 24/38] iter

---
 imblearn/keras/tests/test_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/imblearn/keras/tests/test_generator.py b/imblearn/keras/tests/test_generator.py
index e806ee537..f49ecd0aa 100644
--- a/imblearn/keras/tests/test_generator.py
+++ b/imblearn/keras/tests/test_generator.py
@@ -29,7 +29,7 @@ def data():
     X, y = make_imbalance(
         iris.data, iris.target, sampling_strategy={0: 30, 1: 50, 2: 40}
     )
-    y = LabelBinarizer().fit_transform(y, 3)
+    y = LabelBinarizer().fit_transform(y)
     return X, y
 
 

From 8caf4cbe3c32ee78fdaf49d88c1f520619230f24 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 14:51:51 +0200
Subject: [PATCH 25/38] iter


From 00015d4c1b57b4db8a88aee6e0f3b4526dde5205 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 15:08:07 +0200
Subject: [PATCH 26/38] iter

---
 build_tools/azure/install.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 8d60203f7..b55e994cb 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -41,6 +41,10 @@ if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 
 	make_conda $TO_INSTALL
 
+    if [[ "$CONDA_CHANNEL" == "" ] && [ "$DISTRIB" == "conda" ]]; then
+        mamba install scikit-learn==1.3 -c conda-forge --yes
+    fi
+
 elif [[ "$DISTRIB" == "ubuntu" ]]; then
     sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test
     sudo apt-get update

From a6a7848908e3ac97610dd709e1bb22500fcd7763 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 15:11:57 +0200
Subject: [PATCH 27/38] iter

---
 build_tools/azure/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index b55e994cb..0277bd772 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -41,7 +41,7 @@ if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 
 	make_conda $TO_INSTALL
 
-    if [[ "$CONDA_CHANNEL" == "" ] && [ "$DISTRIB" == "conda" ]]; then
+    if [[ "$CONDA_CHANNEL" == "" && "$DISTRIB" == "conda" ]]; then
         mamba install scikit-learn==1.3 -c conda-forge --yes
     fi
 

From f0ead5935d836f72c662d0c7e45235ea8b7d662a Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 15:16:16 +0200
Subject: [PATCH 28/38] iter

---
 build_tools/azure/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 0277bd772..dcdf0eb61 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -42,7 +42,7 @@ if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 	make_conda $TO_INSTALL
 
     if [[ "$CONDA_CHANNEL" == "" && "$DISTRIB" == "conda" ]]; then
-        mamba install scikit-learn==1.3 -c conda-forge --yes
+        conda install scikit-learn==1.3 -c conda-forge --yes
     fi
 
 elif [[ "$DISTRIB" == "ubuntu" ]]; then

From dd4554c3c04a4fe2a311ed6e70b1973d2c7878a0 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 15:16:43 +0200
Subject: [PATCH 29/38] iter

---
 build_tools/azure/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index dcdf0eb61..7280b6380 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -42,7 +42,7 @@ if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 	make_conda $TO_INSTALL
 
     if [[ "$CONDA_CHANNEL" == "" && "$DISTRIB" == "conda" ]]; then
-        conda install scikit-learn==1.3 -c conda-forge --yes
+        conda install scikit-learn==1.3 --override-channels -c conda-forge --yes
     fi
 
 elif [[ "$DISTRIB" == "ubuntu" ]]; then

From d83a63738f9001366a227c0ba9cb0f22562498f3 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 15:59:22 +0200
Subject: [PATCH 30/38] iter

---
 azure-pipelines.yml          | 12 ++++++++++++
 build_tools/azure/install.sh |  4 ----
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index de47ea840..b79ffbf65 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -229,6 +229,18 @@ jobs:
         TEST_DOCS: 'true'
         TEST_DOCSTRINGS: 'false'  # it is going to fail because of scikit-learn inheritance
         CHECK_WARNINGS: 'false'  # in case the older version raise some FutureWarnings
+      pylatest_conda_forge_mkl_linux:
+        DISTRIB: 'conda'
+        BLAS: 'mkl'
+        CONDA_CHANNEL: 'conda-forge'
+        CPU_COUNT: '3'
+        TEST_DOCS: 'true'
+      pylatest_conda_mkl_no_openmp_linux:
+        DISTRIB: 'conda'
+        BLAS: 'mkl'
+        SKLEARN_SKIP_OPENMP_TEST: 'true'
+        CPU_COUNT: '3'
+        TEST_DOCS: 'true'
 
 # Currently runs on Python 3.8 while only Python 3.7 available
 # - template: build_tools/azure/posix-docker.yml
diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 7280b6380..8d60203f7 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -41,10 +41,6 @@ if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 
 	make_conda $TO_INSTALL
 
-    if [[ "$CONDA_CHANNEL" == "" && "$DISTRIB" == "conda" ]]; then
-        conda install scikit-learn==1.3 --override-channels -c conda-forge --yes
-    fi
-
 elif [[ "$DISTRIB" == "ubuntu" ]]; then
     sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test
     sudo apt-get update

From fa1c7474b2e0553d20555ac22618c2e083f5af95 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 16:01:39 +0200
Subject: [PATCH 31/38] iter

---
 azure-pipelines.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index b79ffbf65..d8661488b 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -285,6 +285,7 @@ jobs:
       pylatest_conda_mkl_no_openmp:
         DISTRIB: 'conda'
         BLAS: 'mkl'
+        JOBLIB_VERSION: '1.3.0'
         SKLEARN_SKIP_OPENMP_TEST: 'true'
         CPU_COUNT: '3'
         TEST_DOCS: 'true'

From ce8e1ff77be52a2e73a9e1359714d86b57795687 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 16:06:01 +0200
Subject: [PATCH 32/38] iter

---
 azure-pipelines.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index d8661488b..b79ffbf65 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -285,7 +285,6 @@ jobs:
       pylatest_conda_mkl_no_openmp:
         DISTRIB: 'conda'
         BLAS: 'mkl'
-        JOBLIB_VERSION: '1.3.0'
         SKLEARN_SKIP_OPENMP_TEST: 'true'
         CPU_COUNT: '3'
         TEST_DOCS: 'true'

From 71a7e9fadfa55ab4b1eace23f7751621ec673187 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 16:23:32 +0200
Subject: [PATCH 33/38] iter

---
 azure-pipelines.yml | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index b79ffbf65..7b8bcaa39 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -282,12 +282,19 @@ jobs:
         CONDA_CHANNEL: 'conda-forge'
         CPU_COUNT: '3'
         TEST_DOCS: 'true'
-      pylatest_conda_mkl_no_openmp:
+      # TODO: re-enable when we find out why MKL on defaults segfaults
+      # pylatest_conda_mkl_no_openmp:
+      #   DISTRIB: 'conda'
+      #   BLAS: 'mkl'
+      #   SKLEARN_SKIP_OPENMP_TEST: 'true'
+      #   CPU_COUNT: '3'
+      #   TEST_DOCS: 'true'
+      conda_defaults_openblas:
         DISTRIB: 'conda'
-        BLAS: 'mkl'
-        SKLEARN_SKIP_OPENMP_TEST: 'true'
-        CPU_COUNT: '3'
+        CONDA_CHANNEL: 'conda-forge'
+        BLAS: 'openblas'
         TEST_DOCS: 'true'
+        CPU_COUNT: '3'
 
 - template: build_tools/azure/windows.yml
   parameters:

From a10202ddc34dbba9e388fdf44b5ad8e490a8b7ac Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 16:31:33 +0200
Subject: [PATCH 34/38] iter

---
 azure-pipelines.yml              | 12 ++++++------
 build_tools/azure/test_script.sh |  3 +++
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 7b8bcaa39..e1c454dc7 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -283,12 +283,12 @@ jobs:
         CPU_COUNT: '3'
         TEST_DOCS: 'true'
       # TODO: re-enable when we find out why MKL on defaults segfaults
-      # pylatest_conda_mkl_no_openmp:
-      #   DISTRIB: 'conda'
-      #   BLAS: 'mkl'
-      #   SKLEARN_SKIP_OPENMP_TEST: 'true'
-      #   CPU_COUNT: '3'
-      #   TEST_DOCS: 'true'
+      pylatest_conda_mkl_no_openmp:
+        DISTRIB: 'conda'
+        BLAS: 'mkl'
+        SKLEARN_SKIP_OPENMP_TEST: 'true'
+        CPU_COUNT: '3'
+        TEST_DOCS: 'true'
       conda_defaults_openblas:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'
diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh
index 446b08b38..823e0b667 100755
--- a/build_tools/azure/test_script.sh
+++ b/build_tools/azure/test_script.sh
@@ -12,7 +12,10 @@ mkdir -p $TEST_DIR
 cp setup.cfg $TEST_DIR
 cd $TEST_DIR
 
+python -c "import joblib; print(f'Number of cores (physical): \
+{joblib.cpu_count()} ({joblib.cpu_count(only_physical_cores=True)})')"
 python -c "import imblearn; imblearn.show_versions()"
+python -c "import sklearn; sklearn.show_versions()"
 
 if ! command -v conda &> /dev/null
 then

From 40d42a3aff43144beae1a1f67996180081ec0543 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 16:55:26 +0200
Subject: [PATCH 35/38] iter

---
 azure-pipelines.yml              | 6 ++++--
 build_tools/azure/install.sh     | 2 +-
 build_tools/azure/test_script.sh | 6 +++---
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index e1c454dc7..01987745c 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -144,7 +144,7 @@ jobs:
         THREADPOOLCTL_VERSION: 'min'
         COVERAGE: 'false'
       # Linux + Python 3.8 build with OpenBLAS and without SITE_JOBLIB
-      py38_conda_defaults_openblas:
+      py38_conda_conda_forge_openblas:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'
         PYTHON_VERSION: '3.8'
@@ -283,13 +283,15 @@ jobs:
         CPU_COUNT: '3'
         TEST_DOCS: 'true'
       # TODO: re-enable when we find out why MKL on defaults segfaults
+      # It seems that scikit-learn from defaults channel is built with LLVM/CLANG OMP
+      # while we use MKL OMP. This could be the cause of the segfaults.
       pylatest_conda_mkl_no_openmp:
         DISTRIB: 'conda'
         BLAS: 'mkl'
         SKLEARN_SKIP_OPENMP_TEST: 'true'
         CPU_COUNT: '3'
         TEST_DOCS: 'true'
-      conda_defaults_openblas:
+      conda_conda_forge_openblas:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'
         BLAS: 'openblas'
diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 8d60203f7..99d5a938c 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -18,7 +18,7 @@ make_conda() {
 }
 
 # imports get_dep
-source build_tools/shared.sh
+source build_tools/shared.shpython= pip 'blas[build=mkl]' numpy scipy scikit-learn joblib pandas Pillow matplotlib
 
 if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 
diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh
index 823e0b667..4d135f3e2 100755
--- a/build_tools/azure/test_script.sh
+++ b/build_tools/azure/test_script.sh
@@ -12,10 +12,10 @@ mkdir -p $TEST_DIR
 cp setup.cfg $TEST_DIR
 cd $TEST_DIR
 
-python -c "import joblib; print(f'Number of cores (physical): \
-{joblib.cpu_count()} ({joblib.cpu_count(only_physical_cores=True)})')"
+# python -c "import joblib; print(f'Number of cores (physical): \
+# {joblib.cpu_count()} ({joblib.cpu_count(only_physical_cores=True)})')"
+# python -c "import sklearn; sklearn.show_versions()"
 python -c "import imblearn; imblearn.show_versions()"
-python -c "import sklearn; sklearn.show_versions()"
 
 if ! command -v conda &> /dev/null
 then

From d217649ea0363bbc62cfa681800352ac7fdc0d2b Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 17:14:32 +0200
Subject: [PATCH 36/38] iter

---
 build_tools/azure/install.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh
index 99d5a938c..8d60203f7 100755
--- a/build_tools/azure/install.sh
+++ b/build_tools/azure/install.sh
@@ -18,7 +18,7 @@ make_conda() {
 }
 
 # imports get_dep
-source build_tools/shared.shpython= pip 'blas[build=mkl]' numpy scipy scikit-learn joblib pandas Pillow matplotlib
+source build_tools/shared.sh
 
 if [[ "$DISTRIB" == "conda" || "$DISTRIB" == *"mamba"* ]]; then
 

From 13f2ea6f26a4101cfca019445f0ecba854d24ef0 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 17:33:05 +0200
Subject: [PATCH 37/38] iter

---
 azure-pipelines.yml | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 01987745c..69dd45b9a 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -229,18 +229,6 @@ jobs:
         TEST_DOCS: 'true'
         TEST_DOCSTRINGS: 'false'  # it is going to fail because of scikit-learn inheritance
         CHECK_WARNINGS: 'false'  # in case the older version raise some FutureWarnings
-      pylatest_conda_forge_mkl_linux:
-        DISTRIB: 'conda'
-        BLAS: 'mkl'
-        CONDA_CHANNEL: 'conda-forge'
-        CPU_COUNT: '3'
-        TEST_DOCS: 'true'
-      pylatest_conda_mkl_no_openmp_linux:
-        DISTRIB: 'conda'
-        BLAS: 'mkl'
-        SKLEARN_SKIP_OPENMP_TEST: 'true'
-        CPU_COUNT: '3'
-        TEST_DOCS: 'true'
 
 # Currently runs on Python 3.8 while only Python 3.7 available
 # - template: build_tools/azure/posix-docker.yml
@@ -290,7 +278,7 @@ jobs:
         BLAS: 'mkl'
         SKLEARN_SKIP_OPENMP_TEST: 'true'
         CPU_COUNT: '3'
-        TEST_DOCS: 'true'
+        TEST_DOCS: 'false'
       conda_conda_forge_openblas:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'

From 9034631f6c0e02e9507c0f942137d3ceeaecf191 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Fri, 7 Jul 2023 17:46:06 +0200
Subject: [PATCH 38/38] iter

---
 azure-pipelines.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 69dd45b9a..0263c4d1d 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -273,12 +273,12 @@ jobs:
       # TODO: re-enable when we find out why MKL on defaults segfaults
       # It seems that scikit-learn from defaults channel is built with LLVM/CLANG OMP
       # while we use MKL OMP. This could be the cause of the segfaults.
-      pylatest_conda_mkl_no_openmp:
-        DISTRIB: 'conda'
-        BLAS: 'mkl'
-        SKLEARN_SKIP_OPENMP_TEST: 'true'
-        CPU_COUNT: '3'
-        TEST_DOCS: 'false'
+      # pylatest_conda_mkl_no_openmp:
+      #   DISTRIB: 'conda'
+      #   BLAS: 'mkl'
+      #   SKLEARN_SKIP_OPENMP_TEST: 'true'
+      #   CPU_COUNT: '3'
+      #   TEST_DOCS: 'true'
       conda_conda_forge_openblas:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'