MAINT cosmetic follow-up switch pixi + GitHub Actions

README.rst

@@ -4,10 +4,10 @@
 
 .. _scikit-learn-contrib: https://github.com/scikit-learn-contrib
 
-|Azure|_ |Codecov|_ |CircleCI|_ |PythonVersion|_ |Pypi|_ |Gitter|_ |Black|_
+|GitHubActions|_ |Codecov|_ |CircleCI|_ |PythonVersion|_ |Pypi|_ |Gitter|_ |Black|_
 
-.. |Azure| image:: https://dev.azure.com/imbalanced-learn/imbalanced-learn/_apis/build/status/scikit-learn-contrib.imbalanced-learn?branchName=master
-.. _Azure: https://dev.azure.com/imbalanced-learn/imbalanced-learn/_build
+.. |GitHubActions| image:: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml/badge.svg
+.. _GitHubActions: https://github.com/scikit-learn-contrib/imbalanced-learn/actions/workflows/tests.yml
 
 .. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn/branch/master/graph/badge.svg
 .. _Codecov: https://codecov.io/gh/scikit-learn-contrib/imbalanced-learn
@@ -66,6 +66,7 @@ Dependencies
 - NumPy (>= |NumPyMinVersion|)
 - SciPy (>= |SciPyMinVersion|)
 - Scikit-learn (>= |ScikitLearnMinVersion|)
+- Pytest (>= |PytestMinVersion|)
 
 Additionally, `imbalanced-learn` requires the following optional dependencies:
 
@@ -128,6 +129,21 @@ of the scikit-learn community. Therefore, you can refer to their
 `Development Guide
 <http://scikit-learn.org/stable/developers>`_.
 
+Endorsement of the Scientific Python Specification
+--------------------------------------------------
+
+We endorse good practices from the Scientific Python Ecosystem Coordination (SPEC).
+The full list of recommendations is available `here`_.
+
+See below the list of recommendations that we endorse for the imbalanced-learn project.
+
+|SPEC 0 — Minimum Supported Dependencies|
+
+.. |SPEC 0 — Minimum Supported Dependencies| image:: https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811&color=%235CA038
+   :target: https://scientific-python.org/specs/spec-0000/
+
+.. _here: https://scientific-python.org/specs/
+
 About
 -----
 

imblearn/base.py

@@ -7,25 +7,14 @@
 from abc import ABCMeta, abstractmethod
 
 import numpy as np
-import sklearn
-from sklearn.base import BaseEstimator
-
-try:
-    # scikit-learn >= 1.2
-    from sklearn.base import OneToOneFeatureMixin
-except ImportError:
-    from sklearn.base import _OneToOneFeatureMixin as OneToOneFeatureMixin
-
+from sklearn.base import BaseEstimator, OneToOneFeatureMixin
 from sklearn.preprocessing import label_binarize
-from sklearn.utils.fixes 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."""

imblearn/ensemble/_bagging.py

@@ -6,32 +6,24 @@
 
 import copy
 import numbers
-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.exceptions import NotFittedError
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils.fixes import parse_version
+from sklearn.utils.metaestimators import available_if
+from sklearn.utils.parallel import Parallel, 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 joblib import Parallel
-    from sklearn.utils.fixes import delayed
-
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
-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
@@ -128,14 +120,6 @@ class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier):
 
         .. versionadded:: 0.10
 
-    n_features_ : int
-        The number of features when `fit` is performed.
-
-        .. deprecated:: 1.0
-           `n_features_` is deprecated in `scikit-learn` 1.0 and will be removed
-           in version 1.2. When the minimum version of `scikit-learn` supported
-           by `imbalanced-learn` will reach 1.2, this attribute will be removed.
-
     estimators_ : list of estimators
         The collection of fitted base estimators.
 
@@ -338,20 +322,6 @@ def _validate_estimator(self, default=DecisionTreeClassifier()):
             [("sampler", self.sampler_), ("classifier", estimator)]
         )
 
-    # TODO: remove when supporting scikit-learn>=1.2
-    @property
-    def n_features_(self):
-        """Number of features when ``fit`` is performed."""
-        warnings.warn(
-            (
-                "`n_features_` was deprecated in scikit-learn 1.0. This attribute will "
-                "not be accessible when the minimum supported version of scikit-learn "
-                "is 1.2."
-            ),
-            FutureWarning,
-        )
-        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).
@@ -443,14 +413,6 @@ def base_estimator_(self):
         error = AttributeError(
             f"{self.__class__.__name__} object has no attribute 'base_estimator_'."
         )
-        if sklearn_version < parse_version("1.2"):
-            # The base class require to have the attribute defined. For scikit-learn
-            # > 1.2, we are going to raise an error.
-            try:
-                check_is_fitted(self)
-                return self.estimator_
-            except NotFittedError:
-                raise error
         raise error
 
     def _more_tags(self):

imblearn/ensemble/_easy_ensemble.py

@@ -14,24 +14,17 @@
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
-from sklearn.exceptions import NotFittedError
 from sklearn.utils._tags import _safe_tags
 from sklearn.utils.fixes import parse_version
+from sklearn.utils.metaestimators import available_if
+from sklearn.utils.parallel import Parallel, 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 joblib import Parallel
-    from sklearn.utils.fixes import delayed
-
 from ..base import _ParamsValidationMixin
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
-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
@@ -107,14 +100,6 @@ class EasyEnsembleClassifier(_ParamsValidationMixin, BaggingClassifier):
     n_classes_ : int or list
         The number of classes.
 
-    n_features_ : int
-        The number of features when `fit` is performed.
-
-        .. deprecated:: 1.0
-           `n_features_` is deprecated in `scikit-learn` 1.0 and will be removed
-           in version 1.2. When the minimum version of `scikit-learn` supported
-           by `imbalanced-learn` will reach 1.2, this attribute will be removed.
-
     n_features_in_ : int
         Number of features in the input dataset.
 
@@ -357,14 +342,6 @@ def base_estimator_(self):
         error = AttributeError(
             f"{self.__class__.__name__} object has no attribute 'base_estimator_'."
         )
-        if sklearn_version < parse_version("1.2"):
-            # The base class require to have the attribute defined. For scikit-learn
-            # > 1.2, we are going to raise an error.
-            try:
-                check_is_fitted(self)
-                return self.estimator_
-            except NotFittedError:
-                raise error
         raise error
 
     def _get_estimator(self):

imblearn/ensemble/_forest.py

@@ -25,15 +25,9 @@
 from sklearn.utils import _safe_indexing, check_random_state
 from sklearn.utils.fixes import parse_version
 from sklearn.utils.multiclass import type_of_target
+from sklearn.utils.parallel import Parallel, delayed
 from sklearn.utils.validation import _check_sample_weight
 
-try:
-    # scikit-learn >= 1.2
-    from sklearn.utils.parallel import Parallel, delayed
-except (ImportError, ModuleNotFoundError):
-    from joblib import Parallel
-    from sklearn.utils.fixes import delayed
-
 from ..base import _ParamsValidationMixin
 from ..pipeline import make_pipeline
 from ..under_sampling import RandomUnderSampler
@@ -80,6 +74,7 @@ def _local_parallel_build_trees(
         "verbose": verbose,
         "class_weight": class_weight,
         "n_samples_bootstrap": n_samples_bootstrap,
+        "bootstrap": bootstrap,
     }
 
     if parse_version(sklearn_version.base_version) >= parse_version("1.4"):
@@ -89,13 +84,6 @@ def _local_parallel_build_trees(
             missing_values_in_feature_mask
         )
 
-    # TODO: remove when the minimum supported version of scikit-learn will be 1.1
-    # change of signature in scikit-learn 1.1
-    if parse_version(sklearn_version.base_version) >= parse_version("1.1"):
-        params_parallel_build_trees["bootstrap"] = bootstrap
-    else:
-        params_parallel_build_trees["forest"] = forest
-
     tree = _parallel_build_trees(**params_parallel_build_trees)
 
     return sampler, tree
@@ -355,14 +343,6 @@ class labels (multi-output problem).
         The number of classes (single output problem), or a list containing the
         number of classes for each output (multi-output problem).
 
-    n_features_ : int
-        The number of features when `fit` is performed.
-
-        .. deprecated:: 1.0
-           `n_features_` is deprecated in `scikit-learn` 1.0 and will be removed
-           in version 1.2. When the minimum version of `scikit-learn` supported
-           by `imbalanced-learn` will reach 1.2, this attribute will be removed.
-
     n_features_in_ : int
         Number of features in the input dataset.
 
@@ -514,13 +494,8 @@ def __init__(
     def _validate_estimator(self, default=DecisionTreeClassifier()):
         """Check the estimator and the n_estimator attribute, set the
         `estimator_` attribute."""
-        if hasattr(self, "estimator"):
-            base_estimator = self.estimator
-        else:
-            base_estimator = self.base_estimator
-
-        if base_estimator is not None:
-            self.estimator_ = clone(base_estimator)
+        if self.estimator is not None:
+            self.estimator_ = clone(self.estimator)
         else:
             self.estimator_ = clone(default)
 
@@ -905,22 +880,5 @@ def _compute_oob_predictions(self, X, y):
 
         return oob_pred
 
-    # TODO: remove when supporting scikit-learn>=1.2
-    @property
-    def n_features_(self):
-        """Number of features when ``fit`` is performed."""
-        warn(
-            (
-                "`n_features_` was deprecated in scikit-learn 1.0. This attribute will "
-                "not be accessible when the minimum supported version of scikit-learn "
-                "is 1.2."
-            ),
-            FutureWarning,
-        )
-        return self.n_features_in_
-
     def _more_tags(self):
-        return {
-            "multioutput": False,
-            "multilabel": False,
-        }
+        return {"multioutput": False, "multilabel": False}