Fixing imports for sklearn 1.2.1

README.rst

@@ -28,15 +28,15 @@
 .. _Black: :target: https://github.com/psf/black
 
 .. |PythonMinVersion| replace:: 3.8
-.. |NumPyMinVersion| replace:: 1.17.3
-.. |SciPyMinVersion| replace:: 1.3.2
-.. |ScikitLearnMinVersion| replace:: 1.0.2
+.. |NumPyMinVersion| replace:: 1.23.5
+.. |SciPyMinVersion| replace:: 1.5.2
+.. |ScikitLearnMinVersion| replace:: 1.2.2
 .. |MatplotlibMinVersion| replace:: 3.1.2
 .. |PandasMinVersion| replace:: 1.0.5
 .. |TensorflowMinVersion| replace:: 2.4.3
 .. |KerasMinVersion| replace:: 2.4.3
 .. |SeabornMinVersion| replace:: 0.9.0
-.. |PytestMinVersion| replace:: 5.0.1
+.. |PytestMinVersion| replace:: 5.3.1
 
 imbalanced-learn
 ================

doc/common_pitfalls.rst

@@ -54,9 +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: 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::

imblearn/_min_dependencies.py

@@ -1,15 +1,15 @@
 """All minimum dependencies for imbalanced-learn."""
 import argparse
 
-NUMPY_MIN_VERSION = "1.17.3"
-SCIPY_MIN_VERSION = "1.3.2"
+NUMPY_MIN_VERSION = "1.23.5"
+SCIPY_MIN_VERSION = "1.5.2"
 PANDAS_MIN_VERSION = "1.0.5"
-SKLEARN_MIN_VERSION = "1.0.2"
+SKLEARN_MIN_VERSION = "1.2.2"
 TENSORFLOW_MIN_VERSION = "2.4.3"
 KERAS_MIN_VERSION = "2.4.3"
 JOBLIB_MIN_VERSION = "1.1.1"
 THREADPOOLCTL_MIN_VERSION = "2.0.0"
-PYTEST_MIN_VERSION = "5.0.1"
+PYTEST_MIN_VERSION = "5.3.1"
 
 # 'build' and 'install' is included to have structured metadata for CI.
 # It will NOT be included in setup's extras_require

imblearn/ensemble/_bagging.py

@@ -10,13 +10,12 @@
 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.parallel import Parallel, delayed
 from sklearn.utils.validation import check_is_fitted
 
 from ..base import _ParamsValidationMixin

imblearn/ensemble/_easy_ensemble.py

@@ -10,12 +10,11 @@
 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.parallel import Parallel, delayed
 from sklearn.utils.validation import check_is_fitted
 
 from ..base import _ParamsValidationMixin

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
@@ -24,8 +23,8 @@
 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.parallel import Parallel, delayed
 from sklearn.utils.validation import _check_sample_weight
 
 from ..base import _ParamsValidationMixin