8000 MAINT avoid warning deprecation sklearn 1.5 by glemaitre · Pull Request #1084 · scikit-learn-contrib/imbalanced-learn · GitHub
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merged 2 commits into from
May 28, 2024
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MAINT avoid warning deprecation sklearn 1.5 #1084

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fea271f
MAINT avoid warning deprecation sklearn 1.5
glemaitre May 28, 2024
b31fa5b
update documentation
glemaitre May 28, 2024
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3 changes: 1 addition & 2 deletions doc/under_sampling.rst
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Expand Up @@ -497,8 +497,7 @@ The class can be used as::
>>> from sklearn.linear_model import LogisticRegression
>>> from imblearn.under_sampling import InstanceHardnessThreshold
>>> iht = InstanceHardnessThreshold(random_state=0,
... estimator=LogisticRegression(
... solver='lbfgs', multi_class='auto'))
... estimator=LogisticRegression())
>>> X_resampled, y_resampled = iht.fit_resample(X, y)
>>> print(sorted(Counter(y_resampled).items()))
[(0, 64), (1, 64), (2, 64)]
2 changes: 1 addition & 1 deletion doc/whats_new/v0.12.rst
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Expand Up @@ -12,7 +12,7 @@ Compatibility
.............

- Compatibility with scikit-learn 1.5
:pr:`1074` by :user:`Guillaume Lemaitre <glemaitre>`.
:pr:`1074` and :pr:`1084` by :user:`Guillaume Lemaitre <glemaitre>`.

Version 0.12.2
==============
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4 changes: 2 additions & 2 deletions examples/applications/plot_outlier_rejections.py
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Expand Up @@ -109,12 +109,12 @@ def outlier_rejection(X, y):

pipe = make_pipeline(
FunctionSampler(func=outlier_rejection),
LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng),
LogisticRegression(random_state=rng),
)
y_pred = pipe.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))

clf = LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng)
clf = LogisticRegression(random_state=rng)
y_pred = clf.fit(X_train, y_train).predict(X_test)
print(classification_report(y_test, y_pred))

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4 changes: 2 additions & 2 deletions imblearn/ensemble/tests/test_bagging.py
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Expand Up @@ -174,7 +174,7 @@ def test_probability():

# Degenerate case, where some classes are missing
ensemble = BalancedBaggingClassifier(
estimator=LogisticRegression(solver="lbfgs", multi_class="auto"),
estimator=LogisticRegression(solver="lbfgs"),
random_state=0,
max_samples=5,
)
Expand Down Expand Up @@ -435,7 +435,7 @@ def test_estimators_samples():
# remap the y outside of the BalancedBaggingclassifier
# _, y = np.unique(y, return_inverse=True)
bagging = BalancedBaggingClassifier(
LogisticRegression(solver="lbfgs", multi_class="auto"),
LogisticRegression(),
max_samples=0.5,
max_features=0.5,
random_state=1,
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5 changes: 0 additions & 5 deletions imblearn/tests/test_docstring_parameters.py
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Expand Up @@ -11,7 +11,6 @@
import pytest
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.utils import IS_PYPY
from sklearn.utils._testing import (
_get_func_name,
check_docstring_parameters,
Expand Down Expand Up @@ -70,7 +69,6 @@
# Python 3.7
@pytest.mark.filterwarnings("ignore::FutureWarning")
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
@pytest.mark.skipif(IS_PYPY, reason="test segfaults on PyPy")
def test_docstring_parameters():
# Test module docstring formatting

Expand Down Expand Up @@ -154,9 +152,6 @@ def test_tabs():
for importer, modname, ispkg in walk_packages(
imblearn.__path__, prefix="imblearn."
):
if IS_PYPY:
continue

# because we don't import
mod = importlib.import_module(modname)

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4 changes: 2 additions & 2 deletions imblearn/tests/test_pipeline.py
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Expand Up @@ -272,7 +272,7 @@ def test_pipeline_methods_anova():
X = iris.data
y = iris.target
# Test with Anova + LogisticRegression
clf = LogisticRegression(solver="lbfgs", multi_class="auto")
clf = LogisticRegression()
filter1 = SelectKBest(f_classif, k=2)
pipe = Pipeline([("anova", filter1), ("logistic", clf)])
pipe.fit(X, y)
Expand Down Expand Up @@ -639,7 +639,7 @@ def test_classes_property():

clf = make_pipeline(
SelectKBest(k=1),
LogisticRegression(solver="lbfgs", multi_class="auto", random_state=0),
LogisticRegression(),
)
with raises(AttributeError):
getattr(clf, "classes_")
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