Dec 28, 2022 · Dec 23, 2022 · Dec 23, 2022 · Dec 28, 2022
diff --git a/doc/whats_new/v0.10.rst b/doc/whats_new/v0.10.rst
 .. _changes_0_10:

 Version 0.10.1
 ==============

 **December 28, 2022**

 Changelog
 ---------

 Bug fixes
 .........

 - Fix a regression in over-sampler where the string `minority` was rejected as
  an unvalid sampling strategy.
  :pr:`964` by :user:`Prakhyath Bhandary <Prakhyath07>`.

 Version 0.10.0
 ==============

diff --git a/imblearn/over_sampling/base.py b/imblearn/over_sampling/base.py
    _parameter_constraints: dict = {
        "sampling_strategy": [
            Interval(numbers.Real, 0, 1, closed="right"),
            StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
            StrOptions({"auto", "minority", "not minority", "not majority", "all"}),
            Mapping,
            callable,
        ],
diff --git a/imblearn/over_sampling/tests/test_random_over_sampler.py b/imblearn/over_sampling/tests/test_random_over_sampler.py

 import numpy as np
 import pytest
 from sklearn.datasets import make_classification
 from sklearn.utils._testing import (
    _convert_container,
    assert_allclose,
    ros = RandomOverSampler(shrinkage=shrinkage)
    with pytest.raises(ValueError, match=err_msg):
        ros.fit_resample(X, y)


 @pytest.mark.parametrize(
    "sampling_strategy", ["auto", "minority", "not minority", "not majority", "all"]
 )
 def test_random_over_sampler_strings(sampling_strategy):
    """Check that we support all supposed strings as `sampling_strategy` in
    a sampler inheriting from `BaseOverSampler`."""

    X, y = make_classification(
        n_samples=100,
        n_clusters_per_class=1,
        n_classes=3,
        weights=[0.1, 0.3, 0.6],
        random_state=0,
    )
    RandomOverSampler(sampling_strategy=sampling_strategy).fit_resample(X, y)
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py b/imblearn/under_sampling/_prototype_selection/tests/test_random_under_sampler.py

 import numpy as np
 import pytest
 from sklearn.datasets import make_classification
 from sklearn.utils._testing import assert_array_equal

 from imblearn.under_sampling import RandomUnderSampler
    assert y_res.shape == (6,)
    assert X_res.shape == (6, 2)
    assert np.any(~np.isfinite(X_res))


 @pytest.mark.parametrize(
    "sampling_strategy", ["auto", "majority", "not minority", "not majority", "all"]
 )
 def test_random_under_sampler_strings(sampling_strategy):
    """Check that we support all supposed strings as `sampling_strategy` in
    a sampler inheriting from `BaseUnderSampler`."""

    X, y = make_classification(
        n_samples=100,
        n_clusters_per_class=1,
        n_classes=3,
        weights=[0.1, 0.3, 0.6],
        random_state=0,
    )
    RandomUnderSampler(sampling_strategy=sampling_strategy).fit_resample(X, y)
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_tomek_links.py b/imblearn/under_sampling/_prototype_selection/tests/test_tomek_links.py
 # License: MIT

 import numpy as np
 import pytest
 from sklearn.datasets import make_classification
 from sklearn.utils._testing import assert_array_equal

 from imblearn.under_sampling import TomekLinks
    y_gt = np.array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0])
    assert_array_equal(X_resampled, X_gt)
    assert_array_equal(y_resampled, y_gt)


 @pytest.mark.parametrize(
    "sampling_strategy", ["auto", "majority", "not minority", "not majority", "all"]
 )
 def test_tomek_links_strings(sampling_strategy):
    """Check that we support all supposed strings as `sampling_strategy` in
    a sampler inheriting from `BaseCleaningSampler`."""

    X, y = make_classification(
        n_samples=100,
        n_clusters_per_class=1,
        n_classes=3,
        weights=[0.1, 0.3, 0.6],
        random_state=0,
    )
    TomekLinks(sampling_strategy=sampling_strategy).fit_resample(X, y)
Original file line number	Diff line number	Diff line change
		@@ -1,5 +1,20 @@
		.. _changes_0_10:

		Version 0.10.1
		==============

		December 28, 2022

		Changelog
		---------

		Bug fixes
		.........

		- Fix a regression in over-sampler where the string `minority` was rejected as
		an unvalid sampling strategy.
		:pr:`964` by :user:`Prakhyath Bhandary <Prakhyath07>`.

		Version 0.10.0
		==============

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -61,7 +61,7 @@ class BaseOverSampler(BaseSampler):
		_parameter_constraints: dict = {
		"sampling_strategy": [
		Interval(numbers.Real, 0, 1, closed="right"),
		StrOptions({"auto", "majority", "not minority", "not majority", "all"}),
		StrOptions({"auto", "minority", "not minority", "not majority", "all"}),
		Mapping,
		callable,
		],
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,6 +7,7 @@

		import numpy as np
		import pytest
		from sklearn.datasets import make_classification
		from sklearn.utils._testing import (
		_convert_container,
		assert_allclose,
Expand DownExpand Up		@@ -255,3 +256,20 @@ def test_random_over_sampler_shrinkage_error(data, shrinkage, err_msg):
		ros = RandomOverSampler(shrinkage=shrinkage)
		with pytest.raises(ValueError, match=err_msg):
		ros.fit_resample(X, y)


		@pytest.mark.parametrize(
		"sampling_strategy", ["auto", "minority", "not minority", "not majority", "all"]
		)
		def test_random_over_sampler_strings(sampling_strategy):
		"""Check that we support all supposed strings as `sampling_strategy` in
		a sampler inheriting from `BaseOverSampler`."""

		X, y = make_classification(
		n_samples=100,
		n_clusters_per_class=1,
		n_classes=3,
		weights=[0.1, 0.3, 0.6],
		random_state=0,
		)
		RandomOverSampler(sampling_strategy=sampling_strategy).fit_resample(X, y)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,6 +4,8 @@
		# License: MIT

		import numpy as np
		import pytest
		from sklearn.datasets import make_classification
		from sklearn.utils._testing import assert_array_equal

		from imblearn.under_sampling import TomekLinks
Expand DownExpand Up		@@ -68,3 +70,20 @@ def test_tl_fit_resample():
		y_gt = np.array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0])
		assert_array_equal(X_resampled, X_gt)
		assert_array_equal(y_resampled, y_gt)


		@pytest.mark.parametrize(
		"sampling_strategy", ["auto", "majority", "not minority", "not majority", "all"]
		)
		def test_tomek_links_strings(sampling_strategy):
		"""Check that we support all supposed strings as `sampling_strategy` in
		a sampler inheriting from `BaseCleaningSampler`."""

		X, y = make_classification(
		n_samples=100,
		n_clusters_per_class=1,
		n_classes=3,
		weights=[0.1, 0.3, 0.6],
		random_state=0,
		)
		TomekLinks(sampling_strategy=sampling_strategy).fit_resample(X, y)