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from collections import Counter
import pytest
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.utils._testing import assert_allclose, assert_array_equal
from imblearn.over_sampling import BorderlineSMOTE
@pytest.mark.parametrize("kind", ["borderline-1", "borderline-2"])
def test_borderline_smote_no_in_danger_samples(kind):
"""Check that the algorithm behave properly even on a dataset without any sample
in danger.
"""
X, y = make_classification(
n_samples=500,
n_features=2,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_clusters_per_class=1,
n_classes=3,
weights=[0.1, 0.2, 0.7],
class_sep=1.5,
random_state=1,
)
smote = BorderlineSMOTE(kind=kind, m_neighbors=3, k_neighbors=5, random_state=0)
X_res, y_res = smote.fit_resample(X, y)
assert_allclose(X, X_res)
assert_allclose(y, y_res)
assert not smote.in_danger_indices
def test_borderline_smote_kind():
"""Check the behaviour of the `kind` parameter.
In short, "borderline-2" generates sample closer to the boundary decision than
"borderline-1". We generate an example where a logistic regression will perform
worse on "borderline-2" than on "borderline-1".
"""
X, y = make_classification(
n_samples=500,
n_features=2,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_clusters_per_class=1,
n_classes=3,
weights=[0.1, 0.2, 0.7],
class_sep=1.0,
random_state=1,
)
smote = BorderlineSMOTE(
kind="borderline-1", m_neighbors=9, k_neighbors=5, random_state=0
)
X_res_borderline_1, y_res_borderline_1 = smote.fit_resample(X, y)
smote.set_params(kind="borderline-2")
X_res_borderline_2, y_res_borderline_2 = smote.fit_resample(X, y)
score_borderline_1 = (
LogisticRegression()
.fit(X_res_borderline_1, y_res_borderline_1)
.score(X_res_borderline_1, y_res_borderline_1)
)
score_borderline_2 = (
LogisticRegression()
.fit(X_res_borderline_2, y_res_borderline_2)
.score(X_res_borderline_2, y_res_borderline_2)
)
assert score_borderline_1 > score_borderline_2
def test_borderline_smote_in_danger():
X, y = make_classification(
n_samples=500,
n_features=2,
n_informative=2,
n_redundant=0,
n_repeated=0,
n_clusters_per_class=1,
n_classes=3,
weights=[0.1, 0.2, 0.7],
class_sep=0.8,
random_state=1,
)
smote = BorderlineSMOTE(
kind="borderline-1",
m_neighbors=9,
k_neighbors=5,
random_state=0,
)
_, y_res_1 = smote.fit_resample(X, y)
in_danger_indices_borderline_1 = smote.in_danger_indices
smote.set_params(kind="borderline-2")
_, y_res_2 = smote.fit_resample(X, y)
in_danger_indices_borderline_2 = smote.in_danger_indices
for key1, key2 in zip(
in_danger_indices_borderline_1, in_danger_indices_borderline_2
):
assert_array_equal(
in_danger_indices_borderline_1[key1], in_danger_indices_borderline_2[key2]
)
assert len(in_danger_indices_borderline_1) == len(in_danger_indices_borderline_2)
counter = Counter(y_res_1)
assert counter[0] == counter[1] == counter[2]
counter = Counter(y_res_2)
assert counter[0] == counter[1] == counter[2]
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