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import numpy as np
import pytest
from sklearn.exceptions import DataConversionWarning
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.utils._testing import _convert_container
from imblearn.over_sampling import SMOTEN
@pytest.fixture
def data():
rng = np.random.RandomState(0)
feature_1 = ["A"] * 10 + ["B"] * 20 + ["C"] * 30
feature_2 = ["A"] * 40 + ["B"] * 20
feature_3 = ["A"] * 20 + ["B"] * 20 + ["C"] * 10 + ["D"] * 10
X = np.array([feature_1, feature_2, feature_3], dtype=object).T
rng.shuffle(X)
y = np.array([0] * 20 + [1] * 40, dtype=np.int32)
y_labels = np.array(["not apple", "apple"], dtype=object)
y = y_labels[y]
return X, y
def test_smoten(data):
# overall check for SMOTEN
X, y = data
sampler = SMOTEN(random_state=0)
X_res, y_res = sampler.fit_resample(X, y)
assert X_res.shape == (80, 3)
assert y_res.shape == (80,)
assert isinstance(sampler.categorical_encoder_, OrdinalEncoder)
def test_smoten_resampling():
# check if the SMOTEN resample data as expected
# we generate data such that "not apple" will be the minority class and
# samples from this class will be generated. We will force the "blue"
# category to be associated with this class. Therefore, the new generated
# samples should as well be from the "blue" category.
X = np.array(["green"] * 5 + ["red"] * 10 + ["blue"] * 7, dtype=object).reshape(
-1, 1
)
y = np.array(
["apple"] * 5
+ ["not apple"] * 3
+ ["apple"] * 7
+ ["not apple"] * 5
+ ["apple"] * 2,
dtype=object,
)
sampler = SMOTEN(random_state=0)
X_res, y_res = sampler.fit_resample(X, y)
X_generated, y_generated = X_res[X.shape[0] :], y_res[X.shape[0] :]
np.testing.assert_array_equal(X_generated, "blue")
np.testing.assert_array_equal(y_generated, "not apple")
@pytest.mark.parametrize("sparse_format", ["sparse_csr", "sparse_csc"])
def test_smoten_sparse_input(data, sparse_format):
"""Check that we handle sparse input in SMOTEN even if it is not efficient.
Non-regression test for:
https://github.com/scikit-learn-contrib/imbalanced-learn/issues/971
"""
X, y = data
X = OneHotEncoder().fit_transform(X).toarray()
X = _convert_container(X, sparse_format)
with pytest.warns(DataConversionWarning, match="is not really efficient"):
X_res, y_res = SMOTEN(random_state=0).fit_resample(X, y)
assert X_res.format == X.format
assert X_res.shape[0] == len(y_res)
def test_smoten_categorical_encoder(data):
"""Check that `categorical_encoder` is used when provided."""
X, y = data
sampler = SMOTEN(random_state=0)
sampler.fit_resample(X, y)
assert isinstance(sampler.categorical_encoder_, OrdinalEncoder)
assert sampler.categorical_encoder_.dtype == np.int32
encoder = OrdinalEncoder(dtype=np.int64)
sampler.set_params(categorical_encoder=encoder).fit_resample(X, y)
assert isinstance(sampler.categorical_encoder_, OrdinalEncoder)
assert sampler.categorical_encoder is encoder
assert sampler.categorical_encoder_ is not encoder
assert sampler.categorical_encoder_.dtype == np.int64
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