def test_pipeline_score_samples_pca_lof():
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.3, 0.7],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=500,
random_state=0,
)
# Test that the score_samples method is implemented on a pipeline.
# Test that the score_samples method on pipeline yields same results as
# applying transform and score_samples steps separately.
rus = RandomUnderSampler(random_state=42)
pca = PCA(svd_solver="full", n_components="mle", whiten=True)
lof = LocalOutlierFactor(novelty=True)
pipe = Pipeline([("rus", rus), ("pca", pca), ("lof", lof)])
pipe.fit(X, y)
# Check the shapes
assert pipe.score_samples(X).shape == (X.shape[0], )
# Check the values
X_res, _ = rus.fit_resample(X, y)
lof.fit(pca.fit_transform(X_res))
assert_allclose(pipe.score_samples(X), lof.score_samples(pca.transform(X)))
def test_pipeline_score_samples_pca_lof():
X, y = load_iris(return_X_y=True)
sampling_strategy = {0: 50, 1: 30, 2: 20}
X, y = make_imbalance(X, y, sampling_strategy=sampling_strategy)
# Test that the score_samples method is implemented on a pipeline.
# Test that the score_samples method on pipeline yields same results as
# applying transform and score_samples steps separately.
rus = RandomUnderSampler()
pca = PCA(svd_solver='full', n_components='mle', whiten=True)
lof = LocalOutlierFactor(novelty=True)
pipe = Pipeline([('rus', rus), ('pca', pca), ('lof', lof)])
pipe.fit(X, y)
# Check the shapes
assert pipe.score_samples(X).shape == (X.shape[0], )
# Check the values
lof.fit(pca.fit_transform(X))
assert_allclose(pipe.score_samples(X), lof.score_samples(pca.transform(X)))
