def test_pipeline_sample():
# Test whether pipeline works with a sampler at the end.
# Also test pipeline.sampler
X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9],
n_informative=3, n_redundant=1, flip_y=0,
n_features=20, n_clusters_per_class=1,
n_samples=5000, random_state=0)
rus = RandomUnderSampler(random_state=0)
pipeline = Pipeline([('rus', rus)])
# test transform and fit_transform:
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_trans2, y_trans2 = pipeline.fit_sample(X, y)
X_trans3, y_trans3 = rus.fit_sample(X, y)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(X_trans, X_trans3)
assert_array_almost_equal(y_trans, y_trans2)
assert_array_almost_equal(y_trans, y_trans3)
pca = PCA()
pipeline = Pipeline([('pca', pca), ('rus', rus)])
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_pca = pca.fit_transform(X)
X_trans2, y_trans2 = rus.fit_sample(X_pca, y)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(y_trans, y_trans2)
def test_pipeline_sample():
# Test whether pipeline works with a sampler at the end.
# Also test pipeline.sampler
X, y = make_classification(n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
rus = RandomUnderSampler(random_state=0)
pipeline = Pipeline([('rus', rus)])
# test transform and fit_transform:
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_trans2, y_trans2 = pipeline.fit_sample(X, y)
X_trans3, y_trans3 = rus.fit_sample(X, y)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(X_trans, X_trans3)
assert_array_almost_equal(y_trans, y_trans2)
assert_array_almost_equal(y_trans, y_trans3)
pca = PCA()
pipeline = Pipeline([('pca', pca), ('rus', rus)])
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_pca = pca.fit_transform(X)
X_trans2, y_trans2 = rus.fit_sample(X_pca, y)
assert_array_almost_equal(X_trans, X_trans2)
assert_array_almost_equal(y_trans, y_trans2)
def test_pipeline_sample():
# Test whether pipeline works with a sampler at the end.
# Also test pipeline.sampler
X, y = make_classification(
n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
rus = RandomUnderSampler(random_state=0)
pipeline = Pipeline([('rus', rus)])
# test transform and fit_transform:
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_trans2, y_trans2 = pipeline.fit_sample(X, y)
X_trans3, y_trans3 = rus.fit_sample(X, y)
assert_allclose(X_trans, X_trans2, rtol=R_TOL)
assert_allclose(X_trans, X_trans3, rtol=R_TOL)
assert_allclose(y_trans, y_trans2, rtol=R_TOL)
assert_allclose(y_trans, y_trans3, rtol=R_TOL)
pca = PCA()
pipeline = Pipeline([('pca', PCA()),
('rus', rus)])
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_pca = pca.fit_transform(X)
X_trans2, y_trans2 = rus.fit_sample(X_pca, y)
# We round the value near to zero. It seems that PCA has some issue
# with that
X_trans[np.bitwise_and(X_trans < R_TOL, X_trans > -R_TOL)] = 0
X_trans2[np.bitwise_and(X_trans2 < R_TOL, X_trans2 > -R_TOL)] = 0
assert_allclose(X_trans, X_trans2, rtol=R_TOL)
assert_allclose(y_trans, y_trans2, rtol=R_TOL)
def test_pipeline_sample():
# Test whether pipeline works with a sampler at the end.
# Also test pipeline.sampler
X, y = make_classification(n_classes=2,
class_sep=2,
weights=[0.1, 0.9],
n_informative=3,
n_redundant=1,
flip_y=0,
n_features=20,
n_clusters_per_class=1,
n_samples=5000,
random_state=0)
rus = RandomUnderSampler(random_state=0)
pipeline = Pipeline([('rus', rus)])
# test transform and fit_transform:
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_trans2, y_trans2 = pipeline.fit_sample(X, y)
X_trans3, y_trans3 = rus.fit_sample(X, y)
assert_allclose(X_trans, X_trans2, rtol=R_TOL)
assert_allclose(X_trans, X_trans3, rtol=R_TOL)
assert_allclose(y_trans, y_trans2, rtol=R_TOL)
assert_allclose(y_trans, y_trans3, rtol=R_TOL)
pca = PCA()
pipeline = Pipeline([('pca', PCA()), ('rus', rus)])
X_trans, y_trans = pipeline.fit(X, y).sample(X, y)
X_pca = pca.fit_transform(X)
X_trans2, y_trans2 = rus.fit_sample(X_pca, y)
# We round the value near to zero. It seems that PCA has some issue
# with that
X_trans[np.bitwise_and(X_trans < R_TOL, X_trans > -R_TOL)] = 0
X_trans2[np.bitwise_and(X_trans2 < R_TOL, X_trans2 > -R_TOL)] = 0
assert_allclose(X_trans, X_trans2, rtol=R_TOL)
assert_allclose(y_trans, y_trans2, rtol=R_TOL)
