def test_sparse_input():
expected = [
[0, 0],
[0, 0],
[1, 0],
[2, 0],
]
dense_X = np.array([[0.1, 0.1], [0.2, 0.4], [0.3, 0.2], [0.4, 0.3]])
X = scipy.sparse.csr_matrix(dense_X)
y = np.array([0, 0, 1, 2])
disc = MDLP(shuffle=False).fit_transform(X, y)
assert_array_equal(expected, disc.toarray())
def test_drop_collapsed_features_sparse():
expected = [
[0, 0],
[0, 0],
[1, 1],
[2, 2],
]
dense_X = np.array([[0.1, 0.1, 0.1, 0.1, 0.1], [0.4, 0.2, 0.4, 0.2, 0.4],
[0.2, 0.3, 0.2, 0.3, 0.2], [0.3, 0.4, 0.3, 0.4, 0.3]])
X = scipy.sparse.csr_matrix(dense_X)
y = np.array([0, 0, 1, 2])
disc = MDLP(drop_collapsed_features=True,
shuffle=False).fit_transform(X, y)
assert_array_equal(expected, disc.toarray())