def test_select_none_competent():
n_classifiers = 3
des_p_test = DESP(create_pool_classifiers())
des_p_test.n_classes_ = 2
competences = np.ones(n_classifiers) * 0.49
indices = des_p_test.select(competences)
expected = np.array([[True, True, True]])
assert np.array_equal(expected, indices)
def test_select_three_classes():
des_p_test = DESP()
des_p_test.n_classes_ = 3
expected = np.array([[True, False, True], [True, False, True],
[False, True, False]])
competences = np.array([[0.34, 0.32, 1.0], [0.50, 0.30, 1.01],
[0.25, 1.0, 0.25]])
selected = des_p_test.select(competences)
assert np.array_equal(selected, expected)
def test_select_two_classes():
des_p_test = DESP()
des_p_test.n_classes_ = 2
expected = np.array([[True, False, True], [True, False, True],
[False, True, False]])
competences = np.array([[0.51, 0.0, 0.51], [0.51, 0.0, 0.51],
[0.49, 1.0, 0.49]])
selected = des_p_test.select(competences)
assert np.array_equal(selected, expected)
def test_select_three_classes(index, expected):
query = np.atleast_2d([1, 1])
des_p_test = DESP(create_pool_classifiers())
des_p_test.fit(X_dsel_ex1, y_dsel_ex1)
des_p_test.n_classes_ = 3
neighbors = neighbors_ex1[index, :].reshape(1, -1)
distances = distances_ex1[index, :].reshape(1, -1)
competences = des_p_test.estimate_competence(query, neighbors, distances)
selected = des_p_test.select(competences)
assert np.array_equal(selected, expected)
