def test_select_none_competent():
knora_e_test = KNORAE()
competences = np.zeros(100)
selected = knora_e_test.select(competences)
expected = np.atleast_2d([True] * 100)
assert np.array_equal(expected, selected)
def test_select(index, expected):
query = np.atleast_2d([1, 1])
knora_e_test = KNORAE(create_pool_classifiers())
knora_e_test.fit(X_dsel_ex1, y_dsel_ex1)
neighbors = neighbors_ex1[index, :].reshape(1, -1)
competences = knora_e_test.estimate_competence(query, neighbors)
selected = knora_e_test.select(competences)
assert np.array_equal(selected, expected)
def test_select(index, expected):
query = np.atleast_2d([1, 1])
knora_e_test = KNORAE(create_pool_classifiers())
knora_e_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_e_test.DFP_mask = np.ones(knora_e_test.n_classifiers)
knora_e_test.neighbors = neighbors_ex1[index, :]
knora_e_test.distances = distances_ex1[index, :]
competences = knora_e_test.estimate_competence(query)
selected = knora_e_test.select(competences)
assert selected == expected
def test_select(index, expected, create_pool_classifiers,
example_estimate_competence):
X, y, neighbors, distances, _, _ = example_estimate_competence
knora_e_test = KNORAE(create_pool_classifiers)
knora_e_test.fit(X, y)
neighbors = neighbors[index, :].reshape(1, -1)
distances = distances[index, :].reshape(1, -1)
competences = knora_e_test.estimate_competence(neighbors,
distances=distances)
selected = knora_e_test.select(competences)
assert np.array_equal(selected, expected)
def test_select_none_competent():
query = np.atleast_2d([1, 1])
knora_e_test = KNORAE(create_pool_all_agree(2, 100))
knora_e_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_e_test.neighbors = neighbors_ex1[0, :]
knora_e_test.distances = distances_ex1[0, :]
knora_e_test.DFP_mask = np.ones(knora_e_test.n_classifiers)
competences = knora_e_test.estimate_competence(query)
indices = knora_e_test.select(competences)
assert indices == list(range(knora_e_test.n_classifiers))