def test_estimate_competence(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)
assert np.isclose(competences, expected, atol=0.01).all()
def test_estimate_competence_batch():
query = np.ones((3, 2))
expected = np.array([[1.0, 0.0, 1.0], [2.0, 0.0, 2.0], [0.0, 3.0, 0.0]])
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
knora_e_test.distances = distances_ex1
competences = knora_e_test.estimate_competence(query)
assert np.allclose(competences, 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_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))
