def test_estimate_competence_kuncheva_ex():
query = np.array([1, 1])
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.processed_dsel = dsel_processed_kuncheva
a_posteriori_test.dsel_scores = dsel_scores_ex_kuncheva
a_posteriori_test.DSEL_target = y_dsel_ex_kuncheva_dependent
a_posteriori_test.n_classes = n_classes_ex_kuncheva
a_posteriori_test.neighbors = neighbors_ex_kuncheva
a_posteriori_test.distances = distances_ex_kuncheva
a_posteriori_test.DFP_mask = [1]
competences = a_posteriori_test.estimate_competence(query.reshape(1, -1))
assert np.isclose(competences, 0.95, atol=0.01)
def test_estimate_competence_all_ones(index):
query = np.array([1, 1])
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.processed_dsel = dsel_processed_ex1
a_posteriori_test.dsel_scores = dsel_scores_all_ones
a_posteriori_test.DSEL_target = y_dsel_ex1
a_posteriori_test.n_classes = 2
a_posteriori_test.neighbors = neighbors_ex1[index, :]
a_posteriori_test.distances = distances_all_ones[index, :]
a_posteriori_test.DFP_mask = [1, 1, 1]
expected = [1.0, 1.0, 1.0]
competences = a_posteriori_test.estimate_competence(query.reshape(1, -1))
assert np.isclose(competences, expected).all()
def test_estimate_competence_kuncheva_ex():
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.processed_dsel = dsel_processed_kuncheva
a_posteriori_test.dsel_scores = dsel_scores_ex_kuncheva
a_posteriori_test.DSEL_target = y_dsel_ex_kuncheva_dependent
a_posteriori_test.n_classes = n_classes_ex_kuncheva
a_posteriori_test.neighbors = neighbors_ex_kuncheva
a_posteriori_test.distances = distances_ex_kuncheva
a_posteriori_test.DFP_mask = [1]
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, predictions=np.array(predictions))
assert np.isclose(competences, 0.95, atol=0.01)
def test_estimate_competence_all_ones(index):
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.processed_dsel = dsel_processed_ex1
a_posteriori_test.dsel_scores = dsel_scores_all_ones
a_posteriori_test.DSEL_target = y_dsel_ex1
a_posteriori_test.n_classes = 2
a_posteriori_test.neighbors = neighbors_ex1[index, :]
a_posteriori_test.distances = distances_all_ones[index, :]
a_posteriori_test.DFP_mask = [1, 1, 1]
expected = [1.0, 1.0, 1.0]
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, predictions=np.array(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_kuncheva_ex_batch():
# considering a batch composed of 10 samples
query = np.ones((10, 2))
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.processed_dsel = dsel_processed_kuncheva
a_posteriori_test.dsel_scores = dsel_scores_ex_kuncheva
a_posteriori_test.DSEL_target = y_dsel_ex_kuncheva_dependent
a_posteriori_test.n_classes = n_classes_ex_kuncheva
# repeating the same matrix in a new axis to simulate a batch input.
a_posteriori_test.neighbors = np.tile(neighbors_ex_kuncheva, (10, 1))
a_posteriori_test.distances = np.tile(distances_ex_kuncheva, (10, 1))
a_posteriori_test.DFP_mask = np.ones((10, 1))
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, predictions=np.array(predictions))
assert np.allclose(competences, 0.95, atol=0.01)