def test_estimate_competence(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test.distances = distances_ex1[index, :]
competences = knop_test.estimate_competence(query)
assert np.isclose(competences, expected, atol=0.01).all()
def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test.distances = distances_ex1[index, :]
prediction = knop_test.classify_instance(query)
assert prediction == expected
def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test.distances = distances_ex1[index, :]
predictions = []
for clf in knop_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = knop_test.classify_instance(query, np.array(predictions))
assert predicted_label == expected
def test_estimate_competence_batch(example_estimate_competence,
create_pool_classifiers):
X, y, neighbors, distances, _, _ = example_estimate_competence
query = np.ones((3, 2))
expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]])
knop_test = KNOP(create_pool_classifiers)
knop_test.fit(X, y)
knop_test.neighbors = neighbors
knop_test.distances = distances
knop_test._get_similar_out_profiles = Mock(return_value=(None, neighbors))
probabilities = np.zeros((3, 6))
competences = knop_test.estimate_competence_from_proba(
query, probabilities)
assert np.allclose(competences, expected, atol=0.01)
def test_estimate_competence_batch():
query = np.ones((3, 2))
expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers_)
knop_test.neighbors = neighbors_ex1
knop_test._get_similar_out_profiles = Mock(return_value=(None,
neighbors_ex1))
knop_test.distances = distances_ex1
probabilities = np.zeros((3, 6)) # not used in this test
competences = knop_test.estimate_competence_from_proba(
query, probabilities)
assert np.allclose(competences, expected, atol=0.01)
def test_estimate_competence(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers_)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test._get_similar_out_profiles = Mock(
return_value=(None, np.atleast_2d(neighbors_ex1[index, :])))
knop_test.distances = distances_ex1[index, :]
probabilities = []
for clf in knop_test.pool_classifiers:
probabilities.append(clf.predict_proba(query))
probabilities = np.array(probabilities).transpose((1, 0, 2))
competences = knop_test.estimate_competence_from_proba(
query, probabilities)
assert np.allclose(competences, expected, atol=0.01)
