def test_estimate_competence(index, expected):
query = np.atleast_2d([1, 1])
knora_u_test = KNORAU(create_pool_classifiers())
knora_u_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_u_test.DFP_mask = np.ones(knora_u_test.n_classifiers)
knora_u_test.neighbors = neighbors_ex1[index, :]
knora_u_test.distances = distances_ex1[index, :]
competences = knora_u_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([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]])
knora_u_test = KNORAU(create_pool_classifiers())
knora_u_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_u_test.DFP_mask = np.ones((3, knora_u_test.n_classifiers))
knora_u_test.neighbors = neighbors_ex1
knora_u_test.distances = distances_ex1
competences = knora_u_test.estimate_competence(query)
assert np.allclose(competences, expected, atol=0.01)
def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knora_u_test = KNORAU(create_pool_classifiers())
knora_u_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_u_test.DFP_mask = np.ones(knora_u_test.n_classifiers)
knora_u_test.neighbors = neighbors_ex1[index, :]
knora_u_test.distances = distances_ex1[index, :]
prediction = knora_u_test.classify_instance(query)
assert prediction == expected
def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knora_u_test = KNORAU(create_pool_classifiers())
knora_u_test.fit(X_dsel_ex1, y_dsel_ex1)
knora_u_test.DFP_mask = np.ones(knora_u_test.n_classifiers)
knora_u_test.neighbors = neighbors_ex1[index, :]
knora_u_test.distances = distances_ex1[index, :]
predictions = []
for clf in knora_u_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
prediction = knora_u_test.classify_instance(query, np.array(predictions))
assert prediction == expected