def test_predict_proba_hybrid():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='hybrid')
des_test.n_classes = 2
selected_indices = [0, 1, 5]
competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]])
expected = np.array([0.5744, 0.4256])
des_test.estimate_competence = MagicMock(return_value=competences)
selected_classifiers = np.zeros((1, 6), dtype=bool)
selected_classifiers[0, selected_indices] = 1
des_test.select = MagicMock(return_value=selected_classifiers)
predictions = []
probabilities = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = des_test.predict_proba_with_ds(query, predictions, probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_weighting():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
des_test = DES(pool_classifiers, mode='weighting')
competences = np.array([0.5, 1.0, 0.2])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.n_classes = 2
expected = np.array([0.5769, 0.4231])
predicted_proba = des_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_weighting():
query = np.atleast_2d([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='weighting')
competences = np.array([0.55, 1.0, 0.2, 0.60, 0.75, 0.3])
des_test.estimate_competence = MagicMock(return_value=competences)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_instance(query, np.array(predictions))
assert predicted_label == 1.0
def test_classify_instance_weighting_batch():
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='weighting')
des_test.classes = np.array([0, 1])
des_test.n_classes = 2
competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1))
des_test.estimate_competence = MagicMock(return_value=competences)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.tile(predictions, (3, 1)))
assert np.allclose(predicted_label, 1) and predicted_label.size == 3
def test_predict_proba_hybrid():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='hybrid')
des_test.n_classes = 2
selected_indices = [0, 1, 5]
competences = np.array([0.55, 1.0, 0.2, 0.60, 0.75, 0.3])
expected = np.array([0.5744, 0.4256])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predicted_proba = des_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_hybrid_batch():
query = np.ones((3, 2))
expected = 1
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='hybrid')
des_test.classes = np.array([0, 1])
des_test.n_classes = 2
selected_indices = np.tile([True, True, False, False, False, True], (3, 1))
competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1))
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.tile(predictions, (3, 1)))
assert np.allclose(predicted_label, expected)
def test_classify_instance_hybrid():
query = np.array([-1, 1])
expected = 1
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
des_test = DES(pool_classifiers, mode='hybrid')
des_test.classes = np.array([0, 1])
des_test.n_classes = 2
selected_indices = np.array([[True, True, False, False, False, True]])
competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.select = MagicMock(return_value=selected_indices)
predictions = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = des_test.classify_with_ds(query, np.array(predictions))
assert expected == predicted_label
def test_predict_proba_weighting():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
des_test = DES(pool_classifiers, mode='weighting')
competences = np.array([[0.5, 1.0, 0.2]])
des_test.estimate_competence = MagicMock(return_value=competences)
des_test.n_classes = 2
expected = np.array([0.5769, 0.4231])
predictions = []
probabilities = []
for clf in des_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
probabilities.append(clf.predict_proba(query)[0])
query = np.atleast_2d(query)
predictions = np.atleast_2d(predictions)
probabilities = np.array(probabilities)
probabilities = np.expand_dims(probabilities, axis=0)
predicted_proba = des_test.predict_proba_with_ds(query, predictions, probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()