def test_classify_instance_all(competences, expected):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers, selection_method='all')
dcs_test.estimate_competence = MagicMock(return_value=competences)
predicted_label = dcs_test.classify_instance(query)
assert predicted_label == expected
def test_predict_proba_instance():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers)
dcs_test.n_classes = 2
competences = np.random.rand(dcs_test.n_classifiers)
dcs_test.estimate_competence = MagicMock(return_value=competences)
expected = pool_classifiers[np.argmax(competences)].predict_proba(query)
predictions = []
probabilities = []
for clf in dcs_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 = dcs_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.array_equal(predicted_proba, expected)
def test_predict_proba_instance_all(competences, expected):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers, selection_method='all')
dcs_test.n_classes = 2
dcs_test.estimate_competence = MagicMock(return_value=competences)
predicted_proba = dcs_test.predict_proba_instance(query)
assert np.isclose(predicted_proba, expected).all()
def test_classify_instance():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers)
competences = np.random.rand(dcs_test.n_classifiers)
dcs_test.estimate_competence = MagicMock(return_value=competences)
expected = pool_classifiers[np.argmax(competences)].predict(query)[0]
predicted_label = dcs_test.classify_instance(query)
assert predicted_label == expected
def test_classify_instance_all(competences, expected):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers, selection_method='all')
dcs_test.estimate_competence = MagicMock(
return_value=np.array(competences))
predictions = []
for clf in dcs_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = dcs_test.classify_with_ds(query, np.array(predictions))
assert predicted_label == expected
def test_predict_proba_instance():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers)
dcs_test.n_classes = 2
competences = np.random.rand(dcs_test.n_classifiers)
dcs_test.estimate_competence = MagicMock(return_value=competences)
expected = pool_classifiers[np.argmax(competences)].predict_proba(query)
predicted_proba = dcs_test.predict_proba_instance(query)
assert np.array_equal(predicted_proba, expected)
def test_classify_instance():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers)
competences = np.random.rand(dcs_test.n_classifiers)
dcs_test.estimate_competence = MagicMock(return_value=competences)
expected = pool_classifiers[np.argmax(competences)].predict(query)[0]
predictions = []
for clf in dcs_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = dcs_test.classify_with_ds(query, np.array(predictions))
assert predicted_label == expected
def test_classify_instance_all_batch():
competences = np.array([[0.6, 0.2, 0.6], [0.5, 0.8, 0.5]])
expected = [0, 1]
n_samples = 2
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers, selection_method='all')
dcs_test.estimate_competence = MagicMock(
return_value=np.array(competences))
predictions = []
for clf in dcs_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = dcs_test.classify_with_ds(
query, np.tile(predictions, (n_samples, 1)))
assert np.array_equal(predicted_label, expected)
def test_classify_instance_batch():
n_samples = 3
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers)
competences = np.random.rand(n_samples, dcs_test.n_classifiers)
dcs_test.estimate_competence = MagicMock(return_value=competences)
expected = []
for ind in range(n_samples):
expected.append(pool_classifiers[np.argmax(
competences[ind, :])].predict(query)[0])
predictions = []
for clf in dcs_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = dcs_test.classify_with_ds(query,
np.tile(predictions, (3, 1)))
assert np.array_equal(predicted_label, expected)
def test_predict_proba_instance_all(competences, expected):
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers()
dcs_test = DCS(pool_classifiers, selection_method='all')
dcs_test.n_classes = 2
dcs_test.estimate_competence = MagicMock(
return_value=np.array(competences))
predictions = []
probabilities = []
for clf in dcs_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 = dcs_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected).all()