def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
dcs_test = DCS(create_pool_classifiers())
with pytest.raises(ValueError):
dcs_test.predict_proba_with_ds(query, predictions, probabilities)
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=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()
