def test_predict_proba_with_ds():
query = np.array([-1, 1])
pool_classifiers = create_pool_classifiers() + create_pool_classifiers()
desmi_test = DESMI(pool_classifiers, DFP=True)
desmi_test.DFP_mask = np.ones((1, 6))
selected_indices = np.array([[0, 1, 5]])
desmi_test.estimate_competence = MagicMock(return_value=np.ones(6))
desmi_test.select = MagicMock(return_value=selected_indices)
desmi_test.n_classes = 2
expected = np.array([0.61, 0.39])
predictions = []
probabilities = []
for clf in desmi_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 = desmi_test.predict_proba_with_ds(query, predictions,
probabilities)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_with_ds_single_sample():
query = np.ones(2)
pool_classifiers = create_pool_classifiers()
# simulated predictions of the pool of classifiers
predictions = np.array([0, 1, 0])
desmi_test = DESMI(pool_classifiers, DFP=True)
desmi_test.DFP_mask = np.ones((1, 3))
desmi_test.estimate_competence = MagicMock(return_value=(np.ones((1, 3))))
desmi_test.select = MagicMock(return_value=np.array([[0, 2]]))
result = desmi_test.classify_with_ds(query, predictions)
assert np.allclose(result, 0)