def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
des_mi = DESMI()
with pytest.raises(ValueError):
des_mi.predict_proba_with_ds(query, predictions, probabilities)
def test_proba_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
probabilities = np.ones((5, 3, 2))
pool_classifiers = create_pool_classifiers()
des_mi = DESMI(pool_classifiers)
with pytest.raises(ValueError):
des_mi.predict_proba_with_ds(query, predictions, probabilities)
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)
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,
DFP_mask=DFP_mask)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_with_ds_soft(create_pool_classifiers):
expected = np.array([0.61, 0.39])
DFP_mask = np.ones((1, 6))
predictions = np.array([[0, 1, 0, 0, 1, 0]])
probabilities = np.array([[[0.5, 0.5], [1, 0], [0.33, 0.67], [0.5, 0.5],
[1, 0], [0.33, 0.67]]])
pool_classifiers = create_pool_classifiers + create_pool_classifiers
desmi_test = DESMI(pool_classifiers, DFP=True, voting='soft')
desmi_test.n_classes_ = 2
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)
predicted_proba = desmi_test.predict_proba_with_ds(predictions,
probabilities,
DFP_mask=DFP_mask)
assert np.isclose(predicted_proba, expected, atol=0.01).all()
