def test_classify_with_ds_batch_samples():
n_samples = 10
# simulated predictions of the pool of classifiers
predictions = np.tile(np.array([0, 1, 0]), (n_samples, 1))
desmi_test = DESMI()
desmi_test.n_classes_ = 2
desmi_test.estimate_competence = MagicMock(
return_value=(np.ones((n_samples, 3))))
desmi_test.select = MagicMock(
return_value=np.tile(np.array([[0, 2]]), (n_samples, 1)))
result = desmi_test.classify_with_ds(predictions)
assert np.allclose(result, np.zeros(10))
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()
