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()