Method test_entropy_sampling

tests/core_tests.py:698–718

(self)

                        shuffled_query_idx, true_query_idx)

    def test_entropy_sampling(self):
        for n_samples in range(1, 10):
            for n_classes in range(2, 10):
                max_proba = np.ones(n_classes)/n_classes
                for true_query_idx in range(n_samples):
                    predict_proba = np.zeros(shape=(n_samples, n_classes))
                    predict_proba[:, 0] = 1.0
                    predict_proba[true_query_idx] = max_proba
                    classifier = mock.MockEstimator(
                        predict_proba_return=predict_proba)

                    query_idx, query_metric = modAL.uncertainty.entropy_sampling(
                        classifier, np.random.rand(n_samples, n_classes)
                    )
                    shuffled_query_idx, shuffled_query_metric = modAL.uncertainty.entropy_sampling(
                        classifier, np.random.rand(n_samples, n_classes),
                        random_tie_break=True
                    )
                    np.testing.assert_array_equal(query_idx, true_query_idx)
                    np.testing.assert_array_equal(
                        shuffled_query_idx, true_query_idx)


# PyTorch model for test cases --> Do not change the layers