Method test_classifier_entropy

tests/core_tests.py:627–655

(self)

            )

    def test_classifier_entropy(self):
        for n_samples in range(1, 100):
            for n_classes in range(1, 20):
                proba = np.zeros(shape=(n_samples, n_classes))
                for sample_idx in range(n_samples):
                    proba[sample_idx, np.random.choice(range(n_classes))] = 1.0

                # _proba_entropy
                np.testing.assert_almost_equal(
                    modAL.uncertainty._proba_entropy(proba),
                    np.zeros(shape=(n_samples,))
                )

                # fitted estimator
                fitted_estimator = mock.MockEstimator(
                    predict_proba_return=proba)
                np.testing.assert_equal(
                    modAL.uncertainty.classifier_entropy(
                        fitted_estimator, np.random.rand(n_samples, 1)),
                    np.zeros(shape=(n_samples, ))
                )

                # not fitted estimator
                not_fitted_estimator = mock.MockEstimator(fitted=False)
                np.testing.assert_almost_equal(
                    modAL.uncertainty.classifier_entropy(
                        not_fitted_estimator, np.random.rand(n_samples, 1)),
                    np.zeros(shape=(n_samples, ))
                )

    def test_uncertainty_sampling(self):
        for n_samples in range(1, 10):