Method test_classifier_margin

tests/core_tests.py:597–625

(self)

            )

    def test_classifier_margin(self):
        test_cases_1 = (Test(p * np.ones(shape=(k, l)), np.zeros(shape=(k,)))
                        for k in range(1, 100) for l in range(1, 10) for p in np.linspace(0, 1, 11))
        test_cases_2 = (Test(p * np.tile(np.asarray(range(k))+1.0, l).reshape(l, k),
                             p * np.ones(shape=(l, ))*int(k != 1))
                        for k in range(1, 10) for l in range(1, 100) for p in np.linspace(0, 1, 11))
        for case in chain(test_cases_1, test_cases_2):
            # _proba_margin
            np.testing.assert_almost_equal(
                modAL.uncertainty._proba_margin(case.input),
                case.output
            )

            # fitted estimator
            fitted_estimator = mock.MockEstimator(
                predict_proba_return=case.input)
            np.testing.assert_almost_equal(
                modAL.uncertainty.classifier_margin(
                    fitted_estimator, np.random.rand(10)),
                case.output
            )

            # not fitted estimator
            not_fitted_estimator = mock.MockEstimator(fitted=False)
            np.testing.assert_almost_equal(
                modAL.uncertainty.classifier_margin(
                    not_fitted_estimator, case.input),
                np.zeros(shape=(len(case.output)))
            )

    def test_classifier_entropy(self):
        for n_samples in range(1, 100):