Method test_KL_max_disagreement

tests/core_tests.py:464–496

(self)

                        consensus_entropy, true_entropy)

    def test_KL_max_disagreement(self):
        for n_samples in range(1, 10):
            for n_classes in range(2, 10):
                for n_learners in range(2, 10):
                    # 1. fitted committee
                    vote_proba = np.zeros(
                        shape=(n_samples, n_learners, n_classes))
                    vote_proba[:, :, 0] = 1.0
                    committee = mock.MockCommittee(
                        n_learners=n_learners, classes_=range(n_classes),
                        vote_proba_return=vote_proba
                    )

                    true_KL_disagreement = np.zeros(shape=(n_samples, ))

                    try:
                        np.testing.assert_array_almost_equal(
                            true_KL_disagreement,
                            modAL.disagreement.KL_max_disagreement(
                                committee, np.random.rand(n_samples, 1))
                        )
                    except:
                        modAL.disagreement.KL_max_disagreement(
                            committee, np.random.rand(n_samples, 1))

                    # 2. unfitted committee
                    committee = mock.MockCommittee(fitted=False)
                    true_KL_disagreement = np.zeros(shape=(n_samples,))
                    returned_KL_disagreement = modAL.disagreement.KL_max_disagreement(
                        committee, np.random.rand(n_samples, n_classes)
                    )
                    np.testing.assert_almost_equal(
                        returned_KL_disagreement, true_KL_disagreement)

    def test_vote_entropy_sampling(self):
        for n_samples, n_features, n_classes in product(range(1, 10), range(1, 10), range(1, 10)):