Class TestCommitteeRegressor

tests/core_tests.py:1532–1607

class TestCommitteeRegressor(unittest.TestCase):

    def test_predict(self):
        for n_members in range(1, 10):
            for n_instances in range(1, 100):
                vote = np.random.rand(n_instances, n_members)
                # assembling the Committee
                learner_list = [mock.MockActiveLearner(predict_return=vote[:, member_idx])
                                for member_idx in range(n_members)]
                committee = modAL.models.learners.CommitteeRegressor(
                    learner_list=learner_list)
                np.testing.assert_array_almost_equal(
                    committee.predict(np.random.rand(
                        n_instances).reshape(-1, 1), return_std=False),
                    np.mean(vote, axis=1)
                )
                np.testing.assert_array_almost_equal(
                    committee.predict(np.random.rand(
                        n_instances).reshape(-1, 1), return_std=True),
                    (np.mean(vote, axis=1), np.std(vote, axis=1))
                )

    def test_vote(self):
        for n_members in range(1, 10):
            for n_instances in range(1, 100):
                vote_output = np.random.rand(n_instances, n_members)
                # assembling the Committee
                learner_list = [mock.MockActiveLearner(predict_return=vote_output[:, member_idx])
                                for member_idx in range(n_members)]
                committee = modAL.models.learners.CommitteeRegressor(
                    learner_list=learner_list)
                np.testing.assert_array_almost_equal(
                    committee.vote(np.random.rand(n_instances).reshape(-1, 1)),
                    vote_output
                )

    def test_on_transformed(self):
        n_samples = 10
        n_features = 5
        query_strategies = [
            # TODO remove, added just to make sure on_transformed doesn't break anything
            # but it has no influence on this strategy, nothing special tested here
            mock.MockFunction(return_val=[np.random.randint(0, n_samples)])

            # add further strategies which work with instance representations
            # no further ones as of 25.09.2020
        ]
        X_pool = np.random.rand(n_samples, n_features)

        # use pandas data frame as X_pool, which will be transformed back to numpy with sklearn pipeline
        X_pool = pd.DataFrame(X_pool)

        y_pool = np.random.rand(n_samples)
        train_idx = np.random.choice(range(n_samples), size=2, replace=False)

        learner_list = [modAL.models.learners.ActiveLearner(
            estimator=make_pipeline(
                FunctionTransformer(func=pd.DataFrame.to_numpy),