Class TestActiveLearner

tests/core_tests.py:926–1223

class TestActiveLearner(unittest.TestCase):

    def test_add_training_data(self):
        for n_samples in range(1, 10):
            for n_features in range(1, 10):
                for n_new_samples in range(1, 10):
                    # testing for valid cases
                    # 1. integer class labels
                    X_initial = np.random.rand(n_samples, n_features)
                    y_initial = np.random.randint(0, 2, size=(n_samples,))
                    X_new = np.random.rand(n_new_samples, n_features)
                    y_new = np.random.randint(0, 2, size=(n_new_samples,))
                    learner = modAL.models.learners.ActiveLearner(
                        estimator=mock.MockEstimator(),
                        X_training=X_initial, y_training=y_initial
                    )
                    learner._add_training_data(X_new, y_new)
                    np.testing.assert_almost_equal(
                        learner.X_training,
                        np.vstack((X_initial, X_new))
                    )
                    np.testing.assert_equal(
                        learner.y_training,
                        np.concatenate((y_initial, y_new))
                    )
                    # 2. vector class labels
                    y_initial = np.random.randint(
                        0, 2, size=(n_samples, n_features+1))
                    y_new = np.random.randint(
                        0, 2, size=(n_new_samples, n_features+1))
                    learner = modAL.models.learners.ActiveLearner(
                        estimator=mock.MockEstimator(),
                        X_training=X_initial, y_training=y_initial
                    )
                    learner._add_training_data(X_new, y_new)
                    np.testing.assert_equal(
                        learner.y_training,
                        np.concatenate((y_initial, y_new))
                    )
                    # 3. data with shape (n, )
                    X_initial = np.random.rand(n_samples, )
                    y_initial = np.random.randint(0, 2, size=(n_samples,))
                    learner = modAL.models.learners.ActiveLearner(
                        estimator=mock.MockEstimator(),
                        X_training=X_initial, y_training=y_initial
                    )
                    X_new = np.random.rand(n_new_samples,)
                    y_new = np.random.randint(0, 2, size=(n_new_samples,))
                    learner._add_training_data(X_new, y_new)

                    # testing for invalid cases
                    # 1. len(X_new) != len(y_new)
                    X_new = np.random.rand(n_new_samples, n_features)
                    y_new = np.random.randint(0, 2, size=(2*n_new_samples,))
                    self.assertRaises(
                        ValueError, learner._add_training_data, X_new, y_new)
                    # 2. X_new has wrong dimensions
                    X_new = np.random.rand(n_new_samples, 2*n_features)