Function test_vectorizer

mne/decoding/tests/test_transformer.py:206–233

Test Vectorizer.

()

def test_vectorizer():
    """Test Vectorizer."""
    data = np.random.rand(150, 18, 6)
    vect = Vectorizer()
    result = vect.fit_transform(data)
    assert_equal(result.ndim, 2)

    # check inverse_trasnform
    orig_data = vect.inverse_transform(result)
    assert_equal(orig_data.ndim, 3)
    assert_array_equal(orig_data, data)
    assert_array_equal(vect.inverse_transform(result[1:]), data[1:])

    # check with different shape
    assert_equal(vect.fit_transform(np.random.rand(150, 18, 6, 3)).shape, (150, 324))
    assert_equal(vect.fit_transform(data[1:]).shape, (149, 108))

    # check if raised errors are working correctly
    X = np.random.default_rng(0).standard_normal((105, 12, 3))
    y = np.arange(X.shape[0]) % 2
    pytest.raises(ValueError, vect.transform, X[..., np.newaxis])
    pytest.raises(ValueError, vect.inverse_transform, X[:, :-1])

    # And that pipelines work properly
    X_arr = EpochsArray(X, create_info(12, 1000.0, "eeg"))
    vect.fit(X_arr)
    clf = make_pipeline(Vectorizer(), StandardScaler(), LinearModel())
    clf.fit(X_arr, y)


def test_unsupervised_spatial_filter():