# Authors: Alexandre Barachant # Jean-Remi King # # License: BSD (3-clause) import numpy as np import os.path as op from nose.tools import assert_equal, assert_raises, assert_true from numpy.testing import assert_array_equal, assert_array_almost_equal from mne import Epochs, read_events, pick_types, compute_raw_covariance from mne.io import read_raw_fif from mne.utils import requires_sklearn, run_tests_if_main from mne.preprocessing.xdawn import Xdawn, _XdawnTransformer base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') event_name = op.join(base_dir, 'test-eve.fif') tmin, tmax = -0.1, 0.2 event_id = dict(cond2=2, cond3=3) def _get_data(): """Get data.""" raw = read_raw_fif(raw_fname, add_eeg_ref=False, verbose=False, preload=True) events = read_events(event_name) picks = pick_types(raw.info, meg=False, eeg=True, stim=False, ecg=False, eog=False, exclude='bads')[::8] return raw, events, picks def test_xdawn(): """Test init of xdawn.""" # Init xdawn with good parameters Xdawn(n_components=2, correct_overlap='auto', signal_cov=None, reg=None) # Init xdawn with bad parameters assert_raises(ValueError, Xdawn, correct_overlap=42) def test_xdawn_fit(): """Test Xdawn fit.""" # Get data raw, events, picks = _get_data() epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, preload=True, baseline=None, verbose=False, add_eeg_ref=False) # =========== Basic Fit test ================= # Test base xdawn xd = Xdawn(n_components=2, correct_overlap='auto') xd.fit(epochs) # With these parameters, the overlap correction must be False assert_equal(xd.correct_overlap_, False) # No overlap correction should give averaged evoked evoked = epochs['cond2'].average() assert_array_equal(evoked.data, xd.evokeds_['cond2'].data) # ========== with signal cov provided ==================== # Provide covariance object signal_cov = compute_raw_covariance(raw, picks=picks) xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=signal_cov) xd.fit(epochs) # Provide ndarray signal_cov = np.eye(len(picks)) xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=signal_cov) xd.fit(epochs) # Provide ndarray of bad shape signal_cov = np.eye(len(picks) - 1) xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=signal_cov) assert_raises(ValueError, xd.fit, epochs) # Provide another type signal_cov = 42 xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=signal_cov) assert_raises(ValueError, xd.fit, epochs) # Fit with baseline correction and overlap correction should throw an # error # XXX This is a buggy test, the epochs here don't overlap epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, preload=True, baseline=(None, 0), verbose=False, add_eeg_ref=False) xd = Xdawn(n_components=2, correct_overlap=True) assert_raises(ValueError, xd.fit, epochs) def test_xdawn_apply_transform(): """Test Xdawn apply and transform.""" # Get data raw, events, picks = _get_data() raw.pick_types(eeg=True, meg=False) epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False, add_eeg_ref=False, preload=True, baseline=None, verbose=False) n_components = 2 # Fit Xdawn xd = Xdawn(n_components=n_components, correct_overlap=False) xd.fit(epochs) # Apply on different types of instances for inst in [raw, epochs.average(), epochs]: denoise = xd.apply(inst) # Apply on other thing should raise an error assert_raises(ValueError, xd.apply, 42) # Transform on epochs xd.transform(epochs) # Transform on ndarray xd.transform(epochs._data) # Transform on someting else assert_raises(ValueError, xd.transform, 42) # Check numerical results with shuffled epochs np.random.seed(0) # random makes unstable linalg idx = np.arange(len(epochs)) np.random.shuffle(idx) xd.fit(epochs[idx]) denoise_shfl = xd.apply(epochs) assert_array_almost_equal(denoise['cond2']._data, denoise_shfl['cond2']._data) @requires_sklearn def test_xdawn_regularization(): """Test Xdawn with regularization.""" # Get data raw, events, picks = _get_data() epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, preload=True, baseline=None, verbose=False, add_eeg_ref=False) # Test with overlapping events. # modify events to simulate one overlap events = epochs.events sel = np.where(events[:, 2] == 2)[0][:2] modified_event = events[sel[0]] modified_event[0] += 1 epochs.events[sel[1]] = modified_event # Fit and check that overlap was found and applied xd = Xdawn(n_components=2, correct_overlap='auto', reg='oas') xd.fit(epochs) assert_equal(xd.correct_overlap_, True) evoked = epochs['cond2'].average() assert_true(np.sum(np.abs(evoked.data - xd.evokeds_['cond2'].data))) # With covariance regularization for reg in [.1, 0.1, 'ledoit_wolf', 'oas']: xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=np.eye(len(picks)), reg=reg) xd.fit(epochs) # With bad shrinkage xd = Xdawn(n_components=2, correct_overlap=False, signal_cov=np.eye(len(picks)), reg=2) assert_raises(ValueError, xd.fit, epochs) @requires_sklearn def test_XdawnTransformer(): """Test _XdawnTransformer.""" # Get data raw, events, picks = _get_data() epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, preload=True, baseline=None, verbose=False, add_eeg_ref=False) X = epochs._data y = epochs.events[:, -1] # Fit xdt = _XdawnTransformer() xdt.fit(X, y) assert_raises(ValueError, xdt.fit, X, y[1:]) assert_raises(ValueError, xdt.fit, 'foo') # Provide covariance object signal_cov = compute_raw_covariance(raw, picks=picks) xdt = _XdawnTransformer(signal_cov=signal_cov) xdt.fit(X, y) # Provide ndarray signal_cov = np.eye(len(picks)) xdt = _XdawnTransformer(signal_cov=signal_cov) xdt.fit(X, y) # Provide ndarray of bad shape signal_cov = np.eye(len(picks) - 1) xdt = _XdawnTransformer(signal_cov=signal_cov) assert_raises(ValueError, xdt.fit, X, y) # Provide another type signal_cov = 42 xdt = _XdawnTransformer(signal_cov=signal_cov) assert_raises(ValueError, xdt.fit, X, y) # Fit with y as None xdt = _XdawnTransformer() xdt.fit(X) # Compare xdawn and _XdawnTransformer xd = Xdawn(correct_overlap=False) xd.fit(epochs) xdt = _XdawnTransformer() xdt.fit(X, y) assert_array_almost_equal(xd.filters_['cond2'][:, :2], xdt.filters_.reshape(2, 2, 8)[0].T) # Transform testing xdt.transform(X[1:, ...]) # different number of epochs xdt.transform(X[:, :, 1:]) # different number of time assert_raises(ValueError, xdt.transform, X[:, 1:, :]) Xt = xdt.transform(X) assert_raises(ValueError, xdt.transform, 42) # Inverse transform testing Xinv = xdt.inverse_transform(Xt) assert_equal(Xinv.shape, X.shape) xdt.inverse_transform(Xt[1:, ...]) xdt.inverse_transform(Xt[:, :, 1:]) # should raise an error if not correct number of components assert_raises(ValueError, xdt.inverse_transform, Xt[:, 1:, :]) assert_raises(ValueError, xdt.inverse_transform, 42) run_tests_if_main()