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# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# 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()
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