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# Author: Denis A. Engemann <d.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_equal
import pytest
from mne import io, Epochs, read_events, pick_types
from mne.utils import requires_version, check_version, run_tests_if_main
from mne.decoding import compute_ems, EMS
data_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
curdir = op.join(op.dirname(__file__))
raw_fname = op.join(data_dir, 'test_raw.fif')
event_name = op.join(data_dir, 'test-eve.fif')
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
@requires_version('sklearn', '0.15')
def test_ems():
"""Test event-matched spatial filters."""
raw = io.read_raw_fif(raw_fname, preload=False)
# create unequal number of events
events = read_events(event_name)
events[-2, 2] = 3
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
pytest.raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
epochs.equalize_event_counts(epochs.event_id)
pytest.raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
surrogates, filters, conditions = compute_ems(epochs)
assert_equal(list(set(conditions)), [1, 3])
events = read_events(event_name)
event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs.equalize_event_counts(epochs.event_id)
n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])
pytest.raises(ValueError, compute_ems, epochs)
surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
assert_equal(n_expected, len(surrogates))
assert_equal(n_expected, len(conditions))
assert_equal(list(set(conditions)), [2, 3])
# test compute_ems cv
epochs = epochs['aud_r', 'vis_l']
epochs.equalize_event_counts(epochs.event_id)
if check_version('sklearn', '0.18'):
from sklearn.model_selection import StratifiedKFold
cv = StratifiedKFold(n_splits=3)
else:
from sklearn.cross_validation import StratifiedKFold
cv = StratifiedKFold(epochs.events[:, 2])
compute_ems(epochs, cv=cv)
compute_ems(epochs, cv=2)
pytest.raises(ValueError, compute_ems, epochs, cv='foo')
pytest.raises(ValueError, compute_ems, epochs, cv=len(epochs) + 1)
raw.close()
# EMS transformer, check that identical to compute_ems
X = epochs.get_data()
y = epochs.events[:, 2]
X = X / np.std(X) # X scaled outside cv in compute_ems
Xt, coefs = list(), list()
ems = EMS()
assert_equal(ems.__repr__(), '<EMS: not fitted.>')
# manual leave-one-out to avoid sklearn version problem
for test in range(len(y)):
train = np.setdiff1d(range(len(y)), np.atleast_1d(test))
ems.fit(X[train], y[train])
coefs.append(ems.filters_)
Xt.append(ems.transform(X[[test]]))
assert_equal(ems.__repr__(), '<EMS: fitted with 4 filters on 2 classes.>')
assert_array_almost_equal(filters, np.mean(coefs, axis=0))
assert_array_almost_equal(surrogates, np.vstack(Xt))
run_tests_if_main()
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