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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from pathlib import Path
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal, assert_equal
pytest.importorskip("sklearn")
from sklearn.model_selection import StratifiedKFold
from mne import Epochs, io, pick_types, read_events
from mne.decoding import EMS, compute_ems
data_dir = Path(__file__).parents[2] / "io" / "tests" / "data"
raw_fname = data_dir / "test_raw.fif"
event_name = data_dir / "test-eve.fif"
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
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)
cv = StratifiedKFold(n_splits=3)
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(copy=False)
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))
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