if (epoch_type == 'Probe') & ('target' in analysis):
sel = np.where((events['Change'] == 1) & (~np.isnan(y)))[0]
else:
sel = np.where(~np.isnan(y))[0]
td = SlidingEstimator(clf,
scoring=make_scorer(scorer),
n_jobs=24,
**kwargs)
# run decoding
cv = StratifiedKFold(8)
scores = list()
patterns = list()
filters = list()
for train, test in cv.split(X[sel], y[sel]):
td.fit(X[sel][train], y[sel][train])
score = td.score(X[sel][test], y[sel][test])
scores.append(score)
patterns.append(get_coef(td, 'patterns_', inverse_transform=True))
filters.append(get_coef(td, 'filters_', inverse_transform=True))
scores = np.mean(scores, axis=0)
patterns = np.mean(patterns, axis=0)
filters = np.mean(filters, axis=0)
if 'angle' in analysis:
patterns = np.mean(np.abs(patterns), axis=1)
filters = np.mean(np.abs(filters), axis=1)
scores = np.reshape(scores, (n_freqs, n_times))
patterns = np.reshape(patterns, (n_channels, n_freqs, n_times))
filters = np.reshape(filters, (n_channels, n_freqs, n_times))
# save cross-validated scores
fname = results_folder +\
'%s_tf_scores_%s_%s.npy' % (subject, epoch_type, analysis)
sel = np.where(y != 0)[0]
le = LabelEncoder()
le.fit(y_con)
y_con = le.transform(y_con)
sel_con = np.where(y_con != 0)[0]
# Define estimators depending on the analysis
clf = make_pipeline(StandardScaler(), LinearModel(LogisticRegression()))
kwargs = dict()
est = SlidingEstimator(clf, scoring='roc_auc', n_jobs=24, **kwargs)
# Run decoding
cv = StratifiedKFold(12)
scores = list()
scores_con = list()
for train, test in cv.split(X[sel], y[sel]):
est.fit(X[sel][train], y[sel][train]) # train during WM task
score = est.score(X[sel][test], y[sel][test]) # test during WM task
score_con = est.score(X_con[sel_con],
y_con[sel_con]) # test during control task
scores.append(score)
scores_con.append(score_con)
scores = np.mean(scores, axis=0)
scores = np.reshape(scores, (n_freqs, n_times))
scores_con = np.mean(scores_con, axis=0)
scores_con = np.reshape(scores_con, (n_freqs, n_times))
# save cross-validated scores
fname = results_folder +\
'%s_scores_tf_%s.npy' % (subject, analysis)
np.save(fname, np.array(scores))
fname = results_folder +\
'%s_scores_tf_%s_con.npy' % (subject, analysis)
np.save(fname, np.array(scores_con))
