clf = make_pipeline(StandardScaler(), clf)
# initialize the GAT object
gat = GeneralizationAcrossTime(clf=clf, scorer=scorer_auc, n_jobs=-1,
cv=10)
# select the trials where a target is presented
for contrast in ['HL', 'EU', 'PR']:
epochs_ = concatenate_epochs((epochs[contrast[0]],
epochs[contrast[1]]))
y = np.hstack((np.zeros(len(epochs[contrast[0]])),
np.ones(len(epochs[contrast[1]]))))
gat.fit(epochs_, y=y)
fname = op.join(data_path, 's%i_%s_fit.pkl' % (subject, contrast))
with open(fname, 'wb') as f:
pickle.dump(gat, f)
# TODO: should save y_pred separately
# predict + score
scores = gat.score(epochs_, y=y)
fname = op.join(data_path,
's%i_%s_scores.npy' % (subject, contrast))
np.save(fname, np.array(scores))
all_scores[contrast].append(np.array(scores))
# plot
fig, axes = plt.subplots(2, 1, facecolor='w')
gat.plot_diagonal(show=False, ax=axes[0], chance=.5)
gat.plot(show=False, ax=axes[1], vmin=.25, vmax=.75)
report.add_figs_to_section(fig, str(subject), contrast)
report.save()
fig_diag, axes_diag = plt.subplots(len(contrasts), 1, figsize=[2, 5])
cmap = plt.get_cmap('rainbow')
colors = cmap(np.linspace(0., 1., len(contrasts)))
for contrast, color, ax in zip(contrasts, colors, axes_diag):
scores = all_scores[contrast]
# plot diagonal
scores_diag = np.array([np.diag(x) for x in scores])
pretty_decod(scores_diag, times=times, color=color, ax=ax, chance=.5)
ax.set_title(contrast)
if ax != axes_diag[-1]:
ax.set_xticklabels([])
ax.set_xlabel('')
# plot gat
fig, ax = plt.subplots(1, figsize=[5, 5])
pretty_gat(np.mean(scores, axis=0), times=times, chance=.5)
report.add_figs_to_section(fig, 'GAT %s' % contrast, contrast)
# plot slices
tois = np.arange(0., .600, .100)
fig, axes = plt.subplots(len(tois), 1, figsize=[5, 5])
pretty_slices(scores, tois=tois, times=times, chance=.5, axes=axes)
report.add_figs_to_section(fig, 'Slices %s' % contrast, contrast)
fig_diag.tight_layout()
report.add_figs_to_section(fig_diag, 'Diagonal', 'All')
# plot subscore
for contrast in ['EU', 'PR']:
fig_diag, ax_diag = plt.subplots(1, figsize=[5, 2])
for HL in 'HL':
subcontrast = '%s_%s' % (contrast, HL)
scores = all_scores[subcontrast]
# plot diagonal
