def eval(model, x, y):
y_pred = np.argmax(model.predict(x), axis=1)
y_test = np.squeeze(y)
bca = utils.bca(y_test, y_pred)
acc = np.sum(y_pred == y_test).astype(np.float32) / len(y_pred)
return acc, bca
# Build Model
xd = XdawnCovariances(nfilter=5,
applyfilters=True,
estimator='lwf')
# es = ElectrodeSelection(nelec=25, metric='riemann')
ts = TangentSpace(metric='logeuclid')
lr = LogisticRegression(solver='liblinear', max_iter=200, C=0.01)
model = Pipeline([('xDAWN', xd), ('TangentSpace', ts), ('LR', lr)])
model.fit(x_train, y_train)
# Test Model
y_pred = np.argmax(model.predict_proba(np.squeeze(x_test)), axis=1)
bca = utils.bca(y_test, y_pred)
acc = np.sum(y_pred == y_test).astype(np.float32) / len(y_pred)
print('{}: acc-{} bca-{}'.format(data_name, acc, bca))
# poison performance
test_asr = []
for test_param in params:
test_npp_params = [amplitude, test_param[0], test_param[1]]
_, x_t_poison, _ = load(data_name,
s_id[s],
test_npp_params,
clean=False,
physical=True,
downsample=False)
idx = y_pred == y_test
x_t_poison, y_t = x_t_poison[idx], y_test[idx]
y1_rate = np.mean(np.where(y == 1, 1, 0))
class_weight = {0: y1_rate, 1: y0_rate}
else:
class_weight = None
# ============================== build model ==================================
processers = [
Blocks.Xdawn(n_filters=8, with_xdawn_templates=True, apply_filters=True),
Blocks.CovarianceFeature(with_mean_templates=False),
Blocks.TangentSpaceFeature(mean_metric='riemann',
name='TangentSpace({})'.format('riemann'))
]
classifier = Blocks.LogisticRegression(class_weight=class_weight,
max_iter=2000)
model = Pipeline(processers, classifier)
print('subject: {}'.format(subject))
model.pipeline_information() # show model information
# ============================== train model ===================================
print('fitting.......')
model.fit(epochs, y)
model.save(save_path)
y_pred = np.argmax(model.predict(epochs), axis=1)
bca = np.round(utils.bca(y, y_pred), decimals=3)
acc = np.round(np.sum(y_pred == y).astype(np.float64) / len(y_pred),
decimals=3)
print('train (target and nontarget): acc={}, bca={}'.format(acc, bca))