return model.fit(
train['x'],
train['y'],
batch_size=32,
epochs=epochs,
verbose=0,
validation_data=(validation['x'], validation['y']),
callbacks=([checkpointer] if checkpointer is not None else []))
### script start
# _compX, _compY = separateXY(readall_comp_epochs('data/competition/epoched/ica'))
_compX, _compY = epoch_comp(prep_comp(load_comp(True),
comp_channel_map3,
GOODS,
l_freq=LO_FREQ,
h_freq=HI_FREQ),
CLASSES,
resample=RESAMPLE,
trange=T_RANGE)
# _pilotX, _pilotY = epoch_pilot(loadall_pilot(True), CLASSES, GOODS, resample=RESAMPLE, trange=T_RANGE, l_freq=LO_FREQ, h_freq=HI_FREQ)
_pilotX, _pilotY = epoch_pilot(
load_pilot('data/rivet/raw/pilot2/BCI_imaginedmoves_3class_7-4-21.vhdr'),
CLASSES,
GOODS,
resample=RESAMPLE,
trange=T_RANGE,
l_freq=LO_FREQ,
h_freq=HI_FREQ)
_compX, _compY = stratify(_compX, _compY, FOLDS)
CLASSES = 2
FOLDS = 9
GOODS = ['Fz','FC5','FC1','FC2','FC6','T7','C3','C1','Cz','C2','C4','T8','CP5','CP1','CPz','CP2','CP6','P3','Pz','P4','POz']
KERNELS = 1
WEIGHT_PATH = f"weights/competition/subject-separated/{CLASSES}class/{FOLDS}fold/channel_map2"
## local functions
def train(model, train, validation, weight_file):
checkpointer = ModelCheckpoint(filepath=weight_file, verbose=1, save_best_only=True)
return model.fit(train['x'], train['y'], batch_size=16, epochs=50, verbose=0,
validation_data=(validation['x'], validation['y']), callbacks=[checkpointer])
### script start
_compX, _compY = epoch_comp(prep_comp(comp_channel_map2, GOODS), CLASSES)
_pilotX, _pilotY = epoch_pilot(prep_pilot('data/rivet/VIPA_BCIpilot_imaginedmovement.vhdr', GOODS), CLASSES)
chans, samples = _pilotX.shape[1], _pilotX.shape[2]
Path(WEIGHT_PATH).mkdir(parents=True, exist_ok=True)
pilotX = add_kernel_dim((_pilotX,), kernels=KERNELS)
pilotY = onehot((_pilotY,))
comp_avg = {'acc': 0, 'bal': 0, 'kap': 0}
pilot_avg = {'acc': 0, 'bal': 0, 'kap': 0}
for i in range(0, FOLDS):
trainX, valX, compX = add_kernel_dim(get_fold(_compX, FOLDS, i, test_val_rest_split), kernels=KERNELS)
trainY, valY, compY = onehot(get_fold(_compY, FOLDS, i, test_val_rest_split))
from mne.decoding import CSP
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
# constants
CLASSES = 2
FOLDS = 10
GOODS = [
'Fz', 'FC3', 'FC1', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6',
'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'
]
# script start
_compX, _compY = epoch_comp(prep_comp(comp_channel_map3, GOODS, h_freq=30.),
CLASSES)
_pilotX, _pilotY = epoch_pilot(prepall_pilot(GOODS, h_freq=30.), CLASSES)
csp = CSP(n_components=4)
csp.fit(_compX, _compY)
csp_compX = csp.transform(_compX)
csp_pilotX = csp.transform(_pilotX)
print(csp_compX)
print(csp_compX.shape)
# clf = SVC(kernel='linear', C=0.05, probability=True)
# clf = MLPClassifier([10, 3], batch_size=16)
clf = RandomForestClassifier()
skf = StratifiedKFold(FOLDS, shuffle=True, random_state=1)
def train(model, train, validation, weight_file=None, epochs=300):
checkpointer = ModelCheckpoint(
filepath=weight_file, verbose=1,
save_best_only=True) if weight_file is not None else None
return model.fit(
train['x'],
train['y'],
batch_size=32,
epochs=epochs,
verbose=0,
validation_data=(validation['x'], validation['y']),
callbacks=([checkpointer] if checkpointer is not None else []))
### script start
subject_epochs, subject_labels = epoch_comp(load_comp_array(True), CLASSES,
comp_channel_map3, GOODS, RESAMPLE,
T_RANGE, LO_FREQ, HI_FREQ)
print(subject_epochs)
print(subject_labels)
for i in range(0, len(subject_epochs)):
rolled_epochs = np.roll(subject_epochs, i, 0)
rolled_labels = np.roll(subject_labels, i, 0)
test_epochs, test_labels = rolled_epochs[0], rolled_labels[0]
trai_epochs, trai_labels = rolled_epochs[1:], rolled_labels[1:]
print(test_labels)
print(len(trai_labels))
def train(model, train, validation, weight_file):
checkpointer = ModelCheckpoint(filepath=weight_file,
verbose=1,
save_best_only=True)
return model.fit(train['x'],
train['y'],
batch_size=16,
epochs=50,
verbose=0,
validation_data=(validation['x'], validation['y']),
callbacks=[checkpointer])
### script start
compX, compY = epoch_comp(prep_comp({}), CLASSES)
chans, samples = compX.shape[1], compX.shape[2]
Path(WEIGHT_PATH).mkdir(parents=True, exist_ok=True)
comp_avg = {'acc': 0, 'bal': 0, 'kap': 0}
for i in range(0, FOLDS):
trainX, valX, testX = add_kernel_dim(get_fold(compX, FOLDS, i,
test_val_rest_split),
kernels=KERNELS)
trainY, valY, testY = onehot(get_fold(compY, FOLDS, i,
test_val_rest_split))
# weight file path
weight_file = f"{WEIGHT_PATH}/{i+1}.h5"
save_best_only=True) if weight_file is not None else None
return model.fit(
train['x'],
train['y'],
batch_size=32,
epochs=epochs,
verbose=0,
validation_data=(validation['x'], validation['y']),
callbacks=([checkpointer] if checkpointer is not None else []))
### script start
_compX, _compY = epoch_comp(prep_comp(comp_channel_map3,
GOODS,
h_freq=LOW_PASS),
CLASSES,
resample=RESAMPLE,
trange=T_RANGE)
_pilotX, _pilotY = epoch_pilot(prepall_pilot(GOODS, h_freq=LOW_PASS),
CLASSES,
resample=RESAMPLE,
trange=T_RANGE)
# _pilotX, _pilotY = epoch_pilot(prep_pilot('data/rivet/VIPA_BCIpilot_realmovement.vhdr', GOODS, l_freq=0.5, h_freq=30.), CLASSES)
chans, samples = _pilotX.shape[1], _pilotX.shape[2]
Path(WEIGHT_PATH).mkdir(parents=True, exist_ok=True)
# stratify and split pilot data for transfer learning
skf = StratifiedKFold(TRANSFER_FOLDS, shuffle=True)
comp_avg = {'acc': 0, 'bal': 0, 'kap': 0}