from __future__ import print_function
import sys
import numpy as np
import chb
subject = chb.load_dataset(sys.argv[1], tiger=True)
sys.stdout.flush()
for sznum in range(1, subject.get_num() + 1):
train, trainlab, test, testlab = chb.loo_epoch(subject, sznum)
print('Seizure %d of %d' % (sznum, subject.get_num()))
print(train.shape)
print(trainlab.shape)
print(sum(trainlab))
print(test.shape)
print(testlab.shape)
print(sum(testlab))
sys.stdout.flush()
from __future__ import print_function
import sys
import os
import numpy as np
import chb
subjname = sys.argv[1]
subject = chb.load_dataset(subjname, tiger=True)
sys.stdout.flush()
print(subjname, 'rec shape:\n')
for idx, eeg in enumerate(subject):
print('%d:\t%s' % (idx + 1, eeg.get_rec().shape))
sys.stdout.flush()
print('-' * 80)
print()
sys.stdout.flush()
def main(subjname, compressed=True):
subject = chb.load_dataset(subjname, tiger=True)
sys.stdout.flush()
op1 = set(['chb14', 'chb20', 'chb21', 'chb22'])
op2 = set(['chb18', 'chb19'])
op3 = set(['chb17'])
op4 = set(['chb16'])
op5 = set(['chb11'])
op6 = set(['chb04'])
op7 = set(['chb09'])
if subjname in op1:
for eeg in subject:
eeg.add_rec(np.delete(eeg.get_rec(), [4, 9, 12, 17, 22], 0))
elif subjname in op2:
del subject[0]
newsubj = chb.CHBsubj(subject.get_name())
for eeg in subject:
eeg.add_rec(np.delete(eeg.get_rec(), [4, 9, 12, 17, 22], 0))
newEEG = eeg.copy_meta()
newsubj.add_file(newEEG)
pklname = pth + subjname + '.p'
os.remove(pklname)
pickle.dump(newsubj, open(pklname, 'wb'))
elif subjname in op3:
del subject[-1]
newsubj = chb.CHBsubj(subject.get_name())
for eeg in subject:
eeg.add_rec(np.delete(eeg.get_rec(), [4, 9, 12, 17, 22], 0))
newEEG = eeg.copy_meta()
newsubj.add_file(newEEG)
pklname = pth + subjname + '.p'
os.remove(pklname)
pickle.dump(newsubj, open(pklname, 'wb'))
elif subjname in op4:
del subject[-2:]
newsubj = chb.CHBsubj(subject.get_name())
for eeg in subject:
eeg.add_rec(np.delete(eeg.get_rec(), [4, 9, 12, 17, 22], 0))
newEEG = eeg.copy_meta()
newsubj.add_file(newEEG)
pklname = pth + subjname + '.p'
os.remove(pklname)
pickle.dump(newsubj, open(pklname, 'wb'))
elif subjname in op5:
for idx, eeg in enumerate(subject):
if not idx:
continue
eeg.add_rec(np.delete(eeg.get_rec(), [4, 9, 12, 17, 22], 0))
elif subjname in op6:
for idx, eeg in enumerate(subject):
if idx < 6:
continue
eeg.add_rec(np.delete(eeg.get_rec(), 23, 0))
elif subjname in op7:
for idx, eeg in enumerate(subject):
if not idx:
continue
eeg.add_rec(np.delete(eeg.get_rec(), 23, 0))
print('Saving...')
savedict = {}
for eeg in subject:
savedict[eeg.get_name()] = eeg.get_rec()
st = time.clock()
savename = pth + subjname + '.npz'
os.remove(savename)
if compressed:
np.savez_compressed(savename, **savedict)
else:
np.savez(savename, **savedict)
print('Done: %f seconds elapsed.' % (time.clock() - st))
def main(subject='chb05',
num_epochs=10,
thresh=0.5,
osr=1,
usp=0,
tiger=False,
tag='test',
plotter=False):
# Load the dataset
subj = chb.load_dataset(subject, tiger=tiger)
sys.stdout.flush()
batch_size = 10
num_szr = subj.get_num()
test_accs = [0] * num_szr
out_dict = {}
for szr in range(1, num_szr + 1):
print('\nLeave-One-Out: %d of %d' % (szr, num_szr))
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
net = nw.simple(input_var)
train_fn, val_fn, prob_fn = compile_model(input_var, target_var, net)
train_err_list = [0] * num_epochs
val_err_list = [0] * num_epochs
print('=' * 80)
print('| epoch\t\t| train loss\t\t| time\t')
print('=' * 80)
x_test, y_test = chb.loowinTest(subj, szr)
for epoch in range(num_epochs):
st = time.clock()
# make generator
data = chb.loowinTrain(subj, szr, osr, usp)\
batch_train_errs = []
for idx, batch in enumerate(data):
x_train, y_train = batch
err = train_fn(x_train, y_train)
batch_train_errs.append(err)
epoch_train_err = np.mean(batch_train_errs)
train_err_list[epoch] = epoch_train_err
en = time.clock()
print('| %d \t\t| %.6f\t\t| %.2f s' %
(epoch + 1, epoch_train_err, en - st))
sys.stdout.flush()
print('-' * 80)
print('Training Complete.\n')
if plotter:
fig = plt.figure()
plt.plot(range(num_epochs), train_err, label='Training error')
plt.plot(range(num_epochs), val_err, label='Validation error')
plt.title('ConvNet Training')
plt.xlabel('Epochs')
plt.ylabel('Error')
plt.legend()
plt.show()
test_err, y_pred, y_prob = nn_test(x_test, y_test, val_fn, prob_fn,
batch_size, thresh)
out_dict['_'.join(['prob', str(szr)])] = y_prob
out_dict['_'.join(['true', str(szr)])] = y_test
np.savez(
''.join(
['./outputs/', subject, 'model', 'LOO',
str(szr), tag, '.npz']),
*lasagne.layers.get_all_param_values(net['out']))
np.savez(''.join([subject, tag, '.npz']), **out_dict)
def main(subject='chb05', num_epochs=10, tiger=False, plotter=False):
# Load the dataset
subj = chb.load_dataset(subject, tiger=tiger)
sys.stdout.flush()
batch_size = 10
num_szr = subj.get_num()
test_accs = [0] * num_szr
out_dict = {}
for szr in range(1, num_szr + 1):
print('\nLeave-One-Out: %d of %d' % (szr, num_szr))
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
net = nw.deep1(input_var)
train_fn, val_fn, prob_fn = compile_model(input_var, target_var, net)
train_err_list = [0] * num_epochs
val_err_list = [0] * num_epochs
print('=' * 80)
print('| epoch \t| train loss\t| val loss\t| time\t')
print('=' * 80)
x_test, y_test = 0, 0
for epoch in range(num_epochs):
st = time.clock()
# make generator
loo_gen = chb.lgus(subj,
szr,
batchsec=60,
drop_prob=0.8,
shuffle=True)
# get test data (same on every epoch, so not really using it until the
# last go-round)
for batch in loo_gen:
x_test, y_test = batch
break
# separate val and train data
x_val = np.zeros((1000, 1, 23, 1280), dtype='float32')
y_val = np.zeros((1000), dtype='int32')
x_train, y_train = 0, 0
batch_train_errs = []
for idx, batch in enumerate(loo_gen):
x_train, y_train = batch
if idx < 1000:
x_train, x_val[idx] = x_train[:-1], x_train[-1:]
y_train, y_val[idx] = y_train[:-1], y_train[-1:]
err = train_fn(x_train, y_train)
batch_train_errs.append(err)
epoch_train_err = np.mean(batch_train_errs)
train_err_list[epoch] = epoch_train_err
batch_val_errs = [0] * int(1000 / batch_size)
for idx, batch in enumerate(
iterate_minibatches(x_val, y_val, batch_size)):
inputs, targets = batch
err = val_fn(inputs, targets)
batch_val_errs[idx] = err
epoch_val_err = np.mean(batch_val_errs)
val_err_list[epoch] = epoch_val_err
en = time.clock()
print('| %d \t\t| %.6f\t| %.6f\t| %.2f s' %
(epoch + 1, epoch_train_err, epoch_val_err, en - st))
print('-' * 80)
print('Training Complete.\n')
if plotter:
fig = plt.figure()
plt.plot(range(num_epochs), train_err, label='Training error')
plt.plot(range(num_epochs), val_err, label='Validation error')
plt.title('ConvNet Training')
plt.xlabel('Epochs')
plt.ylabel('Error')
plt.legend()
plt.show()
test_err, y_pred, y_prob = nn_test(x_test, y_test, val_fn, prob_fn,
batch_size)
out_dict['_'.join(['prob', str(szr)])] = y_prob
out_dict['_'.join(['true', str(szr)])] = y_test
np.savez(''.join([subject, 'deep.npz']), **out_dict)