def load_sentence_data(subject, word, sen_type, experiment, proc, num_instances, reps_to_use, sorted_inds=None):
evokeds, labels, time, sen_ids = load_data.load_raw(subject, word, sen_type,
experiment=experiment, proc=proc)
data, labels, sen_ids = load_data.avg_data(evokeds, labels, sentence_ids_raw=sen_ids,
experiment=experiment,
num_instances=num_instances,
reps_to_use=reps_to_use)
labels = np.array(labels)
label_sort_inds = np.argsort(sen_ids)
labels = labels[label_sort_inds]
data = data[label_sort_inds, :, :]
if sorted_inds is not None:
data = data[:, sorted_inds, :]
return data, labels, time
def run_tgm_exp(experiment,
subject,
sen_type,
word,
win_len,
overlap,
mode='pred',
isPDTW=False,
isPerm=False,
num_folds=2,
alg='GNB',
doFeatSelect=False,
doZscore=False,
doAvg=False,
num_instances=2,
reps_to_use=10,
proc=load_data.DEFAULT_PROC,
random_state_perm=1,
random_state_cv=CV_RAND_STATE,
random_state_sub=SUB_CV_RAND_STATE,
force=False):
warnings.filterwarnings('error')
# Save Directory
top_dir = TOP_DIR.format(exp=experiment)
if not os.path.exists(top_dir):
os.mkdir(top_dir)
save_dir = SAVE_DIR.format(top_dir=top_dir, sub=subject)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if alg not in VALID_ALGS:
raise ValueError('invalid alg {}: must be {}'.format(alg, VALID_ALGS))
if sen_type not in VALID_SEN_TYPE:
raise ValueError('invalid sen_type {}: must be {}'.format(
sen_type, VALID_SEN_TYPE))
if mode not in VALID_MODE:
raise ValueError('invalid mode {}: must be {}'.format(
mode, VALID_MODE))
if alg == 'GNB' and doFeatSelect:
alg_str = alg + '-FS'
else:
alg_str = alg
fname = SAVE_FILE.format(dir=save_dir,
sub=subject,
sen_type=sen_type,
word=word,
win_len=win_len,
overlap=overlap,
pdtw=bool_to_str(isPDTW),
perm=bool_to_str(isPerm),
num_folds=num_folds,
alg=alg_str,
zscore=bool_to_str(doZscore),
doAvg=bool_to_str(doAvg),
inst=num_instances,
rep=reps_to_use,
rsP=random_state_perm,
rsC=random_state_cv,
rsS=random_state_sub,
mode=mode)
if os.path.isfile(fname + '.npz') and not force:
print('Job already completed. Skipping Job.')
print(fname)
return
if isPDTW:
(time_a, time_p, labels, active_data_raw,
passive_data_raw) = load_data.load_pdtw(subject=subject,
word=word,
experiment=experiment,
proc=proc)
if sen_type == 'active':
data_raw = active_data_raw
time = time_a
else:
data_raw = passive_data_raw
time = time_p
else:
data_raw, labels, time, _ = load_data.load_raw(subject=subject,
word=word,
sen_type=sen_type,
experiment=experiment,
proc=proc)
print(data_raw.shape)
data, labels, _ = load_data.avg_data(data_raw=data_raw,
labels_raw=labels,
experiment=experiment,
num_instances=num_instances,
reps_to_use=reps_to_use)
print(data.shape)
if isPerm:
random.seed(random_state_perm)
random.shuffle(labels)
tmin = time.min()
tmax = time.max()
if num_folds > 8:
kf = KFold(n_splits=num_folds,
shuffle=True,
random_state=random_state_cv)
else:
kf = StratifiedKFold(n_splits=num_folds,
shuffle=True,
random_state=random_state_cv)
total_win = int((tmax - tmin) * 500)
if win_len < 0:
win_len = total_win - overlap
win_starts = range(0, total_win - win_len, overlap)
assert total_win <= len(time)
# Run TGM
if mode == 'pred':
if alg == 'LASSO':
(preds, l_ints, cv_membership,
masks) = models.lr_tgm(data=data,
labels=labels,
kf=kf,
win_starts=win_starts,
win_len=win_len,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
preds=preds,
l_ints=l_ints,
cv_membership=cv_membership,
masks=masks,
time=time,
win_starts=win_starts,
proc=proc)
elif alg == 'GNB':
(preds, l_ints, cv_membership, masks,
num_feats) = models.nb_tgm(data=data,
labels=labels,
kf=kf,
sub_rs=random_state_sub,
win_starts=win_starts,
win_len=win_len,
feature_select=doFeatSelect,
doZscore=doZscore,
doAvg=doAvg)
print(num_feats)
np.savez_compressed(fname,
preds=preds,
l_ints=l_ints,
cv_membership=cv_membership,
masks=masks,
time=time,
win_starts=win_starts,
proc=proc,
num_feats=num_feats)
else:
raise ValueError('ENET not implemented yet.')
elif mode == 'uni':
if alg == 'GNB':
(preds, l_ints,
cv_membership) = models.nb_tgm_uni(data=data,
labels=labels,
kf=kf,
win_starts=win_starts,
win_len=win_len,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
preds=preds,
l_ints=l_ints,
cv_membership=cv_membership,
time=time,
win_starts=win_starts,
proc=proc)
else:
raise ValueError('Only GNB available.')
else:
if alg == 'LASSO':
coef = models.lr_tgm_coef(data=data,
labels=labels,
win_starts=win_starts,
win_len=win_len,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
coef=coef,
time=time,
win_starts=win_starts,
proc=proc)
elif alg == 'GNB':
mu_win, std_win, mu_diff_win = models.nb_tgm_coef(
data=data,
labels=labels,
win_starts=win_starts,
win_len=win_len,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
mu_win=mu_win,
std_win=std_win,
mu_diff_win=mu_diff_win,
time=time,
win_starts=win_starts,
proc=proc)
else:
raise ValueError('ENET not implemented yet.')
def run_diag_exp(experiment,
subject,
sen_type,
word,
overlap,
mode='pred',
isPDTW=False,
isPerm=False,
num_folds=2,
alg='GNB',
doZscore=False,
doAvg=False,
num_instances=2,
reps_to_use=10,
proc=load_data.DEFAULT_PROC,
random_state_perm=1,
random_state_cv=CV_RAND_STATE,
random_state_sub=SUB_CV_RAND_STATE,
force=False):
# Save Directory
top_dir = TOP_DIR.format(exp=experiment)
if not os.path.exists(top_dir):
os.mkdir(top_dir)
save_dir = SAVE_DIR.format(top_dir=top_dir, sub=subject)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if alg not in VALID_ALGS:
raise ValueError('invalid alg: must be {}'.format(VALID_ALGS))
if sen_type not in VALID_SEN_TYPE:
raise ValueError('invalid sen_type: must be {}'.format(VALID_SEN_TYPE))
if sen_type not in VALID_MODE:
raise ValueError('invalid mode: must be {}'.format(VALID_MODE))
fname = SAVE_FILE.format(dir=save_dir,
sub=subject,
sen_type=sen_type,
word=word,
overlap=overlap,
pdtw=bool_to_str(isPDTW),
perm=bool_to_str(isPerm),
num_folds=num_folds,
alg=alg,
zscore=bool_to_str(doZscore),
doAvg=bool_to_str(doAvg),
inst=num_instances,
rep=reps_to_use,
rsP=random_state_perm,
rsC=random_state_cv,
rsS=random_state_sub,
mode=mode)
if os.path.isfile(fname) and not force:
print('Job already completed. Skipping Job.')
print(fname)
return
if isPDTW:
(time_a, time_p, labels, active_data_raw,
passive_data_raw) = load_data.load_pdtw(subject=subject,
word=word,
experiment=experiment,
proc=proc)
if sen_type == 'active':
data_raw = active_data_raw
time = time_a
else:
data_raw = passive_data_raw
time = time_p
else:
data_raw, labels, time = load_data.load_raw(subject=subject,
word=word,
sen_type=sen_type,
experiment=experiment,
proc=proc)
data, labels = load_data.avg_data(data_raw=data_raw,
labels_raw=labels,
experiment=experiment,
num_instances=num_instances,
reps_to_use=reps_to_use)
print(data.shape)
if isPerm:
random.seed(random_state_perm)
random.shuffle(labels)
tmin = time.min()
tmax = time.max()
kf = StratifiedKFold(n_splits=num_folds,
shuffle=True,
random_state=CV_RAND_STATE)
sub_kf = StratifiedKFold(n_splits=2,
shuffle=True,
random_state=SUB_CV_RAND_STATE)
total_win = int((tmax - tmin) * 500)
win_starts = range(0, total_win, overlap)
if total_win > len(time):
raise ValueError('Windows are messed up.')
# Run TGM
if mode == 'pred':
if alg == 'LASSO':
(preds, l_ints, cv_membership,
masks) = models.lasso_tgm(data=data,
labels=labels,
kf=kf,
sub_kf=sub_kf,
win_starts=win_starts,
doZscore=doZscore,
doAvg=doAvg)
elif alg == 'GNB':
(preds, l_ints, cv_membership, masks) = models.nb_tgm(
data=data,
labels=labels,
kf=kf,
sub_kf=sub_kf,
win_starts=win_starts,
feature_select='distance_of_means',
feature_select_params={'number_of_features': num_feats},
doZscore=doZscore,
doAvg=doAvg)
else:
(preds, l_ints, cv_membership,
masks) = models.enet_tgm(data=data,
labels=labels,
kf=kf,
sub_kf=sub_kf,
win_starts=win_starts,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
preds=preds,
l_ints=l_ints,
cv_membership=cv_membership,
masks=masks,
time=time,
win_starts=win_starts,
proc=proc)
elif mode == 'coef':
win_lens = load_data.load_win_lens(experiment, subject, sen_type, word,
overlap, isPDTW, num_folds, alg,
doZscore, doAvg, num_instances,
reps_to_use, proc, random_state_cv,
random_state_sub)
if alg == 'LASSO':
coef = models.lasso_tgm_coef(data=data,
labels=labels,
win_starts=win_starts,
win_lens=win_lens,
doZscore=doZscore,
doAvg=doAvg)
elif alg == 'GNB':
coef = models.nb_tgm_coef(
data=data,
labels=labels,
win_starts=win_starts,
win_len=win_lens,
feature_select='distance_of_means',
feature_select_params={'number_of_features': num_feats},
doZscore=False,
doAvg=False,
ddof=1)
else:
coef = models.enet_tgm_coef(data=data,
labels=labels,
win_starts=win_starts,
win_lens=win_lens,
doZscore=doZscore,
doAvg=doAvg)
np.savez_compressed(fname,
coef=coef,
time=time,
win_starts=win_starts,
proc=proc)
def run_sv_exp(experiment,
subject,
sen_type,
word,
model='one_hot',
inc_art1=False,
inc_art2=False,
only_art1=False,
only_art2=False,
direction='encoding',
doPCA=False,
isPDTW=False,
isPerm=False,
num_folds=2,
alg='ridge',
adj='mean_center',
num_instances=1,
reps_to_use=10,
proc=load_data.DEFAULT_PROC,
random_state_perm=1,
random_state_cv=CV_RAND_STATE,
random_state_sub=SUB_CV_RAND_STATE,
force=False):
# warnings.filterwarnings('error')
# Save Directory
top_dir = TOP_DIR.format(exp=experiment)
if not os.path.exists(top_dir):
os.mkdir(top_dir)
save_dir = SAVE_DIR.format(top_dir=top_dir, sub=subject)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if alg not in VALID_ALGS:
raise ValueError('invalid alg {}: must be {}'.format(alg, VALID_ALGS))
if sen_type not in VALID_SEN_TYPE:
raise ValueError('invalid sen_type {}: must be {}'.format(
sen_type, VALID_SEN_TYPE))
if only_art1 and not only_art2:
art1_str = 'O'
art2_str = 'F'
elif only_art2 and not only_art1:
art2_str = 'O'
art1_str = 'F'
elif only_art1 and only_art2:
art2_str = 'O'
art1_str = 'O'
else:
art1_str = bool_to_str(inc_art1)
art2_str = bool_to_str(inc_art2)
fname = SAVE_FILE.format(dir=save_dir,
sub=subject,
sen_type=sen_type,
word=word,
model=model,
art1=art1_str,
art2=art2_str,
direction=direction,
pca=bool_to_str(doPCA),
pdtw=bool_to_str(isPDTW),
perm=bool_to_str(isPerm),
num_folds=num_folds,
alg=alg,
adj=adj,
inst=num_instances,
rep=reps_to_use,
rsP=random_state_perm,
rsC=random_state_cv,
rsS=random_state_sub)
if os.path.isfile(fname + '.npz') and not force:
print('Job already completed. Skipping Job.')
print(fname)
return
if isPDTW:
(time_a, time_p, labels, active_data_raw,
passive_data_raw) = load_data.load_pdtw(subject=subject,
word=word,
experiment=experiment,
proc=proc)
if sen_type == 'active':
data_raw = active_data_raw
time = time_a
else:
data_raw = passive_data_raw
time = time_p
sentence_ids = range(data_raw.shape[0])
else:
data_raw, labels, time, sentence_ids = load_data.load_raw(
subject=subject,
word=word,
sen_type=sen_type,
experiment=experiment,
proc=proc)
print(data_raw.shape)
data, labels, sentence_ids = load_data.avg_data(
data_raw=data_raw,
labels_raw=labels,
sentence_ids_raw=sentence_ids,
experiment=experiment,
num_instances=num_instances,
reps_to_use=reps_to_use)
print(data.shape)
l_set = np.unique(labels)
n_l = len(l_set)
l_index = {l_set[i]: i for i in xrange(n_l)}
l_ints = np.array([l_index[l] for l in labels])
if model == 'glove':
semantic_vectors = load_data.load_glove_vectors(labels)
else:
if only_art1 and not only_art2:
semantic_vectors = load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 1))
elif only_art2 and not only_art1:
semantic_vectors = load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 2))
elif only_art1 and only_art2:
semantic_vectors = np.concatenate([
load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 1)),
load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 2))
],
axis=1)
else:
semantic_vectors = load_data.load_one_hot(labels)
if inc_art1:
semantic_vectors = np.concatenate([
semantic_vectors,
load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 1))
],
axis=1)
if inc_art2:
semantic_vectors = np.concatenate([
semantic_vectors,
load_data.load_one_hot(
load_data.get_arts_from_senid(sentence_ids, 2))
],
axis=1)
if doPCA:
if direction == 'encoding':
pca = PCA()
pca.fit(semantic_vectors)
semantic_vectors *= np.transpose(pca.components_)
else:
reshaped_data = np.reshape(data, (data.shape[0], -1))
pca = PCA()
pca.fit(semantic_vectors)
semantic_vectors *= np.transpose(pca.components_)
if isPerm:
random.seed(random_state_perm)
random.shuffle(labels)
if num_folds > 8:
kf = KFold(n_splits=num_folds,
shuffle=True,
random_state=random_state_cv)
else:
kf = StratifiedKFold(n_splits=num_folds,
shuffle=True,
random_state=random_state_cv)
preds, l_ints, cv_membership, scores = models.lin_reg(data,
semantic_vectors,
l_ints,
kf,
reg=alg,
adj=adj,
ddof=1)
np.savez_compressed(fname,
preds=preds,
l_ints=l_ints,
cv_membership=cv_membership,
scores=scores,
time=time,
proc=proc)
parser.add_argument('--isPDTW', default='False')
parser.add_argument('--num_instances', type=int, default=1)
parser.add_argument('--reps_to_use', type=int, default=10)
parser.add_argument('--proc', default=load_data.DEFAULT_PROC)
args = parser.parse_args()
evokeds, labels, time, _ = load_data.load_raw(args.subject,
args.word,
args.sen_type,
experiment=args.experiment,
proc=args.proc)
evokeds = baseline_correct(evokeds, time)
avg_data, labels_avg, _ = load_data.avg_data(
evokeds,
labels,
experiment=args.experiment,
num_instances=args.num_instances,
reps_to_use=args.reps_to_use)
sorted_inds, sorted_reg = sort_sensors()
avg_data = avg_data[:, sorted_inds, :]
uni_reg = np.unique(sorted_reg)
yticks_sens = [sorted_reg.index(reg) for reg in uni_reg]
num_time = time.size
for i in range(2):
fig, ax = plt.subplots()
h = ax.imshow(np.squeeze(avg_data[i, :, :]),
interpolation='nearest',
aspect='auto',
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--experiment', default='krns2')
parser.add_argument('--subject', default='B')
parser.add_argument('--sen_type', default='active')
parser.add_argument('--word', default='firstNoun')
parser.add_argument('--isPDTW', default='False')
parser.add_argument('--num_instances', type=int, default=1)
parser.add_argument('--reps_to_use', type=int, default=10)
parser.add_argument('--proc', default=load_data.DEFAULT_PROC)
args = parser.parse_args()
save_dir = SAVE_DIR.format(args.experiment)
evokeds, labels, time, sentence_ids = load_data.load_raw(args.subject, args.word, args.sen_type,
experiment=args.experiment, proc=args.proc)
fname_raw = DATA_FNAME.format(save_dir, args.subject, args.sen_type, args.word, 'raw')
sio.savemat(fname_raw, mdict={'evokeds': evokeds, 'labels': labels, 'time': time, 'sentence_ids': sentence_ids})
avg_data, labels_avg, sentence_ids = load_data.avg_data(evokeds, labels, sentence_ids_raw=sentence_ids, experiment=args.experiment,
num_instances=args.num_instances, reps_to_use=args.reps_to_use)
fname_avg = DATA_FNAME.format(save_dir, args.subject, args.sen_type, args.word, 'avg' + str(args.reps_to_use))
sio.savemat(fname_avg, mdict={'avg_data': avg_data, 'labels_avg': labels_avg, 'time': time, 'sentence_ids_avg': sentence_ids})
sorted_inds, sorted_reg = sort_sensors()
sio.savemat(SENSOR_FNAME.format(save_dir), mdict={'sorted_inds': sorted_inds, 'sorted_reg': sorted_reg})