def _train(df, bhv_df, args):
# get X-y from df
feature = [ii for ii in df.columns if 'feat' in ii]
k_feat = len(feature)
print('number of features = %d' % (k_feat))
k_clip = len(np.unique(df['c']))
print('number of clip = %d' % (k_clip))
subject_list = bhv_df['Subject'].unique()
train_list = subject_list[:args.train_size]
test_list = subject_list[args.train_size:]
# length of each clip
clip_time = np.zeros(k_clip)
for ii in range(k_clip):
class_df = df[df['c'] == ii]
clip_time[ii] = np.max(np.unique(class_df['timepoint'])) + 1
clip_time = clip_time.astype(int) # df saves float
_info('seq lengths = %s' % clip_time)
# init dict for all results
results = {}
# true and predicted scores and clip label
results['y'] = {}
results['y_hat'] = {}
results['c'] = {}
for score in SCORES:
# mean scores across time
results['train_%s' % score] = np.zeros(args.k_fold)
results['val_%s' % score] = np.zeros(args.k_fold)
# per clip temporal score
results['t_train_%s' % score] = {}
results['t_val_%s' % score] = {}
for ii in range(k_clip):
results['t_train_%s' % score][ii] = np.zeros(
(args.k_fold, clip_time[ii]))
results['t_val_%s' % score][ii] = np.zeros(
(args.k_fold, clip_time[ii]))
kf = KFold(n_splits=args.k_fold, random_state=K_SEED)
# get participant lists for each assigned class
# ensure they're only in train_list
class_list = {}
for ii in range(args.k_class):
class_list[ii] = bhv_df[(bhv_df['Subject'].isin(train_list))
& (bhv_df['y'] == ii)]['Subject'].values
print('No. of participants in class {} = {}'.format(
ii, len(class_list[ii])))
'''
split participants in each class with kf
nearly identical ratio of train and val,
in all classes
'''
split = {}
for ii in range(args.k_class):
split[ii] = kf.split(class_list[ii])
for i_fold in range(args.k_fold):
_info('fold: %d/%d' % (i_fold + 1, args.k_fold))
# ***between-subject train-val split
train_subs, val_subs = [], []
for ii in range(args.k_class):
train, val = next(split[ii])
for jj in train:
train_subs.append(class_list[ii][jj])
for jj in val:
val_subs.append(class_list[ii][jj])
'''
model main
'''
X_train, train_len, y_train, c_train = _get_seq(df, train_subs, args)
X_val, val_len, y_val, c_val = _get_seq(df, val_subs, args)
max_length = tf.reduce_max(train_len)
'''
train regression model
'''
then = time.time()
model = GRURegressor(X_train,
k_hidden=args.k_hidden,
k_layers=args.k_layers,
l2=args.l2,
dropout=args.dropout,
lr=args.lr)
model.fit(X_train,
y_train.reshape(y_train.shape[0], y_train.shape[1], 1),
batch_size=args.batch_size,
epochs=args.num_epochs,
verbose=1,
validation_split=0.2)
print('--- train time = %0.4f seconds ---' % (time.time() - then))
'''
results on train data
'''
s, s_t, _, _, _ = dnn_score(model,
X_train,
y_train,
c_train,
train_len,
max_length,
clip_time,
model_type=model_type)
for score in SCORES:
results['train_%s' % score][i_fold] = s[score]
for ii in range(k_clip):
results['t_train_%s' % score][ii][i_fold] = s_t[ii][score]
print('train p = %0.3f' % s['p'])
'''
results on val data
'''
s, s_t, y, y_hat, c = dnn_score(model,
X_val,
y_val,
c_val,
val_len,
max_length,
clip_time,
model_type=model_type)
for score in SCORES:
results['val_%s' % score][i_fold] = s[score]
for ii in range(k_clip):
results['t_val_%s' % score][ii][i_fold] = s_t[ii][score]
print('val p = %0.3f' % s['p'])
results['y'][i_fold] = y
results['y_hat'][i_fold] = y_hat
results['c'][i_fold] = c
return results
def _test(df, bhv_df, args):
_info('test mode')
# get X-y from df
features = [ii for ii in df.columns if 'feat' in ii]
k_feat = len(features)
print('number of features = %d' % (k_feat))
k_clip = len(np.unique(df['c']))
print('number of clips = %d' % (k_clip))
subject_list = bhv_df['Subject'].unique()
train_list = subject_list[:args.train_size]
test_list = subject_list[args.train_size:]
# length of each clip
clip_time = np.zeros(k_clip)
for ii in range(k_clip):
class_df = df[df['c'] == ii]
clip_time[ii] = np.max(np.unique(class_df['timepoint'])) + 1
clip_time = clip_time.astype(int) # df saves float
_info('seq lengths = %s' % clip_time)
# init dict for all results
results = {}
for score in SCORES:
# per clip temporal score
results['t_train_%s' % score] = {}
results['t_test_%s' % score] = {}
for ii in range(k_clip):
results['t_train_%s' % score][ii] = np.zeros(clip_time[ii])
results['t_test_%s' % score][ii] = np.zeros(clip_time[ii])
'''
model main
'''
# get train, test sequences
X_train, train_len, y_train, c_train = _get_seq(df, train_list, args)
X_test, test_len, y_test, c_test = _get_seq(df, test_list, args)
max_length = tf.reduce_max(train_len)
'''
test regression model
'''
then = time.time()
model = GRURegressor(X_train,
k_hidden=args.k_hidden,
k_layers=args.k_layers,
l2=args.l2,
dropout=args.dropout,
lr=args.lr)
model.fit(X_train,
y_train.reshape(y_train.shape[0], y_train.shape[1], 1),
batch_size=args.batch_size,
epochs=args.num_epochs,
verbose=1,
validation_split=0.2)
print('--- train time = %0.4f seconds ---' % (time.time() - then))
'''
results on train data
'''
s, s_t, _, _, _ = dnn_score(model,
X_train,
y_train,
c_train,
train_len,
max_length,
clip_time,
model_type=model_type)
for score in SCORES:
results['train_%s' % score] = s[score]
for ii in range(k_clip):
results['t_train_%s' % score][ii] = s_t[ii][score]
print('train p = %0.3f' % s['p'])
'''
results on test data
'''
s, s_t, y, y_hat, c = dnn_score(model,
X_test,
y_test,
c_test,
test_len,
max_length,
clip_time,
model_type=model_type)
for score in SCORES:
results['test_%s' % score] = s[score]
for ii in range(k_clip):
results['t_test_%s' % score][ii] = s_t[ii][score]
print('test p = %0.3f' % s['p'])
results['y'] = y
results['y_hat'] = y_hat
results['c'] = c
return results