def run(args):
_info(args.roi_name)
param_grid = {'k_hidden':args.k_hidden,'k_layers':args.k_layers}
param_grid = [comb for comb in ParameterGrid(param_grid)]
print(len(param_grid))
print(len(args.k_layers))
if len(param_grid) == 1:
res_path = (RES_DIR +
'/%s_%d_net_%d' %(args.roi_name, args.roi, args.net) +
'_trainsize_%d' %(args.train_size) +
'_k_hidden_%d' %(args.k_hidden[0]) +
'_k_layers_%d_batch_size_%d' %(args.k_layers[0], args.batch_size) +
'_num_epochs_%d_z_%d.pkl' %(args.num_epochs, args.zscore))
mod_path = res_path.replace('results','models')
mod_path = mod_path.replace('pkl','h5')
elif len(param_grid) > 1:
res_path = (RES_DIR +
'/%s_%d_net_%d' %(args.roi_name, args.roi, args.net) +
'_trainsize_%d' %(args.train_size) +
'_kfold_%d' %(args.k_fold) +
'_batch_size_%d' %(args.batch_size) +
'_num_epochs_%d_z_%d_GSCV.pkl' %(args.num_epochs, args.zscore))
'''
get dataframe
'''
if not os.path.isfile(res_path):
start = time.time()
df = _clip_class_df(args)
print('data loading time: %.2f seconds' %(time.time()-start))
if len(param_grid) == 1:
results, results_prob, model = _test(df,args,param_grid[0])
# save results
with open(res_path, 'wb') as f:
pickle.dump([results, results_prob], f)
# save model
if not os.path.exists(os.path.dirname(mod_path)):
os.makedirs(os.path.dirname(mod_path),exist_ok=True)
model.save(mod_path)
elif len(param_grid) > 1:
results = {}
for mm, params in enumerate(param_grid):
print('---')
print('model{:02d}'.format(mm) + ': ')
print(params)
print('---')
results['model{:02d}'.format(mm)] = _train(df, args, params)
# save grid-search CV results
with open(res_path, 'wb') as f:
pickle.dump([results, param_grid], f)
def run(args):
_info(args.subnet)
'''
get dataframe
'''
# all-but-subnetwork (invert_flag)
if 'minus' in args.subnet:
args.invert_flag = True
res_path = (RES_DIR + '/%s_%d_net_%d' %
(args.roi_name, args.roi, args.net) + '_nw_%s' %
(args.subnet) + '_trainsize_%d' % (args.train_size) +
'_kdim_%d_batch_size_%d' % (args.k_dim, args.batch_size) +
'_num_epochs_%d_z_%d.pkl' % (args.num_epochs, args.zscore))
if not os.path.isfile(res_path):
df = _clip_class_df(args)
results = _test(df, args)
with open(res_path, 'wb') as f:
pickle.dump(results, f)
def run(args):
# Get all combinations of the parameter grid
_info(args.roi_name)
# Get all combinations of the parameter grid
param_grid = {'k_hidden': args.k_hidden, 'k_layers': args.k_layers}
param_grid = [comb for comb in ParameterGrid(param_grid)]
print(len(param_grid))
print(len(args.k_layers))
_info('Number of hyperparameter combinations: ' + str(len(param_grid)))
_info(args.roi_name)
'''
get dataframe
'''
if len(param_grid) == 1:
res_path = (RES_DIR + '/%s_%d_net_%d' %
(args.roi_name, args.roi, args.net) + '_trainsize_%d' %
(args.train_size) + '_k_hidden_%d' % (args.k_hidden[0]) +
'_k_layers_%d_batch_size_%d' %
(args.k_layers[0], args.batch_size) +
'_num_epochs_%d_z_%d.pkl' % (args.num_epochs, args.zscore))
gru_mod_path = res_path.replace('results', 'models')
gru_mod_path = gru_mod_path.replace('pkl', 'h5')
gru_model_path = gru_mod_path.replace('saliency', 'gru')
args.gru_model_path = gru_model_path
elif len(param_grid) > 1:
res_path = (RES_DIR + '/%s_%d_net_%d' %
(args.roi_name, args.roi, args.net) + '_trainsize_%d' %
(args.train_size) + '_kfold_%d' % (args.k_fold) +
'_batch_size_%d' % (args.batch_size) +
'_num_epochs_%d_z_%d_GSCV.pkl' %
(args.num_epochs, args.zscore))
if not os.path.isfile(res_path):
start = time.clock()
df = _clip_class_df(args)
print('data loading time: %.2f seconds' % (time.clock() - start))
if len(param_grid) == 1:
sal_df = _saliency(df, args)
# save results
with open(res_path, 'wb') as f:
pickle.dump(sal_df, f)
elif len(param_grid) > 1:
results = {}
for mm, params in enumerate(param_grid):
print('---')
print('model{:02d}'.format(mm) + ': ')
print(params)
print('---')
results['model{:02d}'.format(mm)] = _train(df, args, params)
# save grid-search CV results
with open(res_path, 'wb') as f:
pickle.dump([results, param_grid], f)
def run(args):
# Get all combinations of the parameter grid
_info(args.roi_name)
# Get all combinations of the parameter grid
param_grid = {'k_hidden': args.k_hidden, 'k_layers': args.k_layers}
param_grid = [comb for comb in ParameterGrid(param_grid)]
print(len(param_grid))
print(len(args.k_layers))
_info('Number of hyperparameter combinations: ' + str(len(param_grid)))
_info(args.roi_name)
'''
get dataframe
'''
if len(param_grid) == 1:
res_path = (RES_DIR + '/%s_%d_net_%d' %
(args.roi_name, args.roi, args.net) + '_trainsize_%d' %
(args.train_size) + '_k_hidden_%d' % (args.k_hidden[0]) +
'_kdim_%d' % (args.k_dim) + '_k_layers_%d_batch_size_%d' %
(args.k_layers[0], args.batch_size) +
'_num_epochs_%d_z_%d.pkl' % (args.num_epochs, args.zscore))
mod_path = res_path.replace('results', 'models')
mod_path = mod_path.replace('pkl', 'h5')
gru_model_path = mod_path.replace('gruencoder', 'gru')
gru_model_path = gru_model_path.replace('_kdim_%d' % (args.k_dim), '')
args.gru_model_path = gru_model_path
elif len(param_grid) > 1:
res_path = (RES_DIR + '/%s_%d_net_%d' %
(args.roi_name, args.roi, args.net) + '_trainsize_%d' %
(args.train_size) + '_kfold_%d' % (args.k_fold) +
'_batch_size_%d' % (args.batch_size) +
'_num_epochs_%d_z_%d_GSCV.pkl' %
(args.num_epochs, args.zscore))
if not os.path.isfile(res_path):
start = time.clock()
df = _clip_class_df(args)
print('data loading time: %.2f seconds' % (time.clock() - start))
if len(param_grid) == 1:
results, results_prob, model = _test(df, args, param_grid[0])
print('TESTING DONE :)')
# forward pass data and save encodings
#traj_df = {}
#for run in range(K_RUNS):
# df = _clip_class_rest_df(args, run)
# traj_df[run] = _get_trajectories(df, model, args)
# save results
with open(res_path, 'wb') as f:
pickle.dump([results, results_prob], f)
# save model
#model.save(mod_path)
elif len(param_grid) > 1:
results = {}
for mm, params in enumerate(param_grid):
print('---')
print('model{:02d}'.format(mm) + ': ')
print(params)
print('---')
results['model{:02d}'.format(mm)] = _train(df, args, params)
# save grid-search CV results
with open(res_path, 'wb') as f:
pickle.dump([results, param_grid], f)