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)