def test_config(): try: get_config_file('tests') except SystemExit: print("it's okay, there are probably several config files") config = process_config(config_file) print(config.cell_args)
def test_deepar_init(): config = process_config(config_path) model = deeparsys.DeepARSysModel(config) model.delete_checkpoints() create_dirs([ config.summary_dir, config.checkpoint_dir, config.plots_dir, config.output_dir ]) assert os.path.exists(config.summary_dir) assert os.path.exists(config.output_dir) assert os.path.exists(config.checkpoint_dir) data = data_generator.DataGenerator(config) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) logger = Logger(sess, config) trainer = deeparsys.DeepARSysTrainer(sess, model, data, config, logger) trainer.eval_step() trainer.train_step() model.load(sess) trainer.train() trainer.eval_step()
def test_data_config_update(): config = process_config(config_path_2) data = data_generator.DataGenerator(config) config = data.update_config() assert 'num_cov' in config assert 'num_features' in config assert 'num_ts' in config assert config.batch_size == config.num_ts
def test_data(): config = process_config(config_path) data = data_generator.DataGenerator(config) batch_Z, batch_X = next(data.next_batch(config.batch_size)) assert batch_Z.shape[0] == config.batch_size == batch_X.shape[0] assert batch_Z.shape[ 1] == config.cond_length + config.pred_length == batch_X.shape[1] assert data.Z.shape[0] == data.X.shape[0]
def test_splitting_config(): config = process_config(config_file) config_list = split_grid_config(config) assert isinstance(config_list, list) assert len(config_list) for c in config_list: for k, v in c.items(): print(k, v) assert not isinstance(v, list)
def test_deepar_init(): config = process_config(config_path) create_dirs([config.summary_dir, config.checkpoint_dir]) model = deepar.DeepARModel(config) # model.delete_checkpoints() data = data_generator.DataGenerator(config) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) logger = Logger(sess, config) trainer = deepar.DeepARTrainer(sess, model, data, config, logger) trainer.train()
if __name__ == '__main__': # config_path = os.path.join('deepartransit','experiments', 'deepar_dev','deepar_config.yml') try: args = get_args() print(args.experiment) if args.experiment: print('ok') config_file = get_config_file( os.path.join("experiments", args.experiment.strip())) print(config_file) else: config_file = args.config print('ok2') config = process_config(config_file) except: print("missing or invalid arguments") exit(0) model = deepar.DeepARModel(config) data = data_generator.DataGenerator(config) create_dirs([ config.summary_dir, config.checkpoint_dir, config.plots_dir, config.output_dir ]) init = tf.global_variables_initializer() with tf.Session() as sess:
import pandas as pd from timeit import default_timer as timer if __name__ == '__main__': # config_path = os.path.join('deepartransit','experiments', 'deeparsys_dev','deeparsys_config.yml') try: args = get_args() print(args.experiment) if args.experiment: print('found an experiment argument:', args.experiment) meta_config_file = get_config_file(os.path.join("experiments", args.experiment)) print("which constains a config file", meta_config_file) else: meta_config_file = args.config print('processing the config from the config file') meta_config = process_config(meta_config_file) except: print("missing or invalid arguments") exit(0) grid_config = split_grid_config(meta_config) list_configs = [c for c in grid_config if c['total_length'] == c['pretrans_length'] + c['trans_length'] + c['postrans_length']] df_scores = pd.DataFrame(index=list(range(len(list_configs))), columns=list(list_configs[0].keys()) + ['loss_pred', 'nb_epochs', 'mse_pred', 'init_time', 'training_time']) print('Starting to run {} models'.format(len(list_configs))) for i, config in enumerate(list_configs): df_scores.loc[i, config.keys()] = list(config.values()) print('\n\t\t >>>>>>>>> model ', i)