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)
