metrics_names = [
m.__name__ if not isinstance(m, str) else m for m in metrics
]
return None, \
dict(zip(metrics_names, test_scores)), \
None
if __name__ == '__main__':
grid_search = False
if grid_search:
run_grid_search(experimentclass=DTSExperiment,
parameters=yaml.load(
open(os.path.join(config['config'],
'static.yaml'))),
db_name=config['db'],
ex_name='static+grid_search',
f_main=main,
f_config=ex_config,
f_metrics=log_metrics,
cmd_args=vars(args),
observer_type='mongodb')
else:
run_single_experiment(experimentclass=DTSExperiment,
db_name=config['db'],
ex_name='static',
f_main=main,
f_config=ex_config,
f_metrics=log_metrics,
cmd_args=vars(args),
observer_type='mongodb')
fn_plot=fn_plot)
else:
test_scores = ffnn.evaluate([X_test, y_test],
fn_inverse=fn_inverse_test,
fn_plot=fn_plot)
metrics_names = [
m.__name__ if not isinstance(m, str) else m for m in model.metrics
]
return dict(zip(metrics_names, val_scores)), \
dict(zip(metrics_names, test_scores)), \
model_filepath
if __name__ == '__main__':
if args.grid_search:
run_grid_search(experimentclass=DTSExperiment,
db_name=config['db'],
ex_name='ffnn_grid_search',
f_main=main,
f_metrics=log_metrics,
f_config=args.add_config,
observer_type=args.observer)
else:
run_single_experiment(experimentclass=DTSExperiment,
db_name=config['db'],
ex_name='ffnn',
f_main=main,
f_config=args.add_config,
f_metrics=log_metrics,
observer_type=args.observer)