def grid_search(self, settings_file, filename):
"""
Grid search for parameters/hyperparameters using network.
Args:
settings_file (str): File with GridSearch setting ranges.
filename (str): Output .csv file with entry data and resulting
testing losses.
"""
settings_set = SettingsParser('GridSearch').read(settings_file)
keys = list(settings_set.keys())
values = list(settings_set.values())
range_keys = [
keys[j] for j, range_ in enumerate(values) if len(range_) > 1
]
print('\nGrid Search:', ','.join(range_keys))
setting_combinations = list(product(*values))
run_entries = []
for i, setting_combination in enumerate(setting_combinations):
print('\n\nRun {}:'.format(i + 1), end=' ')
run_settings = {k: v for k, v in zip(keys, setting_combination)}
entry = [str(val) for val in setting_combinations]
for key in range_keys:
print('{} = {}'.format(key, run_settings[key]), end=' ')
self.load_data(run_settings['inputs_name'],
run_settings['partitions_file'], run_settings)
run_testing_loss = self.train_network(run_settings,
tag='_' + str(i + 1))
entry.append('{0:.4f}'.format(run_testing_loss))
run_entries.append(entry)
with open(filename, 'w') as search_record:
lines = [','.join(entry) + ';\n' for entry in run_entries]
lines.insert(0, ','.join(keys) + ';\n')
search_record.writelines(lines)
if __name__ == '__main__':
description = 'Create collated molecules file.'
argparser = argparse.ArgumentParser(description=description)
argparser.add_argument('--settings_file',
'-s',
default='settings.cfg',
help='Filename of settings.')
argparser.add_argument('--verbosity', '-v', default=0, action='count')
argparser.add_argument('--export',
'-E',
action='store_true',
help='Export entries to csv.')
args = argparser.parse_args()
settings = SettingsParser('Collate').read(args.settings_file)
settings['verbosity'] = args.verbosity
input_name = settings['inputs_name']
output_name = settings['outputs_name']
energies_file = settings['energies_file']
sys_elements = settings['sys_elements']
assert sys_elements != ['None']
collator = BatchCollator(output_name, settings, sys_elements)
if os.path.isfile(input_name):
collator.parse_molecules(input_name, energies_file)
else:
collator.parse_loose_molecules(input_name, energies_file)
if args.export:
collator.export_entries(input_name.split('.')[0] + '.csv')
raise ValueError('invalid keyword!')
return np.asarray(pairs), np.asarray(triplets)
if __name__ == '__main__':
description = 'Create fingerprints from collated molecules file.'
argparser = argparse.ArgumentParser(description=description)
argparser.add_argument('--settings_file',
'-s',
default='settings.cfg',
help='Filename of settings.')
argparser.add_argument('--verbosity', '-v', default=0, action='count')
argparser.add_argument('--export',
'-E',
action='store_true',
help='Export entries to csv.')
args = argparser.parse_args()
settings = SettingsParser('Fingerprint').read(args.settings_file)
settings['verbosity'] = args.verbosity
input_name = settings['inputs_name']
output_name = settings['outputs_name']
parameters_file = settings['parameters_file']
sys_elements = settings['sys_elements']
assert sys_elements != ['None']
processor = FingerprintProcessor(output_name, parameters_file, settings)
processor.process_collated(input_name)
if args.export:
processor.export_entries(input_name.split('.')[0] + '.csv')
argparser.add_argument('--export', '-e', action='store_true',
help='Export entries to csv.')
argparser.add_argument('--GridSearch', '-g', action='store_true',
help='Initialized grid search with network.')
# argparser.add_argument('--plot', '-p', action='store_true',
# help='Plot and Save figures.')
argparser.add_argument('--force', '-f', action='store_true',
help='Force overwrite/merge. (prompt otherwise)')
args = argparser.parse_args()
return args
if __name__ == '__main__':
args = initialize_argparser()
action = args.action
settings = SettingsParser(action).read(args.settings_file)
settings['verbosity'] = args.verbosity
inputs_name = settings['inputs_name']
outputs_name = settings['outputs_name']
force = args.force
if os.path.isfile(outputs_name) and not force:
while True:
reply = str(input('File "{}"exists. \
Merge/overwrite? (y/n) '.format(outputs_name)).lower().strip())
try:
if reply[0] == 'y':
break
elif reply[0] == 'n':
ind = 1
outputs_path = os.path.splitext(outputs_name)
if __name__ == '__main__':
description = 'Train an artificial neural network.'
argparser = argparse.ArgumentParser(description=description)
argparser.add_argument('--settings_file',
'-s',
default='settings.cfg',
help='Filename of settings.')
argparser.add_argument('--verbosity', '-v', default=0, action='count')
argparser.add_argument('--grid',
'-g',
action='store_true',
help='Begin grid search.')
args = argparser.parse_args()
settings = SettingsParser('Network').read(args.settings_file)
settings['verbosity'] = args.verbosity
input_name = settings['inputs_name']
partitions_file = settings['partitions_file']
output_name = settings['outputs_name']
network = Network(settings)
if args.grid:
network.grid_search(args.settings_file, output_name)
else:
network.load_data(input_name, partitions_file, settings)
final_loss = network.train_network(settings)
print('Final loss:', final_loss)
print(model.save_weights(self.final_weights_name.format(tag)))