def main():
parser = argparse.ArgumentParser()
parser.add_argument('--save',
'-s',
type=str,
default='',
help='save name to test')
args = parser.parse_args()
save = args.save
# Prepare logger
# - create logger with 'spam_application'
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# - create file handler which logs even debug messages
fh = logging.FileHandler('test_result/{}.log'.format(save.split('/')[1]),
mode='w')
fh.setLevel(logging.INFO)
# - create formatter and add it to the handlers
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# - add the handlers to the logger
logger.addHandler(fh)
# Parse save name
hp_dict = parse_hyparams(save.split('/')[1])
# Prepare datasets
train, dev, test = reader.read(hp_dict['x'], hp_dict['y'])
# Prepare linearizer and converter
global l, c
# Model setup
if hp_dict['r'] == 0:
l = linearizer.linearize_tree
c = converter.convert
params = {
'n_embed': hp_dict['x'] + 3,
'd': hp_dict['d'],
'k': hp_dict['k']
}
if hp_dict['m'] == 'n':
model = RNN(**params)
elif hp_dict['m'] == 't':
model = RNTN(**params)
elif hp_dict['m'] == 'd':
model = RNTNd(**params)
elif hp_dict['m'] == 'c':
model = RNTNc(**params)
elif hp_dict['r'] == 1:
l = linearizer.linearize_tree_relational
c = converter.convert_relational
params = {
'n_embed': hp_dict['x'] * 2,
'd': hp_dict['d'],
'k': hp_dict['k']
}
if hp_dict['m'] == 'rn':
model = RNNr(**params)
elif hp_dict['m'] == 'rt':
params['mp'] = hp_dict['v']
model = RNTNr(**params)
elif hp_dict['m'] == 'rd':
params['mp'] = hp_dict['v']
model = RNTNrd(**params)
elif hp_dict['m'] == 'rc':
params['mp'] = hp_dict['v']
model = RNTNrc(**params)
elif hp_dict['m'] == 'rs':
model = RNTNrs(**params, p=hp_dict['p'], q=hp_dict['q'])
print(save)
serializers.load_hdf5(save, model)
model = L.Classifier(model)
model.to_cpu()
# Dev
logger.info('---dev---')
accuracy = get_accuracy(model, dev)
logger.info('dev: {}'.format(accuracy))
# Test
logger.info('---test---')
for length, a_bin in enumerate(test[1:]):
accuracy = get_accuracy(model, a_bin)
logger.info('test {} : {}'.format(length + 1, accuracy))
def main():
"""
parser = argparse.ArgumentParser()
parser.add_argument('--save', '-s', type=str, default='',
help='save name to test')
args = parser.parse_args()
"""
# Parse save file name
path = './trained_model_12-11'
saves = os.listdir(path)
for save in saves:
# Prepare logger
# - create logger with 'spam_application'
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# - create file handler which logs even debug messages
fh = logging.FileHandler('test_result/{}.log'.format(save), mode='w')
fh.setLevel(logging.INFO)
# - create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# - add the handlers to the logger
logger.addHandler(fh)
# Parse save name
hp_dict = parse_hyparams(save)
# Prepare datasets
train, dev, test = reader.read(hp_dict['x'], hp_dict['y'])
# Prepare linearizer and converter
global l, c
# Model setup
if hp_dict['r'] == 0:
l = linearizer.linearize_tree
c = converter.convert
params = {'n_embed': hp_dict['x'] + 3, 'd': hp_dict['d'], 'k': hp_dict['k']}
if hp_dict['m'] == 'n':
model = RNN(**params)
elif hp_dict['m'] == 't':
model = RNTN(**params)
elif hp_dict['m'] == 'd':
model = RNTNd(**params)
elif hp_dict['m'] == 'c':
model = RNTNc(**params)
elif hp_dict['r'] == 1:
l = linearizer.linearize_tree_relational
c = converter.convert_relational
params = {'n_embed': hp_dict['x'] * 2, 'd': hp_dict['d'], 'k': hp_dict['k']}
if hp_dict['m'] == 'rn':
model = RNNr(**params)
elif hp_dict['m'] == 'rt':
model = RNTNr(**params)
elif hp_dict['m'] == 'rd':
model = RNTNrd(**params)
elif hp_dict['m'] == 'rc':
model = RNTNrc(**params)
elif hp_dict['m'] == 'rs':
model = RNTNrs(**params, p=hp_dict['p'], q=hp_dict['q'])
print(save)
serializers.load_hdf5(path + '/' + save, model)
model = L.Classifier(model)
model.to_cpu()
m = hp_dict['m']
w = hp_dict['w']
i = hp_dict['i'] - 1
p = hp_dict['p']
# Dev
logger.info('---dev---')
accuracy = get_accuracy(model, dev)
logger.info('dev: {}'.format(accuracy))
if m == 'rs':
results[p][w][0][i] = accuracy
else:
results[m][w][0][i] = accuracy
# Test
logger.info('---test---')
for length, a_bin in enumerate(test[1:]):
accuracy = get_accuracy(model, a_bin)
logger.info('test {} : {}'.format(length + 1, accuracy))
if hp_dict['m'] == 'rs':
results[p][w][length + 1][i] = accuracy
else:
results[m][w][length + 1][i] = accuracy
logger.removeHandler(fh)
logger.setLevel(logging.INFO)
fh = logging.FileHandler('test_result/final.log', mode='w')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
for m in results.keys():
logger.info('')
logger.info('m: {}'.format(m))
for w in results[m].keys():
logger.info('w: {}---------'.format(w))
for j, accuracies in enumerate(results[m][w]):
print(accuracies)
std = np.std(accuracies)
mean = np.mean(accuracies)
if j == 0:
logger.info('dev: {} ({})'.format(mean, std))
else:
logger.info('test {} : {} ({})'.format(j, mean, std))
with open('final.p', 'wb') as f:
pickle.dump(results, f)