def evaluate_model(lc_quad, args, assignments):
print(assignments)
args.gamma = assignments['gamma']
# args.positive_reward = assignments['positive_reward']
args.negetive_reward = assignments['negetive_reward']
args.lr = assignments['lr']
args.dropout = assignments['dropout']
runner = Runner(lc_quad, args)
runner.train(lc_quad, args)
final_value = runner.test(lc_quad, args)
return final_value
except RuntimeError as expt:
logger.error(expt)
return flask.jsonify({'error': str(expt)}), 408
except Exception as expt:
logger.error(expt)
return flask.jsonify({'error': str(expt)}), 422
@app.errorhandler(404)
def not_found(error):
return flask.make_response(flask.jsonify({'error': 'Command Not found'}), 404)
if __name__ == '__main__':
logger = logging.getLogger(__name__)
Utils.setup_logging()
args = parse_args()
dataset = LC_QuAD(config['lc_quad']['train'], config['lc_quad']['test'], config['lc_quad']['vocab'],
False, args.remove_stop_words)
runner = Runner(dataset, args)
runner.load_checkpoint()
runner.environment.entity_linker = None
runner.environment.relation_linker = None
print(runner.link("Who has been married to both Penny Lancaster and Alana Stewart?", k=10, e=0.1))
logger.info("Starting the HTTP server")
http_server = WSGIServer(('', args.port), app)
http_server.serve_forever()
start = time.time()
args = parse_args()
logger = logging.getLogger('main')
logger.setLevel(logging.INFO)
formatter = logging.Formatter("%(levelname)s:%(name)s:%(message)s")
ch = logging.StreamHandler()
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.info(args)
dataset = None
if args.dataset == 'lcquad':
dataset = LC_QuAD(config['lc_quad']['train'],
config['lc_quad']['test'],
config['lc_quad']['vocab'], False,
args.remove_stop_words)
elif args.dataset == 'qald_7_ml':
dataset = Qald_7_ml(config['qald_7_ml']['train'],
config['qald_7_ml']['test'],
config['qald_7_ml']['vocab'], False, False)
runner = Runner(dataset, args)
if args.mode == 'test':
runner.load_checkpoint()
else:
runner.train(dataset, args)
logger.setLevel(logging.DEBUG)
runner.test(dataset, args, use_elastic=True)
finish = time.time()
print('total runtime:', finish - start)
try:
with open('eval-{}.json'.format(args.dataset), 'rt') as json_file:
eval_results = json.load(json_file)
except:
eval_results = {}
if args.mode == 'test':
for file_name in os.listdir(config['chk_path']):
if file_name.startswith(args.dataset) and 'bilstm' in file_name:
try:
l = len(args.dataset)
args.policy = file_name[l + 1:-6]
args.b = int(file_name[-4:-3])
args.k = 1
args.checkpoint = file_name
runner = Runner(dataset, args)
runner.load_checkpoint(
os.path.join(config['chk_path'], args.checkpoint))
print(args)
results = runner.test(dataset, args,
use_elastic=True) # use_EARL=True)
eval_results[file_name] = results
finish = time.time()
print('total runtime:', finish - start)
except Exception as e:
print(e)
print(file_name)
eval_results[file_name] = [0, 0]
with open('eval-{}.json'.format(args.dataset), 'wt') as json_file:
json.dump(eval_results, json_file)
from common.dataset.qald_7_ml import Qald_7_ml
from scripts.config_args import parse_args
if __name__ == '__main__':
args = parse_args()
dataset = LC_QuAD(config['lc_quad']['train'], config['lc_quad']['test'],
config['lc_quad']['vocab'], False,
args.remove_stop_words)
# dataset = Qald_6_ml(config['qald_6_ml']['train'], config['qald_6_ml']['test'], config['qald_6_ml']['vocab'],
# False, False)
# dataset = Qald_7_ml(config['qald_7_ml']['train'], config['qald_7_ml']['test'], config['qald_7_ml']['vocab'],
# False, False)
runner = Runner(dataset, args)
runner.load_checkpoint(
checkpoint_filename=
'/Users/hamid/workspace/DeepShallowParsingQA/data/checkpoints/lctmp')
runner.environment.entity_linker = None
runner.environment.relation_linker = None
connecting_relations = False
free_relation_match = False
connecting_relation = False
k = 10
results = {}
for idx, qarow in tqdm(enumerate(dataset.test_set)):
result = runner.link(qarow.question, 0.1, k, connecting_relations,
free_relation_match, connecting_relation, True)
results[qarow.question] = result