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code2vec.py
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54 lines (50 loc) · 2.65 KB
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from common import Config, VocabType
from argparse import ArgumentParser
from interactive_predict import InteractivePredictor
from model import Model
import sys
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("-d", "--data", dest="data_path",
help="path to preprocessed dataset", required=False)
parser.add_argument("-te", "--test", dest="test_path",
help="path to test file", metavar="FILE", required=False)
is_training = '--train' in sys.argv or '-tr' in sys.argv
parser.add_argument("-s", "--save", dest="save_path",
help="path to save file", metavar="FILE", required=False)
parser.add_argument("-w2v", "--save_word2v", dest="save_w2v",
help="path to save file", metavar="FILE", required=False)
parser.add_argument("-t2v", "--save_target2v", dest="save_t2v",
help="path to save file", metavar="FILE", required=False)
parser.add_argument("-l", "--load", dest="load_path",
help="path to save file", metavar="FILE", required=False)
parser.add_argument('--save_w2v', dest='save_w2v', required=False,
help="save word (token) vectors in word2vec format")
parser.add_argument('--save_t2v', dest='save_t2v', required=False,
help="save target vectors in word2vec format")
parser.add_argument('--release', action='store_true',
help='if specified and loading a trained model, release the loaded model for a lower model '
'size.')
parser.add_argument('--predict', action='store_true')
args = parser.parse_args()
config = Config.get_default_config(args)
model = Model(config)
print('Created model')
if config.TRAIN_PATH:
model.train()
if args.save_w2v is not None:
model.save_word2vec_format(args.save_w2v, source=VocabType.Token)
print('Origin word vectors saved in word2vec text format in: %s' % args.save_w2v)
if args.save_t2v is not None:
model.save_word2vec_format(args.save_t2v, source=VocabType.Target)
print('Target word vectors saved in word2vec text format in: %s' % args.save_t2v)
if config.TEST_PATH and not args.data_path:
eval_results = model.evaluate()
if eval_results is not None:
results, precision, recall, f1 = eval_results
print(results)
print('Precision: ' + str(precision) + ', recall: ' + str(recall) + ', F1: ' + str(f1))
if args.predict:
predictor = InteractivePredictor(config, model)
predictor.predict()
model.close_session()