code2seq.py

from argparse import ArgumentParser
import numpy as np
import tensorflow as tf

from config import Config
from interactive_predict import InteractivePredictor
from model import Model

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)

 parser.add_argument("-s", "--save_prefix", dest="save_path_prefix",
 help="path to save file", metavar="FILE", required=False)
 parser.add_argument("-l", "--load", dest="load_path",
 help="path to saved file", metavar="FILE", required=False)
 parser.add_argument('--release', action='store_true',
 help='if specified and loading a trained model, release the loaded model for a smaller model '
 'size.')
 parser.add_argument('--predict', action='store_true')
 parser.add_argument('--debug', action='store_true')
 parser.add_argument('--seed', type=int, default=239)
 args = parser.parse_args()

 np.random.seed(args.seed)
 tf.set_random_seed(args.seed)

 if args.debug:
 config = Config.get_debug_config(args)
 else:
 config = Config.get_default_config(args)

 model = Model(config)
 print('Created model')
 if config.TRAIN_PATH:
 model.train()
 if config.TEST_PATH and not args.data_path:
 results, precision, recall, f1, rouge = model.evaluate()
 print('Accuracy: ' + str(results))
 print('Precision: ' + str(precision) + ', recall: ' + str(recall) + ', F1: ' + str(f1))
 print('Rouge: ', rouge)
 if args.predict:
 predictor = InteractivePredictor(config, model)
 predictor.predict()
 if args.release and args.load_path:
 model.evaluate(release=True)
 model.close_session()