from argparse import ArgumentParser
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')
args = parser.parse_args()
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 = model.evaluate()
print('Accuracy: ' + str(results))
print('Precision: ' + str(precision) + ', recall: ' + str(recall) + ', F1: ' + str(f1))
if args.predict:
predictor = InteractivePredictor(config, model)
predictor.predict()
if args.release and args.load_path:
model.evaluate(release=True)
model.close_session()