interactive_predict.py

import traceback

from common import common
from python_extractor.extractor import Extractor

SHOW_TOP_CONTEXTS = 10
MAX_PATH_LENGTH = 8
MAX_PATH_WIDTH = 2


class InteractivePredictor:
 exit_keywords = ['exit', 'quit', 'q']

 def __init__(self, config, model):
 model.predict([])
 self.model = model
 self.config = config
 self.path_extractor = Extractor(max_path_length=MAX_PATH_LENGTH,
 max_path_width=MAX_PATH_WIDTH)

 def read_file(self, input_filename):
 with open(input_filename, 'r') as file:
 return file.readlines()

 def predict(self):
 # input_filename = 'Input.java'
 input_filename = 'input.py'
 print('Starting interactive prediction...')
 while True:
 print(
 'Modify the file: "%s" and press any key when ready, or "q" / "quit" / "exit" to exit' % input_filename)
 user_input = input()
 if user_input.lower() in self.exit_keywords:
 print('Exiting...')
 return
 try:
 predict_lines = list(path.strip() for path in self.path_extractor.extract_paths(input_filename))
 contexts = predict_lines[0].split()
 space_padding = ' ' * (self.config.MAX_CONTEXTS - len(contexts) + 1)
 predict_lines[0] = ' '.join(contexts) + space_padding
 print(predict_lines)
 except ValueError as e:
 print(e)
 continue
 hash_to_string_dict = UnitDict()
 try:
 raw_prediction_results = self.model.predict(predict_lines)
 except Exception as exc:
 print(exc)
 continue
 method_prediction_results = common.parse_prediction_results(
 raw_prediction_results, hash_to_string_dict,
 self.model.vocabs.target_vocab.special_words, topk=SHOW_TOP_CONTEXTS)
 for raw_prediction, method_prediction in zip(raw_prediction_results, method_prediction_results):
 print('Original name:\t' + method_prediction.original_name)
 for name_prob_pair in method_prediction.predictions:
 print('\t(%f) predicted: %s' % (name_prob_pair['probability'], name_prob_pair['name']))
 print('Attention:')
 for attention_obj in method_prediction.attention_paths:
 print('%f\tcontext: %s,%s,%s' % (
 attention_obj['score'], attention_obj['token1'], attention_obj['path'], attention_obj['token2']))
 if self.config.EXPORT_CODE_VECTORS:
 print('Code vector:')
 print(' '.join(map(str, raw_prediction.code_vector)))

class UnitDict(dict):

 def __getitem__(self, key):
 return key