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common.py
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242 lines (214 loc) · 8.98 KB
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import re
import json
import sys
from enum import Enum
class Config:
@staticmethod
def get_default_config(args):
config = Config()
config.NUM_EPOCHS = 20
config.SAVE_EVERY_EPOCHS = 1
config.BATCH_SIZE = 1024
config.TEST_BATCH_SIZE = config.BATCH_SIZE
config.READING_BATCH_SIZE = 1300 * 4
config.NUM_BATCHING_THREADS = 2
config.BATCH_QUEUE_SIZE = 300000
config.MAX_CONTEXTS = 200
config.WORDS_VOCAB_SIZE = 1301136
config.TARGET_VOCAB_SIZE = 261245
config.PATHS_VOCAB_SIZE = 911417
config.EMBEDDINGS_SIZE = 128
config.MAX_TO_KEEP = 10
# Automatically filled, do not edit:
config.TRAIN_PATH = args.data_path
config.TEST_PATH = args.test_path
config.SAVE_PATH = args.save_path
config.LOAD_PATH = args.load_path
config.RELEASE = args.release
config.EXPORT_CODE_VECTORS = args.export_code_vectors
return config
def __init__(self):
self.NUM_EPOCHS = 0
self.SAVE_EVERY_EPOCHS = 0
self.BATCH_SIZE = 0
self.TEST_BATCH_SIZE = 0
self.READING_BATCH_SIZE = 0
self.NUM_BATCHING_THREADS = 0
self.BATCH_QUEUE_SIZE = 0
self.TRAIN_PATH = ''
self.TEST_PATH = ''
self.MAX_CONTEXTS = 0
self.WORDS_VOCAB_SIZE = 0
self.TARGET_VOCAB_SIZE = 0
self.PATHS_VOCAB_SIZE = 0
self.EMBEDDINGS_SIZE = 0
self.SAVE_PATH = ''
self.LOAD_PATH = ''
self.MAX_TO_KEEP = 0
self.RELEASE = False
self.EXPORT_CODE_VECTORS = False
class common:
noSuchWord = "NoSuchWord"
@staticmethod
def normalize_word(word):
stripped = re.sub(r'[^a-zA-Z]', '', word)
if len(stripped) == 0:
return word.lower()
else:
return stripped.lower()
@staticmethod
def _load_vocab_from_histogram(path, min_count=0, start_from=0, return_counts=False):
with open(path, 'r') as file:
word_to_index = {}
index_to_word = {}
word_to_count = {}
next_index = start_from
for line in file:
line_values = line.rstrip().split(' ')
if len(line_values) != 2:
continue
word = line_values[0]
count = int(line_values[1])
if count < min_count:
continue
if word in word_to_index:
continue
word_to_index[word] = next_index
index_to_word[next_index] = word
word_to_count[word] = count
next_index += 1
result = word_to_index, index_to_word, next_index - start_from
if return_counts:
result = (*result, word_to_count)
return result
@staticmethod
def _load_vocab_from_dict(word_to_count, min_count=0, start_from=0):
word_to_index = {}
index_to_word = {}
next_index = start_from
for word, count in word_to_count.items():
if count < min_count:
continue
if word in word_to_index:
continue
word_to_index[word] = next_index
index_to_word[next_index] = word
word_to_count[word] = count
next_index += 1
return word_to_index, index_to_word, next_index - start_from
@staticmethod
def load_vocab_from_histogram(path, min_count=0, start_from=0, max_size=None, return_counts=False):
if max_size is not None:
word_to_index, index_to_word, next_index, word_to_count = \
common._load_vocab_from_histogram(path, min_count, start_from, return_counts=True)
if next_index <= max_size:
results = (word_to_index, index_to_word, next_index)
if return_counts:
results = (*results, word_to_count)
return results
# Take min_count to be one plus the count of the max_size'th word
min_count = sorted(word_to_count.values(), reverse=True)[max_size] + 1
return common._load_vocab_from_histogram(path, min_count, start_from, return_counts)
@staticmethod
def load_vocab_from_dict(word_to_count, max_size=None, start_from=0):
if max_size is not None:
if max_size > len(word_to_count):
min_count = 0
else:
min_count = sorted(word_to_count.values(), reverse=True)[max_size] + 1
return common._load_vocab_from_dict(word_to_count, min_count, start_from)
@staticmethod
def load_json(json_file):
data = []
with open(json_file, 'r') as file:
for line in file:
current_program = common.process_single_json_line(line)
if current_program is None:
continue
for element, scope in current_program.items():
data.append((element, scope))
return data
@staticmethod
def load_json_streaming(json_file):
with open(json_file, 'r') as file:
for line in file:
current_program = common.process_single_json_line(line)
if current_program is None:
continue
for element, scope in current_program.items():
yield (element, scope)
@staticmethod
def save_word2vec_file(file, vocab_size, dimension, index_to_word, vectors):
file.write('%d %d\n' % (vocab_size, dimension))
for i in range(1, vocab_size + 1):
if i in index_to_word:
file.write(index_to_word[i] + ' ')
file.write(' '.join(map(str, vectors[i])) + '\n')
@staticmethod
def calculate_max_contexts(file):
contexts_per_word = common.process_test_input(file)
return max(
[max(l, default=0) for l in [[len(contexts) for contexts in prog.values()] for prog in contexts_per_word]],
default=0)
@staticmethod
def binary_to_string(binary_string):
return binary_string.decode("utf-8")
@staticmethod
def binary_to_string_list(binary_string_list):
return [common.binary_to_string(w) for w in binary_string_list]
@staticmethod
def binary_to_string_matrix(binary_string_matrix):
return [common.binary_to_string_list(l) for l in binary_string_matrix]
@staticmethod
def load_file_lines(path):
with open(path, 'r') as f:
ret
6C56
urn f.read().splitlines()
@staticmethod
def split_to_batches(data_lines, batch_size):
return [data_lines[x:x + batch_size] for x in range(0, len(data_lines), batch_size)]
@staticmethod
def legal_method_names_checker(name):
return name != common.noSuchWord and re.match('^[a-zA-Z\|]+$', name)
@staticmethod
def filter_impossible_names(top_words):
result = list(filter(common.legal_method_names_checker, top_words))
return result
@staticmethod
def get_subtokens(str):
return str.split('|')
@staticmethod
def parse_results(result, unhash_dict, topk=5):
prediction_results = []
for single_method in result:
original_name, top_suggestions, top_scores, attention_per_context = list(single_method)
current_method_prediction_results = PredictionResults(original_name)
for i, predicted in enumerate(top_suggestions):
if predicted == common.noSuchWord:
continue
suggestion_subtokens = common.get_subtokens(predicted)
current_method_prediction_results.append_prediction(suggestion_subtokens, top_scores[i].item())
for context, attention in [(key, attention_per_context[key]) for key in
sorted(attention_per_context, key=attention_per_context.get, reverse=True)][
:topk]:
token1, hashed_path, token2 = context
if hashed_path in unhash_dict:
unhashed_path = unhash_dict[hashed_path]
current_method_prediction_results.append_attention_path(attention.item(), token1=token1,
path=unhashed_path, token2=token2)
prediction_results.append(current_method_prediction_results)
return prediction_results
class PredictionResults:
def __init__(self, original_name):
self.original_name = original_name
self.predictions = list()
self.attention_paths = list()
def append_prediction(self, name, probability):
self.predictions.append({'name': name, 'probability': probability})
def append_attention_path(self, attention_score, token1, path, token2):
self.attention_paths.append({'score': attention_score,
'path': path,
'token1': token1,
'token2': token2})
class VocabType(Enum):
Token = 1
Target = 2