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common.py
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230 lines (199 loc) · 9.66 KB
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import re
import numpy as np
import tensorflow as tf
from itertools import takewhile, repeat
from typing import List, Optional, Tuple, Iterable
from datetime import datetime
from collections import OrderedDict
class common:
@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_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_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(output_file, index_to_word, vocab_embedding_matrix: np.ndarray):
assert len(vocab_embedding_matrix.shape) == 2
vocab_size, embedding_dimension = vocab_embedding_matrix.shape
output_file.write('%d %d\n' % (vocab_size, embedding_dimension))
for word_idx in range(0, vocab_size):
assert word_idx in index_to_word
word_str = index_to_word[word_idx]
output_file.write(word_str + ' ')
output_file.write(' '.join(map(str, vocab_embedding_matrix[word_idx])) + '\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:
return f.read().splitlines()
@staticmethod
def split_to_batches(data_lines, batch_size):
for x in range(0, len(data_lines), batch_size):
yield data_lines[x:x + batch_size]
@staticmethod
def legal_method_names_checker(special_words, name):
return name != special_words.OOV and re.match(r'^[a-zA-Z|]+$', name)
@staticmethod
def filter_impossible_names(special_words, top_words):
result = list(filter(lambda word: common.legal_method_names_checker(special_words, word), top_words))
return result
@staticmethod
def get_subtokens(str):
return str.split('|')
@staticmethod
def parse_prediction_results(raw_prediction_results, unhash_dict, special_words, topk: int = 5) -> List['MethodPredictionResults']:
prediction_results = []
for single_method_prediction in raw_prediction_results:
current_method_prediction_results = MethodPredictionResults(single_method_prediction.original_name)
for i, predicted in enumerate(single_method_prediction.topk_predicted_words):
if predicted == special_words.OOV:
continue
suggestion_subtokens = common.get_subtokens(predicted)
current_method_prediction_results.append_prediction(
suggestion_subtokens, single_method_prediction.topk_predicted_words_scores[i].item())
topk_attention_per_context = [
(key, single_method_prediction.attention_per_context[key])
for key in sorted(single_method_prediction.attention_per_context,
key=single_method_prediction.attention_per_context.get, reverse=True)
][:topk]
for context, attention in topk_attention_per_context:
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
@staticmethod
def tf_get_first_true(bool_tensor: tf.Tensor) -> tf.Tensor:
bool_tensor_as_int32 = tf.cast(bool_tensor, dtype=tf.int32)
cumsum = tf.cumsum(bool_tensor_as_int32, axis=-1, exclusive=False)
return tf.logical_and(tf.equal(cumsum, 1), bool_tensor)
@staticmethod
def count_lines_in_file(file_path: str):
with open(file_path, 'rb') as f:
bufgen = takewhile(lambda x: x, (f.raw.read(1024 * 1024) for _ in repeat(None)))
return sum(buf.count(b'\n') for buf in bufgen)
@staticmethod
def squeeze_single_batch_dimension_for_np_arrays(arrays):
assert all(array is None or isinstance(array, np.ndarray) or isinstance(array, tf.Tensor) for array in arrays)
return tuple(
None if array is None else np.squeeze(array, axis=0)
for array in arrays
)
@staticmethod
def get_first_match_word_from_top_predictions_with_score(special_words, original_name, top_predicted_words, scores)\
-> Optional[Tuple[int, str, float]]:
normalized_original_name = common.normalize_word(original_name)
scores_dict = dict(zip(top_predicted_words, scores))
for suggestion_idx, predicted_word in enumerate(
common.filter_impossible_names(special_words, top_predicted_words)):
normalized_possible_suggestion = common.normalize_word(predicted_word)
if normalized_original_name == normalized_possible_suggestion:
return suggestion_idx, predicted_word, scores_dict[predicted_word]
return None
@staticmethod
def get_first_match_word_from_top_predictions(special_words, original_name, top_predicted_words) -> Optional[Tuple[int, str]]:
normalized_original_name = common.normalize_word(original_name)
for suggestion_idx, predicted_word in enumerate(common.filter_impossible_names(special_words, top_predicted_words)):
normalized_possible_suggestion = common.normalize_word(predicted_word)
if normalized_original_name == normalized_possible_suggestion:
return suggestion_idx, predicted_word
return None
@staticmethod
def now_str():
return datetime.now().strftime("%Y%m%d-%H%M%S: ")
@staticmethod
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
@staticmethod
def get_unique_list(lst: Iterable) -> list:
return list(OrderedDict(((item, 0) for item in lst)).keys())
class MethodPredictionResults:
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})