-
Notifications
You must be signed in to change notification settings - Fork 165
Expand file tree
/
Copy pathextract.py
More file actions
189 lines (138 loc) · 5.26 KB
/
extract.py
File metadata and controls
189 lines (138 loc) · 5.26 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
import argparse
import re
import json
import multiprocessing
import itertools
import tqdm
import joblib
import numpy as np
from pathlib import Path
from sklearn import model_selection as sklearn_model_selection
METHOD_NAME, NUM = 'METHODNAME', 'NUM'
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', required=True, type=str)
parser.add_argument('--valid_p', type=float, default=0.2)
parser.add_argument('--max_path_length', type=int, default=8)
parser.add_argument('--max_path_width', type=int, default=2)
parser.add_argument('--use_method_name', type=bool, default=True)
parser.add_argument('--use_nums', type=bool, default=True)
parser.add_argument('--output_dir', required=True, type=str)
parser.add_argument('--n_jobs', type=int, default=multiprocessing.cpu_count())
parser.add_argument('--seed', type=int, default=239)
def __collect_asts(json_file):
with open(json_file, 'r', encoding='utf-8') as f:
for line in tqdm.tqdm(f):
yield line
def __terminals(ast, node_index, args):
stack, paths = [], []
def dfs(v):
stack.append(v)
v_node = ast[v]
if 'value' in v_node:
if v == node_index: # Top-level func def node.
if args.use_method_name:
paths.append((stack.copy(), METHOD_NAME))
else:
v_type = v_node['type']
if v_type.startswith('Name'):
paths.append((stack.copy(), v_node['value']))
elif args.use_nums and v_type == 'Num':
paths.append((stack.copy(), NUM))
else:
pass
if 'children' in v_node:
for child in v_node['children']:
dfs(child)
stack.pop()
dfs(node_index)
return paths
def __merge_terminals2_paths(v_path, u_path):
s, n, m = 0, len(v_path), len(u_path)
while s < min(n, m) and v_path[s] == u_path[s]:
s += 1
prefix = list(reversed(v_path[s:]))
lca = v_path[s - 1]
suffix = u_path[s:]
return prefix, lca, suffix
def __raw_tree_paths(ast, node_index, args):
tnodes = __terminals(ast, node_index, args)
tree_paths = []
for (v_path, v_value), (u_path,
97C8
u_value) in itertools.combinations(
iterable=tnodes,
r=2,
):
prefix, lca, suffix = __merge_terminals2_paths(v_path, u_path)
if (len(prefix) + 1 + len(suffix) <= args.max_path_length) \
and (abs(len(prefix) - len(suffix)) <= args.max_path_width):
path = prefix + [lca] + suffix
tree_path = v_value, path, u_value
tree_paths.append(tree_path)
return tree_paths
def __delim_name(name):
if name in {METHOD_NAME, NUM}:
return name
def camel_case_split(identifier):
matches = re.finditer(
'.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)',
identifier,
)
return [m.group(0) for m in matches]
blocks = []
for underscore_block in name.split('_'):
blocks.extend(camel_case_split(underscore_block))
return '|'.join(block.lower() for block in blocks)
def __collect_sample(ast, fd_index, args):
root = ast[fd_index]
if root['type'] != 'FunctionDef':
raise ValueError('Wrong node type.')
target = root['value']
tree_paths = __raw_tree_paths(ast, fd_index, args)
contexts = []
for tree_path in tree_paths:
start, connector, finish = tree_path
start, finish = __delim_name(start), __delim_name(finish)
connector = '|'.join(ast[v]['type'] for v in connector)
context = f'{start},{connector},{finish}'
contexts.append(context)
if len(contexts) == 0:
return None
target = __delim_name(target)
context = ' '.join(contexts)
return f'{target} {context}'
def __collect_samples(ast, args):
samples = []
for node_index, node in enumerate(ast):
if node['type'] == 'FunctionDef':
sample = __collect_sample(ast, node_index, args)
if sample is not None:
samples.append(sample)
return samples
def __collect_all_and_save(asts, args, output_file):
parallel = joblib.Parallel(n_jobs=args.n_jobs)
func = joblib.delayed(__collect_samples)
samples = parallel(func(ast, args) for ast in tqdm.tqdm(asts))
samples = list(itertools.chain.from_iterable(samples))
with open(output_file, 'w') as f:
for line_index, line in enumerate(samples):
f.write(line + ('' if line_index == len(samples) - 1 else '\n'))
def main():
args = parser.parse_args()
np.random.seed(args.seed)
data_dir = Path(args.data_dir)
trains = list(__collect_asts(data_dir / 'python100k_train.json'))
evals = list(__collect_asts(data_dir / 'python50k_eval.json'))
train, valid = sklearn_model_selection.train_test_split(
trains,
test_size=args.valid_p,
)
test = evals
output_dir = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
for split_name, split in zip(
('train', 'valid', 'test'),
(train, valid, test),
):
output_file = output_dir / f'{split_name}_output_file.txt'
__collect_all_and_save((json.loads(line) for line in split), args, output_file)
if __name__ == '__main__':
main()