Datasets:
File size: 2,282 Bytes
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import argparse
import json
import os
from pathlib import Path
import numpy as np
import pybktree
from sklearn.model_selection import GroupShuffleSplit
import tqdm
import unionfind
import Levenshtein
def files_list():
data_path = Path("valid_data")
files = [f for f in data_path.rglob("*.json") if f.is_file()]
return files
def main(similarity, split, seed):
files = files_list()
if similarity:
tree = pybktree.BKTree(
lambda a, b: Levenshtein.distance(a, b) / max(len(a), len(b))
)
uf = unionfind.UnionFind()
repo_map = {}
for schema_file in tqdm.tqdm(files):
path_str = str(schema_file)
repo = schema_file.parts[1:4]
uf.add(str(schema_file))
if repo not in repo_map:
repo_map[repo] = str(schema_file)
else:
uf.union(repo_map[repo], str(schema_file))
if similarity:
tree.add((str(schema_file), open(schema_file).read().strip()))
del repo_map
# Optionally group together similar files
if similarity:
for schema_file in tqdm.tqdm(files):
path_str = str(schema_file)
data = open(schema_file).read().strip()
for other_path, _ in tree.find(data, similarity):
uf.union(path_str, other_path)
all_schemas = list()
schema_groups = list()
for group, schemas in enumerate(uf.components()):
all_schemas.extend(schemas)
schema_groups.extend([group] * len(schemas))
all_schemas = np.array(all_schemas)
schema_groups = np.array(schema_groups)
gss = GroupShuffleSplit(n_splits=1, train_size=split, random_state=seed)
(train_indexes, test_indexes) = next(gss.split(all_schemas, groups=schema_groups))
open("train_schemas.json", "w").write(
json.dumps(all_schemas[train_indexes].tolist())
)
open("test_schemas.json", "w").write(json.dumps(all_schemas[test_indexes].tolist()))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--similarity", default=None, type=float)
parser.add_argument("--seed", default=15, type=int)
parser.add_argument("--split", default=0.8, type=float)
args = parser.parse_args()
main(args.similarity, args.split, args.seed)
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