import argparse import csv import gzip import json import os from pathlib import Path import sys 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 write_schemas(filename, schema_list, schema_data): sys.stderr.write(f"Writing {filename}…\n") with gzip.open(filename, "wt") as f: for schema in tqdm.tqdm(list(schema_list)): filename = str(os.path.join(*Path(schema).parts[1:])) data = schema_data[filename] schema = open(schema).read() obj = { "repository": data["repository"], "commit": data["commit"], "path": data["path"], "repoStars": data["repoStars"], "repoLastFetched": data["repoLastFetched"], "content": schema, } json.dump(obj, f) f.write("\n") def main(similarity, split, seed, repo_file): files = files_list() # Prepare a BK Tree if we're doing similarity grouping if similarity: tree = pybktree.BKTree( lambda a, b: Levenshtein.distance(a, b) / max(len(a), len(b)) ) # Initialize a union-find data structure uf = unionfind.UnionFind() # Track the first schema added to each org so we can group them org_map = {} sys.stderr.write("Grouping by repository…\n") for schema_file in tqdm.tqdm(files): path_str = str(schema_file) # Get the organization name from the path org = schema_file.parts[1:3] uf.add(str(schema_file)) if org not in org_map: # Track the first schema for this organization org_map[org] = str(schema_file) else: # Merge with the previous group if this # organization has been seen before uf.union(org_map[org], str(schema_file)) # Add to the BK Tree if similarity: tree.add((str(schema_file), open(schema_file).read().strip())) del org_map # Optionally group together similar files if similarity: sys.stderr.write("Grouping similar files…\n") for schema_file in tqdm.tqdm(files): path_str = str(schema_file) data = open(schema_file).read().strip() # Find similar schemas for this schema and group them together for other_path, _ in tree.find(data, similarity): uf.union(path_str, other_path) # Produce a list of schemas and their associated groups all_schemas = list() schema_groups = list() for group, schemas in enumerate(uf.components()): all_schemas.extend(schemas) schema_groups.extend([group] * len(schemas)) # Split the schemas into training and test 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)) test_schemas = all_schemas[test_indexes] test_groups = schema_groups[test_indexes] gss = GroupShuffleSplit(n_splits=1, train_size=0.5, random_state=seed) (test_indexes, val_indexes) = next(gss.split(test_schemas, groups=test_groups)) schema_data = {} with open(repo_file) as csvfile: reader = csv.DictReader(csvfile) for row in reader: filename = os.path.join(row["repository"], row["path"]) schema_data[filename] = row # Write the train and test sets write_schemas("train.jsonl.gz", all_schemas[train_indexes], schema_data) write_schemas("test.jsonl.gz", test_schemas[test_indexes], schema_data) write_schemas("validation.jsonl.gz", test_schemas[val_indexes], schema_data) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--similarity", default=None, type=float) parser.add_argument("--seed", default=94, type=int) parser.add_argument("--split", default=0.8, type=float) parser.add_argument("--repo_file", default="repos.csv") args = parser.parse_args() main(args.similarity, args.split, args.seed, args.repo_file)