| import argparse | |
| import gzip | |
| import json | |
| from sklearn.model_selection import GroupShuffleSplit | |
| def read_groups(): | |
| groups = [set()] | |
| for line in open("schema-groups.txt"): | |
| if line.strip() != "": | |
| groups[-1].add(line.strip()) | |
| else: | |
| groups.append(set()) | |
| return groups | |
| def sample(random_state, train_pct): | |
| groups = read_groups() | |
| next_id = len(groups) | |
| names = [] | |
| name_ids = [] | |
| for line in gzip.open("all.jsonl.gz", "rt"): | |
| obj = json.loads(line) | |
| names.append(obj["name"]) | |
| found = False | |
| for (i, group) in enumerate(groups): | |
| if obj["name"] in group: | |
| assert(not found) | |
| found = True | |
| name_ids.append(i) | |
| if not found: | |
| name_ids.append(next_id) | |
| next_id += 1 | |
| gss = GroupShuffleSplit(n_splits=10, train_size=train_pct, random_state=random_state) | |
| train_idx, test_idx = next(gss.split(names, groups=name_ids)) | |
| train_file = gzip.open("train.jsonl.gz", "wt") | |
| val_file = gzip.open("validation.jsonl.gz", "wt") | |
| for (idx, line) in enumerate(gzip.open("all.jsonl.gz", "rt")): | |
| if idx in train_idx: | |
| train_file.write(line) | |
| elif idx in test_idx: | |
| val_file.write(line) | |
| train_file.close() | |
| val_file.close() | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--train_pct", type=float, default=0.8) | |
| parser.add_argument("--random_state", type=int, default=16) | |
| args = parser.parse_args() | |
| sample(args.random_state, args.train_pct) | |