| 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) |
|
|
