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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)
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