Datasets:
File size: 4,308 Bytes
565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d 80a02b9 565784d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #!/usr/bin/env python3
"""Build a seeded uniform-random subset of a generated JSONL dataset directly as Parquet.
Every *cluster* is kept with probability --frac, decided by a hash of (seed, cluster_id), so clusters
stay whole (coreference labels remain usable), the sample is reproducible, independent of file order,
and stratification follows automatically (language / label / positive-negative mix match the parent in
expectation). Entity tables remain embedded on each retained cluster's doc_index == 0 record.
Usage: python preview_subset.py --src /tmp/poc/v2 --out /tmp/poc/preview_parquet --frac 0.36 --seed 42 --group 9 --workers 4
"""
import argparse, collections, glob, hashlib, json, os
from multiprocessing import Pool
import pyarrow as pa, pyarrow.parquet as pq
from to_parquet import SCHEMA, normalize
def keep(rid, seed, frac):
h = hashlib.blake2b(f"{seed}:{rid}".encode(), digest_size=8).digest()
return int.from_bytes(h, "big") / 2 ** 64 < frac
def work(args):
srcs, dst, seed, frac = args
C = collections.Counter
st = {"files": 0, "clusters": 0, "entities": 0, "injected": 0, "spans": 0, "negs": 0, "label": C(), "lang": C(), "value_type": C(), "context": C(), "jsonl_bytes": 0}
w = pq.ParquetWriter(dst + ".tmp", SCHEMA, compression="zstd"); buf = []
def flush():
if buf:
w.write_table(pa.Table.from_pylist(buf, schema=SCHEMA)); buf.clear()
for src in srcs:
with open(src, encoding="utf-8") as fh:
for line in fh:
r = json.loads(line)
if not keep(r["cluster_id"], seed, frac):
continue
normalize(r)
st["clusters"] += r["doc_index"] == 0
buf.append(r); st["files"] += 1; st["jsonl_bytes"] += len(line)
st["injected"] += bool(r["spans"]); st["spans"] += len(r["spans"]); st["negs"] += len(r["hard_negatives"])
st["lang"][r["language"]] += 1
for s in r["spans"]:
st["label"][s["label"]] += 1; st["value_type"][s["value_type"]] += 1; st["context"][s["context"]] += 1
if len(buf) >= 10000:
flush()
flush(); w.close(); os.replace(dst + ".tmp", dst)
st["parquet_bytes"] = os.path.getsize(dst); st["file"] = os.path.basename(dst)
print(st["file"], st["files"], "records", f"{st['parquet_bytes']/1e6:.0f} MB", flush=True)
return st
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--src", required=True); ap.add_argument("--out", required=True)
ap.add_argument("--frac", type=float, default=0.36); ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--group", type=int, default=9, help="JSONL parts per output parquet file")
ap.add_argument("--workers", type=int, default=4)
a = ap.parse_args()
os.makedirs(a.out, exist_ok=True)
parts = sorted(glob.glob(os.path.join(a.src, "part-?????.jsonl")))
jobs = []
for i in range(0, len(parts), a.group):
chunk = parts[i:i + a.group]
s, e = os.path.basename(chunk[0])[5:10], os.path.basename(chunk[-1])[5:10]
jobs.append((chunk, os.path.join(a.out, f"train-{s}-{e}.parquet"), a.seed, a.frac))
with Pool(a.workers) as pool:
stats = pool.map(work, jobs)
C = collections.Counter
agg = {"frac": a.frac, "seed": a.seed, "source_parts": len(parts), "files": 0, "clusters": 0, "entities": 0, "injected": 0, "spans": 0, "negs": 0, "jsonl_bytes": 0, "parquet_bytes": 0,
"label": C(), "lang": C(), "value_type": C(), "context": C(), "per_file": []}
for st in stats:
for k in ("files", "clusters", "entities", "injected", "spans", "negs", "jsonl_bytes", "parquet_bytes"): agg[k] += st[k]
for k in ("label", "lang", "value_type", "context"): agg[k].update(st[k])
agg["per_file"].append({"file": st["file"], "records": st["files"], "bytes": st["parquet_bytes"]})
for k in ("label", "lang", "value_type", "context"): agg[k] = dict(agg[k].most_common())
json.dump(agg, open(os.path.join(a.out, "preview_stats.json"), "w"), indent=1)
print(json.dumps({k: agg[k] for k in ("files", "clusters", "entities", "injected", "spans", "negs", "jsonl_bytes", "parquet_bytes")}, indent=1))
if __name__ == "__main__":
main()
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