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import argparse
import json
import random
from collections import defaultdict
from pathlib import Path
from typing import Any, Callable, Hashable, Iterable
PROJECT_ROOT = Path(__file__).resolve().parents[1]
SEED = 20260718
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Build the four-task textual-reasoning HCL data splits."
)
parser.add_argument(
"--musique-root",
type=Path,
default=PROJECT_ROOT / "data" / "musique_data" / "data",
help="Directory containing musique_ans_v1.0_{train,dev}.jsonl.",
)
parser.add_argument(
"--source-root",
type=Path,
default=PROJECT_ROOT / "data" / "source_4task",
help="Directory containing existing gsm8k/proofwriter/hotpotqa split folders.",
)
parser.add_argument(
"--output-root",
type=Path,
default=PROJECT_ROOT / "data" / "datasets",
help="Destination used by configs/taskstream_textual_main_250_50_500.json.",
)
parser.add_argument("--train-size", type=int, default=500)
parser.add_argument("--validation-size", type=int, default=100)
parser.add_argument("--anchor-size", type=int, default=100)
parser.add_argument("--test-size", type=int, default=500)
parser.add_argument("--seed", type=int, default=SEED)
return parser
def main() -> int:
args = build_parser().parse_args()
sizes = {
"train": args.train_size,
"validation": args.validation_size,
"anchor": args.anchor_size,
"test": args.test_size,
}
if any(size < 0 for size in sizes.values()):
raise ValueError("Split sizes must be non-negative.")
for task_name in ("gsm8k", "proofwriter", "hotpotqa"):
build_existing_task(args.source_root, args.output_root, task_name, sizes=sizes)
build_musique(
args.musique_root,
args.output_root / "musique",
sizes=sizes,
seed=args.seed,
)
print(f"Prepared four-task data at {args.output_root.resolve()}")
for task_name in ("gsm8k", "proofwriter", "hotpotqa", "musique"):
counts = {
path.stem: count_jsonl(path)
for path in sorted((args.output_root / task_name).glob("*.jsonl"))
}
print(f"{task_name}: {counts}")
return 0
def build_existing_task(
source_root: Path,
output_root: Path,
task_name: str,
*,
sizes: dict[str, int],
) -> None:
source_dir = source_root / task_name
splits = {
split: read_jsonl(source_dir / f"{split}.jsonl")[: sizes[split]]
for split in ("train", "validation", "test")
}
for split, rows in splits.items():
require_count(f"{task_name} {split}", rows, sizes[split])
# This matches the recovered experiment protocol for the three existing tasks.
# A distinct MuSiQue anchor sample is constructed below.
require_count(
f"{task_name} validation-as-anchor",
splits["validation"],
sizes["anchor"],
)
splits["anchor"] = [dict(row) for row in splits["validation"][: sizes["anchor"]]]
write_splits(output_root / task_name, splits)
def build_musique(
source_root: Path,
output_dir: Path,
*,
sizes: dict[str, int],
seed: int,
) -> None:
train_source = read_jsonl(source_root / "musique_ans_v1.0_train.jsonl")
dev_source = read_jsonl(source_root / "musique_ans_v1.0_dev.jsonl")
train_pool = stratified_shuffle(train_source, key=musique_hop_count, seed=seed + 300)
required = sizes["train"] + sizes["validation"] + sizes["anchor"]
require_count("musique train/validation/anchor", train_pool, required)
raw_splits = {
"train": train_pool[: sizes["train"]],
"validation": train_pool[
sizes["train"] : sizes["train"] + sizes["validation"]
],
"anchor": train_pool[
sizes["train"] + sizes["validation"] : required
],
"test": stratified_sample(
dev_source,
size=sizes["test"],
key=musique_hop_count,
seed=seed + 301,
),
}
splits = {
split: [convert_musique(row, split, index) for index, row in enumerate(rows)]
for split, rows in raw_splits.items()
}
assert_disjoint_source_ids(splits)
write_splits(output_dir, splits)
def convert_musique(row: dict[str, Any], split: str, index: int) -> dict[str, Any]:
answer = str(row.get("answer", "")).strip()
if not answer:
raise ValueError(f"MuSiQue row has no answer: {row.get('id')}")
context = [
{
"idx": paragraph.get("idx"),
"title": str(paragraph.get("title", "")),
"paragraph_text": str(paragraph.get("paragraph_text", "")),
}
for paragraph in row.get("paragraphs", [])
]
if not context:
raise ValueError(f"MuSiQue row has no paragraphs: {row.get('id')}")
return {
"id": f"musique_{split}_{index:05d}",
"question": (
f"{str(row.get('question', '')).strip()}\n\n"
"Answer using the provided context. Return only the shortest final answer; do not explain."
),
"visible_context": context,
"answer": answer,
"answers": unique_nonempty([answer, *row.get("answer_aliases", [])]),
"task_name": "musique",
"task_type": "compositional_multi_hop_qa",
"metadata": {
"source": "musique",
"original_id": row.get("id"),
"hop_count": musique_hop_count(row),
"paragraph_count": len(context),
},
}
def musique_hop_count(row: dict[str, Any]) -> int:
identifier = str(row.get("id", ""))
try:
return int(identifier.split("hop", 1)[0])
except ValueError:
decomposition = row.get("question_decomposition", [])
return len(decomposition) if isinstance(decomposition, list) else 0
def stratified_sample(
rows: list[dict[str, Any]],
*,
size: int,
key: Callable[[dict[str, Any]], Hashable],
seed: int,
) -> list[dict[str, Any]]:
if size > len(rows):
raise ValueError(f"Cannot sample {size} rows from a population of {len(rows)}")
grouped = _shuffled_groups(rows, key=key, seed=seed)
exact = {name: size * len(group) / len(rows) for name, group in grouped.items()}
allocation = {name: int(value) for name, value in exact.items()}
remaining = size - sum(allocation.values())
remainder_order = sorted(
grouped,
key=lambda name: (exact[name] - allocation[name], str(name)),
reverse=True,
)
for name in remainder_order[:remaining]:
allocation[name] += 1
selected = [
row
for name in sorted(grouped, key=str)
for row in grouped[name][: allocation[name]]
]
random.Random(seed).shuffle(selected)
return selected
def stratified_shuffle(
rows: list[dict[str, Any]],
*,
key: Callable[[dict[str, Any]], Hashable],
seed: int,
) -> list[dict[str, Any]]:
"""Interleave shuffled strata so contiguous slices retain every stratum."""
grouped = _shuffled_groups(rows, key=key, seed=seed)
consumed = {name: 0 for name in grouped}
output: list[dict[str, Any]] = []
while len(output) < len(rows):
candidates = [name for name in grouped if consumed[name] < len(grouped[name])]
name = max(
candidates,
key=lambda candidate: (
len(grouped[candidate]) / len(rows)
- consumed[candidate] / max(len(output), 1),
-consumed[candidate],
str(candidate),
),
)
output.append(grouped[name][consumed[name]])
consumed[name] += 1
return output
def _shuffled_groups(
rows: list[dict[str, Any]],
*,
key: Callable[[dict[str, Any]], Hashable],
seed: int,
) -> dict[Hashable, list[dict[str, Any]]]:
grouped: dict[Hashable, list[dict[str, Any]]] = defaultdict(list)
for row in rows:
grouped[key(row)].append(row)
rng = random.Random(seed)
for group in grouped.values():
rng.shuffle(group)
return grouped
def assert_disjoint_source_ids(splits: dict[str, list[dict[str, Any]]]) -> None:
seen: dict[Any, str] = {}
for split, rows in splits.items():
for row in rows:
identifier = row["metadata"]["original_id"]
if identifier in seen:
raise ValueError(
f"MuSiQue source id {identifier!r} appears in both {seen[identifier]} and {split}"
)
seen[identifier] = split
def unique_nonempty(values: Iterable[Any]) -> list[str]:
output: list[str] = []
for value in values:
text = str(value).strip()
if text and text not in output:
output.append(text)
return output
def read_jsonl(path: Path) -> list[dict[str, Any]]:
with path.open("r", encoding="utf-8") as handle:
return [json.loads(line) for line in handle if line.strip()]
def write_splits(output_dir: Path, splits: dict[str, list[dict[str, Any]]]) -> None:
output_dir.mkdir(parents=True, exist_ok=True)
for split, rows in splits.items():
with (output_dir / f"{split}.jsonl").open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
def require_count(label: str, rows: list[Any], expected: int) -> None:
if len(rows) < expected:
raise ValueError(f"{label} has {len(rows)} rows, but {expected} are required")
def count_jsonl(path: Path) -> int:
with path.open("r", encoding="utf-8") as handle:
return sum(1 for line in handle if line.strip())
if __name__ == "__main__":
raise SystemExit(main())
|