| """Dataset splitter — combines per-area JSONL files into stratified train / valid / curation pool. |
| |
| Stratification ensures: |
| - task_type ratio preserved across splits (60/40 teaching vs interp by default) |
| - For task 1: each method represented proportionally |
| - For task 2: each pattern represented proportionally |
| |
| Usage: |
| uv run python src/split_data.py |
| uv run python src/split_data.py --seed 42 --out-dir data/splits |
| """ |
|
|
| import argparse |
| import json |
| import random |
| import sys |
| from collections import defaultdict |
| from pathlib import Path |
|
|
| REPO_ROOT = Path(__file__).resolve().parent.parent |
| sys.path.insert(0, str(REPO_ROOT)) |
|
|
| from src.generators.base import load_yaml |
|
|
| CONFIG_DIR = REPO_ROOT / "configs" |
|
|
|
|
| def load_all_area_files(input_dir: Path, areas: list[str]) -> list[dict]: |
| """Load every per-area JSONL file; deduplicate by example_id (a content hash).""" |
| seen: set[str] = set() |
| combined: list[dict] = [] |
| n_raw = 0 |
| for area in areas: |
| path = input_dir / f"{area}.jsonl" |
| if not path.exists(): |
| print(f" Skipping {area}: {path} not found", file=sys.stderr) |
| continue |
| with open(path) as f: |
| for line in f: |
| n_raw += 1 |
| ex = json.loads(line) |
| eid = ex["meta"]["example_id"] |
| if eid in seen: |
| continue |
| seen.add(eid) |
| combined.append(ex) |
| dropped = n_raw - len(combined) |
| if dropped: |
| print(f" Deduplication: dropped {dropped} exact-duplicate example(s) ({n_raw} -> {len(combined)}).") |
| return combined |
|
|
|
|
| def stratify_key(example: dict) -> str: |
| """Return a stratification bucket key for an example.""" |
| task = example["meta"]["task_type"] |
| gl = example["meta"]["gold_labels"] |
| if task == "teaching_program": |
| return f"t1:{gl['method']}" |
| return f"t2:{gl['pattern_class']}" |
|
|
|
|
| def stratified_split( |
| examples: list[dict], |
| train_ratio: float, |
| valid_ratio: float, |
| curation_ratio: float, |
| rng: random.Random, |
| ) -> tuple[list[dict], list[dict], list[dict]]: |
| """Stratified three-way split preserving per-bucket proportions.""" |
| buckets: dict[str, list[dict]] = defaultdict(list) |
| for ex in examples: |
| buckets[stratify_key(ex)].append(ex) |
|
|
| train, valid, curation = [], [], [] |
| for key in sorted(buckets.keys()): |
| items = buckets[key] |
| rng.shuffle(items) |
| n = len(items) |
| n_train = int(round(n * train_ratio)) |
| n_valid = int(round(n * valid_ratio)) |
| n_curation = n - n_train - n_valid |
| |
| if n >= 10: |
| n_valid = max(1, n_valid) |
| n_curation = max(1, n_curation) |
| n_train = n - n_valid - n_curation |
| train.extend(items[:n_train]) |
| valid.extend(items[n_train : n_train + n_valid]) |
| curation.extend(items[n_train + n_valid :]) |
| rng.shuffle(train) |
| rng.shuffle(valid) |
| rng.shuffle(curation) |
| return train, valid, curation |
|
|
|
|
| def write_jsonl(examples: list[dict], path: Path) -> None: |
| with open(path, "w") as f: |
| for ex in examples: |
| f.write(json.dumps(ex) + "\n") |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--seed", type=int, default=None) |
| parser.add_argument("--out-dir", type=str, default=None) |
| args = parser.parse_args() |
|
|
| gen_config = load_yaml(CONFIG_DIR / "generation.yaml") |
| seed = args.seed if args.seed is not None else gen_config["seed"] |
| out_dir = Path(args.out_dir) if args.out_dir else REPO_ROOT / gen_config["output_dir"] |
|
|
| splits_cfg = gen_config["splits"] |
| tr = splits_cfg["train"] |
| va = splits_cfg["valid"] |
| cu = splits_cfg["curation_pool"] |
| if abs(tr + va + cu - 1.0) >= 1e-6: |
| raise ValueError(f"Splits must sum to 1.0 (got {tr + va + cu})") |
|
|
| |
| enabled_areas = [a for a, c in gen_config["areas"].items() if c.get("enabled")] |
| examples = load_all_area_files(out_dir, enabled_areas) |
| print(f"Loaded {len(examples)} total examples from {len(enabled_areas)} areas.\n") |
|
|
| rng = random.Random(seed) |
| train, valid, curation = stratified_split(examples, tr, va, cu, rng) |
|
|
| write_jsonl(train, out_dir / "train.jsonl") |
| write_jsonl(valid, out_dir / "valid.jsonl") |
| write_jsonl(curation, out_dir / "curation_pool.jsonl") |
|
|
| print("Splits written:") |
| print(f" train.jsonl {len(train):5d} ({100*len(train)/len(examples):.1f}%)") |
| print(f" valid.jsonl {len(valid):5d} ({100*len(valid)/len(examples):.1f}%)") |
| print(f" curation_pool.jsonl {len(curation):5d} ({100*len(curation)/len(examples):.1f}%)") |
| print() |
| print("Curation pool is the source for the author-curated test (100) + sanity (20) splits.") |
|
|
| |
| bucket_counts = defaultdict(int) |
| for ex in curation: |
| bucket_counts[stratify_key(ex)] += 1 |
| print("\nCuration-pool stratification:") |
| for key, c in sorted(bucket_counts.items()): |
| print(f" {key:45s} {c:3d}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
