File size: 8,367 Bytes
5f4e9ea | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | """CSV → jsonl splits + axiom rewrite + coverage report. No model imports."""
from __future__ import annotations
import argparse
import csv
import json
import random
import sys
from collections import Counter
from pathlib import Path
from .axioms import match_cot, rewrite_cot_assistant
TEXT_CSV = Path("Physics_Dataset/Physics_Dataset_no_image.csv")
IMAGE_CSV = Path("Physics_Dataset/Physics_dataset_with_image.csv")
IMAGE_DIR = Path("Physics_Dataset/Image")
DEFAULT_OUT = Path("data/physics_sft")
SPLIT_SEED = 42
TRAIN_RATIO = 0.90
VAL_RATIO = 0.05
def repo_root_from_here() -> Path:
here = Path(__file__).resolve()
for parent in here.parents:
if (parent / "Physics_Dataset").is_dir() and (parent / "CLAUDE.md").exists():
return parent
return Path.cwd()
def load_csv(path: Path) -> list[dict[str, str]]:
with path.open(newline="", encoding="utf-8") as handle:
return list(csv.DictReader(handle))
def is_empty_answer(value: str | None) -> bool:
return not (value or "").strip()
def resolve_image_path(id_image: str, image_dir: Path) -> Path:
candidate = image_dir / id_image
if candidate.is_file():
return candidate
raise FileNotFoundError(f"Missing diagram for id_image={id_image!r}: {candidate}")
def split_keys(keys: list[str], seed: int) -> dict[str, str]:
rng = random.Random(seed)
ordered = list(keys)
rng.shuffle(ordered)
n = len(ordered)
n_val = max(1, int(round(n * VAL_RATIO))) if n >= 3 else (1 if n >= 2 else 0)
n_test = max(1, int(round(n * VAL_RATIO))) if n >= 3 else (1 if n >= 3 else 0)
if n_val + n_test >= n:
n_val = 1 if n >= 3 else 0
n_test = 1 if n >= 3 else 0
n_train = n - n_val - n_test
assignment: dict[str, str] = {}
for i, key in enumerate(ordered):
if i < n_train:
assignment[key] = "train"
elif i < n_train + n_val:
assignment[key] = "val"
else:
assignment[key] = "test"
return assignment
def record_from_row(
row: dict[str, str],
*,
has_image: bool,
image_path: str | None,
) -> dict:
assistant, axiom_ids = rewrite_cot_assistant(row.get("cot") or "", row.get("answer") or "", row.get("unit") or "")
return {
"id": row["id"],
"has_image": has_image,
"image_path": image_path,
"question": row.get("question") or "",
"axiom_ids": axiom_ids,
"assistant": assistant,
}
def write_jsonl(path: Path, rows: list[dict]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
def coverage_report(records: list[dict]) -> dict:
hist: Counter[str] = Counter()
n_with = 0
for rec in records:
ids = rec.get("axiom_ids") or []
if ids:
n_with += 1
for axiom_id in ids:
hist[axiom_id] += 1
n = len(records)
n_zero = n - n_with
return {
"n_rows": n,
"n_with_axiom": n_with,
"n_without_axiom": n_zero,
"pct_with_axiom": round(100.0 * n_with / n, 2) if n else 0.0,
"pct_without_axiom": round(100.0 * n_zero / n, 2) if n else 0.0,
"axiom_histogram": dict(hist.most_common()),
}
def print_coverage(report: dict) -> None:
print("=== axiom matcher coverage ===")
print(f"rows {report['n_rows']}")
print(f">=1 axiom {report['n_with_axiom']} ({report['pct_with_axiom']}%)")
print(f"0 axioms {report['n_without_axiom']} ({report['pct_without_axiom']}%)")
print("histogram:")
for axiom_id, count in report["axiom_histogram"].items():
print(f" {axiom_id:8} {count}")
def build_records(root: Path) -> tuple[list[dict], list[str], dict]:
text_rows = load_csv(root / TEXT_CSV)
image_rows = load_csv(root / IMAGE_CSV)
image_dir = root / IMAGE_DIR
dropped: list[str] = []
records: list[dict] = []
for row in text_rows:
if is_empty_answer(row.get("answer")):
dropped.append(row["id"])
continue
rec = record_from_row(row, has_image=False, image_path=None)
rec["_split_key"] = row["id"]
rec["_split_kind"] = "text"
records.append(rec)
for row in image_rows:
if is_empty_answer(row.get("answer")):
dropped.append(row["id"])
continue
resolved = resolve_image_path(row["id_image"], image_dir)
rel = resolved.relative_to(root).as_posix()
rec = record_from_row(row, has_image=True, image_path=rel)
rec["_id_image"] = row["id_image"]
rec["_split_key"] = row["id_image"]
rec["_split_kind"] = "image"
records.append(rec)
return records, dropped, {
"n_text_csv": len(text_rows),
"n_image_csv": len(image_rows),
}
def assign_splits(records: list[dict], seed: int) -> dict:
text_keys = sorted({r["_split_key"] for r in records if r["_split_kind"] == "text"})
image_keys = sorted({r["_split_key"] for r in records if r["_split_kind"] == "image"})
text_map = split_keys(text_keys, seed)
image_map = split_keys(image_keys, seed + 1)
manifest = {"seed": seed, "text": {"train": [], "val": [], "test": []}, "image": {"train": [], "val": [], "test": []}}
for rec in records:
if rec["_split_kind"] == "text":
split = text_map[rec["_split_key"]]
rec["split"] = split
manifest["text"][split].append(rec["id"])
else:
split = image_map[rec["_split_key"]]
rec["split"] = split
manifest["image"][split].append({"id": rec["id"], "id_image": rec["_id_image"]})
return manifest
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Prepare physics SFT jsonl splits.")
parser.add_argument("--root", type=Path, default=None)
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
parser.add_argument("--seed", type=int, default=SPLIT_SEED)
args = parser.parse_args(argv)
root = (args.root or repo_root_from_here()).resolve()
out = args.out if args.out.is_absolute() else root / args.out
out.mkdir(parents=True, exist_ok=True)
records, dropped, counts = build_records(root)
print(f"dropped empty-answer ids ({len(dropped)}): {dropped}")
print(f"csv text={counts['n_text_csv']} image={counts['n_image_csv']}")
manifest = assign_splits(records, args.seed)
splits: dict[str, list[dict]] = {"train": [], "val": [], "test": []}
for rec in records:
splits[rec["split"]].append(rec)
clean_splits: dict[str, list[dict]] = {}
for name, rows in splits.items():
cleaned = []
for rec in rows:
cleaned.append(
{
"id": rec["id"],
"has_image": rec["has_image"],
"image_path": rec["image_path"],
"question": rec["question"],
"axiom_ids": rec["axiom_ids"],
"assistant": rec["assistant"],
}
)
clean_splits[name] = cleaned
write_jsonl(out / f"{name}.jsonl", cleaned)
report = coverage_report([rec for recs in clean_splits.values() for rec in recs])
(out / "coverage.json").write_text(json.dumps(report, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
(out / "split_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
(out / "dropped_ids.json").write_text(json.dumps(dropped, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
print_coverage(report)
print("=== split sizes ===")
for name, rows in clean_splits.items():
n_img = sum(1 for r in rows if r["has_image"])
n_txt = len(rows) - n_img
max_chars = max((len(r["assistant"]) for r in rows), default=0)
print(f"{name:5} n={len(rows):5} text={n_txt:4} image={n_img:4} max_assistant_chars={max_chars}")
if report["pct_with_axiom"] < 25.0:
print("WARNING: >=1 axiom coverage is under 25%. Loosen matcher rules before training.", file=sys.stderr)
return 2
return 0
if __name__ == "__main__":
raise SystemExit(main())
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