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34c53b5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 | from __future__ import annotations
import argparse
import csv
import json
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Sequence, Set
from scripts.eval_pipeline import run_eval
REPO_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_EVAL_PATH = REPO_ROOT / "data" / "eval_samples" / "e621_sfw_sample_1000_seed123_buffer10000_caption_evident_n30.jsonl"
def _canon_tag(tag: str) -> str:
t = " ".join(str(tag or "").strip().split()).lower()
return t.replace(" ", "_").replace("\\(", "(").replace("\\)", ")")
def _parse_tag_set(text: str) -> Set[str]:
out: Set[str] = set()
for raw in (text or "").split(","):
t = _canon_tag(raw)
if t:
out.add(t)
return out
def _set_metrics(pred_sets: Sequence[Set[str]], gold_sets: Sequence[Set[str]]) -> Dict[str, float]:
n = len(pred_sets)
if n == 0:
return {
"set_precision": 0.0,
"set_recall": 0.0,
"set_f1": 0.0,
"avg_pred_tags": 0.0,
"avg_gold_tags": 0.0,
}
p_vals: List[float] = []
r_vals: List[float] = []
f_vals: List[float] = []
pred_sizes: List[int] = []
gold_sizes: List[int] = []
for pset, gset in zip(pred_sets, gold_sets):
pred_sizes.append(len(pset))
gold_sizes.append(len(gset))
if not pset or not gset:
p_vals.append(0.0 if pset or gset else 1.0)
r_vals.append(0.0 if pset or gset else 1.0)
f_vals.append(0.0 if pset or gset else 1.0)
continue
tp = len(pset & gset)
p = tp / len(pset)
r = tp / len(gset)
f1 = (2 * p * r / (p + r)) if (p + r) > 0 else 0.0
p_vals.append(p)
r_vals.append(r)
f_vals.append(f1)
return {
"set_precision": sum(p_vals) / n,
"set_recall": sum(r_vals) / n,
"set_f1": sum(f_vals) / n,
"avg_pred_tags": sum(pred_sizes) / n,
"avg_gold_tags": sum(gold_sizes) / n,
}
def _summarize(results) -> Dict[str, float]:
valid = [r for r in results if r.error is None]
if not valid:
return {
"n_valid": 0,
"n_errors": len(results),
"ret_R": 0.0,
"P": 0.0,
"R": 0.0,
"F1": 0.0,
"leaf_F1": 0.0,
"t1": 0.0,
"t2": 0.0,
"t3": 0.0,
"t_total": 0.0,
"rw_P": 0.0,
"rw_R": 0.0,
"rw_F1": 0.0,
"rw_avg_pred": 0.0,
"rw_avg_gt": 0.0,
}
n = len(valid)
avg = lambda xs: sum(xs) / n
pred_sets = []
gold_sets = []
for r in valid:
phrase_text = ", ".join((r.rewrite_phrases or []))
pred_sets.append(_parse_tag_set(phrase_text))
gold_sets.append({_canon_tag(t) for t in (r.ground_truth_tags or set()) if t})
rewrite = _set_metrics(pred_sets, gold_sets)
t1 = avg([r.stage1_time for r in valid])
t2 = avg([r.stage2_time for r in valid])
t3 = avg([r.stage3_time for r in valid])
return {
"n_valid": n,
"n_errors": len(results) - n,
"ret_R": avg([r.retrieval_recall for r in valid]),
"P": avg([r.selection_precision for r in valid]),
"R": avg([r.selection_recall for r in valid]),
"F1": avg([r.selection_f1 for r in valid]),
"leaf_F1": avg([r.leaf_f1 for r in valid]),
"t1": t1,
"t2": t2,
"t3": t3,
"t_total": t1 + t2 + t3,
"rw_P": rewrite["set_precision"],
"rw_R": rewrite["set_recall"],
"rw_F1": rewrite["set_f1"],
"rw_avg_pred": rewrite["avg_pred_tags"],
"rw_avg_gt": rewrite["avg_gold_tags"],
}
def main() -> int:
ap = argparse.ArgumentParser(description="Run n30 rewrite ablation: LLM vs T5, heuristic phrase append off/on")
ap.add_argument("--eval-path", type=Path, default=DEFAULT_EVAL_PATH)
ap.add_argument("--caption-field", type=str, default="caption_cogvlm")
ap.add_argument("--n", type=int, default=30)
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--workers", type=int, default=1)
ap.add_argument("--mode", type=str, default="chunked_map_union", choices=["single_shot", "chunked_map_union"])
ap.add_argument("--chunk-size", type=int, default=60)
ap.add_argument("--per-phrase-k", type=int, default=2)
ap.add_argument("--per-phrase-final-k", type=int, default=1)
ap.add_argument("--min-why", type=str, default="strong_implied")
ap.add_argument("--infer-structural", action="store_true", default=True)
ap.add_argument("--no-infer-structural", dest="infer_structural", action="store_false")
ap.add_argument("--infer-probe", action="store_true", default=True)
ap.add_argument("--no-infer-probe", dest="infer_probe", action="store_false")
ap.add_argument("--t5-model-dir", type=str, default="models/finetune/t5-rewrite")
ap.add_argument("--t5-num-beams", type=int, default=4)
ap.add_argument("--t5-max-new-tokens", type=int, default=128)
args = ap.parse_args()
eval_path = args.eval_path if args.eval_path.is_absolute() else (REPO_ROOT / args.eval_path).resolve()
if not eval_path.is_file():
raise FileNotFoundError(f"Eval path not found: {eval_path}")
configs = [
{"rewrite_source": "llm", "append_heuristic_phrases": False},
{"rewrite_source": "llm", "append_heuristic_phrases": True},
{"rewrite_source": "t5", "append_heuristic_phrases": False},
{"rewrite_source": "t5", "append_heuristic_phrases": True},
]
rows: List[Dict[str, Any]] = []
details: Dict[str, Any] = {}
for cfg in configs:
name = f"{cfg['rewrite_source']}_heur_{'on' if cfg['append_heuristic_phrases'] else 'off'}"
print("\n" + "=" * 80)
print(f"Running config: {name}")
print("=" * 80)
results = run_eval(
n_samples=args.n,
caption_field=args.caption_field,
skip_rewrite=False,
allow_nsfw=False,
mode=args.mode,
chunk_size=args.chunk_size,
per_phrase_k=args.per_phrase_k,
per_phrase_final_k=args.per_phrase_final_k,
temperature=0.0,
max_tokens=512,
verbose=False,
shuffle=True,
seed=args.seed,
workers=args.workers,
min_why=None if args.min_why == "none" else args.min_why,
eval_path=str(eval_path),
expand_implications=False,
infer_structural=args.infer_structural,
infer_probe=args.infer_probe,
rewrite_source=cfg["rewrite_source"],
t5_model_dir=args.t5_model_dir,
t5_num_beams=args.t5_num_beams,
t5_max_new_tokens=args.t5_max_new_tokens,
append_heuristic_phrases=cfg["append_heuristic_phrases"],
)
summary = _summarize(results)
summary.update(cfg)
rows.append(summary)
details[name] = {
"summary": summary,
"errors": [
{
"id": r.sample_id,
"error": r.error,
"issues": r.issues,
}
for r in results
if r.error
],
}
print(json.dumps(summary, ensure_ascii=False, indent=2))
out_dir = REPO_ROOT / "data" / "eval_results"
out_dir.mkdir(parents=True, exist_ok=True)
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
json_out = out_dir / f"rewrite_ablation_n{args.n}_{ts}.json"
csv_out = out_dir / f"rewrite_ablation_n{args.n}_{ts}.csv"
payload = {
"meta": {
"timestamp": datetime.now().isoformat(),
"eval_path": str(eval_path),
"caption_field": args.caption_field,
"n": args.n,
"seed": args.seed,
"workers": args.workers,
"mode": args.mode,
"chunk_size": args.chunk_size,
"per_phrase_k": args.per_phrase_k,
"per_phrase_final_k": args.per_phrase_final_k,
"min_why": args.min_why,
"infer_structural": args.infer_structural,
"infer_probe": args.infer_probe,
"t5_model_dir": args.t5_model_dir,
"t5_num_beams": args.t5_num_beams,
"t5_max_new_tokens": args.t5_max_new_tokens,
},
"rows": rows,
"details": details,
}
with json_out.open("w", encoding="utf-8") as f:
json.dump(payload, f, ensure_ascii=False, indent=2)
fieldnames = [
"rewrite_source",
"append_heuristic_phrases",
"n_valid",
"n_errors",
"rw_P",
"rw_R",
"rw_F1",
"rw_avg_pred",
"rw_avg_gt",
"ret_R",
"P",
"R",
"F1",
"leaf_F1",
"t1",
"t2",
"t3",
"t_total",
]
with csv_out.open("w", encoding="utf-8", newline="") as f:
w = csv.DictWriter(f, fieldnames=fieldnames)
w.writeheader()
for row in rows:
w.writerow(row)
print(f"\nSaved ablation JSON: {json_out}")
print(f"Saved ablation CSV: {csv_out}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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