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"""Export paper-facing CBU tables with caption-level bootstrap CIs.
The script consumes existing CBU response JSONL artifacts. It does not call a
model and does not modify source captions.
"""
from __future__ import annotations
import argparse
import csv
import json
import re
from collections import Counter, defaultdict
from pathlib import Path
from typing import Any
import numpy as np
UNIT_CATEGORIES = [
"object",
"attribute",
"relation",
"style",
"camera",
"lighting",
"count",
"text_rendering",
]
VISUAL_STATUSES = {"grounded", "unsupported", "uncertain"}
TOKEN_RE = re.compile(r"[^\W_]+(?:'[^\W_]+)*", re.UNICODE)
ARTICLE_UNITS = {"a", "an", "the"}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--claimed", action="append", default=[], metavar="LABEL=PATH")
parser.add_argument("--grounded", action="append", default=[], metavar="LABEL=PATH")
parser.add_argument("--output-dir", required=True)
parser.add_argument("--bootstrap-reps", type=int, default=2000)
parser.add_argument("--seed", type=int, default=0)
return parser.parse_args()
def parse_label_path(value: str) -> tuple[str, Path]:
if "=" not in value:
raise ValueError(f"Expected LABEL=PATH, got {value!r}")
label, path = value.split("=", 1)
return label, Path(path)
def normalize_unit(text: str) -> str:
tokens = TOKEN_RE.findall(text.lower())
while tokens and tokens[0] in ARTICLE_UNITS:
tokens.pop(0)
return " ".join(tokens)
def normalize_key_part(text: str) -> str:
return normalize_unit(text) or ""
def unit_records(group: Any) -> list[dict[str, str]]:
records: list[dict[str, str]] = []
if not isinstance(group, list):
return records
for item in group:
if not isinstance(item, dict):
continue
category = item.get("category")
unit = item.get("unit")
if category not in UNIT_CATEGORIES or not isinstance(unit, str) or not unit.strip():
continue
target = item.get("target", "")
records.append(
{
"category": category,
"unit": unit.strip(),
"target": target.strip() if isinstance(target, str) else "",
}
)
return records
def dedup_counts(group: Any) -> tuple[int, dict[str, int], int]:
counts = {category: 0 for category in UNIT_CATEGORIES}
seen: set[str] = set()
duplicate = 0
for record in unit_records(group):
norm = normalize_unit(record["unit"])
if not norm:
continue
key = f"{record['category']}|{norm}|{normalize_key_part(record.get('target', ''))}"
if key in seen:
duplicate += 1
continue
seen.add(key)
counts[record["category"]] += 1
return sum(counts.values()), counts, duplicate
def caption_tokens(request: dict[str, Any]) -> int:
caption = request.get("caption", "")
return len(TOKEN_RE.findall(caption)) if isinstance(caption, str) else 0
def read_claimed(path: Path, label: str) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as handle:
for line in handle:
if not line.strip():
continue
raw = json.loads(line)
if not raw.get("ok") or not isinstance(raw.get("parsed"), dict):
continue
total, counts, duplicate = dedup_counts(raw["parsed"].get("claimed_units"))
request = raw.get("request", {})
rows.append(
{
"label": label,
"caption_id": request.get("caption_id"),
"tokens": caption_tokens(request),
"dedup_units": total,
"duplicate_units": duplicate,
**{f"{category}_units": counts[category] for category in UNIT_CATEGORIES},
}
)
return rows
def request_unit_lookup(request: dict[str, Any]) -> dict[str, dict[str, Any]]:
return {
unit.get("unit_id"): unit
for unit in request.get("claimed_units", [])
if isinstance(unit, dict) and isinstance(unit.get("unit_id"), str)
}
def read_grounded(path: Path, label: str) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as handle:
for line in handle:
if not line.strip():
continue
raw = json.loads(line)
if not raw.get("ok") or not isinstance(raw.get("parsed"), dict):
continue
lookup = request_unit_lookup(raw.get("request", {}))
counter: Counter[str] = Counter()
for result in raw["parsed"].get("unit_results", []):
if not isinstance(result, dict):
continue
unit = lookup.get(result.get("unit_id"), {})
category = unit.get("category", "__unknown__")
status = result.get("status", "__bad_status__")
counter["valid"] += 1
counter[status] += 1
if status in VISUAL_STATUSES:
counter["visual"] += 1
if category in UNIT_CATEGORIES:
counter[f"{category}_visual"] += 1
counter[f"{category}_{status}"] += 1
rows.append(
{
"label": label,
"caption_id": raw.get("request", {}).get("caption_id"),
"valid": counter["valid"],
"visual": counter["visual"],
"grounded": counter["grounded"],
"unsupported": counter["unsupported"],
"uncertain": counter["uncertain"],
**{key: counter[key] for key in counter if "_" in key},
}
)
return rows
def ci(values: np.ndarray) -> tuple[float, float]:
return float(np.quantile(values, 0.025)), float(np.quantile(values, 0.975))
def bootstrap_indices(n: int, reps: int, rng: np.random.Generator) -> np.ndarray:
return rng.integers(0, n, size=(reps, n), endpoint=False)
def summarize_claimed(rows: list[dict[str, Any]], reps: int, rng: np.random.Generator) -> dict[str, Any]:
n = len(rows)
units = np.asarray([row["dedup_units"] for row in rows], dtype=np.float64)
tokens = np.asarray([max(row["tokens"], 1) for row in rows], dtype=np.float64)
dups = np.asarray([row["duplicate_units"] for row in rows], dtype=np.float64)
idx = bootstrap_indices(n, reps, rng) if n else np.empty((0, 0), dtype=np.int64)
def mean_metric(arr: np.ndarray) -> dict[str, float]:
point = float(arr.mean()) if len(arr) else 0.0
boot = arr[idx].mean(axis=1) if len(arr) else np.asarray([0.0])
low, high = ci(boot)
return {"mean": point, "ci95_low": low, "ci95_high": high}
ratio = float(100.0 * units.sum() / tokens.sum()) if tokens.sum() else 0.0
ratio_boot = 100.0 * units[idx].sum(axis=1) / tokens[idx].sum(axis=1) if n else np.asarray([0.0])
low, high = ci(ratio_boot)
out: dict[str, Any] = {
"captions": n,
"dedup_units_per_caption": mean_metric(units),
"dedup_units_per_100_tokens": {"mean": ratio, "ci95_low": low, "ci95_high": high},
"duplicate_units_per_caption": mean_metric(dups),
}
for category in UNIT_CATEGORIES:
arr = np.asarray([row[f"{category}_units"] for row in rows], dtype=np.float64)
out[f"{category}_per_caption"] = mean_metric(arr)
return out
def summarize_grounded(rows: list[dict[str, Any]], reps: int, rng: np.random.Generator) -> dict[str, Any]:
n = len(rows)
grounded = np.asarray([row["grounded"] for row in rows], dtype=np.float64)
unsupported = np.asarray([row["unsupported"] for row in rows], dtype=np.float64)
uncertain = np.asarray([row["uncertain"] for row in rows], dtype=np.float64)
visual = np.asarray([max(row["visual"], 0) for row in rows], dtype=np.float64)
idx = bootstrap_indices(n, reps, rng) if n else np.empty((0, 0), dtype=np.int64)
def ratio_metric(num: np.ndarray, den: np.ndarray) -> dict[str, float]:
point = float(num.sum() / den.sum()) if den.sum() else 0.0
if not n:
return {"mean": point, "ci95_low": point, "ci95_high": point}
boot_den = den[idx].sum(axis=1)
boot = np.divide(num[idx].sum(axis=1), boot_den, out=np.zeros_like(boot_den), where=boot_den != 0)
low, high = ci(boot)
return {"mean": point, "ci95_low": low, "ci95_high": high}
def mean_metric(arr: np.ndarray) -> dict[str, float]:
point = float(arr.mean()) if len(arr) else 0.0
boot = arr[idx].mean(axis=1) if len(arr) else np.asarray([0.0])
low, high = ci(boot)
return {"mean": point, "ci95_low": low, "ci95_high": high}
out: dict[str, Any] = {
"captions": n,
"visual_units": int(visual.sum()),
"grounded_units_per_caption": mean_metric(grounded),
"grounded_precision": ratio_metric(grounded, visual),
"unsupported_rate": ratio_metric(unsupported, visual),
"uncertain_rate": ratio_metric(uncertain, visual),
}
categories: dict[str, Any] = {}
for category in UNIT_CATEGORIES:
den = np.asarray([row.get(f"{category}_visual", 0) for row in rows], dtype=np.float64)
cat_grounded = np.asarray([row.get(f"{category}_grounded", 0) for row in rows], dtype=np.float64)
cat_unsupported = np.asarray([row.get(f"{category}_unsupported", 0) for row in rows], dtype=np.float64)
cat_uncertain = np.asarray([row.get(f"{category}_uncertain", 0) for row in rows], dtype=np.float64)
categories[category] = {
"visual_units": int(den.sum()),
"grounded_precision": ratio_metric(cat_grounded, den),
"unsupported_rate": ratio_metric(cat_unsupported, den),
"uncertain_rate": ratio_metric(cat_uncertain, den),
}
out["categories"] = categories
return out
def write_tsv(path: Path, rows: list[dict[str, Any]], fieldnames: list[str]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8", newline="") as handle:
writer = csv.DictWriter(handle, fieldnames=fieldnames, delimiter="\t")
writer.writeheader()
writer.writerows(rows)
def fmt_metric(metric: dict[str, float]) -> str:
return f"{metric['mean']:.4f} [{metric['ci95_low']:.4f}, {metric['ci95_high']:.4f}]"
def main() -> int:
args = parse_args()
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
rng = np.random.default_rng(args.seed)
payload: dict[str, Any] = {
"bootstrap_reps": args.bootstrap_reps,
"seed": args.seed,
"claimed": {},
"grounded": {},
}
claimed_tsv: list[dict[str, Any]] = []
for item in args.claimed:
label, path = parse_label_path(item)
rows = read_claimed(path, label)
summary = summarize_claimed(rows, args.bootstrap_reps, rng)
payload["claimed"][label] = {"input": str(path), **summary}
claimed_tsv.append(
{
"surface": label,
"captions": summary["captions"],
"cbu_per_caption_ci95": fmt_metric(summary["dedup_units_per_caption"]),
"cbu_per_100_tokens_ci95": fmt_metric(summary["dedup_units_per_100_tokens"]),
"object_per_caption_ci95": fmt_metric(summary["object_per_caption"]),
"attribute_per_caption_ci95": fmt_metric(summary["attribute_per_caption"]),
"relation_per_caption_ci95": fmt_metric(summary["relation_per_caption"]),
"camera_per_caption_ci95": fmt_metric(summary["camera_per_caption"]),
"lighting_per_caption_ci95": fmt_metric(summary["lighting_per_caption"]),
"text_rendering_per_caption_ci95": fmt_metric(summary["text_rendering_per_caption"]),
}
)
grounded_tsv: list[dict[str, Any]] = []
category_tsv: list[dict[str, Any]] = []
for item in args.grounded:
label, path = parse_label_path(item)
rows = read_grounded(path, label)
summary = summarize_grounded(rows, args.bootstrap_reps, rng)
payload["grounded"][label] = {"input": str(path), **summary}
grounded_tsv.append(
{
"surface": label,
"captions": summary["captions"],
"visual_units": summary["visual_units"],
"grounded_units_per_caption_ci95": fmt_metric(summary["grounded_units_per_caption"]),
"grounded_precision_ci95": fmt_metric(summary["grounded_precision"]),
"unsupported_rate_ci95": fmt_metric(summary["unsupported_rate"]),
"uncertain_rate_ci95": fmt_metric(summary["uncertain_rate"]),
}
)
for category, cat in summary["categories"].items():
category_tsv.append(
{
"surface": label,
"category": category,
"visual_units": cat["visual_units"],
"grounded_precision_ci95": fmt_metric(cat["grounded_precision"]),
"unsupported_rate_ci95": fmt_metric(cat["unsupported_rate"]),
"uncertain_rate_ci95": fmt_metric(cat["uncertain_rate"]),
}
)
(out_dir / "cbu_bootstrap_summary.json").write_text(json.dumps(payload, indent=2), encoding="utf-8")
write_tsv(
out_dir / "claimed_cbu_ci.tsv",
claimed_tsv,
[
"surface",
"captions",
"cbu_per_caption_ci95",
"cbu_per_100_tokens_ci95",
"object_per_caption_ci95",
"attribute_per_caption_ci95",
"relation_per_caption_ci95",
"camera_per_caption_ci95",
"lighting_per_caption_ci95",
"text_rendering_per_caption_ci95",
],
)
write_tsv(
out_dir / "grounded_cbu_ci.tsv",
grounded_tsv,
[
"surface",
"captions",
"visual_units",
"grounded_units_per_caption_ci95",
"grounded_precision_ci95",
"unsupported_rate_ci95",
"uncertain_rate_ci95",
],
)
write_tsv(
out_dir / "grounded_cbu_category_ci.tsv",
category_tsv,
[
"surface",
"category",
"visual_units",
"grounded_precision_ci95",
"unsupported_rate_ci95",
"uncertain_rate_ci95",
],
)
print(json.dumps({"output_dir": str(out_dir), "claimed": len(claimed_tsv), "grounded": len(grounded_tsv)}, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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