| |
| """Score the runs. Two metrics and, crucially, the right random baseline. |
| |
| RunningBench mixes 3..8 options with 1..5 correct, so the chance line is NOT 1/n and NOT |
| one number for the whole set: it is C(n_options, n_select) per question, averaged over the |
| questions each model actually answered. Reporting a single total against a single baseline |
| lets a model that simply prefers single-answer questions look better than it is. |
| """ |
| import argparse, collections, json, math, os, sys |
| from math import comb |
|
|
| EVAL = "/mnt/data/cvhci_video_understanding/eval" |
|
|
|
|
| def load(tag): |
| rows = [] |
| for l in open(f"{EVAL}/results/{tag}.jsonl"): |
| try: |
| r = json.loads(l) |
| except Exception: |
| continue |
| if not r.get("error"): |
| rows.append(r) |
| ded = {r["review_id"]: r for r in rows} |
| return list(ded.values()) |
|
|
|
|
| def baselines(rows): |
| """Exact-match chance is averaged over questions; overlap chance is averaged over |
| answer SLOTS, which is a different weighting and easy to get wrong.""" |
| ex = sum(1.0 / comb(r["n_options"], r["n_select"]) for r in rows) / len(rows) |
| num = sum(r["n_select"] ** 2 / r["n_options"] for r in rows) |
| den = sum(r["n_select"] for r in rows) |
| return ex, num / den |
|
|
|
|
| def wilson(k, n, z=1.96): |
| if not n: |
| return (0.0, 0.0) |
| p = k / n; d = 1 + z * z / n |
| c = (p + z * z / (2 * n)) / d |
| m = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / d |
| return max(0.0, c - m), min(1.0, c + m) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("tags", nargs="*") |
| ap.add_argument("--by", default="unit", choices=["unit", "question_type", "n_clips", "spec"]) |
| a = ap.parse_args() |
| tags = a.tags or sorted(x[:-6] for x in os.listdir(f"{EVAL}/results") if x.endswith(".jsonl")) |
| print(f"{'model':32s} {'n':>5s} {'exact':>7s} {'95% CI':>14s} {'chance':>7s} {'overlap':>8s} {'chance':>7s} {'多选够数':>8s}") |
| allrows = {} |
| for t in tags: |
| rows = load(t) |
| if not rows: |
| continue |
| allrows[t] = rows |
| n = len(rows); ex = sum(r["exact"] for r in rows) |
| ov = sum(r["overlap"] for r in rows) / n |
| bex, bov = baselines(rows) |
| lo, hi = wilson(ex, n) |
| wellformed = sum(1 for r in rows if r["n_pred"] == r["n_select"]) / n |
| print(f"{t:32s} {n:5d} {100*ex/n:6.2f}% {100*lo:5.1f}-{100*hi:5.1f}% {100*bex:6.2f}% " |
| f"{100*ov:7.1f}% {100*bov:6.1f}% {100*wellformed:7.1f}%") |
| if not allrows: |
| return |
| key = {"unit": "unit", "question_type": "question_type", "n_clips": "n_clips"}.get(a.by) |
| print(f"\n--- 按 {a.by} 拆分（exact）---") |
| cats = sorted({(r[key] if key != "n_clips" else min(r["n_clips"], 5)) for rs in allrows.values() for r in rs}, |
| key=str) |
| hdr = "".join(f"{str(c)[:16]:>17s}" for c in cats) |
| print(f"{'model':32s}{hdr}") |
| for t, rows in allrows.items(): |
| g = collections.defaultdict(list) |
| for r in rows: |
| g[r[key] if key != "n_clips" else min(r["n_clips"], 5)].append(r["exact"]) |
| cells = "".join(f"{(100*sum(g[c])/len(g[c]) if g[c] else float('nan')):16.1f}%" for c in cats) |
| print(f"{t:32s}{cells}") |
| print("\n注: n_clips 一列的 5 表示 >=5 个片段。chance 为按题目规格 C(n_options, n_select) 实算。") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
