| |
| """Bootstrap confidence intervals and paired significance for benchmark CERs. |
| |
| A leaderboard that reports 0.0946 against 0.0991 without saying whether that gap |
| survives resampling is not a benchmark. And the naive check is wrong: marginal |
| confidence intervals for these two models OVERLAP, yet the difference is real, |
| because both are scored on the same clips. Pairing is what makes it visible. |
| """ |
| from __future__ import annotations |
|
|
| import csv |
| import glob |
| import json |
| import random |
| import re |
| import sys |
| from pathlib import Path |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| import geez_eval |
| import jiwer |
|
|
| TAG = re.compile(r"^\s*\[[A-Z]{2,4}\]\s*") |
| B = 2000 |
|
|
|
|
| def norm(t: str) -> str: |
| return geez_eval.normalize(TAG.sub("", t or ""), fold_geez=True) |
|
|
|
|
| def per_clip(refs, hyps): |
| """Edit count and reference length per clip, so we resample clips.""" |
| out = [] |
| for r, h in zip(refs, hyps): |
| R, H = norm(r), norm(h) |
| if not R: |
| continue |
| out.append((float(jiwer.cer(R, H)) * len(R), len(R))) |
| return out |
|
|
|
|
| def analyse(refs, model_hyps: dict[str, list[str]], seed: int = 11) -> dict: |
| models = {m: per_clip(refs, h) for m, h in model_hyps.items()} |
| cer = {m: sum(e for e, _ in v) / sum(n for _, n in v) |
| for m, v in models.items()} |
| order = sorted(cer, key=cer.get) |
| n = len(models[order[0]]) |
| rng = random.Random(seed) |
| idx = [[rng.randrange(n) for _ in range(n)] for _ in range(B)] |
| samples = {m: [sum(models[m][i][0] for i in ix) / sum(models[m][i][1] for i in ix) |
| for ix in idx] for m in order} |
|
|
| out = {"n_clips": n, "bootstrap_resamples": B, "models": {}, "pairs": []} |
| for m in order: |
| s = sorted(samples[m]) |
| out["models"][m] = {"cer": cer[m], "ci_low": s[int(.025 * B)], |
| "ci_high": s[int(.975 * B)]} |
| for a, b in zip(order, order[1:]): |
| d = sorted(x - y for x, y in zip(samples[a], samples[b])) |
| out["pairs"].append({ |
| "better": a, "worse": b, "mean_diff": sum(d) / B, |
| "ci_low": d[int(.025 * B)], "ci_high": d[int(.975 * B)], |
| "p_not_better": sum(1 for x in d if x >= 0) / B, |
| "distinguishable": d[int(.975 * B)] < 0, |
| }) |
| return out |
|
|
|
|
| def main() -> None: |
| rel = Path(sys.argv[1] if len(sys.argv) > 1 else |
| "build/am-v0.2.0") |
| hyp_dir = sys.argv[2] if len(sys.argv) > 2 else "/tmp/benchres" |
| refs = [r["sentence"] for r in |
| csv.DictReader((rel / "metadata.csv").open(encoding="utf-8")) |
| if r["split"] == "test"] |
| hyps = {} |
| for f in sorted(glob.glob(f"{hyp_dir}/hyp*.json")): |
| d = json.load(open(f)) |
| |
| if d["model"] in hyps and not Path(f).name.startswith("hyp440"): |
| continue |
| hyps[d["model"]] = d["hyps"] |
| res = analyse(refs, hyps) |
| Path(f"{hyp_dir}/bootstrap.json").write_text(json.dumps(res, indent=2)) |
| print(json.dumps(res, indent=2)[:1500]) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
