| |
| """Parser audit (criterion B1): draw a stratified random sample of answers for hand-labelling. |
| |
| python experiment/audit/make_sample.py [--n 150] |
| -> audit/sample.csv (fill in human_pick and human_status; do NOT look at sample_key.json first) |
| -> audit/sample_key.json (parser output, hidden from the labeller) |
| |
| Stratified so that every main model and every answer type (paragraph answers, name-only |
| follow-ups, pairwise) is represented. human_status: ok | hedge (declines / no favourite) | none. |
| """ |
| import argparse, csv, json, os, random, sys |
| HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| sys.path.insert(0, os.path.join(os.path.dirname(HERE), "pib")) |
| from pib.score_v1 import reparse |
|
|
| ap = argparse.ArgumentParser(); ap.add_argument("--n", type=int, default=150); a = ap.parse_args() |
| cfg = json.load(open(os.path.join(HERE, "config.json"))) |
| QS = [q for q in json.load(open(os.path.join(HERE, "materials", "questions.json")))["questions"] if q["id"] in cfg["questions"]] |
| items = {q["id"]: q["items"] for q in QS} |
| roster = [m for m in json.load(open(os.path.join(HERE, "models.json")))["models"] if m["role"] == "main"] |
| pool = [] |
| for m in roster: |
| p = os.path.join(os.environ.get("PIB_RUNS", os.path.join(HERE, "runs")), m["id"] + ".jsonl") |
| if not os.path.exists(p): |
| continue |
| for r in reparse([json.loads(l) for l in open(p)], QS): |
| if r["block"] in ("base", "pre", "ctx"): |
| pool.append((m["id"], "paragraph", r, r["answer"], items[r["q"]], r["pick"], r["status"])) |
| elif r["block"] == "post" and len(r["turns"]) == 4: |
| pool.append((m["id"], "follow-up", r, r["turns"][3]["content"], items[r["q"]], r["pick"], r["status"])) |
| elif r["block"] == "pair": |
| pool.append((m["id"], "pairwise", r, r["answer"], r["order"], r["pick"], "ok" if r["pick"] else "none")) |
| rng = random.Random("pib-v1.0-audit") |
| strata = {} |
| for x in pool: |
| strata.setdefault((x[0], x[1]), []).append(x) |
| per = max(1, a.n // max(1, len(strata))) |
| sample = [] |
| for k in sorted(strata): |
| sample += rng.sample(strata[k], min(per, len(strata[k]))) |
| rest = [x for x in pool if x not in sample] |
| sample += rng.sample(rest, max(0, min(len(rest), a.n - len(sample)))) |
| rng.shuffle(sample) |
| with open(os.path.join(HERE, "audit", "sample.csv"), "w", newline="") as f: |
| w = csv.writer(f) |
| w.writerow(["audit_id", "answer_type", "options", "answer", "human_pick", "human_status"]) |
| for i, (mid, kind, r, ans, opts, _, _) in enumerate(sample): |
| w.writerow([i, kind, " | ".join(opts), ans, "", ""]) |
| json.dump([{"audit_id": i, "model_id": mid, "key": r["key"], "parser_pick": pk, "parser_status": st} |
| for i, (mid, kind, r, ans, opts, pk, st) in enumerate(sample)], |
| open(os.path.join(HERE, "audit", "sample_key.json"), "w"), indent=1, ensure_ascii=False) |
| print(len(sample), "answers from", len(pool), "->", "audit/sample.csv") |
|
|
