evaluate/score_rubric.py

#!/usr/bin/env python3
"""Rubric scoring for the AudioSpan release.

Calls a judge model (via the OpenAI chat-completions API) to score each
criterion on semantic + temporal dimensions, then aggregates with
importance weights. Depends only on `openai` and `tenacity`.

The answer file carries only what the model produced, one record per
question: {"qa_id": ..., "answer": "..."}. Questions and criteria are
joined in from metadata/rubric/{S,M,L}.jsonl; questions with no answer
score zero.

Usage:
    python score_rubric.py --input results/<model>/rubric.jsonl \
                           --judges gpt-5.4-2026-03-05 [--rounds 1]
"""

import argparse
import json
import logging
import os
import re
import sys
from pathlib import Path

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

IMPORTANCE_WEIGHT = {"essential": 1.0, "supporting": 0.5}
VALID_SEMANTIC = {"correct", "partial", "wrong", "none"}
VALID_TEMPORAL = {"correct", "wrong", "missing", "none"}

SCRIPT_DIR = Path(__file__).resolve().parent
DEFAULT_JUDGE_PROMPT = SCRIPT_DIR / "judge_criteria.md"
DEFAULT_JUDGES = ["gpt-5.4-2026-03-05"]
DEFAULT_ROUNDS = 1

# (prefix, base_url, api_key_env_var). Order matters: first match wins.
DEFAULT_API_BASEES: list[tuple[str, str, str]] = [
    ("qwen",    "https://dashscope.aliyuncs.com/compatible-mode/v1",        "DASHSCOPE_API_KEY"),
    ("gpt",     "https://api.openai.com/v1",                                "OPENAI_API_KEY"),
    ("o1",      "https://api.openai.com/v1",                                "OPENAI_API_KEY"),
    ("o3",      "https://api.openai.com/v1",                                "OPENAI_API_KEY"),
    ("o4",      "https://api.openai.com/v1",                                "OPENAI_API_KEY"),
    ("gemini",  "https://generativelanguage.googleapis.com/v1beta/openai/", "GEMINI_API_KEY"),
    ("doubao",  "https://ark.cn-beijing.volces.com/api/v3",                 "ARK_API_KEY"),
    ("seed",    "https://ark.cn-beijing.volces.com/api/v3",                 "ARK_API_KEY"),
    ("deepseek","https://api.deepseek.com/v1",                              "DEEPSEEK_API_KEY"),
]


def _resolve_default_api_base(model: str) -> tuple[str, str]:
    m = model.lower()
    for prefix, base, env in DEFAULT_API_BASEES:
        if m == prefix or m.startswith(prefix):
            nxt = m[len(prefix):len(prefix) + 1]
            if nxt == "" or not nxt.isalpha():
                return base, env
    return "https://api.openai.com/v1", "OPENAI_API_KEY"


RELEASE_ROOT = Path(__file__).resolve().parent.parent
TIERS = ("S", "M", "L")


def load_records(input_path: str, data_root: Path) -> list[dict]:
    """Join answer records with rubric metadata by qa_id."""
    preds: dict[str, str] = {}
    with open(input_path, encoding="utf-8", errors="replace") as fh:
        for lineno, line in enumerate(fh, 1):
            line = line.strip()
            if not line:
                continue
            try:
                rec = json.loads(line)
            except json.JSONDecodeError as e:
                logger.warning("Skipping bad line %d: %s", lineno, e)
                continue
            if rec.get("qa_id"):
                preds[rec["qa_id"]] = rec.get("answer")
    if not preds:
        sys.exit(f"ERROR: no valid answers in {input_path}")

    records = []
    for tier in TIERS:
        path = data_root / "metadata" / "rubric" / f"{tier}.jsonl"
        if not path.is_file():
            sys.exit(f"ERROR: metadata not found: {path}")
        with open(path, encoding="utf-8") as fh:
            for line in fh:
                if not line.strip():
                    continue
                q = json.loads(line)
                q["model_response"] = preds.get(q["qa_id"])
                records.append(q)

    unknown = sorted(set(preds) - {r["qa_id"] for r in records})
    if unknown:
        logger.warning("Ignoring %d unknown qa_id(s), e.g. %s", len(unknown), unknown[0])
    covered = sum(1 for r in records if r["model_response"] is not None)
    logger.info("Answers cover %d/%d questions (missing score zero)", covered, len(records))
    return records


def format_criteria(criteria: list[dict]) -> str:
    lines = []
    for i, c in enumerate(criteria):
        tr = c.get("time_range")
        tr_str = f" [{tr[0]}-{tr[1]}]" if tr and len(tr) == 2 else ""
        lines.append(f"{i+1}. [{c.get('importance', 'essential')}]{tr_str} {c['text']}")
    return "\n".join(lines)


_SEM_SCORE = {"correct": 1.0, "partial": 0.5, "wrong": 0.0}


def _criterion_score(semantic: str, temporal: str, has_time_range: bool) -> float:
    sem = _SEM_SCORE.get(semantic, 0.0)
    if has_time_range:
        if semantic == "wrong":
            return 0.0
        if temporal in ("none", "missing"):
            return sem
        return (sem + (1.0 if temporal == "correct" else 0.0)) / 2
    if temporal in ("correct", "wrong"):
        return 1.0 if temporal == "correct" else 0.0
    return sem


def _rubric_score(judged: list[dict], ref_criteria: list[dict]) -> float:
    judged_by_idx = {jc.get("index", i): jc for i, jc in enumerate(judged)}
    weighted_sum, weight_total = 0.0, 0.0
    for ref_idx, ref in enumerate(ref_criteria):
        jc = judged_by_idx.get(ref_idx)
        if not jc:
            continue
        has_tr = ref.get("importance", "essential") != "essential" and ref.get("time_range") is not None
        w = IMPORTANCE_WEIGHT.get(ref.get("importance", "essential"), 1.0)
        weighted_sum += w * _criterion_score(jc.get("semantic", "wrong"), jc.get("temporal", "none"), has_tr)
        weight_total += w
    return round(weighted_sum / weight_total if weight_total else 0.0, 4)


def _trimmed_mean(scores: list[float]) -> float:
    s = sorted(scores)[1:-1] if len(scores) >= 3 else list(scores)
    return sum(s) / len(s) if s else 0.0


def _extract_json(text: str) -> dict | None:
    stripped = text.strip()
    fence = re.search(r"```(?:json)?\s*\n(.*?)\n\s*```", stripped, re.DOTALL)
    if fence:
        stripped = fence.group(1).strip()
    try:
        return json.loads(stripped)
    except json.JSONDecodeError:
        first, last = stripped.find("{"), stripped.rfind("}")
        if first != -1 and last > first:
            try:
                return json.loads(stripped[first:last + 1])
            except json.JSONDecodeError:
                return None
    return None


def _load_completed(evals_path: str) -> set[tuple[str, str, int]]:
    done: set[tuple[str, str, int]] = set()
    if not os.path.isfile(evals_path):
        return done
    with open(evals_path, encoding="utf-8", errors="replace") as fh:
        for line in fh:
            line = line.strip()
            if not line:
                continue
            try:
                ev = json.loads(line)
                done.add((ev.get("qa_id"), ev["judge"], ev["round"]))
            except (json.JSONDecodeError, KeyError):
                continue
    return done


def run_rubric(records: list[dict], judges: list[str], rounds: int,
               evals_path: str, judge_prompt_path: str,
               api_base: str, api_key: str) -> dict:
    from openai import OpenAI
    from tenacity import retry, stop_after_attempt, wait_exponential_jitter

    system_prompt = Path(judge_prompt_path).read_text(encoding="utf-8")
    client = OpenAI(api_key=api_key, base_url=api_base)

    done = _load_completed(evals_path)
    pending = [
        (rec, judge, r)
        for rec in records
        if rec.get("criteria")
        for judge in judges
        for r in range(rounds)
        if (rec["qa_id"], judge, r) not in done
    ]
    logger.info("Rubric: %d pending / %d total evals", len(pending),
                len(records) * len(judges) * rounds)

    write_mode = "a" if done else "w"
    fh = open(evals_path, write_mode, encoding="utf-8")
    completed = 0

    @retry(wait=wait_exponential_jitter(initial=3, max=15, exp_base=2, jitter=2),
           stop=stop_after_attempt(3))
    def _call_judge(judge: str, user_msg: str) -> str:
        resp = client.chat.completions.create(
            model=judge,
            messages=[{"role": "system", "content": system_prompt},
                      {"role": "user", "content": user_msg}],
            max_tokens=3000, temperature=0.0,
            response_format={"type": "json_object"},
        )
        return resp.choices[0].message.content or ""

    try:
        for rec, judge, round_idx in pending:
            user_msg = (
                f"## Question\n\n{rec['question']}\n\n"
                f"## Evaluation Criteria\n\n{format_criteria(rec['criteria'])}\n\n"
                f"## Model Response\n\n{rec.get('model_response') or ''}"
            )
            judged = None
            for _ in range(3):
                try:
                    raw = _call_judge(judge, user_msg)
                    result = _extract_json(raw) or {}
                    judged = result.get("criteria", [])
                    for j in judged:
                        if j.get("temporal") == "partial":
                            j["temporal"] = "correct"
                    if len(judged) != len(rec["criteria"]):
                        judged = None
                        continue
                    if any(j.get("semantic") not in VALID_SEMANTIC
                           or j.get("temporal") not in VALID_TEMPORAL
                           for j in judged):
                        judged = None
                        continue
                    break
                except Exception as e:
                    logger.warning("Judge %s id=%s: %s", judge, rec["qa_id"], str(e)[:120])
                    judged = None
            if not judged:
                logger.warning("Judge %s id=%s: all attempts failed", judge, rec["qa_id"])
                continue
            score = _rubric_score(judged, rec["criteria"])
            ev = {
                "qa_id": rec["qa_id"], "judge": judge, "round": round_idx, "score": score,
                "criteria": [
                    {"index": j.get("index", j_idx),
                     "importance": rec["criteria"][j_idx].get("importance", "essential"),
                     "semantic": j.get("semantic"), "temporal": j.get("temporal"),
                     "score": _criterion_score(
                         j.get("semantic"), j.get("temporal"),
                         rec["criteria"][j_idx].get("importance", "essential") != "essential"
                         and rec["criteria"][j_idx].get("time_range") is not None,
                     )}
                    for j_idx, j in enumerate(judged)
                ],
            }
            fh.write(json.dumps(ev, ensure_ascii=False) + "\n")
            fh.flush()
            completed += 1
            if completed % 50 == 0:
                logger.info("Rubric progress: %d/%d", completed, len(pending))
    finally:
        fh.close()

    return _aggregate(evals_path, records, judges)


def _aggregate(evals_path: str, records: list[dict], judges: list[str]) -> dict:
    evals_by_id: dict[str, list[dict]] = {}
    with open(evals_path, encoding="utf-8", errors="replace") as fh:
        for line in fh:
            line = line.strip()
            if not line:
                continue
            try:
                ev = json.loads(line)
            except json.JSONDecodeError:
                continue
            evals_by_id.setdefault(ev.get("qa_id"), []).append(ev)

    scored = []
    for rec in records:
        evals = [e for e in evals_by_id.get(rec["qa_id"], [])
                 if e.get("judge") == judges[0]]
        score = round(_trimmed_mean([e["score"] for e in evals]) * 100, 2) if evals else 0
        scored.append({"qa_id": rec["qa_id"], "score": score})

    total = len(scored)
    mean = sum(s["score"] for s in scored) / total if total else 0
    return {"mode": "rubric", "total": total,
            "avg_score": round(mean, 2), "scored_records": scored}


def main():
    parser = argparse.ArgumentParser(description="Rubric-score AudioSpan model outputs")
    parser.add_argument("--input", required=True,
                        help="Prediction JSONL: {\"qa_id\": ..., \"answer\": \"...\"} per line")
    parser.add_argument("--output", help="Aggregated scored JSONL (default: <input>_scored.jsonl)")
    parser.add_argument("--evals", help="Per-eval JSONL (default: <input>_evals.jsonl)")
    parser.add_argument("--data-root", type=Path, default=RELEASE_ROOT,
                        help="release root holding metadata/ (default: parent of evaluate/)")
    parser.add_argument("--judges", help="Comma-separated judge models")
    parser.add_argument("--rounds", type=int, default=DEFAULT_ROUNDS)
    parser.add_argument("--judge-prompt", default=str(DEFAULT_JUDGE_PROMPT))
    parser.add_argument("--api-base",
                        help="OpenAI-compatible base URL (default: inferred from first judge)")
    parser.add_argument("--api-key",
                        help="API key (default: provider env var, see module docstring)")
    args = parser.parse_args()

    records = load_records(args.input, args.data_root.resolve())

    judges = args.judges.split(",") if args.judges else DEFAULT_JUDGES
    default_base, default_env = _resolve_default_api_base(judges[0])
    if not args.api_base:
        args.api_base = default_base
        logger.info("Resolved --api-base from judge %r: %s", judges[0], args.api_base)
    if not args.api_key:
        args.api_key = os.environ.get(default_env) or os.environ.get("OPENAI_API_KEY")
    if not args.api_key:
        parser.error(f"--api-key or {default_env} required for judge {judges[0]!r}")

    evals_path = args.evals or args.input.replace(".jsonl", "_evals.jsonl")
    summary = run_rubric(
        records, judges, args.rounds, evals_path,
        judge_prompt_path=args.judge_prompt,
        api_base=args.api_base, api_key=args.api_key,
    )

    out_path = args.output or args.input.replace(".jsonl", "_scored.jsonl")
    with open(out_path, "w", encoding="utf-8") as fh:
        for s in summary.get("scored_records", []):
            fh.write(json.dumps(s, ensure_ascii=False) + "\n")
    logger.info("Scored records: %s", out_path)

    print(json.dumps({k: v for k, v in summary.items() if k != "scored_records"},
                     indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()