| """Score a predictor (anything with `.ask(question, history)`) on generated eval examples.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from collections import defaultdict |
| from pathlib import Path |
|
|
| from zenith.engines.base import Result |
| from zenith.evals.metrics import caveats_recalled, faithful, params_correct, routed, sim_op_correct |
| from zenith.sft.generate import Example |
|
|
| SHOWCASE = Path(__file__).with_name("showcase.txt") |
| TARGETS = {"sim_op_accuracy": 0.90, "param_accuracy": 0.75, "faithfulness": 0.95, "caveat_recall": 0.70, "routing": 0.85} |
|
|
|
|
| def _mean(xs: list[bool]) -> float | None: |
| return sum(xs) / len(xs) if xs else None |
|
|
|
|
| def score(example: Example, response) -> dict[str, bool]: |
| """Metric -> pass/fail for one example; metrics that don't apply are absent.""" |
| gold = example.turns[-1] |
| question = gold.question |
| if example.kind in ("none", "unsupported"): |
| return {"routing": routed(example.kind, response.spec)} |
| out = {"sim_op_accuracy": sim_op_correct(gold.spec, response.spec), |
| "param_accuracy": params_correct(gold.spec, response.spec)} |
| if response.result is not None and response.answer is not None: |
| result = Result(**response.result) |
| out["faithfulness"] = faithful(response.answer, result, response.spec or {}, question) |
| if result.notes: |
| out["caveat_recall"] = caveats_recalled(response.answer, result) |
| return out |
|
|
|
|
| def _group(example: Example) -> str: |
| spec = example.turns[-1].spec |
| return f"{spec['sim']}/{spec['op']}" if "op" in spec else example.kind |
|
|
|
|
| def run_eval(predictor, examples: list[Example], out_dir: Path, ppl: dict | None = None) -> dict: |
| out_dir = Path(out_dir) |
| out_dir.mkdir(parents=True, exist_ok=True) |
| by_group: dict[str, dict[str, list[bool]]] = defaultdict(lambda: defaultdict(list)) |
| overall: dict[str, list[bool]] = defaultdict(list) |
| for e in examples: |
| response = predictor.ask(e.turns[-1].question, e.turns[:-1]) |
| for metric, ok in score(e, response).items(): |
| by_group[_group(e)][metric].append(ok) |
| overall[metric].append(ok) |
| report = { |
| "examples": len(examples), |
| "overall": {m: _mean(overall.get(m, [])) for m in TARGETS}, |
| "groups": {g: {m: _mean(v.get(m, [])) for m in TARGETS} for g, v in sorted(by_group.items())}, |
| "targets": TARGETS, |
| "perplexity": ppl, |
| } |
| (out_dir / "report.json").write_text(json.dumps(report, indent=2)) |
| (out_dir / "report.md").write_text(_markdown(report)) |
| return report |
|
|
|
|
| def _cell(v: float | None) -> str: |
| return "—" if v is None else f"{v:.1%}" |
|
|
|
|
| def _markdown(report: dict) -> str: |
| metrics = list(TARGETS) |
| lines = [f"# Evaluation ({report['examples']} examples)", "", |
| "| group | " + " | ".join(metrics) + " |", "|---" * (len(metrics) + 1) + "|"] |
| for g, row in report["groups"].items(): |
| lines.append(f"| {g} | " + " | ".join(_cell(row[m]) for m in metrics) + " |") |
| lines.append("| **overall** | " + " | ".join(_cell(report["overall"][m]) for m in metrics) + " |") |
| lines.append("| *target* | " + " | ".join(f"≥ {TARGETS[m]:.0%}" for m in metrics) + " |") |
| if report.get("perplexity"): |
| lines += ["", "Validation bits per byte: " + ", ".join(f"{k} {v:.3f}" for k, v in report["perplexity"].items())] |
| return "\n".join(lines) + "\n" |
|
|
|
|
| def run_showcase(predictor, out_path: Path) -> None: |
| sections = ["# Showcase transcripts", ""] |
| for i, q in enumerate([l for l in SHOWCASE.read_text().splitlines() if l.strip()], 1): |
| r = predictor.ask(q, []) |
| sections += [f"### {i}. {q}", "", f"- **spec:** `{json.dumps(r.spec)}`"] |
| if r.error: |
| sections.append(f"- **error:** {r.error}") |
| else: |
| sections.append(f"- **answer:** {r.answer}" + (" *(truncated)*" if getattr(r, "truncated", False) else "")) |
| sections.append("") |
| Path(out_path).write_text("\n".join(sections)) |
|
|
