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ThearchyHelios
83 lines
4.7 KiB
Python
83 lines
4.7 KiB
Python
"""评测的统计（app/eval/stats.py；细案 01 做法 11，评测-D10）。纯计算，和手算对照。
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- Wilson 95% 区间、精确 McNemar 双侧 p 值、P50 / P90；
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- 总表：准确率和可追溯准确率分开报（分母只算能判的）、作答路径、出具档位、复核、look、按角色的用量、引用率、逐题 E1、
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重复 k 次的题给通过率。
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和上一次比、A/B 配对表、选基准在 test_eval_compare.py。
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"""
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from __future__ import annotations
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import pytest
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import app.db.models # noqa: F401 - conftest 按已导入的模型建表
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from app.eval import compare, stats
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def test_wilson_and_mcnemar_match_hand_computation():
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assert stats.wilson(8, 10) == (0.4902, 0.9433)
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assert stats.wilson(0, 0) is None
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low, high = stats.wilson(0, 5)
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assert low == 0.0 and 0.43 < high < 0.44
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assert stats.mcnemar_exact(1, 0) == 1.0
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assert stats.mcnemar_exact(6, 0) == 0.03125
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assert stats.mcnemar_exact(7, 2) == 0.179688
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assert stats.mcnemar_exact(0, 0) == 1.0
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assert stats.percentile([5, 1, 3, 2, 4], 50) == 3 and stats.percentile([5, 1, 3, 2, 4], 90) == 5
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assert stats.percentile([], 50) is None
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def _row(case: str, verdict: str | None, *, path: str = "ran", status: str = "ok", repeat: int = 0, **extra) -> dict:
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scores = [{"id": "e1", "kind": "scalar", "required": True,
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"outcome": {"pass": "traced", "fail": "wrong"}.get(verdict or "", "ungradable")}]
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return {"case_id": case, "case_seq": int(case[-1]) if case[-1].isdigit() else 1, "case_hash": f"h-{case}",
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"verdict": verdict, "path": path, "status": status, "repeat": repeat, "scores": scores,
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"traced": verdict == "pass", **extra}
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def test_summary_counts_only_gradable_items_and_splits_roles():
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usage = {"assistant": {"by_model": {"m": {"calls": 3, "cost_usd": 0.03}}, "calls": 3, "cost_usd": 0.03,
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"input_tokens": 10, "output_tokens": 5, "unpriced_calls": 0},
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"writer": {"by_model": {}, "calls": 1, "cost_usd": 0.01, "input_tokens": 1, "output_tokens": 1,
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"unpriced_calls": 1},
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"judge": {"cost_usd": 0.0, "calls": 0}}
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rows = [
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_row("c1", "pass", tier="formal", usage=usage, looks={"model": 3, "system": 0, "limit_hit": True},
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timings={"build_ms": 1000, "total_ms": 4000, "review_ms": 30_000}, review={"verdict": "annotated",
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"severity": "degraded"},
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answer_stats={"numbers": 2, "numbers_cited": 1, "uncited_numbers": 1}, attempts=1,
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sql_match={"status": "proven", "aspects": []}),
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_row("c2", "fail", usage=usage, timings={"build_ms": 3000, "total_ms": 6000},
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sql_match={"status": "related", "aspects": ["time"]}),
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_row("c3", "ungradable"),
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_row("c4", None, path=None, status="skipped"),
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]
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rows[0]["scores"][0]["outcome"] = "traced"
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summary = stats.summarize(rows)
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assert summary["accuracy"] == {"passed": 1, "n": 2, "rate": 0.5, "ci": stats.wilson(1, 2)}
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assert summary["traced_accuracy"]["passed"] == 1
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assert summary["verdicts"] == {"pass": 1, "fail": 1, "ungradable": 1, "skipped": 0} and summary["not_run"] == 1
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assert summary["paths"] == {"ran": 3} and summary["tiers"]["formal"] == 1 and summary["tiers"]["explore"] == 2
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assert summary["review"]["retries"] == 1 and summary["review"]["slow"] == 1
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assert summary["review"]["severity"] == {"degraded": 1}
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assert summary["looks"]["model"] == 3 and summary["looks"]["limit_hit"] == round(1 / 3, 4)
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assert summary["timings"]["build_ms"] == {"p50": 1000, "p90": 3000, "n": 2}
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assert summary["usage"]["assistant"]["calls"] == 6 and summary["usage"]["writer"]["unpriced_calls"] == 2
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assert summary["cost_usd"] == 0.08 and summary["unpriced_calls"] == 2
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assert summary["per_case_cost_usd"] == round(0.08 / 3, 6), "按评完了的题平均，没开评的不摊"
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assert summary["usage"]["gold"] == {"cost_usd": 0.0, "calls": 0}, "期望探针不调模型"
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assert summary["citations"]["rate"] == 0.5
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assert summary["e1"]["passed"] == 1 and summary["e1"]["n"] == 2 and summary["e1"]["aspects"] == {"time": 1}
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def test_repeats_give_a_pass_rate_per_case():
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rows = [_row("c1", "pass", repeat=0), _row("c1", "fail", repeat=1), _row("c1", "pass", repeat=2)]
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summary = stats.summarize(rows)
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assert summary["pass_rates"]["c1"]["passed"] == 2 and summary["pass_rates"]["c1"]["n"] == 3
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assert compare.case_outcome(rows)["verdict"] == "pass", "多数决"
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@pytest.mark.parametrize("n, k", [(1, 0), (3, 3)])
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def test_rates_carry_their_sample_size(n, k):
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r = stats.rate(k, n)
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assert r["n"] == n and r["passed"] == k and r["ci"] == stats.wilson(k, n)
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