93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
"""平台门禁：均分提升 + 受保护 session 无明显退步。"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from app.core.config import settings
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from app.features.evaluation.models import EvalCase, EvalRun
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@dataclass
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class GateResult:
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passed: bool
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mean_baseline_composite: float
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mean_candidate_composite: float
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mean_delta: float
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protected_regressions: list[dict[str, Any]]
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per_case: list[dict[str, Any]]
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def compute_gate(
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*,
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cases: list[EvalCase],
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runs: list[EvalRun],
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regression_threshold: float | None = None,
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) -> GateResult:
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thr = (
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regression_threshold
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if regression_threshold is not None
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else settings.eval_gate_protected_regression_threshold
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)
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by_case: dict[str, dict[str, EvalRun]] = {}
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for r in runs:
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if r.status != "completed":
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continue
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by_case.setdefault(r.case_id, {})[r.side] = r
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per_case: list[dict[str, Any]] = []
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base_scores: list[float] = []
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cand_scores: list[float] = []
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protected_regs: list[dict[str, Any]] = []
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case_map = {c.id: c for c in cases}
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for cid, sides in by_case.items():
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b = sides.get("baseline")
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c_run = sides.get("candidate")
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if not b or not c_run:
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continue
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if b.composite_score is None or c_run.composite_score is None:
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continue
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bs = float(b.composite_score)
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cs = float(c_run.composite_score)
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delta = cs - bs
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base_scores.append(bs)
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cand_scores.append(cs)
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ec = case_map.get(cid)
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protected = bool(ec and ec.is_protected)
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row = {
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"case_id": cid,
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"title": ec.title if ec else None,
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"baseline_composite": bs,
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"candidate_composite": cs,
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"delta": delta,
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"protected": protected,
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}
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per_case.append(row)
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if protected and delta < -thr:
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protected_regs.append(
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{
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"case_id": cid,
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"title": ec.title if ec else None,
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"delta": delta,
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"threshold": thr,
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}
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)
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mean_b = sum(base_scores) / len(base_scores) if base_scores else 0.0
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mean_c = sum(cand_scores) / len(cand_scores) if cand_scores else 0.0
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mean_delta = mean_c - mean_b
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passed = (
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mean_c > mean_b + 1e-6 and len(protected_regs) == 0 and len(base_scores) > 0
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)
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return GateResult(
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passed=passed,
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mean_baseline_composite=mean_b,
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mean_candidate_composite=mean_c,
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mean_delta=mean_delta,
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protected_regressions=protected_regs,
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per_case=per_case,
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)
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