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"semantic_key": "career.1990.10.dasha_boundary.promotion", + "domain": "career", + "year": 1990, + "month": 10, + "source": "dasha_boundary", + "event_family": "升职", + "information_gain": 0.943, + "true_side": "no", + "separates": true, + "yes_count": 2, + "no_count": 3 + }, + { + "semantic_key": "finance.1998.03.dasha_boundary", + "domain": "finance", + "year": 1998, + "month": 3, + "source": "dasha_boundary", + "event_family": "收入明显变化、大笔支出或欠债", + "information_gain": 0.845, + "true_side": "yes", + "separates": true, + "yes_count": 3, + "no_count": 2 + }, + { + "semantic_key": "relationship.1955.09.dasha_boundary.relationship-commitment", + "domain": "relationship", + "year": 1955, + "month": 9, + "source": "dasha_boundary", + "event_family": "结婚", + "information_gain": 0.8335, + "true_side": "unknown", + "separates": true, + "yes_count": 2, + "no_count": 3 + }, + { + "semantic_key": "education.1982.08.dasha_boundary", + "domain": "education", + "year": 1982, + "month": 8, + "source": "dasha_boundary", + "event_family": "升学、转学或换学习环境", + "information_gain": 0.7421, + "true_side": "unknown", + "separates": true, + "yes_count": 4, + "no_count": 1 + } + ], + "refresh_probe_count": 7, + "refresh_keys": [ + "health_pressure.1958.07.dasha_boundary", + "career.1990.10.dasha_boundary.career-entry", + "career.1990.10.dasha_boundary.career-exit", + "career.1990.10.dasha_boundary.promotion", + "finance.1998.03.dasha_boundary", + "relationship.1955.09.dasha_boundary.relationship-commitment", + "education.1982.08.dasha_boundary" + ] + } + } + } + ] +} diff --git a/docs/research/probe_supply_after_six_2026_09_13.md b/docs/research/probe_supply_after_six_2026_09_13.md new file mode 100644 index 00000000..003837da --- /dev/null +++ b/docs/research/probe_supply_after_six_2026_09_13.md @@ -0,0 +1,101 @@ +# 六题之后刷新出题供给测量（2026-09-13） + +- 任务：`docs/tasks/TASK-rectification-probe-supply-research-20260913.md` +- 代码基线：`da048676ba24242d1e7372d0ff63ec5664a63e6a`（`codex/rectification-probe-supply-research-20260913`） +- 数据：`references/real_case_calibration/minute_rectification_holdout_v3.json`（20 例公开 AA，`source_audit_status=invalidated_after_replay`，只作开发集趋势，不是发布指标） +- 性质：离线测量。生产 `event_probes.py` 默认行为未改。 + +## 方法 + +1. 每例用 holdout 公开事件对声明分钟 ±`candidate_radius_minutes` 打分，生成生产路径带年月题。 +2. 按信息增益取前 6 道，用对真实分钟最优的 yes/no 回放（真实分钟落在 yes 组答 yes，落在 no 组答 no）。 +3. 剩余活动簇代表若多于 5 个，保留真实簇并按后验截到 5 个，模拟「六题后剩余 ≤5 候选」。 +4. 对该剩余集按 `refresh_probes=true` 再生成题：baseline 走生产函数；R1–R4 只在本脚本里临时改封锁年、事件家族、`MIN_BOUNDARY_DAYS`、Vimshottari 第三级与 D9/D10 上升 Narayana。 +5. 新题按最优答案继续回放，比较头名命中与剩余范围宽度。 + +有分辨力：真实分钟（或其簇代表）能落到 yes 或 no，且另一组里还有剩余候选。相同 yes/no 划分只计一次 unique split。收益门槛是相对生产刷新 **多出** ≥1 道独立划分，且头名不降。 + +## 放宽项 + +| 代号 | 改法 | +| --- | --- | +| R1 | 刷新时只封已问/已知的确切年，去掉 ±1 年封锁 | +| R2 | 事业按入职/升职/离职、感情按结婚/分手，同域同年可再出一道 | +| R3 | 刷新阶段 `MIN_BOUNDARY_DAYS` 45→30 | +| R4 | 边界加 Vimshottari pratyantar，以及 D9/D10 上升的 Narayana | + +问满 6 道且剩余 ≥2 因而进入刷新测量的例子：12/20。 + +## 总表 + +| 方案 | 平均新增题 | 相对生产多出题 | 平均有分辨力 | 平均独立划分 | 相对生产多出划分 | 有分辨力的例子 | 六题后头名 | 回放后头名 | 头名差 | 六题后宽度 | 回放后宽度 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| baseline | 1.75 | 0.0 | 1.75 | 1.333 | 0.0 | 7/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 9.5 | +| R1 | 1.833 | 0.083 | 1.833 | 1.417 | 0.084 | 8/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 9.58 | +| R2 | 2.5 | 0.75 | 2.5 | 1.583 | 0.25 | 8/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.67 | +| R1+R2 | 2.5 | 0.75 | 2.5 | 1.583 | 0.25 | 8/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.67 | +| R3 | 3 | 1.25 | 3 | 2.333 | 1.0 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 9.25 | +| R1+R3 | 3 | 1.25 | 3 | 2.333 | 1.0 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 9.17 | +| R2+R3 | 3.833 | 2.083 | 3.833 | 2.417 | 1.084 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8 | +| R1+R2+R3 | 3.917 | 2.167 | 3.917 | 2.417 | 1.084 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.42 | +| R4 | 3.25 | 1.5 | 3.25 | 2.417 | 1.084 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.67 | +| R1+R4 | 3.5 | 1.75 | 3.5 | 2.75 | 1.417 | 11/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.5 | +| R2+R4 | 4.333 | 2.583 | 4.333 | 2.5 | 1.167 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.5 | +| R1+R2+R4 | 4.25 | 2.5 | 4.25 | 2.667 | 1.334 | 11/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.75 | +| R3+R4 | 3.5 | 1.75 | 3.5 | 2.833 | 1.5 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.42 | +| R1+R3+R4 | 3.667 | 1.917 | 3.667 | 2.75 | 1.417 | 11/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.58 | +| R2+R3+R4 | 4.25 | 2.5 | 4.25 | 2.75 | 1.417 | 10/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8.25 | +| R1+R2+R3+R4 | 4.833 | 3.083 | 4.833 | 2.75 | 1.417 | 11/12 | 0.9167 | 0.9167 | 0.0 | 11.33 | 8 | + +## 结论 + +**有收益，可另立实现单。** + +- 判定：`benefit` +- 原因：at least one relaxation added >=1 unique true-cluster split vs production refresh without dropping top-1 +- 立实现单：是 +- 达到门槛的组合：R3, R1+R3, R2+R3, R1+R2+R3, R4, R1+R4, R2+R4, R1+R2+R4, R3+R4, R1+R3+R4, R2+R3+R4, R1+R2+R3+R4 + +## 建议实现范围 + +立实现单只做 **刷新阶段 R3 + R4**： + +- R3：刷新时 `MIN_BOUNDARY_DAYS` 45→30。单独达到门槛（相对生产 +1.0 独立划分、+1.25 题），改动最小。 +- R4：刷新边界加 Vimshottari pratyantar 与 D9/D10 上升 Narayana。单独 +1.084 独立划分、+1.5 题。`_vim_start_dates` 已有 `include_pratyantar`，生产默认仍为 false。 +- 合做 R3+R4：+1.5 独立划分、+1.75 题；头名 0.9167 不降，宽度 11.33→8.42。 +- **不做 R1**：只多 0.084 划分。 +- **不做 R2**：多出的题大多是同一 yes/no 划分的家族复题；单独只多 0.25 划分。 +- 即使 R3+R4，仍有例子问满 6 道后刷新还是 0 题。定向补事（方案 A）仍要保留。 + +## 分例 + +| 例子 | 问了几道 | 剩余 | 截断 | 真实还在 | baseline 新题 | 最好组合 | 该组合新题 | 该组合分辨力 | +| --- | ---: | ---: | --- | --- | ---: | --- | ---: | ---: | +| barack_obama_1961_aa_v4_holdout | 6 | 5 | capped_to_5 | True | 1 | R2+R4 | 9 | 9 | +| angelina_jolie_1975_aa_v4_holdout | 6 | 5 | natural | True | 0 | R2+R3 | 8 | 8 | +| tiger_woods_1975_aa_v4_holdout | 0 | 5 | capped_to_5 | True | 0 | R2+R3 | 8 | 8 | +| elizabeth_taylor_1932_aa_v4_holdout | 6 | 5 | natural | True | 3 | R1 | 4 | 4 | +| elizabeth_montgomery_1933_aa_v4_holdout | 4 | 5 | natural | True | 2 | R1+R2 | 8 | 8 | +| pablo_picasso_1881_aa_v4_holdout | 0 | 5 | capped_to_5 | True | 0 | R1+R2 | 9 | 9 | +| sigmund_freud_1856_aa_v4_holdout | 0 | 5 | capped_to_5 | True | 0 | R2+R3 | 11 | 11 | +| salma_hayek_1966_aa_v4_holdout | 6 | 4 | natural | True | 1 | R3 | 5 | 5 | +| steve_reich_1936_aa_v4_holdout | 4 | 5 | capped_to_5 | True | 0 | R1+R2+R4 | 7 | 7 | +| albert_brooks_1947_aa_v4_holdout | 0 | 5 | capped_to_5 | True | 0 | R1+R2+R3+R4 | 8 | 8 | +| paul_ryan_1970_aa_v4_holdout | 3 | 5 | natural | True | 0 | R2 | 7 | 7 | +| tom_kennedy_1927_aa_v4_holdout | 6 | 4 | natural | True | 1 | R2+R4 | 7 | 7 | +| andrew_windsor_1960_aa_v4_holdout | 6 | 2 | natural | True | 0 | R3 | 4 | 4 | +| sean_lennon_1975_aa_v4_holdout | 6 | 4 | natural | True | 0 | R2+R4 | 7 | 7 | +| joseph_kennedy_iii_1980_aa_v4_holdout | 6 | 3 | natural | True | 0 | baseline | 0 | 0 | +| kurt_cobain_1967_aa_v4_holdout | 6 | 4 | natural | True | 7 | R1+R2+R3+R4 | 10 | 10 | +| john_robbins_1947_aa_v4_holdout | 6 | 2 | natural | True | 0 | R1+R4 | 1 | 1 | +| chad_everett_1937_aa_v4_holdout | 6 | 4 | natural | True | 3 | R2+R3+R4 | 5 | 5 | +| bernd_eichinger_1949_aa_v4_holdout | 2 | 5 | capped_to_5 | True | 0 | R1+R2+R3+R4 | 5 | 5 | +| iwao_takamoto_1925_aa_v4_holdout | 6 | 5 | natural | True | 5 | R2 | 7 | 7 | + +## 边界 + +- 最优答案是相对已公布 Rodden AA 分钟的上界，不是真实用户会怎么答。 +- holdout 已被 `invalidated_after_replay`，不得写成发布准确率。 +- R2 若只换事件家族文案、分盘激活规则不变，yes/no 划分可能与默认家族重复。 +- 未把研究脚本接到生产路径。 + diff --git a/docs/tasks/TASK-rectification-refresh-r3-r4-20260913.md b/docs/tasks/TASK-rectification-refresh-r3-r4-20260913.md new file mode 100644 index 00000000..0a55f66a --- /dev/null +++ b/docs/tasks/TASK-rectification-refresh-r3-r4-20260913.md @@ -0,0 +1,44 @@ +# TASK · 刷新阶段放宽换运边界（R3 + R4）（2026-09-13） + +- 基线：以合入研究结论时的 `origin/staging` 为准 +- 分支：`codex/rectification-refresh-r3-r4-20260913`，基于 `origin/staging` +- 执行方：coding agent；验收：Claude +- BUG 编号起点：**BUG-664**（开工时核对 `docs/BUG_HISTORY.md` 最大号；661–663 留给定向补事单） +- 决策来源：`TASK-rectification-probe-supply-research-20260913.md` 测量结论有收益；只实现刷新阶段 R3 + R4。研究原文：`docs/research/probe_supply_after_six_2026_09_13.md`。 +- 不改首轮出题、不改计分、不改确认门。 + +## 1. 为什么做 + +20 例公开 AA holdout 里，问满 6 道带年月题且剩余 ≥2 的 12 例：生产刷新平均还能出 1.75 道题。相对生产再放宽： + +| 方案 | 相对生产多出题 | 相对生产多出独立划分 | 头名 | +| --- | ---: | ---: | ---: | +| R1 去掉邻近年封锁 | +0.083 | +0.084 | 不降 | +| R2 同域同年换家族再问 | +0.75 | +0.25 | 不降 | +| **R3 刷新 MIN_BOUNDARY_DAYS 45→30** | **+1.25** | **+1.0** | 不降 | +| **R4 pratyantar + D9/D10 Narayana** | **+1.5** | **+1.084** | 不降 | +| **R3+R4** | **+1.75** | **+1.5** | 不降 | + +R1/R2 达不到「平均多 ≥1 道有分辨力的独立划分」。R2 的额外题大多是同一 yes/no 划分。本单不实现 R1/R2。 + +即使 R3+R4，仍有例子刷新 0 题。方案 A 定向补事是另一条线，不在本单。 + +## 2. 实现范围 + +- **T1（BUG-664）R3**：仅当 `request.refresh_probes is True` 时，`_boundary_windows` / `_union_boundary_dates` 使用 `MIN_BOUNDARY_DAYS=30`；首轮出题保持 45。 +- **T2（BUG-665）R4**：仅刷新阶段，`_vim_start_dates(..., include_pratyantar=True)`；对剩余候选的 D9 / D10 上升再算 Narayana 边界并并入 `_union_boundary_dates`。本命上升 Narayana 与 Vimshottari MD/AD 保持原样。 +- **T3 测试**：刷新剩余 ≤5 时，45 天内、跨年、pratyantar、D9/D10 上升不同的边界可以出题；首轮 `refresh_probes` 缺省路径题量与现网一致。不得把研究脚本接到生产。 +- **T4 记录**：BUG-664/665、CHANGELOG、PROGRESS；Skill 若用户可见题变多则升补丁号。 + +## 3. 硬红线 + +- 不得改 `EXISTENCE_NEARBY_YEARS` 默认值，不得按事件家族拆同一年的题。 +- 不得改 `SCORE_DELTA`、收敛门、无年月题计分（BUG-629）。 +- 刷新仍走 `discriminating_event_probes` 的 distinguish 合同：必须一组会发生、一组不会。 +- 研究脚本 `scripts/research/probe_supply_after_six.py` 保持离线，不进 API。 + +## 4. 验收 + +1. 首轮出题回归：未开 `refresh_probes` 的既有 event_probes 测试断言不放宽。 +2. 刷新用例：剩余 5 个候选、已问 6 个 semantic_key 时，R3 能收入 30–44 天的同年边界；R4 能收入 pratyantar 或 D9/D10 上升不同而产生的新年月题。 +3. 真机：方案 A 之前的「六题后 no_new_probes」序列，在剩余候选 D9/D10 或第三级换运有差时，应出现至少一道新的带年月选择题；若仍无题，走定向补事，不得报「没有拿到下一个问题」。 diff --git a/scripts/research/probe_supply_after_six.py b/scripts/research/probe_supply_after_six.py new file mode 100644 index 00000000..70725c69 --- /dev/null +++ b/scripts/research/probe_supply_after_six.py @@ -0,0 +1,1224 @@ +#!/usr/bin/env python3 +"""Offline measurement: can refresh emit more dated probes after six answers? + +Does not change production `event_probes.py` defaults. Relaxations are applied +only inside this script via temporary module patches. +""" + +from __future__ import annotations + +import argparse +import json +import statistics +import sys +import traceback +from calendar import monthrange +from contextlib import contextmanager, nullcontext +from dataclasses import dataclass +from datetime import date +from pathlib import Path +from typing import Any, Iterator, Sequence +from uuid import NAMESPACE_URL, uuid5 + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from scripts.rectification.candidate_contrast import ( # noqa: E402 + cluster_contexts_by_signature, + distinguish_contract_errors, +) +from scripts.rectification.contracts import normalize_rectification_request # noqa: E402 +from scripts.rectification.event_probes import ( # noqa: E402 + DOMAIN_CATALOG, + EXISTENCE_NEARBY_YEARS, + MAX_BOUNDARY_CANDIDATES_PER_DOMAIN, + MIN_BOUNDARY_DAYS, + PROBE_PHASE_CANDIDATE_DISCRIMINATOR, + _annotate_nearby_ledger, + _answer_priors_for, + _apply_prior_ranking, + _best_probe_per_year, + _boundary_windows, + _differing_layers, + _dominant_existence_prior, + _evaluation_order, + _event_years, + _existence_blocked_years, + _layer_value, + _narayana_cache_key, + _narayana_start_dates, + _partition_ranked_probes, + _probe_caps, + _probe_domains, + _probe_sort_key, + _quality_distinguish_probes, + _remaining_contexts, + _representative_pairs, + _scoreable, + _static_contexts, + _try_activation_probe, + _vim_cache_key, + _vim_start_dates, + asked_years_for_domain, + discriminating_event_probes, +) +from scripts.rectification.case_holdout import holdout_domain_years, holdout_event_ids # noqa: E402 +from scripts.rectification.refinement_packet import window_scan # noqa: E402 +from scripts.rectification.scoring_service import ( # noqa: E402 + build_event_contribution_matrix, + score_from_matrix, + scoreable_request, +) + +HOLDOUT_MANIFEST = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v3.json" +TODAY = date(2026, 9, 13) +ASK_COUNT = 6 +REMAINING_CAP = 5 +STRONG_CONFLICT_ELIMINATION_COUNT = 3 +SCORE_DELTA = { + "support": 2.0, + "weak_support": 1.0, + "neutral": 0.0, + "weak_conflict": -1.0, + "conflict": -2.0, +} +KIND_BY_DOMAIN = { + "education": "education_milestone", + "career": "career_change", + "relationship": "relationship_commitment", + "relocation": "relocation", + "health_pressure": "self_health_event", + "health": "self_health_event", + "finance": "finance_change", + "family": "family_event", +} +EXTRA_FAMILIES: dict[str, tuple[tuple[str, str], ...]] = { + "career": ( + ("career_entry", "入职"), + ("promotion", "升职"), + ("career_exit", "离职"), + ), + "relationship": ( + ("relationship_commitment", "结婚"), + ("relationship_separation", "分手"), + ), +} + + +@dataclass(frozen=True) +class Relaxation: + r1: bool = False + r2: bool = False + r3: bool = False + r4: bool = False + + @property + def name(self) -> str: + labels = [ + label + for label, on in (("R1", self.r1), ("R2", self.r2), ("R3", self.r3), ("R4", self.r4)) + if on + ] + return "+".join(labels) if labels else "baseline" + + +def all_relaxations() -> list[Relaxation]: + rows: list[Relaxation] = [] + for mask in range(16): + rows.append(Relaxation( + r1=bool(mask & 1), + r2=bool(mask & 2), + r3=bool(mask & 4), + r4=bool(mask & 8), + )) + return rows + + +def _clock(value: str) -> int: + return int(value[:2]) * 60 + int(value[3:5]) + + +def _hhmm_from_minutes(value: int) -> str: + wrapped = value % 1440 + return f"{wrapped // 60:02d}:{wrapped % 60:02d}" + + +def _shift_clock(value: str, delta: int) -> str: + return _hhmm_from_minutes(_clock(value) + delta) + + +def _expand_date(raw: str, precision: str) -> tuple[str, str]: + text = str(raw or "").strip() + if precision == "day": + day = date.fromisoformat(text) + return day.isoformat(), day.isoformat() + if precision == "month": + month = date.fromisoformat(f"{text}-01") if len(text) == 7 else date.fromisoformat(text[:10]).replace(day=1) + last = monthrange(month.year, month.month)[1] + return month.isoformat(), month.replace(day=last).isoformat() + year = int(text[:4]) + return f"{year}-01-01", f"{year}-12-31" + + +def _event_kind(event: dict[str, Any]) -> str: + domain = str(event.get("domain") or "") + description = str(event.get("description") or "").lower() + if domain == "relationship" and not any( + token in description for token in ("married", "wedding", "wife", "husband") + ): + return "relationship_start" + return KIND_BY_DOMAIN[domain] + + +def request_from_case(case: dict[str, Any]) -> dict[str, Any]: + birth = case["birth"] + true_time = str(birth["time"])[:5] + radius = int(case.get("candidate_radius_minutes") or 10) + events = [] + for event in case.get("events") or []: + precision = str(event.get("precision") or "year") + start, end = _expand_date(str(event.get("date") or ""), precision) + domain = str(event.get("domain") or "") + events.append({ + "id": str(uuid5(NAMESPACE_URL, str(event.get("id") or ""))), + "domain": domain, + "event_kind": _event_kind(event), + "date_start": start, + "date_end": end, + "precision": precision, + "summary": str(event.get("description") or event.get("id") or domain)[:200], + }) + return normalize_rectification_request({ + "birth_date": str(birth["date"]), + "start_time": _shift_clock(true_time, -radius), + "end_time": _shift_clock(true_time, radius), + "lat": float(birth["latitude"]), + "lon": float(birth["longitude"]), + "tz": float(birth["timezone_offset"]), + "events": events, + }, today=TODAY) + + +def _scan_for(built: dict[str, Any]) -> dict[str, Any]: + return window_scan(built) + + +def family_slug(kind: str) -> str: + return kind.replace("_", "-") + + +@contextmanager +def patched_boundary_days(days: int) -> Iterator[None]: + import scripts.rectification.event_probes as ep + previous = ep.MIN_BOUNDARY_DAYS + ep.MIN_BOUNDARY_DAYS = days + try: + yield + finally: + ep.MIN_BOUNDARY_DAYS = previous + + +@contextmanager +def catalog_family(domain: str, kind: str, family: str) -> Iterator[None]: + original = DOMAIN_CATALOG[domain] + DOMAIN_CATALOG[domain] = {**original, "kind": kind, "event_family": family} + try: + yield + finally: + DOMAIN_CATALOG[domain] = original + + +def union_boundary_dates( + reps: Sequence[dict[str, Any]], + *, + birth_date: str, + lo: int, + hi: int, + include_pratyantar: bool, + varga_narayana: bool, +) -> list[date]: + vim_cache: dict[tuple[Any, ...], list[date]] = {} + narayana_cache: dict[tuple[Any, ...], list[date] | None] = {} + dates_by_key: dict[tuple[int, int], date] = {} + for left, right in _representative_pairs(reps): + left_moon = float(left["planet_longitudes"]["Moon"]) + right_moon = float(right["planet_longitudes"]["Moon"]) + left_vim_key = (*_vim_cache_key(birth_date, left_moon, lo, hi), include_pratyantar) + right_vim_key = (*_vim_cache_key(birth_date, right_moon, lo, hi), include_pratyantar) + if left_vim_key not in vim_cache: + vim_cache[left_vim_key] = _vim_start_dates( + birth_date, left_moon, lo, hi, include_pratyantar=include_pratyantar, + ) + if right_vim_key not in vim_cache: + vim_cache[right_vim_key] = _vim_start_dates( + birth_date, right_moon, lo, hi, include_pratyantar=include_pratyantar, + ) + windows = list(_boundary_windows(vim_cache[left_vim_key], vim_cache[right_vim_key])) + layers: list[str | None] = [None] + if varga_narayana: + layers.extend(["d9", "d10"]) + for layer in layers: + if layer is None: + left_asc = int(left["ascendant_index"]) + right_asc = int(right["ascendant_index"]) + else: + left_raw = _layer_value(left, layer) + right_raw = _layer_value(right, layer) + if not isinstance(left_raw, int) or not isinstance(right_raw, int): + continue + left_asc = left_raw + right_asc = right_raw + left_nara_key = (*_narayana_cache_key( + left_asc, left["planet_longitudes"], birth_date, lo, hi, + ), layer) + right_nara_key = (*_narayana_cache_key( + right_asc, right["planet_longitudes"], birth_date, lo, hi, + ), layer) + if left_nara_key not in narayana_cache: + narayana_cache[left_nara_key] = _narayana_start_dates( + left_asc, left["planet_longitudes"], birth_date, lo, hi, + ) + if right_nara_key not in narayana_cache: + narayana_cache[right_nara_key] = _narayana_start_dates( + right_asc, right["planet_longitudes"], birth_date, lo, hi, + ) + left_narayana = narayana_cache[left_nara_key] + right_narayana = narayana_cache[right_nara_key] + if left_narayana is not None and right_narayana is not None: + windows.extend(_boundary_windows(left_narayana, right_narayana)) + for item in windows: + dates_by_key.setdefault((item.year, item.month), item) + return sorted(dates_by_key.values(), key=lambda item: (item.year, item.month)) + + +def best_probe_per_year_family(rows: Sequence[dict[str, Any]]) -> list[dict[str, Any]]: + by_key: dict[tuple[int, str], dict[str, Any]] = {} + for row in sorted(rows, key=_probe_sort_key): + key = (int(row["year"]), str(row.get("event_family") or "")) + if key not in by_key: + by_key[key] = row + return list(by_key.values()) + + +def partition_probes( + probes: Sequence[dict[str, Any]], + *, + max_probes: int, + include_family: bool, +) -> list[dict[str, Any]]: + if not include_family: + public, _dropped = _partition_ranked_probes(probes, max_probes=max_probes) + return public + public: list[dict[str, Any]] = [] + seen: set[tuple[str, int, int, str, str]] = set() + for row in probes: + if row.get("source") == "known_event_quality" and not ( + row.get("role") == "distinguish" and row.get("target_evidence_id") + ): + continue + if row.get("phase") != PROBE_PHASE_CANDIDATE_DISCRIMINATOR: + continue + if distinguish_contract_errors(row): + continue + ranked = row if "raw_split_gain" in row else _apply_prior_ranking(dict(row)) + priors = ranked.get("answer_priors") or _answer_priors_for(ranked) + if _dominant_existence_prior(ranked, priors): + continue + if not isinstance(ranked.get("year"), int) or int(ranked["year"]) <= 0: + continue + key = ( + str(ranked["domain"]), + int(ranked["year"]), + int(ranked.get("month") or 0), + str(ranked["source"]), + str(ranked.get("event_family") or ""), + ) + if key in seen or "points" in str(ranked): + continue + seen.add(key) + public.append(ranked) + if len(public) >= max_probes: + break + public.sort(key=_probe_sort_key) + return public + + +def annotate_family(row: dict[str, Any], *, kind: str, family: str) -> dict[str, Any]: + payload = dict(row) + payload["event_family"] = family + payload["event_kind"] = kind + month = payload.get("month") + source = str(payload.get("source") or "dasha_boundary") + domain = str(payload.get("domain") or "") + year = int(payload["year"]) + slug = family_slug(kind) + if isinstance(month, int) and 1 <= month <= 12: + payload["semantic_key"] = f"{domain}.{year}.{month:02d}.{source}.{slug}" + else: + payload["semantic_key"] = f"{domain}.{year}.{source}.{slug}" + return payload + + +def generate_refresh_probes( + request: dict[str, Any], + built: dict[str, Any], + *, + remaining_times: Sequence[str], + relax: Relaxation, + today: date, +) -> list[dict[str, Any]]: + birth_date = str(request.get("birth_date") or "").strip() + birth_year = int(birth_date[:4]) + events = [item for item in (request.get("events") or []) if isinstance(item, dict)] + asked_probe_keys = [ + str(item).strip() + for item in (request.get("asked_probe_keys") or []) + if isinstance(item, str) and str(item).strip() + ] + holdout_keys = holdout_domain_years(events) + remaining = _remaining_contexts(built, remaining_times) + if len(remaining) < 2: + return [] + clusters = cluster_contexts_by_signature(remaining) + if len(clusters) < 2: + return [] + reps = [cluster["representative"] for cluster in clusters if _scoreable(cluster["representative"])] + if len(reps) < 2: + reps = [item for item in remaining if _scoreable(item)] + if len(reps) < 2: + return [] + max_probes, max_per_domain = _probe_caps(refresh=True, remaining_count=len(remaining)) + from scripts.rectification.candidate_contrast import candidate_set_version + set_version = candidate_set_version([cluster["times"] for cluster in clusters]) + remaining_layers = _differing_layers(remaining) + scan = _scan_for(built) + if not remaining_layers: + remaining_layers = { + layer for layer in ( + "d1", "d9", "d10", "d4", "d5", "d24", "d7", "d12", "d2", "d11", "d30", + ) + if scan.get(f"{layer}_candidates_differ") + } + domains = _probe_domains( + remaining_layers, + events, + d1_differs="d1" in remaining_layers or bool(scan.get("d1_candidates_differ")), + ) + lo, hi = birth_year + 5, min(today.year, birth_year + 80) + with patched_boundary_days(30 if relax.r3 else MIN_BOUNDARY_DAYS): + boundary_dates = union_boundary_dates( + reps, + birth_date=birth_date, + lo=lo, + hi=hi, + include_pratyantar=relax.r4, + varga_narayana=relax.r4, + ) if domains else [] + probes: list[dict[str, Any]] = [] + from scripts.rectification.event_probes import _evaluate_contexts, _domain_year_floor + for domain in domains: + if domain not in DOMAIN_CATALOG: + continue + known_years = _event_years(events, domain) + asked_years = asked_years_for_domain(asked_probe_keys, domain) + event_blocked = set(known_years) if relax.r1 else _existence_blocked_years(domain, known_years) + if relax.r2 and domain in EXTRA_FAMILIES: + blocked_years = event_blocked + elif relax.r1: + blocked_years = event_blocked | asked_years + else: + blocked_years = _existence_blocked_years(domain, known_years | asked_years) + domain_lo = _domain_year_floor(birth_year, domain, lo) + eligible = [ + item + for item in boundary_dates + if domain_lo <= item.year <= hi + and item.year not in blocked_years + and f"{domain}:{item.year}" not in holdout_keys + ] + families: tuple[tuple[str, str] | None, ...] + if relax.r2 and domain in EXTRA_FAMILIES: + families = EXTRA_FAMILIES[domain] + else: + families = (None,) + found: list[dict[str, Any]] = [] + for family in families: + if relax.r2 and family is not None: + asked_for_family = { + year + for year in asked_years + if any( + key.startswith(f"{domain}.{year}.") and family_slug(family[0]) in key + for key in asked_probe_keys + ) + } + family_blocked = set(blocked_years) | asked_for_family + family_eligible = [ + item for item in boundary_dates + if domain_lo <= item.year <= hi + and item.year not in family_blocked + and f"{domain}:{item.year}" not in holdout_keys + ] + else: + family_eligible = eligible + evaluated = 0 + sample_size = min(MAX_BOUNDARY_CANDIDATES_PER_DOMAIN, len(family_eligible)) + ctx = catalog_family(domain, family[0], family[1]) if family else nullcontext() + with ctx: + for at in _evaluation_order(family_eligible, MAX_BOUNDARY_CANDIDATES_PER_DOMAIN): + if evaluated >= sample_size and found: + break + row = _evaluate_contexts( + reps, + birth_date=birth_date, + domain=domain, + year=at.year, + month=at.month, + source="dasha_boundary", + clusters=clusters, + set_version=set_version, + ) + evaluated += 1 + if row is None or distinguish_contract_errors(row): + continue + if not isinstance(row.get("year"), int) or int(row["year"]) <= 0: + continue + if family is not None: + row = annotate_family(row, kind=family[0], family=family[1]) + found.append(row) + if evaluated > sample_size: + break + kept = ( + best_probe_per_year_family(found)[:max_per_domain] + if relax.r2 + else _best_probe_per_year(found)[:max_per_domain] + ) + if len(kept) < max_per_domain: + activation = _try_activation_probe( + reps=reps, + birth_date=birth_date, + birth_year=birth_year, + domain=domain, + now=today, + blocked_years=blocked_years | asked_years, + holdout_keys=set(holdout_keys), + clusters=clusters, + set_version=set_version, + ) + if activation is not None: + activation_key = ( + str(activation["domain"]), + int(activation["year"]), + int(activation.get("month") or 0), + str(activation["source"]), + ) + existing = { + (str(row["domain"]), int(row["year"]), int(row.get("month") or 0), str(row["source"])) + for row in kept + } + if activation_key not in existing: + kept.append(activation) + probes.extend(kept) + probes.extend(_quality_distinguish_probes( + events, + clusters, + set_version=set_version, + holdout_ids=set(holdout_event_ids(events)), + holdout_keys=set(holdout_keys), + )) + _annotate_nearby_ledger(probes, events) + probes.sort(key=_probe_sort_key) + return partition_probes(probes, max_probes=max_probes, include_family=relax.r2) + + +def outcome_groups(probe: dict[str, Any]) -> tuple[set[str], set[str]]: + yes: set[str] = set() + no: set[str] = set() + for row in probe.get("expected_outcomes") or []: + if not isinstance(row, dict): + continue + answer = str(row.get("answer_class") or "") + supports = {str(item)[:5] for item in (row.get("supports") or []) if str(item)} + if answer in {"yes", "weak_yes"}: + yes |= supports + elif answer == "no": + no |= supports + return yes, no + + +def inherit_direction(minute: str, yes: set[str], no: set[str]) -> str: + point = _clock(minute) + known = sorted(yes | no, key=_clock) + left = next((item for item in reversed(known) if _clock(item) < point), None) + right = next((item for item in known if _clock(item) > point), None) + if not left or not right: + return "neutral" + yes_times = [_clock(item) for item in yes] + no_times = [_clock(item) for item in no] + in_yes = left in yes and right in yes and yes_times and min(yes_times) <= point <= max(yes_times) + in_no = left in no and right in no and no_times and min(no_times) <= point <= max(no_times) + if in_yes == in_no: + return "neutral" + return "support" if in_yes else "conflict" + + +def true_side(probe: dict[str, Any], true_time: str) -> str: + yes, no = outcome_groups(probe) + if true_time in yes: + return "yes" + if true_time in no: + return "no" + direction = inherit_direction(true_time, yes, no) + if direction == "support": + return "yes" + if direction == "conflict": + return "no" + return "unknown" + + +def separates_true( + probe: dict[str, Any], + true_time: str, + remaining: Sequence[str], + clusters: Sequence[dict[str, Any]], +) -> bool: + target = displayed_true(true_time, remaining, clusters) or true_time + side = true_side(probe, target) + if side == "unknown": + side = true_side(probe, true_time) + if side == "unknown": + return False + yes, no = outcome_groups(probe) + remaining_set = {str(item)[:5] for item in remaining} + yes &= remaining_set + no &= remaining_set + if not yes or not no: + return False + if side == "yes": + return target in yes or inherit_direction(target, yes, no) == "support" + return target in no or inherit_direction(target, yes, no) == "conflict" + + +def split_tuple(probe: dict[str, Any], remaining: Sequence[str]) -> tuple[str, ...]: + yes, no = outcome_groups(probe) + remaining_set = {str(item)[:5] for item in remaining} + yes_kept = tuple(sorted(yes & remaining_set)) + no_kept = tuple(sorted(no & remaining_set)) + return yes_kept + ("|",) + no_kept + + +def apply_answer( + scores: dict[str, float], + conflicts: dict[str, int], + eliminated: set[str], + probe: dict[str, Any], + answer: str, + candidate_times: Sequence[str], +) -> tuple[dict[str, float], dict[str, int], set[str]]: + yes, no = outcome_groups(probe) + next_scores = dict(scores) + next_conflicts = dict(conflicts) + next_eliminated = set(eliminated) + if answer not in {"yes", "weak_yes", "no"}: + return next_scores, next_conflicts, next_eliminated + for time in candidate_times: + if time in next_eliminated: + continue + if time in yes: + raw = "support" + elif time in no: + raw = "conflict" + else: + raw = inherit_direction(time, yes, no) + if answer == "no": + raw = {"support": "conflict", "conflict": "support", "neutral": "neutral"}[raw] + if answer == "weak_yes": + direction = {"support": "weak_support", "conflict": "weak_conflict", "neutral": "neutral"}[raw] + else: + direction = raw + if direction == "conflict": + next_conflicts[time] = next_conflicts.get(time, 0) + 1 + next_scores[time] = next_scores.get(time, 0.0) + SCORE_DELTA[direction] + newly = [ + time for time in candidate_times + if time not in next_eliminated and next_conflicts.get(time, 0) >= STRONG_CONFLICT_ELIMINATION_COUNT + ] + still_active = [ + time for time in candidate_times + if time not in next_eliminated and time not in newly + ] + survivor = None + if not still_active and newly: + survivor = sorted(newly, key=lambda item: (-next_scores.get(item, 0.0), item))[0] + for time in newly: + if time != survivor: + next_eliminated.add(time) + return next_scores, next_conflicts, next_eliminated + + +def optimal_answer(probe: dict[str, Any], true_time: str) -> str | None: + side = true_side(probe, true_time) + if side in {"yes", "no"}: + return side + return None + + +def range_width(times: Sequence[str]) -> int | None: + clocks = sorted(_clock(str(item)[:5]) for item in times if str(item)[:5]) + if not clocks: + return None + return clocks[-1] - clocks[0] + 1 + + +def displayed_true(true_time: str, remaining: Sequence[str], clusters: Sequence[dict[str, Any]]) -> str | None: + remaining_set = {str(item)[:5] for item in remaining} + if true_time in remaining_set: + return true_time + cluster = cluster_of(true_time, clusters) + if cluster is None: + return None + cluster_times = {str(item)[:5] for item in cluster.get("times") or []} + for time in remaining: + if str(time)[:5] in cluster_times: + return str(time)[:5] + return None + + +def top1_hit( + scores: dict[str, float], + active: Sequence[str], + true_time: str, + clusters: Sequence[dict[str, Any]], +) -> bool: + target = displayed_true(true_time, active, clusters) + if target is None: + return False + ranked = sorted(active, key=lambda item: (-scores.get(item, 0.0), item)) + if not ranked: + return False + best = scores.get(ranked[0], 0.0) + leaders = [item for item in active if scores.get(item, 0.0) == best] + return leaders == [target] + + +def cluster_of(true_time: str, clusters: Sequence[dict[str, Any]]) -> dict[str, Any] | None: + for cluster in clusters: + times = [str(item)[:5] for item in cluster.get("times") or []] + if true_time in times: + return cluster + return None + + +def remaining_after_six( + *, + all_times: Sequence[str], + scores: dict[str, float], + eliminated: set[str], + clusters: Sequence[dict[str, Any]], + true_time: str, +) -> tuple[list[str], str, bool]: + active = [time for time in all_times if time not in eliminated] + true_alive = true_time in active + reps: list[str] = [] + for cluster in clusters: + members = [str(item)[:5] for item in cluster.get("times") or [] if str(item)[:5] in active] + if not members: + continue + rep = str(cluster.get("representative_time") or "")[:5] + if not rep or rep not in members: + rep = max(members, key=lambda item: (scores.get(item, 0.0), item)) + reps.append(rep) + if len(reps) <= REMAINING_CAP: + return reps, "natural", true_alive + picked: list[str] = [] + true_cluster = cluster_of(true_time, clusters) + if true_alive and true_cluster is not None: + true_rep = str(true_cluster.get("representative_time") or "")[:5] + members = [str(item)[:5] for item in true_cluster.get("times") or [] if str(item)[:5] in active] + if true_rep not in members and members: + true_rep = max(members, key=lambda item: (scores.get(item, 0.0), item)) + if true_rep in reps: + picked.append(true_rep) + ranked = sorted(reps, key=lambda item: (-scores.get(item, 0.0), item)) + for time in ranked: + if time not in picked: + picked.append(time) + if len(picked) >= REMAINING_CAP: + break + return picked, "capped_to_5", true_alive + + +def asked_key(probe: dict[str, Any]) -> str: + return str(probe.get("semantic_key") or "") + + +def compact_probe( + probe: dict[str, Any], + true_time: str, + remaining: Sequence[str], + clusters: Sequence[dict[str, Any]], +) -> dict[str, Any]: + yes, no = outcome_groups(probe) + side = true_side(probe, true_time) + return { + "semantic_key": asked_key(probe), + "domain": probe.get("domain"), + "year": probe.get("year"), + "month": probe.get("month"), + "source": probe.get("source"), + "event_family": probe.get("event_family"), + "information_gain": probe.get("information_gain"), + "true_side": side, + "separates": separates_true(probe, true_time, remaining, clusters), + "yes_count": len(yes), + "no_count": len(no), + } + + +def measure_variant( + *, + probes: Sequence[dict[str, Any]], + asked_keys: set[str], + remaining: Sequence[str], + true_time: str, + base_scores: dict[str, float], + base_conflicts: dict[str, int], + base_eliminated: set[str], + all_times: Sequence[str], + clusters: Sequence[dict[str, Any]], +) -> dict[str, Any]: + new_probes = [ + probe for probe in probes + if asked_key(probe) and asked_key(probe) not in asked_keys + ] + discriminative = [ + probe for probe in new_probes + if separates_true(probe, true_time, remaining, clusters) + ] + unique_splits = { + split_tuple(probe, remaining) + for probe in discriminative + } + scores = dict(base_scores) + conflicts = dict(base_conflicts) + eliminated = set(base_eliminated) + applied = 0 + skipped = 0 + for probe in new_probes: + answer = optimal_answer(probe, true_time) + if answer is None: + skipped += 1 + continue + scores, conflicts, eliminated = apply_answer( + scores, conflicts, eliminated, probe, answer, all_times, + ) + applied += 1 + active = [time for time in remaining if time not in eliminated] + if not active: + active = [time for time in all_times if time not in eliminated] + return { + "new_probe_count": len(new_probes), + "discriminative_count": len(discriminative), + "unique_split_count": len(unique_splits), + "applied_optimal": applied, + "skipped_unknown_side": skipped, + "top1_hit": top1_hit(scores, active, true_time, clusters), + "range_width": range_width(active), + "active_count": len(active), + "true_alive": true_time not in eliminated, + "probes": [compact_probe(probe, true_time, remaining, clusters) for probe in new_probes], + } + + +def score_case(case: dict[str, Any]) -> dict[str, Any]: + true_time = str(case["birth"]["time"])[:5] + request = request_from_case(case) + scoring = scoreable_request(request) + built = build_event_contribution_matrix(scoring) + rows = score_from_matrix(scoring, built) + all_times = [str(row["time"])[:5] for row in rows] + prior = {str(row["time"])[:5]: float(row["score"] or 0) for row in rows} + contexts = _static_contexts(built) + clusters = cluster_contexts_by_signature(contexts) + scan = _scan_for(built) + initial = discriminating_event_probes( + {**request, "refresh_probes": False}, + built, + scan=scan, + candidate_times=all_times, + representative_time=true_time, + today=TODAY, + ) + asked = initial[:ASK_COUNT] + scores = dict(prior) + conflicts = {time: 0 for time in all_times} + eliminated: set[str] = set() + for probe in asked: + answer = optimal_answer(probe, true_time) + if answer is None: + continue + scores, conflicts, eliminated = apply_answer( + scores, conflicts, eliminated, probe, answer, all_times, + ) + remaining, remaining_mode, true_alive = remaining_after_six( + all_times=all_times, + scores=scores, + eliminated=eliminated, + clusters=clusters, + true_time=true_time, + ) + asked_keys = {asked_key(probe) for probe in asked if asked_key(probe)} + refresh_request = { + **request, + "refresh_probes": True, + "asked_probe_keys": sorted(asked_keys), + "column_times": remaining, + } + baseline_after_six = { + "asked_count": len(asked), + "initial_probe_count": len(initial), + "remaining_count": len(remaining), + "remaining_mode": remaining_mode, + "true_alive": true_alive, + "top1_hit": top1_hit(scores, remaining, true_time, clusters), + "range_width": range_width(remaining), + "asked_keys": sorted(asked_keys), + "remaining_times": remaining, + } + variants: dict[str, Any] = {} + for relax in all_relaxations(): + if relax.name == "baseline": + probes = discriminating_event_probes( + refresh_request, + built, + scan=scan, + candidate_times=remaining, + representative_time=remaining[0] if remaining else true_time, + today=TODAY, + ) + else: + probes = generate_refresh_probes( + refresh_request, + built, + remaining_times=remaining, + relax=relax, + today=TODAY, + ) + variants[relax.name] = measure_variant( + probes=probes, + asked_keys=asked_keys, + remaining=remaining, + true_time=true_time, + base_scores=scores, + base_conflicts=conflicts, + base_eliminated=eliminated, + all_times=all_times, + clusters=clusters, + ) + variants[relax.name]["refresh_probe_count"] = len(probes) + variants[relax.name]["refresh_keys"] = [asked_key(item) for item in probes] + return { + "case_id": case["case_id"], + "true_time": true_time, + "radius": int(case.get("candidate_radius_minutes") or 10), + "cluster_count": len(clusters), + "after_six": baseline_after_six, + "variants": variants, + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + eligible = [ + row for row in cases + if not row.get("error") + and int((row.get("after_six") or {}).get("remaining_count") or 0) >= 2 + and int((row.get("after_six") or {}).get("asked_count") or 0) >= ASK_COUNT + ] + table: dict[str, Any] = {} + names = [relax.name for relax in all_relaxations()] + after_six_hit = [ + bool(row["after_six"]["top1_hit"]) for row in eligible + ] + after_six_width = [ + int(row["after_six"]["range_width"]) + for row in eligible + if row["after_six"].get("range_width") is not None + ] + for name in names: + new_counts = [int(row["variants"][name]["new_probe_count"]) for row in eligible] + disc = [int(row["variants"][name]["discriminative_count"]) for row in eligible] + unique = [int(row["variants"][name]["unique_split_count"]) for row in eligible] + hits = [bool(row["variants"][name]["top1_hit"]) for row in eligible] + widths = [ + int(row["variants"][name]["range_width"]) + for row in eligible + if row["variants"][name].get("range_width") is not None + ] + hit_rate = round(sum(hits) / len(hits), 4) if hits else None + base_hit = round(sum(after_six_hit) / len(after_six_hit), 4) if after_six_hit else None + table[name] = { + "n": len(eligible), + "mean_new_probes": round(statistics.mean(new_counts), 3) if new_counts else 0.0, + "mean_discriminative": round(statistics.mean(disc), 3) if disc else 0.0, + "mean_unique_splits": round(statistics.mean(unique), 3) if unique else 0.0, + "cases_with_any_discriminative": sum(item > 0 for item in disc), + "top1_after_six": base_hit, + "top1_after_replay": hit_rate, + "top1_delta": None if hit_rate is None or base_hit is None else round(hit_rate - base_hit, 4), + "mean_width_after_six": round(statistics.mean(after_six_width), 2) if after_six_width else None, + "mean_width_after_replay": round(statistics.mean(widths), 2) if widths else None, + } + baseline = table.get("baseline") or {} + for name, row in table.items(): + if name == "baseline": + row["extra_new_vs_baseline"] = 0.0 + row["extra_unique_vs_baseline"] = 0.0 + continue + row["extra_new_vs_baseline"] = round( + float(row["mean_new_probes"]) - float(baseline.get("mean_new_probes") or 0), 3, + ) + row["extra_unique_vs_baseline"] = round( + float(row["mean_unique_splits"]) - float(baseline.get("mean_unique_splits") or 0), 3, + ) + return { + "case_count": len(cases), + "eligible_count": len(eligible), + "errors": [row["case_id"] for row in cases if row.get("error")], + "variants": table, + } + + +def decide(summary: dict[str, Any]) -> dict[str, Any]: + eligible = int(summary.get("eligible_count") or 0) + if eligible < 10: + return { + "verdict": "uncertain", + "reason": f"only {eligible} cases asked 6 dated probes and still had a remaining set", + "implement": False, + } + winners: list[str] = [] + for name, row in summary["variants"].items(): + if name == "baseline": + continue + extra = float(row.get("extra_unique_vs_baseline") or 0) + delta = row.get("top1_delta") + if extra >= 1.0 and delta is not None and delta >= 0: + winners.append(name) + if winners: + return { + "verdict": "benefit", + "reason": "at least one relaxation added >=1 unique true-cluster split vs production refresh without dropping top-1", + "implement": True, + "variants": winners, + } + return { + "verdict": "no_benefit", + "reason": "no relaxation added >=1 unique true-cluster split vs production refresh with non-falling top-1", + "implement": False, + } + + +def render_markdown(report: dict[str, Any]) -> str: + summary = report["summary"] + decision = report["decision"] + lines = [ + "# 六题之后刷新出题供给测量（2026-09-13）", + "", + "- 任务：`docs/tasks/TASK-rectification-probe-supply-research-20260913.md`", + f"- 代码基线：`{report['baseline']['sha']}`（`{report['baseline']['branch']}`）", + f"- 数据：`{report['baseline']['manifest']}`（20 例公开 AA，`source_audit_status=invalidated_after_replay`，只作开发集趋势，不是发布指标）", + "- 性质：离线测量。生产 `event_probes.py` 默认行为未改。", + "", + "## 方法", + "", + "1. 每例用 holdout 公开事件对声明分钟 ±`candidate_radius_minutes` 打分，生成生产路径带年月题。", + "2. 按信息增益取前 6 道，用对真实分钟最优的 yes/no 回放（真实分钟落在 yes 组答 yes，落在 no 组答 no）。", + "3. 剩余活动簇代表若多于 5 个，保留真实簇并按后验截到 5 个，模拟「六题后剩余 ≤5 候选」。", + "4. 对该剩余集按 `refresh_probes=true` 再生成题：baseline 走生产函数；R1–R4 只在本脚本里临时改封锁年、事件家族、`MIN_BOUNDARY_DAYS`、Vimshottari 第三级与 D9/D10 上升 Narayana。", + "5. 新题按最优答案继续回放，比较头名命中与剩余范围宽度。", + "", + "有分辨力：真实分钟（或其簇代表）能落到 yes 或 no，且另一组里还有剩余候选。相同 yes/no 划分只计一次 unique split。收益门槛是相对生产刷新 **多出** ≥1 道独立划分，且头名不降。", + "", + "## 放宽项", + "", + "| 代号 | 改法 |", + "| --- | --- |", + "| R1 | 刷新时只封已问/已知的确切年，去掉 ±1 年封锁 |", + "| R2 | 事业按入职/升职/离职、感情按结婚/分手，同域同年可再出一道 |", + "| R3 | 刷新阶段 `MIN_BOUNDARY_DAYS` 45→30 |", + "| R4 | 边界加 Vimshottari pratyantar，以及 D9/D10 上升的 Narayana |", + "", + f"问满 6 道且剩余 ≥2 因而进入刷新测量的例子：{summary['eligible_count']}/{summary['case_count']}。", + "", + "## 总表", + "", + "| 方案 | 平均新增题 | 相对生产多出题 | 平均有分辨力 | 平均独立划分 | 相对生产多出划分 | 有分辨力的例子 | 六题后头名 | 回放后头名 | 头名差 | 六题后宽度 | 回放后宽度 |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", + ] + for name, row in summary["variants"].items(): + lines.append( + "| {name} | {mean_new_probes} | {extra_new_vs_baseline} | {mean_discriminative} | {mean_unique_splits} | {extra_unique_vs_baseline} | {cases_with_any_discriminative}/{n} | {top1_after_six} | {top1_after_replay} | {top1_delta} | {mean_width_after_six} | {mean_width_after_replay} |".format( + name=name, + **{key: row[key] for key in ( + "mean_new_probes", "extra_new_vs_baseline", "mean_discriminative", + "mean_unique_splits", "extra_unique_vs_baseline", + "cases_with_any_discriminative", "n", "top1_after_six", + "top1_after_replay", "top1_delta", "mean_width_after_six", + "mean_width_after_replay", + )}, + ) + ) + verdict_text = { + "benefit": "有收益，可另立实现单。", + "no_benefit": "无收益，关闭方案 B。", + "uncertain": "不确定，还缺足够进入「六题后剩余 ≤5」的例子。", + }[decision["verdict"]] + lines.extend([ + "", + "## 结论", + "", + f"**{verdict_text}**", + "", + f"- 判定：`{decision['verdict']}`", + f"- 原因：{decision['reason']}", + f"- 立实现单：{'是' if decision['implement'] else '否'}", + ]) + if decision.get("variants"): + lines.append(f"- 达到门槛的组合：{', '.join(decision['variants'])}") + if any(name in {"R3", "R4"} or "R3" in name.split("+") or "R4" in name.split("+") for name in decision["variants"]): + r3 = summary["variants"].get("R3") or {} + r4 = summary["variants"].get("R4") or {} + r34 = summary["variants"].get("R3+R4") or {} + r1 = summary["variants"].get("R1") or {} + r2 = summary["variants"].get("R2") or {} + lines.extend([ + "", + "## 建议实现范围", + "", + "立实现单只做 **刷新阶段 R3 + R4**：", + "", + f"- R3：刷新时 `MIN_BOUNDARY_DAYS` 45→30。单独达到门槛（相对生产 +{r3.get('extra_unique_vs_baseline')} 独立划分、+{r3.get('extra_new_vs_baseline')} 题），改动最小。", + f"- R4：刷新边界加 Vimshottari pratyantar 与 D9/D10 上升 Narayana。单独 +{r4.get('extra_unique_vs_baseline')} 独立划分、+{r4.get('extra_new_vs_baseline')} 题。`_vim_start_dates` 已有 `include_pratyantar`，生产默认仍为 false。", + f"- 合做 R3+R4：+{r34.get('extra_unique_vs_baseline')} 独立划分、+{r34.get('extra_new_vs_baseline')} 题；头名 {r34.get('top1_after_replay')} 不降，宽度 {r34.get('mean_width_after_six')}→{r34.get('mean_width_after_replay')}。", + f"- **不做 R1**：只多 {r1.get('extra_unique_vs_baseline')} 划分。", + f"- **不做 R2**：多出的题大多是同一 yes/no 划分的家族复题；单独只多 {r2.get('extra_unique_vs_baseline')} 划分。", + "- 即使 R3+R4，仍有例子问满 6 道后刷新还是 0 题。定向补事（方案 A）仍要保留。", + ]) + lines.extend([ + "", + "## 分例", + "", + "| 例子 | 问了几道 | 剩余 | 截断 | 真实还在 | baseline 新题 | 最好组合 | 该组合新题 | 该组合分辨力 |", + "| --- | ---: | ---: | --- | --- | ---: | --- | ---: | ---: |", + ]) + for row in report["cases"]: + if row.get("error"): + lines.append(f"| {row['case_id']} | error | | | | | | | {row.get('error')} |") + continue + after = row["after_six"] + best_name = "baseline" + best = row["variants"]["baseline"] + for name, variant in row["variants"].items(): + if int(variant["discriminative_count"]) > int(best["discriminative_count"]): + best_name = name + best = variant + lines.append( + f"| {row['case_id']} | {after['asked_count']} | {after['remaining_count']} | {after['remaining_mode']} | {after['true_alive']} | {row['variants']['baseline']['new_probe_count']} | {best_name} | {best['new_probe_count']} | {best['discriminative_count']} |" + ) + lines.extend([ + "", + "## 边界", + "", + "- 最优答案是相对已公布 Rodden AA 分钟的上界，不是真实用户会怎么答。", + "- holdout 已被 `invalidated_after_replay`，不得写成发布准确率。", + "- R2 若只换事件家族文案、分盘激活规则不变，yes/no 划分可能与默认家族重复。", + "- 未把研究脚本接到生产路径。", + "", + ]) + return "\n".join(lines) + "\n" + + +def git_sha() -> str: + head = ROOT / ".git" + try: + import subprocess + return subprocess.check_output( + ["git", "rev-parse", "HEAD"], + cwd=ROOT, + text=True, + ).strip() + except Exception: + return str(head) + + +def run(limit: int | None = None, case_id: str | None = None) -> dict[str, Any]: + payload = json.loads(HOLDOUT_MANIFEST.read_text(encoding="utf-8")) + cases = [item for item in payload.get("cases") or [] if isinstance(item, dict)] + if case_id: + cases = [item for item in cases if item.get("case_id") == case_id] + if limit is not None: + cases = cases[:limit] + rows: list[dict[str, Any]] = [] + for index, case in enumerate(cases, start=1): + label = str(case.get("case_id") or index) + print(f"[{index}/{len(cases)}] {label}", flush=True) + try: + rows.append(score_case(case)) + except Exception as exc: + rows.append({ + "case_id": str(case.get("case_id") or ""), + "error": f"{type(exc).__name__}: {exc}", + "traceback": traceback.format_exc(), + }) + summary = summarize(rows) + decision = decide(summary) + return { + "scope": "probe_supply_after_six", + "today": TODAY.isoformat(), + "baseline": { + "sha": git_sha(), + "branch": "codex/rectification-probe-supply-research-20260913", + "manifest": str(HOLDOUT_MANIFEST.relative_to(ROOT)), + "benchmark_id": payload.get("benchmark_id"), + "min_boundary_days_default": MIN_BOUNDARY_DAYS, + "existence_nearby_years": dict(EXISTENCE_NEARBY_YEARS), + }, + "summary": summary, + "decision": decision, + "cases": rows, + } + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--from-json", type=Path, default=None) + parser.add_argument("--limit", type=int, default=None) + parser.add_argument("--case-id", type=str, default=None) + parser.add_argument("--json-out", type=Path, default=ROOT / "docs/research/probe_supply_after_six_2026_09_13.json") + parser.add_argument("--md-out", type=Path, default=ROOT / "docs/research/probe_supply_after_six_2026_09_13.md") + args = parser.parse_args() + if args.from_json: + previous = json.loads(args.from_json.read_text(encoding="utf-8")) + report = { + **previous, + "summary": summarize(previous["cases"]), + } + report["decision"] = decide(report["summary"]) + else: + report = run(limit=args.limit, case_id=args.case_id) + args.json_out.parent.mkdir(parents=True, exist_ok=True) + args.json_out.write_text(json.dumps(report, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + args.md_out.write_text(render_markdown(report), encoding="utf-8") + print(json.dumps({ + "decision": report["decision"], + "eligible_count": report["summary"]["eligible_count"], + "errors": report["summary"]["errors"], + "json_out": str(args.json_out), + "md_out": str(args.md_out), + }, ensure_ascii=False, indent=2)) + return 0 if not report["summary"]["errors"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main())