From 0f5442cea272be94130e5c624b8db2499ee8e415 Mon Sep 17 00:00:00 2001 From: Jesse_Chen Date: Sat, 26 Sep 2026 14:47:21 +0800 Subject: [PATCH] =?UTF-8?q?research(rectification):=20offline=20R1/R2/R3?= =?UTF-8?q?=20=E2=80=94=20answer-flip=20tolerance,=20V1/V2=20rerun,=20dash?= =?UTF-8?q?a=20shift=20arithmetic?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - R1: flipping 1 answer keeps truth in range 98-100% but cuts head hit by a third or more; 2 flips squeeze truth out in 7-10% of ±30/±60 replays (two flips = 8 points = SEPARATION_LEAD). - R2: weights do apply (research scorer == production at V0); V1/V2 are identity at ±30/±60 by construction and leave six-question metrics unchanged at ±10 -> no_benefit (measured). Supplementary V1n does not pass the gate. - R3: boundary shift is ~3.8 days/minute (1.3-5.9), not 1.1; the 45-day gate is ~8-34 minutes. The _representative_pairs hypothesis is refuted (all-pairs adds no dated probes); the bottleneck is monthly evaluation. New finding recorded as BUG-1048 (investigating): _boundary_windows year-straddle exemption and positional zip misalignment bypass the gate. - Dated errata appended (no deletions) to the 09-14/09-16 briefs and research docs; README board row -> 待验收. No production code, scoring, thresholds, gates or Skill changed. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_017eEAG8HD3mm8gsKXgk8uU8 --- docs/BUG_HISTORY.md | 18 + .../answer_flip_tolerance_2026_09_26.json | 1577 +++ .../dasha_shift_per_minute_2026_09_26.json | 415 + docs/research/precision_gate_2026_09_14.md | 9 + ...on_minute_resolution_closure_2026_09_14.md | 7 + ...ctification_offline_research_2026_09_26.md | 210 + ...representative_pairs_probe_2026_09_26.json | 9433 +++++++++++++++++ .../varga_sensitivity_rerun_2026_09_26.json | 5681 ++++++++++ ...rectification-offline-research-20260926.md | 79 + docs/tasks/README.md | 2 +- ...tification-open-collect-invite-20260914.md | 9 + ...ion-adaptive-boundary-research-20260914.md | 19 + ...-precision-gate-guided-collect-20260916.md | 9 + scripts/research/answer_flip_tolerance.py | 281 + scripts/research/dasha_shift_per_minute.py | 263 + .../research/offline_research_20260926_lib.py | 147 + .../research/representative_pairs_probe.py | 474 + scripts/research/varga_sensitivity_rerun.py | 303 + tests/test_offline_research_20260926.py | 77 + 19 files changed, 19012 insertions(+), 1 deletion(-) create mode 100644 docs/research/answer_flip_tolerance_2026_09_26.json create mode 100644 docs/research/dasha_shift_per_minute_2026_09_26.json create mode 100644 docs/research/rectification_offline_research_2026_09_26.md create mode 100644 docs/research/representative_pairs_probe_2026_09_26.json create mode 100644 docs/research/varga_sensitivity_rerun_2026_09_26.json create mode 100644 docs/tasks/PROGRESS-rectification-offline-research-20260926.md create mode 100644 scripts/research/answer_flip_tolerance.py create mode 100644 scripts/research/dasha_shift_per_minute.py create mode 100644 scripts/research/offline_research_20260926_lib.py create mode 100644 scripts/research/representative_pairs_probe.py create mode 100644 scripts/research/varga_sensitivity_rerun.py create mode 100644 tests/test_offline_research_20260926.py diff --git a/docs/BUG_HISTORY.md b/docs/BUG_HISTORY.md index 5d0f71d9..066b0a89 100644 --- a/docs/BUG_HISTORY.md +++ b/docs/BUG_HISTORY.md @@ -10789,6 +10789,7 @@ - 修复：邀请语按候选数出话（2 → 「这两分钟」；>2 → 「这几个候选」；未知 → 不说这半句）。`exitAppendCalls()` 加入「收到 … 这 N 分钟 / 按现有信息分不开 / 说出来我接着算」，保持 `assert.equal(gates.length, 1)`，不加「只微调排序」。公开字段深比较补 `birth_time_source`。M1b 的 V1/V2 从 `no_benefit` 勘误为 `not_measured`。 - 验证：`frontend/tests/rectification-open-collect-invite-20260914.test.ts`、`rectification-exhaustion-exit-20260906.test.ts`、`rectification-decision-authority.test.ts`。 - 防复发：文案里出现的数字必须来自同一份数据，不得硬编码；改用户可见文案或公开字段时，必须同步跑 `npm test` 并把失败数与基线比对。 +- 补充（2026-09-26 离线研究 R2）：M1b 的 V1/V2 已重跑。权重确实生效（研究计分器 V0 与线上逐分相同）；V1/V2 在 ±30 / ±60 上按定义等于等权（0 个候选分数变化），在 ±10 上只微调分数、六题后指标不变。判定由 `not_measured` 改为 `no_benefit`（已实测）。见 `docs/research/rectification_offline_research_2026_09_26.md` §R2。 - 相关记录：BUG-689、BUG-690、BUG-688、BUG-687、BUG-587 - 复发自：BUG-689（邀请语复用两候选句）、BUG-690（公开字段未进深比较） - 修复版本：待发布 @@ -14036,3 +14037,20 @@ - 相关记录：BUG-721、BUG-722、BUG-723、BUG-724、BUG-725、BUG-726、BUG-388、BUG-1046。 - 复发自：无。 - 修复版本：`ab0f01a9`，已在 staging，deploy-staging run 2930（`8a409434`）部署（2026-09-26 对账）。 + +## BUG-1048 | 出题闸门 `_boundary_windows` 两处绕过：跨 1 月 1 日的边界不卡最小间隔，列表错位时比的是不对应的边界 + +- 状态：investigating（离线研究确认了代码行为，未确认对真人作答的影响；未改代码） +- 首次发现 / 最近更新：2026-09-26 / 2026-09-26 +- 影响面：`scripts/rectification/event_probes.py` `_boundary_windows`，经 `_union_boundary_dates` 影响所有带日期的自动点选题（`source = dasha_boundary`），初始与刷新两阶段；Vimshottari 与 Narayana 两条轨道都走这个函数。`event_probes.py` 属于冻结评分文件（ERR-110）。 +- 用户现象：无直接用户报告。离线研究（公开 AA 开放集 v4 与虚构时刻）发现，文档所说的「候选间大运边界差 < 45 天（刷新 30 天）不出题」并没有对所有配对生效。 +- 触发条件：（1）两个候选的同一条边界分别落在 12 月底和次年 1 月初：代码条件是 `one.year == two.year and 差 < 阈值` 才跳过，跨年的配对即使只差几天也放行。（2）两个候选的边界列表按位置 `zip`：某条边界跨出 `[出生+5, 出生+80]` 的年份边缘时，两边列表长度差一到两个元素（MD 起点与其第一个 AD 起点是同一天）。此后每一对比较的都是不对应的边界，间隔很大，于是全部放行。 +- 根因：闸门只实现了同年分支；按位置配对假设两边列表逐项对应，而 Vimshottari 的真实关系是整体平移同一天数。 +- 已确认事实：20 分钟虚构配对（MD+AD）有 10.7% 错位；v4 刷新阶段（含 PD）线上配对有 35–44% 错位。研究补丁把闸门改为对所有配对生效、错位时对齐后：±10 初始阶段没有带日期题的例子从 4 例变成 8 例，刷新阶段从 3 例变成 4 例，带日期题均值 4.85 → 4.10；±30 / ±60 基本不变。窄窗上现有的一部分带日期题依赖这两个漏洞。 +- 未确认：这些题问的是「几个候选的边界只差几天」的那个月份，按月精度回答能否可靠区分，没有评估；修掉漏洞会让窄窗更早出现「没有带日期题」，是否可以接受需要产品决定。 +- 修复：未修。任何修改都要按 ERR-110 重新冻结 sealed holdout / reported offset 记录。 +- 验证：`python3 scripts/research/representative_pairs_probe.py`（`strict_gate` 对照）、`python3 scripts/research/dasha_shift_per_minute.py`（`repo_raw_zip_misaligned_share`）。 +- 防复发：待定。改闸门时，同时用同年、跨年、列表错位三类虚构配对做回归。 +- 相关记录：BUG-689、BUG-740、BUG-1047；ERR-110。 +- 复发自：无。 +- 修复版本：未修。 diff --git a/docs/research/answer_flip_tolerance_2026_09_26.json b/docs/research/answer_flip_tolerance_2026_09_26.json new file mode 100644 index 00000000..a1cb7db8 --- /dev/null +++ b/docs/research/answer_flip_tolerance_2026_09_26.json @@ -0,0 +1,1577 @@ +{ + "generated_at": "2026-09-26", + "nature": "offline replay on the public AA open set (v4); not a blind test, not accuracy", + "ayanamsa": "raman", + "node_mode": "mean", + "holdout": 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"case_id": "iwao_takamoto_1925_aa_v4_holdout", + "radius": 60, + "answerable": 6, + "k0": { + "truth_in_range": 1.0, + "head_hit": 0.0, + "width_median": 17 + }, + "k1": { + "truth_in_range": 1.0, + "head_hit": 0.0, + "width_median": 85.0 + }, + "k2": { + "truth_in_range": 0.8667, + "head_hit": 0.0, + "width_median": 79 + } + } + ], + "errors": [], + "command": "python3 scripts/research/answer_flip_tolerance.py" +} diff --git a/docs/research/dasha_shift_per_minute_2026_09_26.json b/docs/research/dasha_shift_per_minute_2026_09_26.json new file mode 100644 index 00000000..2e2850b8 --- /dev/null +++ b/docs/research/dasha_shift_per_minute_2026_09_26.json @@ -0,0 +1,415 @@ +{ + "generated_at": "2026-09-26", + "nature": "fictional instants only; pure astronomy + repo Vimshottari helper", + "ayanamsa": "raman (swisseph SIDM_RAMAN)", + "dasha_year_days": 365.25, + "samples": 2000, + "seed": 7, + "old_claim": { + "days_per_minute": 1.1, + "vimshottari_days_per_degree": 122.0, + "correct_mean_days_per_degree": 365.25, + "moon_deg_per_minute_mean": 0.00914 + }, + "analytic_days_per_minute": { + "n": 2000, + "mean": 3.32, + "p0": 1.34, + "p10": 1.58, + "p25": 1.8, + "p50": 3.82, + "p75": 4.54, + "p90": 5.0, + "p100": 5.85 + }, + "finite_difference_days_per_minute": { + "n": 1997, + "mean": 3.32, + "p0": 1.34, + "p10": 1.58, + "p25": 1.8, + "p50": 3.82, + "p75": 4.54, + "p90": 5.0, + "p100": 5.85 + }, + "repo_vim_start_dates_days_per_minute": { + "n": 299, + "mean": 3.5, + "p0": 1.35, + "p10": 1.6, + "p25": 1.95, + "p50": 4.05, + "p75": 4.55, + "p90": 5.0, + "p100": 5.75 + }, + "repo_samples": 299, + "repo_crossed_nakshatra_in_20_min": 1, + "repo_max_spread_days": 1, + "repo_raw_zip_misaligned_share": 0.107, + "by_lord": { + "Ketu": { + "n": 222, + "mean": 1.76, + "p0": 1.57, + "p10": 1.58, + "p25": 1.61, + "p50": 1.77, + "p75": 1.89, + "p90": 1.95, + "p100": 2.04, + "years": 7.0 + }, + "Venus": { + "n": 224, + "mean": 4.98, + "p0": 4.48, + "p10": 4.49, + "p25": 4.57, + "p50": 4.93, + "p75": 5.32, + "p90": 5.59, + "p100": 5.85, + "years": 20.0 + }, + "Sun": { + "n": 250, + "mean": 1.51, + "p0": 1.34, + "p10": 1.35, + "p25": 1.39, + "p50": 1.5, + "p75": 1.62, + "p90": 1.69, + "p100": 1.75, + "years": 6.0 + }, + "Moon": { + "n": 233, + "mean": 2.49, + "p0": 2.25, + "p10": 2.26, + "p25": 2.3, + "p50": 2.47, + "p75": 2.65, + "p90": 2.76, + "p100": 2.91, + "years": 10.0 + }, + "Mars": { + "n": 206, + "mean": 1.75, + "p0": 1.57, + "p10": 1.58, + "p25": 1.6, + "p50": 1.71, + "p75": 1.88, + "p90": 1.95, + "p100": 2.03, + "years": 7.0 + }, + "Rahu": { + "n": 224, + "mean": 4.52, + "p0": 4.05, + "p10": 4.07, + "p25": 4.18, + "p50": 4.54, + "p75": 4.82, + "p90": 4.99, + "p100": 5.23, + "years": 18.0 + }, + "Jupiter": { + "n": 202, + "mean": 4.01, + "p0": 3.6, + "p10": 3.62, + "p25": 3.68, + "p50": 3.96, + "p75": 4.28, + "p90": 4.43, + "p100": 4.64, + "years": 16.0 + }, + "Saturn": { + "n": 227, + "mean": 4.76, + "p0": 4.26, + "p10": 4.31, + "p25": 4.4, + 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"p75": 9.1, + "p90": 10.0, + "p100": 11.7 + }, + "share_ge_45_days": 0.0, + "share_ge_30_days": 0.0, + "old_claim_days": 2.2 + }, + { + "candidate_gap_minutes": 4, + "boundary_gap_days": { + "n": 2000, + "mean": 13.3, + "p0": 5.4, + "p10": 6.3, + "p25": 7.2, + "p50": 15.3, + "p75": 18.2, + "p90": 20.0, + "p100": 23.4 + }, + "share_ge_45_days": 0.0, + "share_ge_30_days": 0.0, + "old_claim_days": 4.4 + }, + { + "candidate_gap_minutes": 6, + "boundary_gap_days": { + "n": 2000, + "mean": 19.9, + "p0": 8.1, + "p10": 9.5, + "p25": 10.8, + "p50": 22.9, + "p75": 27.2, + "p90": 30.0, + "p100": 35.1 + }, + "share_ge_45_days": 0.0, + "share_ge_30_days": 0.1, + "old_claim_days": 6.6 + }, + { + "candidate_gap_minutes": 10, + "boundary_gap_days": { + "n": 2000, + "mean": 33.2, + "p0": 13.4, + "p10": 15.8, + "p25": 18.0, + "p50": 38.2, + "p75": 45.4, + "p90": 50.0, + "p100": 58.5 + }, + "share_ge_45_days": 0.273, + "share_ge_30_days": 0.544, + "old_claim_days": 11.0 + }, + { + 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229.5, + "p75": 272.3, + "p90": 299.9, + "p100": 351.2 + }, + "share_ge_45_days": 1.0, + "share_ge_30_days": 1.0, + "old_claim_days": 66.0 + } + ], + "precision_table": [ + { + "evidence": "day_exact", + "span_days": 1.0, + "min_separable_minutes_p90_shift": 0.2, + "min_separable_minutes_median_shift": 0.3, + "min_separable_minutes_p10_shift": 0.6, + "min_separable_minutes_slowest": 0.7 + }, + { + "evidence": "day_pm3", + "span_days": 7.0, + "min_separable_minutes_p90_shift": 1.4, + "min_separable_minutes_median_shift": 1.8, + "min_separable_minutes_p10_shift": 4.4, + "min_separable_minutes_slowest": 5.2 + }, + { + "evidence": "day_pm7", + "span_days": 15.0, + "min_separable_minutes_p90_shift": 3.0, + "min_separable_minutes_median_shift": 3.9, + "min_separable_minutes_p10_shift": 9.5, + "min_separable_minutes_slowest": 11.2 + }, + { + "evidence": "month", + "span_days": 30.44, + "min_separable_minutes_p90_shift": 6.1, + "min_separable_minutes_median_shift": 8.0, + "min_separable_minutes_p10_shift": 19.3, + "min_separable_minutes_slowest": 22.6 + }, + { + "evidence": "gate_45_same_year", + "span_days": 45.0, + "min_separable_minutes_p90_shift": 9.0, + "min_separable_minutes_median_shift": 11.8, + "min_separable_minutes_p10_shift": 28.5, + "min_separable_minutes_slowest": 33.5 + }, + { + "evidence": "year", + "span_days": 365.25, + "min_separable_minutes_p90_shift": 73.1, + "min_separable_minutes_median_shift": 95.5, + "min_separable_minutes_p10_shift": 231.6, + "min_separable_minutes_slowest": 271.7 + } + ], + "straddle_table": [ + { + "candidate_gap_minutes": 2, + "boundary_gap_days_median": 7.6, + "p_different_month": 0.251, + "p_different_year": 0.021 + }, + { + "candidate_gap_minutes": 6, + "boundary_gap_days_median": 22.9, + "p_different_month": 0.754, + "p_different_year": 0.063 + }, + { + "candidate_gap_minutes": 10, + "boundary_gap_days_median": 38.2, + "p_different_month": 1.0, + "p_different_year": 0.105 + }, + { + "candidate_gap_minutes": 20, + "boundary_gap_days_median": 76.5, + "p_different_month": 1.0, + "p_different_year": 0.209 + } + ], + "command": "python3 scripts/research/dasha_shift_per_minute.py" +} diff --git a/docs/research/precision_gate_2026_09_14.md b/docs/research/precision_gate_2026_09_14.md index 0f2e0af5..f0542d13 100644 --- a/docs/research/precision_gate_2026_09_14.md +++ b/docs/research/precision_gate_2026_09_14.md @@ -279,3 +279,12 @@ V3 在 ±7 天偏移下： 错误 0 例。20 例跑完。 + +### 勘误（2026-09-26，离线研究 R2） + +上面 2026-09-15 勘误把 V1 / V2 改成 `not_measured`，理由是「更像权重没真正打上」。本轮已重跑（`python3 scripts/research/varga_sensitivity_rerun.py`，`docs/research/varga_sensitivity_rerun_2026_09_26.json`）： + +- 研究计分器在 V0 时与线上计分逐分相同（0 / 2060 候选有差）。 +- V1 / V2 权重确实打上了。但在 ±30 / ±60 上按定义就是恒等：窗宽 60 / 120 分钟时 V1 的系数全部截断到 1，V2 不丢任何分盘，0 个候选分数变化。在 ±10 上 V1 改了 168 / 220、V2 改了 159 / 220 个候选的分数（最大 0.43 / 0.63 分），六题后头名、覆盖、宽度仍与 V0 完全相同。 +- **V1 / V2 判定改为 `no_benefit`（已实测）**，取代 `not_measured`。原表与原判定文字保留。 +- 补充方案 V1n（按敏感度反比、归一化到平均 1）头名 0.85 / 0.60 / 0.50，宽度 15 / 33 / 73，不过门。详见 `docs/research/rectification_offline_research_2026_09_26.md` §R2。 diff --git a/docs/research/rectification_minute_resolution_closure_2026_09_14.md b/docs/research/rectification_minute_resolution_closure_2026_09_14.md index ca2e0c0d..a1293351 100644 --- a/docs/research/rectification_minute_resolution_closure_2026_09_14.md +++ b/docs/research/rectification_minute_resolution_closure_2026_09_14.md @@ -87,3 +87,10 @@ 1. **先确认指标口径再下结论。** 第一轮的头条结论就是被"不含淘汰"的宽度口径带偏的；第二轮 M0 才把它纠回来。 2. **不要从 docstring 推断行为。** 簇合并那条假线索就是这么来的——实测它从未触发。 3. **真值覆盖率优先于区间宽度。** 把区间做窄很容易，把真实分钟挤出去是更严重的错误（`AGENTS.md` Part B B4）。W3 就是卡在这一条。 + +## 7. 勘误与补充（2026-09-26 离线研究） + +- §5 索引里「M1b 的 V1/V2 标为未测」已过时：2026-09-26 重跑后，V1/V2 **已实测、无收益**。它们在 ±30 / ±60 上按定义等于等权，在 ±10 上只微调分数、不改六题后指标。 +- 任务书和 BUG-689 邀请语依据里的「1 分钟 ≈ 1.1 天大运边界位移」有误，实测中位 3.8 天（1.3–5.9）。45 天闸门对应 8–34 分钟，不是 40 分钟。 +- 新增答错容错：答错 1 题，真值在区间 98–100%；答错 2 题，±30 / ±60 上 7–10% 的回放会挤出真值（两道反答 = 8 分 = 淘汰线）。 +- 全文见 `docs/research/rectification_offline_research_2026_09_26.md`。 diff --git a/docs/research/rectification_offline_research_2026_09_26.md b/docs/research/rectification_offline_research_2026_09_26.md new file mode 100644 index 00000000..70815f87 --- /dev/null +++ b/docs/research/rectification_offline_research_2026_09_26.md @@ -0,0 +1,210 @@ +# 生时校正三项离线研究（2026-09-26） + +- 任务书：`docs/tasks/TASK-rectification-offline-research-20260926.md`；进度：`docs/tasks/PROGRESS-rectification-offline-research-20260926.md` +- 分支：`codex/rectification-offline-research-20260926`，测量在 `origin/staging` @ `7ddce2c9` 上完成（引擎代码与任务书基线 `abb05b67` 相同），提交已变基到 `7203d94e`（其间只新增 `fewer_probes_card_replay.py` 与文档，引擎与打分代码未变）。 +- 性质：**离线测量。不改线上代码、打分、阈值、出题闸门、Skill。** 研究脚本读引擎内部函数，个别测量在一次调用内临时替换模块属性，调用结束立即恢复，脚本末尾断言已恢复。 +- 数据：`references/real_case_calibration/minute_rectification_holdout_v4.json`，20 例公开 Rodden-AA 名人。R3a 只用随机虚构时刻。**这是开放集回放，不是盲测，下面的数字不是准确率。** +- 口径：ayanamsa `raman`，node mode `mean`，步长 2 分钟，半径 ±10 / ±30 / ±60。交付区间 = 未淘汰、且落后头名不足 8 分的簇的并集（线上 `unionStillValidRange`）。六题回放沿用 09-14 研究：同一份六道题，不随作答重新出题。 +- 同机 A/B：R1、R3a 在 `PYTHONHASHSEED` 不同的两次运行下 JSON 逐字节一致；R2、R3c 换 `PYTHONHASHSEED` 复跑，汇总与已存 JSON 完全一致（见进度记录）。不与其他机器的浮点结果比对（BUG-985）。 + +## 结论 + +| 项 | 一句话结论 | 是否建议立实现单 | +| --- | --- | --- | +| R1 答错容错 | 答错 1 题，真值几乎都还在区间里（98–100%），但头名命中降三到四成。答错 2 题，±30 / ±60 上约 7–10% 的回放会把真值挤出区间，半数以上的例子至少有一种两题组合会挤出。原因是两道反答正好造成 8 分差，等于淘汰线 | **不立实现单**。给产品一条口径：区间变窄不等于更准（±10 反答后宽度反而从 15 收到 11） | +| R2 V1/V2 分盘配权 | 权重确实生效了：研究计分器在 V0 与线上逐分相同。V1/V2 在 ±30 / ±60 上按定义就是恒等，0 个候选分数变化。在 ±10 上改了约 3/4 候选的分数（最多 0.4–0.6 分），六题后指标与基线完全相同。**判定由 `not_measured` 改为 `no_benefit`（已实测）** | **不立实现单**。补充的 V1n（按敏感度归一化重新分配权重）头名 +1/+1/+2 例，但宽度没降，±60 宽度 56→73，不过门 | +| R3 算术改正 | 1 分钟对应大运边界位移中位 **3.8 天**，不是 1.1 天（p10 1.6、p90 5.0，范围 1.3–5.9），按出生星宿主星分两档。45 天闸门对应 **8–34 分钟**（中位 12，p10–p90 为 9–29），不是 40 分钟。`_representative_pairs` 推断**被推翻**：改成全部两两配对后，带日期的题一道都没多。真正的卡点在逐月评估：只有 3%（刷新阶段 0.4%）的边界月份能让代表候选分到两边 | **不立实现单**。另记一个新发现：`_boundary_windows` 有两处绕过闸门的漏洞（跨 1 月 1 日豁免、按位置 zip 错位），记为 BUG-1048 `investigating`，是否修要等产品决定 | + +--- + +## R1 · 答错 1–2 题的容错 + +**方法。** 每例每个半径用线上 G0 闸门出题，取前六题。按真值给出最优是 / 否，再从能作答的题里随机选 1 题或 2 题反过来答（固定种子 `20260926`，按例、半径、题数、重复序号生成随机数，每格重复 30 次）。另外对所有组合穷举一遍作对照。能作答的题少于 k 的例子不进该格；所以每格另列「同一批例子不答错」的基线。 + +复跑：`python3 scripts/research/answer_flip_tolerance.py`（约 1 分钟）→ `docs/research/answer_flip_tolerance_2026_09_26.json` + +| 半径 | 答错题数 | 例数 | 回放次数 | 真值在区间 | 头名命中 | 宽度中位（分钟） | 至少一次挤出真值的例子 | 同批例子不答错：命中 / 宽度 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| ±10 | 0 | 20 | 20 | 1.000 | 0.80 | 15 | 0 | — | +| ±10 | 1 | 16 | 480 | 1.000 | 0.58 | 13 | 0 | 0.94 / 12 | +| ±10 | 2 | 15 | 450 | 0.996 | 0.40 | 11 | 1 | 0.93 / 11 | +| ±30 | 0 | 20 | 20 | 1.000 | 0.55 | 33 | 0 | — | +| ±30 | 1 | 18 | 540 | 0.989 | 0.33 | 35 | 1 | 0.61 / 32 | +| ±30 | 2 | 18 | 540 | 0.926 | 0.08 | 33 | 9 | 0.61 / 32 | +| ±60 | 0 | 20 | 20 | 1.000 | 0.40 | 56 | 0 | — | +| ±60 | 1 | 18 | 540 | 0.981 | 0.24 | 73 | 2 | 0.44 / 54 | +| ±60 | 2 | 18 | 540 | 0.898 | 0.06 | 71 | 12 | 0.44 / 54 | + +穷举对照（每例所有 1 题 / 2 题组合）：真值在区间 ±10 1.000 / 0.990，±30 0.991 / 0.919，±60 0.981 / 0.907；头名 ±10 0.66 / 0.43，±30 0.33 / 0.09，±60 0.23 / 0.06。随机抽样与穷举一致。 + +不答错的基线与 09-14 精度闸门研究的 G0 完全一致（0.80 / 0.55 / 0.40；15 / 33 / 56；20/20），说明回放链路没有漂移。 + +**读法。** + +1. **真值从未被淘汰。** 强冲突淘汰要 3 次；答错 2 题最多给真值 2 次冲突。 +2. **挤出真值靠的是 8 分线。** 一道反答让真值 −2、另一侧 +2，相对差 4 分；两道反答正好 8 分，等于 `SEPARATION_LEAD = 8`。所以 k=2 开始挤出真值，k=1 只有真值原本就贴线的例子才会被挤出。 +3. **±10 上答错反而让区间更窄**（15 → 13 → 11），头名却从 0.94 掉到 0.40。窄是因为错误答案把一个假头名推高了，不是因为更准。**产品口径：不要把「范围变窄」讲成「更确定了」。** +4. 回放的六道题不随作答重出，这一点对答对和答错两边都一样。真人答错后，后续题目可能被带偏，实际影响可能更大，本回放测不到。 + +**是否建议立实现单：不建议。** 容错与淘汰线（8 分、3 次冲突）是同一组常数，要改就会同时影响答对时的宽度，需要另立研究。可选方向（均未测）：答题之间出现矛盾时放宽交付并集；在交付卡上允许「改一道答案」。 + +--- + +## R2 · V1/V2 分盘敏感度配权重跑 + +**背景。** 09-14 精度闸门研究的 M1b 里，V1/V2 与 V0 数字完全一样。BUG-692 把判定改成 `not_measured`，文中怀疑「权重没真正打上」。本轮先证明权重有没有生效，再重跑。 + +复跑：`python3 scripts/research/varga_sensitivity_rerun.py`（约 16 分钟）→ `docs/research/varga_sensitivity_rerun_2026_09_26.json` + +### 第一步：权重是否改了分数 + +| 半径 | 研究计分器 V0 vs 线上 | V1 vs V0：分数变化的候选 | V1 最大分差 | V1 头名变化例 | V2 vs V0：分数变化的候选 | V2 最大分差 | V2 头名变化例 | V2 丢掉某领域分盘的例子 | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| ±10 | 0 / 220 不同 | 168 / 220（17 例） | 0.428 | 0 | 159 / 220（17 例） | 0.625 | 1 | 12 | +| ±30 | 0 / 620 不同 | **0 / 620** | 0 | 0 | **0 / 620** | 0 | 0 | 0 | +| ±60 | 0 / 1220 不同 | **0 / 1220** | 0 | 0 | **0 / 1220** | 0 | 0 | 0 | + +V1 系数 `min(窗宽 / 分盘换一次上升的分钟数, 1)`： + +| 窗宽 | D2 | D3 | D5 | 其余 8 张（D4/D7/D9/D10/D11/D12/D24/D30） | +| --- | ---: | ---: | ---: | ---: | +| 20 分钟（±10） | 0.33 | 0.50 | 0.83 | 1.00 | +| 60 分钟（±30） | 1.00 | 1.00 | 1.00 | 1.00 | +| 120 分钟（±60） | 1.00 | 1.00 | 1.00 | 1.00 | + +**结论：权重生效了，是 V1/V2 的定义本身在宽窗上等于恒等。** 11 张计分分盘里最慢的 D2 每 60 分钟换一次上升，窗宽 ≥ 60 分钟时 V1 每个系数都截断到 1。V2 只丢掉窗内一次都不变的分盘，窗宽 ≥ 60 分钟时每张都会变。±10 上两者确实改了分数，但幅度小于候选之间的分差。09-14「与 V0 相同」的数字是真实结果，不是没打上权重。 + +### 第二步：按 closure 口径重跑（G0 闸门、六题回放） + +| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 引擎头名（答题前） | 六题后再出 | 判定 | +| --- | --- | ---: | ---: | ---: | ---: | ---: | --- | +| ±10 | V0 | 0.80 | 20/20 | 15 | 0.30 | 3.25 | 基线 | +| ±10 | V1 | 0.80 | 20/20 | 15 | 0.30 | 3.25 | no_benefit | +| ±10 | V2 | 0.80 | 20/20 | 15 | 0.35 | 3.25 | no_benefit | +| ±10 | V1n（补充） | 0.85 | 20/20 | 15 | 0.45 | 3.25 | no_benefit（宽度未降） | +| ±30 | V0 | 0.55 | 20/20 | 33 | 0.20 | 5.45 | 基线 | +| ±30 | V1 / V2 | 0.55 | 20/20 | 33 | 0.20 | 5.45 | no_benefit（恒等） | +| ±30 | V1n（补充） | 0.60 | 20/20 | 33 | 0.15 | 5.45 | no_benefit（宽度未降） | +| ±60 | V0 | 0.40 | 20/20 | 56 | 0.05 | 5.75 | 基线 | +| ±60 | V1 / V2 | 0.40 | 20/20 | 56 | 0.05 | 5.75 | no_benefit（恒等） | +| ±60 | V1n（补充） | 0.50 | 20/20 | **73** | 0.10 | 5.90 | no_benefit（宽度变宽） | + +判定沿用 `precision_gate_lib.gate_verdict`：头名不降**且**宽度下降才算有收益，覆盖不得下降。 + +**V1n 是什么。** 这是 09-14 设计里没有的补充方案。每张分盘的系数与「换一次上升的分钟数」成反比，再归一化到 11 张盘的平均系数等于 1。总分量不变，只把权重从慢盘挪到快盘（D2 0.17、D3 0.26、D5 0.43、D7 0.60、D9 0.77、D10 0.85、D11 0.94、D12 1.02、D4 1.36、D24 2.05、D30 2.56）。它改了全部候选的分数，头名多中 1 / 1 / 2 例（20 例里），但宽度没有收窄，±60 反而变宽。样本只有 20 例，+1 例在噪声范围内。 + +**是否建议立实现单：不建议。** V1/V2 结论更新为 `no_benefit`（已实测）。V1n 若要继续，先补 ±7 天日期偏移和 R1 答错两项稳健性，再谈实现。 + +--- + +## R3 · 改正「1 分钟 ≈ 1.1 天」与 `_representative_pairs` 推断 + +### R3a · 1 分钟对应多少天 + +复跑：`python3 scripts/research/dasha_shift_per_minute.py`（1 秒）→ `docs/research/dasha_shift_per_minute_2026_09_26.json` + +**推导。** 第一段大运的余额是 `(1 − 星宿内进度) × 主星年数`。之后每一个 MD / AD / PD 的起点都等于「出生 + 余额 + 固定时长」，所以出生时间挪 1 分钟，**所有边界整体平移同一个天数**： + +> 平移天数 / 分钟 = 月亮每分钟走的度数 ÷ 13.333° × 主星年数 × 365.25 + +月亮平均每分钟 0.0091°。Vimshottari 120 年覆盖 9 个星宿（120°），**平均 1° ≈ 365 天**，按主星从 164 天（太阳 6 年）到 548 天（金星 20 年）不等。09-14 文档写的「1° ≈ 122 天」是错的，于是算成 0.0092 × 122 ≈ 1.1 天，把位移低估了约 3.5 倍。 + +**实测（2000 个虚构时刻，种子 7，swisseph 恒星黄道 Raman）：** + +| 测法 | n | 平均 | p0 | p10 | p25 | 中位 | p75 | p90 | p100 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 解析式（月速 × 主星年数） | 2000 | 3.32 | 1.34 | 1.58 | 1.80 | **3.82** | 4.54 | 5.00 | 5.85 | +| 差分（余额在 t 与 t+1 分钟） | 1997 | 3.32 | 1.34 | 1.58 | 1.80 | 3.82 | 4.54 | 5.00 | 5.85 | +| 仓库 `_vim_start_dates`（t 与 t+20 分钟，÷20） | 299 | 3.50 | 1.35 | 1.60 | 1.95 | 4.05 | 4.55 | 5.00 | 5.75 | + +仓库函数的所有 MD / AD 起点在 20 分钟内平移同一个天数（对齐后差值最大相差 1 天，是日期取整）。它是前 300 个时刻的子样本，中位 4.05 与全样本 3.82 的差异来自抽样。 + +**按出生星宿主星分两档**（中位，天 / 分钟）：太阳 1.50、火星 1.71、计都 1.77、月亮 2.47 ｜ 木星 3.96、水星 4.18、罗睺 4.54、土星 4.71、金星 4.93。前一档约占 45%。所以「中位 3.8」落在两档之间，单个人的实际值要么约 1.5–2.5，要么约 4–5。 + +### 闸门与窗宽换算（改正后） + +| 候选相隔 | 边界相差（天，中位 / p10 / p90） | ≥45 天的比例 | ≥30 天的比例 | 旧文档写的 | +| --- | --- | ---: | ---: | --- | +| 2 分钟 | 7.6 / 3.2 / 10.0 | 0% | 0% | ≈ 2 天 | +| 4 分钟 | 15.3 / 6.3 / 20.0 | 0% | 0% | — | +| 10 分钟 | 38.2 / 15.8 / 50.0 | 27% | 54% | — | +| 20 分钟 | 76.5 / 31.5 / 100.0 | 66% | 94% | ≈ 22 天、不可出题 | +| 30 分钟 | 114.7 / 47.3 / 149.9 | 94% | 100% | — | +| 40 分钟 | 153.0 / 63.1 / 199.9 | 100% | 100% | ≈ 45 天 | + +- **45 天闸门 ↔ 中位 11.8 分钟**（p10 9.0，p90 28.5，全范围 7.7–33.5）。 +- **30 天刷新闸门 ↔ 中位 7.8 分钟**（6.0–19.0，全范围 5.1–22.3）。 + +### 不同精度的证据能分开多近的候选 + +证据日期要落在两个候选的边界之间，才能把它们分到两边。所需边界差约等于证据的日期跨度，再除以每分钟平移天数： + +| 证据精度 | 日期跨度 | 稳定可分的候选间距（分钟） 快档 p90 | 中位 | 慢档 p10 | 最慢 | +| --- | ---: | ---: | ---: | ---: | ---: | +| 确切某一天 | 1 天 | 0.2 | 0.3 | 0.6 | 0.7 | +| 某天 ±3 天 | 7 天 | 1.4 | 1.8 | 4.4 | 5.2 | +| 某天 ±7 天 | 15 天 | 3.0 | 3.9 | 9.5 | 11.2 | +| 记得到月 | 30.4 天 | 6.1 | 8.0 | 19.3 | 22.6 | +| 线上 45 天闸门 | 45 天 | 9.0 | 11.8 | 28.5 | 33.5 | +| 只记得年 | 365 天 | 73 | 95 | 232 | 272 | + +低于这个间距时，只有两条边界恰好跨在月初（或年初）两侧才能分开，概率约为「边界差 ÷ 跨度」。例如按中位平移，相隔 2 分钟的一对候选：跨月概率 25%，跨年概率 2%；相隔 20 分钟：跨年概率 21%。 + +**所以：** 记得到月的事件，在候选相隔约 8 分钟以上（慢档约 20 分钟以上）时就够用；记得到天（容许几天误差）的事件，能分开相隔 2–5 分钟的候选。只记得年的事件单靠大运边界分不开 ±60 以内的候选，只有跨年的那一小部分边界例外。 + +### R3c · `_representative_pairs` 推断核实 + +**任务书的推断：** 「出不来题」的真正原因可能是 `_representative_pairs` 只比较相邻代表分钟和首尾一对，相邻代表太近，边界差过不了闸门。 + +复跑：`python3 scripts/research/representative_pairs_probe.py`（约 6 分钟）→ `docs/research/representative_pairs_probe_2026_09_26.json` + +对每例每个半径，在「初始」（全部候选）与「刷新」（六题回放后剩余候选，与 09-14 相同）两个阶段，比较三种设置：线上配对；研究补丁返回**全部两两配对**；研究补丁把 `_boundary_windows` 改成**严格闸门**（对所有配对都卡最小间隔、不豁免跨年，并把错位的 zip 对齐）。题目数在公开截断之前统计。 + +| 半径 · 阶段 | 代表数中位 | 线上配对：过闸对数均 | 全配对：过闸对数均 | 带日期的题（截断前，每例均值）线上 / 全配对 / 严格闸门 | 公开题里没有新的带日期题的例子 线上 / 全配对 / 严格闸门 | 边界月份评估次数均 | 能分开代表的比例 | +| --- | ---: | ---: | ---: | --- | --- | ---: | ---: | +| ±10 初始 | 6 | 0.60 | 3.30 | 4.85 / 4.80 / 4.10 | 4 / 4 / **8** | 168 | 3.2% | +| ±10 刷新 | 4 | 0.90 | 1.65 | 3.25 / 3.30 / 3.10 | 3 / 3 / 4 | 809 | 0.4% | +| ±30 初始 | 16 | 1.00 | 76.5 | 6.75 / 6.75 / 6.55 | 2 / 2 / 2 | 226 | 4.2% | +| ±30 刷新 | 5 | 1.80 | 4.95 | 5.30 / 5.25 / 5.30 | 2 / 2 / 2 | 1130 | 0.5% | +| ±60 初始 | 31 | 1.00 | 400.6 | 6.95 / 7.05 / 6.85 | 2 / 2 / 2 | 255 | 4.5% | +| ±60 刷新 | 5 | 1.95 | 5.55 | 5.55 / 5.60 / 5.60 | 2 / 2 / 2 | 1057 | 0.5% | + +「没有带日期题」的例子里，每格都有 2 例是 1900 年前出生的（毕加索 1881、弗洛伊德 1856）：`_birth_year` 只接受 1900–2100，出题器对它们直接返回空，与闸门无关。 + +**结论：推断被推翻。** + +1. **全部两两配对没有多出一道带日期的题。** 过闸的配对多了好几倍，日期并集也变大，但带日期的题数不变（差异 ≤0.1 道），没有题的例子数完全相同。首尾那一对已经是间距最大的一对，中间补上的配对只贡献更靠内的月份，这些月份照样分不开代表。 +2. **真正的卡点在逐月评估。** `_evaluate_contexts` 在每个边界月份判断各代表在该领域是否被激活，要求一边激活、一边不激活。初始阶段每例评估约 170–250 个边界月份，只有 3–4.5% 能分开；刷新阶段带上 PD 后评估 800–1100 个，只有约 0.5% 能分开。 +3. **v4 回放里六题后题池并不空。** ±10 刷新阶段平均还能出 3.25 道新的带日期题，1900 年后出生的 18 例里只有 1 例为 0。真机上「问完了」的原因用 v4 复现不出来。候选原因包括：采集线已关或被拒、已问年份、封存年份、存在性封锁年份、真人事件多为月精度，都未测。需要真机统计（见 `TASK-rectification-telemetry-20260926.md`）才能定性。 +4. **新发现：`_boundary_windows` 有两处绕过闸门。** + - 跨年豁免：代码写的是 `one.year == two.year and 差 < 阈值` 才跳过。两条边界只差 7 天、但分别落在 12 月底和 1 月初，也会被放行。例：某例 ±10 初始阶段，放行的 6 个日期全是这种一周内跨年的对（12-27 / 01-03 这类），逐月评估全部失败。 + - zip 错位：两个候选的边界列表按位置配对。一旦某条边界跨出 `[出生+5, 出生+80]` 的年份边缘，列表会差一到两个元素（MD 起点与其第一个 AD 起点同一天，所以一次可能差两个）。此后每一对比的都是不对应的边界，间隔很大，于是全部放行。20 分钟虚构对（只算 MD+AD）有 10.7% 错位；刷新阶段（含 PD）线上配对有 35–44% 错位。 + - 改成严格闸门后，±10 初始阶段没有带日期题的例子从 4 例变成 8 例，刷新阶段从 3 例变成 4 例，宽窗基本不变。也就是说，**窄窗上现有的一部分带日期题是靠这两个漏洞出来的**。这些题问的是「边界只差几天的那个月」，按月精度回答是否可靠，本轮没有评估。已记为 BUG-1048（`investigating`），不改代码。 + +### 改正与文案建议 + +**已加勘误段（不删原文）：** + +- `docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md`（1.1 天、122 天 / 度、换算表） +- `docs/tasks/TASK-rectification-open-collect-invite-20260914.md`（D1b 的依据句） +- `docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md`（实证第 5 条「20 分钟 ≈ 22 天，题池必然为空」） +- `docs/research/precision_gate_2026_09_14.md`（M1b：V1/V2 改为已实测 `no_benefit`） +- `docs/research/rectification_minute_resolution_closure_2026_09_14.md`（索引里 V1/V2「未测」的说明） + +**引导邀请语的依据：建议改写（不改线上文案）。** 线上现在的引导题是「YYYY 年 M 月前后，有没有 X？」加年 / 月选择器，日期可选（BUG-740 起）。BUG-689 / D1b 的依据句建议改成： + +> 出生时间每差 1 分钟，Vimshottari 边界整体平移约 1.3–5.9 天（中位约 3.8 天，取决于出生星宿的主星）。记得到月的事件，在候选相隔约 8 分钟以上（慢的星宿约 20 分钟以上）时就能把它们分开；记得到具体哪天（容许几天误差）的事件，能分开相隔 2–5 分钟的候选。但 v4 开放集上把日精度降成月精度，头名与宽度都没有变化（精度闸门研究 M0），所以不能写成「记得到天明显更有用」已经证实，更不能承诺能定到分钟。 + +对用户文案的建议（仅供产品决定）：选择器保持「日可选」，旁边一句「记得哪天就选上，记不清选到月也可以」。只在剩余候选相隔不到约 8 分钟时（交付区间 ≤ 10 分钟的收尾阶段）才值得主动追问具体哪天。不写任何「能定到分钟」的承诺。 + +--- + +## 附：全部复跑命令 + +```bash +python3 scripts/research/answer_flip_tolerance.py # R1，约 1 分钟 +python3 scripts/research/varga_sensitivity_rerun.py # R2，约 16 分钟 +python3 scripts/research/dasha_shift_per_minute.py # R3a，1 秒 +python3 scripts/research/representative_pairs_probe.py # R3c，约 6 分钟 +python3 -m pytest tests/test_offline_research_20260926.py # 纯函数回归 +``` diff --git a/docs/research/representative_pairs_probe_2026_09_26.json b/docs/research/representative_pairs_probe_2026_09_26.json new file mode 100644 index 00000000..3fe13468 --- /dev/null +++ b/docs/research/representative_pairs_probe_2026_09_26.json @@ -0,0 +1,9433 @@ +{ + "generated_at": "2026-09-26", + "nature": "offline replay on the public AA open set (v4); not a blind test", + "ayanamsa": "raman", + "node_mode": "mean", + "holdout": "references/real_case_calibration/minute_rectification_holdout_v4.json", + "case_count": 20, + "radii": [ + 10, + 30, + 60 + ], + "minute_step": 2, + "today": "2026-09-14", + "summary": { + "10": { + "initial": { + "cases": 20, + "cases_birth_before_1900": 2, + "cases_measured": 20, + "cases_skipped": 0, + "reps": { + "n": 20, + "mean": 6.0, + "p0": 4.0, + "p10": 5.0, + "p25": 5.0, + "p50": 6.0, + "p75": 7.0, + "p90": 8.0, + "p100": 9.0 + }, + "rep_span_minutes": { + "n": 20, + "mean": 18.6, + "p0": 16.0, + "p10": 16.0, + "p25": 18.0, + "p50": 18.0, + "p75": 20.0, + "p90": 20.0, + "p100": 20.0 + }, + "production_pairs": { + "pairs_mean": 6.05, + "pairs_clearing_gate_mean": 0.6, + "cases_with_zero_pairs_clearing_gate": 8, + "pairs_zip_misaligned_share": 0.0, + "pair_minutes": { + "n": 121, + "mean": 6.1, + "p0": 2.0, + "p10": 2.0, + "p25": 2.0, + "p50": 4.0, + "p75": 6.0, + "p90": 18.0, + "p100": 20.0 + }, + "pair_shift_days": { + "n": 121, + "mean": 20.0, + "p0": 3.0, + "p10": 4.0, + "p25": 7.0, + "p50": 11.0, + "p75": 22.0, + "p90": 37.0, + "p100": 102.0 + }, + "union_dates_mean": 99.25, + "cases_zero_union_dates": 0, + "dasha_boundary_before_cap_mean": 4.85, + "cases_zero_dasha_boundary_before_cap": 4, + "public_mean": 5.1, + "public_new_mean": 5.1, + "public_new_dasha_boundary_mean": 4.85, + "public_new_separating_truth_mean": 5.1, + "cases_zero_public_new": 4, + "dasha_boundary_evaluated_mean": 167.75, + "dasha_boundary_split_rate": 0.032, + "vim_pass_by_gap_total": 616, + "vim_pass_by_year_straddle_total": 216, + "vim_pass_by_misaligned_zip_total": 0, + "cases_zero_public_new_dasha_boundary": 4 + }, + "all_pairs": { + "pairs_mean": 16.1, + "pairs_clearing_gate_mean": 3.3, + "cases_with_zero_pairs_clearing_gate": 8, + "pairs_zip_misaligned_share": 0.0, + "pair_minutes": { + "n": 322, + "mean": 8.9, + "p0": 2.0, + "p10": 2.0, + "p25": 4.0, + "p50": 8.0, + "p75": 12.0, + "p90": 16.0, + "p100": 20.0 + }, + "pair_shift_days": { + "n": 322, + "mean": 28.8, + "p0": 3.0, + "p10": 7.0, + "p25": 11.0, + "p50": 22.0, + "p75": 41.0, + "p90": 63.0, + "p100": 102.0 + }, + "union_dates_mean": 130.55, + "cases_zero_union_dates": 0, + "dasha_boundary_before_cap_mean": 4.8, + "cases_zero_dasha_boundary_before_cap": 4, + 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4.0, + "p10": 6.0, + "p25": 10.0, + "p50": 14.0, + "p75": 16.0, + "p90": 20.0, + "p100": 20.0 + }, + "production_pairs": { + "pairs_mean": 3.85, + "pairs_clearing_gate_mean": 0.9, + "cases_with_zero_pairs_clearing_gate": 7, + "pairs_zip_misaligned_share": 0.351, + "pair_minutes": { + "n": 77, + "mean": 6.6, + "p0": 2.0, + "p10": 2.0, + "p25": 2.0, + "p50": 4.0, + "p75": 10.0, + "p90": 14.0, + "p100": 20.0 + }, + "pair_shift_days": { + "n": 77, + "mean": 19.5, + "p0": 3.0, + "p10": 4.0, + "p25": 7.0, + "p50": 14.0, + "p75": 27.0, + "p90": 43.0, + "p100": 79.0 + }, + "union_dates_mean": 521.3, + "cases_zero_union_dates": 0, + "dasha_boundary_before_cap_mean": 3.25, + "cases_zero_dasha_boundary_before_cap": 3, + "public_mean": 3.25, + "public_new_mean": 3.25, + "public_new_dasha_boundary_mean": 3.25, + "public_new_separating_truth_mean": 3.25, + "cases_zero_public_new": 3, + "dasha_boundary_evaluated_mean": 809.45, + "dasha_boundary_split_rate": 0.004, + "vim_pass_by_gap_total": 4963, + 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17, + "engine_top1": false, + "probes": 8, + "refresh": 6 + } + } + } + ], + "errors": [], + "command": "python3 scripts/research/varga_sensitivity_rerun.py" +} diff --git a/docs/tasks/PROGRESS-rectification-offline-research-20260926.md b/docs/tasks/PROGRESS-rectification-offline-research-20260926.md new file mode 100644 index 00000000..113a3e63 --- /dev/null +++ b/docs/tasks/PROGRESS-rectification-offline-research-20260926.md @@ -0,0 +1,79 @@ +# 进度 · 生时校正三项离线研究（2026-09-26） + +## 范围 + +- 任务书：`docs/tasks/TASK-rectification-offline-research-20260926.md` +- 分支：`codex/rectification-offline-research-20260926`，worktree `.worktrees/rectification-offline-research-20260926`。开工基线 `origin/staging` @ `abb05b67`；测量在快进后的 `7ddce2c9` 上完成；提交最后变基到 `7203d94e`（其间只新增 `scripts/research/fewer_probes_card_replay.py` 与文档，引擎与打分代码未变，测量结果不受影响）。**本地提交，未推送。** +- 执行方式：产品授权 Claude 子代理直接执行。 +- 报告：`docs/research/rectification_offline_research_2026_09_26.md`（三节 + 一句话结论 + 是否立实现单） +- 性质：离线研究。**未改线上代码、打分、阈值、出题闸门、Skill、workflow。** 研究脚本在一次调用内临时替换模块属性（`event_probes._representative_pairs`、`event_probes._boundary_windows`、`event_probes._evaluate_contexts` 计数包装、研究模块 `precision_gate_lib.varga_factor`），调用结束立即恢复，脚本末尾断言已恢复，并断言线上闸门仍是 45 / 30。 +- 数据：v4 公开 AA 开放集（20 例）与虚构时刻。没有真实用户资料。**开放集回放，不是盲测，不是准确率。** + +## 开工预检 + +- 读了 `AGENTS.md`（Part A、Part B B4）、`CLAUDE.md`、任务书、closure / minute_resolution / cluster_width / precision_gate / holdout_v4 / guided_collect_holdout 各文档、BUG-692、`event_probes.py`（`_boundary_windows`、`_representative_pairs`、`_union_boundary_dates`、`_evaluate_contexts`、`guided_collect_windows`）、`decision_policy.py`、v4 回放脚本、上一轮审计的 `shift.py` / `repo_shift.py`、`docs/research/pre_work_error_ledger.md`（重点 ERR-110 / ERR-111）。 +- `python3 scripts/pre_work_check.py --remote-timeout 8 --command-timeout 45`：**exit 0**。remote verified，碎片扫描、外部引擎适配器诊断、5 个 focused 目标全部通过。本机没有复现台账里 09-24 / 09-25 的 `.workbuddy` 镜像断言失败，与 09-26 BUG-1047 预检记录一致。无新台账条目。 +- 本机没有 `.venv`，系统 `python3`（带 swisseph、pytest）运行全部命令。 + +## 完成 + +| 项 | 脚本 | 结果 | 耗时 | +| --- | --- | --- | ---: | +| R1 答错容错 | `scripts/research/answer_flip_tolerance.py` | `docs/research/answer_flip_tolerance_2026_09_26.json` | ~1 分钟 | +| R2 V1/V2 重跑 | `scripts/research/varga_sensitivity_rerun.py` | `docs/research/varga_sensitivity_rerun_2026_09_26.json` | ~16 分钟 | +| R3a 每分钟位移 | `scripts/research/dasha_shift_per_minute.py` | `docs/research/dasha_shift_per_minute_2026_09_26.json` | 1 秒 | +| R3c 配对推断 | `scripts/research/representative_pairs_probe.py` | `docs/research/representative_pairs_probe_2026_09_26.json` | ~6 分钟 | +| 共用纯函数 | `scripts/research/offline_research_20260926_lib.py` | — | — | +| 回归测试 | `tests/test_offline_research_20260926.py`（5 条，只测研究纯函数） | 5 passed | — | + +全部运行错误 0 例。 + +## 关键数字 + +- **R1**：不答错的基线与 09-14 G0 逐项一致（0.80 / 0.55 / 0.40；15 / 33 / 56；20/20）。答错 1 题：真值在区间 1.000 / 0.989 / 0.981，头名 0.58 / 0.33 / 0.24（同批不答错为 0.94 / 0.61 / 0.44）。答错 2 题：真值在区间 0.996 / 0.926 / 0.898，头名 0.40 / 0.08 / 0.06；±30 有 9/18 例、±60 有 12/18 例至少一种组合挤出真值。真值从未被淘汰（需 3 次强冲突）。随机抽样（种子 20260926，每格 30 次）与穷举一致。 +- **R2**：研究计分器 V0 与线上 0 / 2060 候选有差。V1 / V2 在 ±30 / ±60 上 0 个候选分数变化（系数全截断到 1 / 不丢分盘），±10 上 168 / 159 个候选分数变化，六题后指标与 V0 完全相同 → `no_benefit`（已实测）。补充 V1n 头名 0.85 / 0.60 / 0.50，宽度 15 / 33 / 73，不过门。 +- **R3a**：每分钟边界位移中位 3.82 天（p10 1.58、p90 5.00、范围 1.34–5.85，n=2000），差分与解析式一致，仓库 `_vim_start_dates` 中位 4.05（n=299 子样本），边界整体平移（对齐后差值 ≤1 天）。45 天闸门 ↔ 中位 11.8 分钟（7.7–33.5）。 +- **R3c**：全部两两配对 vs 线上配对，带日期题（截断前）4.85 vs 4.80（±10 初始）、3.25 vs 3.30（±10 刷新），其余半径差异 ≤0.1；没有带日期题的例子数完全相同 → 推断被推翻。边界月份能分开代表的比例：初始 3.2–4.5%，刷新约 0.4–0.5%。严格闸门（研究补丁）下 ±10 初始没有带日期题的例子 4 → 8。 + +## 同机复跑（BUG-985：只做同机 A/B，不写死跨机哈希） + +| 脚本 | 复跑方式 | 结果 | +| --- | --- | --- | +| R1 | `PYTHONHASHSEED=12345` 再跑一次 | JSON 逐字节一致 | +| R3a | `PYTHONHASHSEED=999` 再跑一次 | JSON 逐字节一致 | +| R3c | `PYTHONHASHSEED=4242 … --no-write` | summary 与已存 JSON 完全一致 | +| R2 | `PYTHONHASHSEED=4242 … --no-write` | proof 摘要与 12 行指标与已存 JSON 完全一致 | + +## 文档改动（只加勘误段，不删原文） + +- `docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md`：1.1 天 / 122 天每度 / 换算表 +- `docs/tasks/TASK-rectification-open-collect-invite-20260914.md`：D1b 依据句 +- `docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md`：实证第 5 条「20 分钟≈22 天，题池必然为空」 +- `docs/research/precision_gate_2026_09_14.md`：M1b V1/V2 → `no_benefit`（已实测） +- `docs/research/rectification_minute_resolution_closure_2026_09_14.md`：新增 §7 勘误与补充 +- `docs/BUG_HISTORY.md`：BUG-692 加一条补充；新增 **BUG-1048**（`investigating`，闸门跨年豁免 + zip 错位，未修）。开工时最大号是 BUG-1047；合入前请再核对一次编号，避免与并行分支冲突。 +- `docs/tasks/README.md`：本单状态改为「待验收」。 + +线上引导文案**未改**；改写建议写在报告 §R3「改正与文案建议」。 + +## 测试 + +- `python3 -m pytest tests/test_offline_research_20260926.py tests/test_bug_history_workflow.py`：7 passed +- `python3 -m pytest tests/test_repo_privacy_markers.py`（新文件暂存后）：全部 passed +- `python3 -m pytest tests/test_rectification_validation_integrity_gate.py tests/test_minute_resolution_research.py tests/test_cluster_width_research.py`：全部 passed（没有改冻结评分文件，见 ERR-110） +- 快速门 `python3 scripts/run_quality_gate.py --profile quick`：Python 部分 **948 passed / 1 skipped**（含隐私、校正完整性门禁）；随后 `npm test` 步骤 exit 127，原因是本 worktree 没有 `frontend/node_modules`（环境缺口，本单没有前端改动），整体报 `Quality gate failed`。不记为通过。快速门没有改动任何跟踪文件。 + +## 偏离与注意 + +1. **R2 加了一个任务书没有的补充方案 V1n。** 原因：V1 按定义在宽窗上截断成恒等，只报告「V1/V2 无变化」回答不了「按敏感度配权有没有用」。V1n 单列、标「补充」，不参与原判定。 +2. **R3c 加了「严格闸门」对照**，由此发现 BUG-1048。只做测量，不修。任何修改都要按 ERR-110 重新冻结 sealed holdout 记录。 +3. `scripts/research/precision_gate_sweep.py` 重跑会整篇覆盖 `docs/research/precision_gate_2026_09_14.md`，冲掉 09-15 与 09-26 两段手写勘误（既有风险，本单未改那个脚本）。 +4. 六题回放沿用 09-14 设计，题目不随作答重出。R1 因此测不到「答错后后续题被带偏」的影响，报告里已写明。 +5. v4 回放复现不出真机上「六题后问完了」的现象（±10 刷新阶段平均还有 3.25 道新的带日期题）。真机原因需要真机统计（`TASK-rectification-telemetry-20260926.md`），本单不下结论。 +6. `scripts/research/**` 与 `tests/**` 在门禁路径里，推送会触发 staging 门禁。本单未推送。 + +## 未做 + +- 未推送、未合入 staging、未部署（按指令）。 +- 未改线上引导文案（任务书要求只给建议）。 +- 未评估 BUG-1048 两个漏洞放出来的题目在真人作答下是否可靠。 diff --git a/docs/tasks/README.md b/docs/tasks/README.md index 1a644021..963af1ea 100644 --- a/docs/tasks/README.md +++ b/docs/tasks/README.md @@ -37,7 +37,7 @@ | --- | --- | --- | --- | --- | | `TASK-rectification-fewer-probes-card-20260926.md` | [PROGRESS](PROGRESS-rectification-fewer-probes-card-20260926.md) | **减少无效追问 + 卡片区间为主**：引导补件离线新增达标 0 → 上限 6→2、定向题问完门槛未达直接出卡；卡片区间为主标题、代表分钟副标题，第一二名差距 ≥5 个百分点才显示百分比，否则写「目前区分不开」。不放宽任何置信度。先做 | 待验收 | `codex/rectification-fewer-probes-card-20260926`（未推送；D1 改在前端每校正 2 道，引擎常量因冻结评分身份未动；回放真值不降、宽度中位 ±1 分钟、提问 11.4→7.8；Skill 10.0.30） | | `TASK-rectification-telemetry-20260926.md` | — | **匿名聚合统计**：每会话一行只存数字 / 枚举（题数分类、宽度、差距、停止原因、门槛达标、耗时、版本），管理后台只看汇总、保留 180 天；动表须 test:db。排在 fewer-probes 后 | 待领取 | — | -| `TASK-rectification-offline-research-20260926.md` | — | **三项离线研究**：答错 1–2 题的容错、V1/V2 分盘配权正确重跑、改正「1 分钟≈1.1 天」（实测中位 3.8 天）并核实 `_representative_pairs` 推断。不改线上 | 待领取 | — | +| `TASK-rectification-offline-research-20260926.md` | `PROGRESS-rectification-offline-research-20260926.md` | **三项离线研究**：答错 1–2 题的容错、V1/V2 分盘配权正确重跑、改正「1 分钟≈1.1 天」（实测中位 3.8 天）并核实 `_representative_pairs` 推断。不改线上。结论（`docs/research/rectification_offline_research_2026_09_26.md`）：R1 答错 1 题真值在区间 98–100%、头名降三到四成，答错 2 题 ±30/±60 挤出 7–10%（两道反答=8 分=淘汰线）；R2 权重生效，V1/V2 在 ±30/±60 按定义恒等、±10 六题后指标不变 → `no_benefit`（已实测）；R3 3.8 天/分钟复现，45 天闸≈8–34 分钟，`_representative_pairs` 推断被推翻（全配对题数不变，卡在逐月评估），另记 BUG-1048 `investigating`（闸门跨年豁免 + zip 错位）。三项均不建议立实现单 | 待验收 | `codex/rectification-offline-research-20260926`（本地未推） | | `TASK-rectification-code-split-20260926.md` | — | **代码拆分（只搬不改）**：聊天组件 2043 行 / 35 useState、`POST` 926 行、`runV9AgentTurn` 1047 行，269 处切源码测试；拆分 + 增长合同 + 切片测试改调用函数。排在 fewer-probes、telemetry 之后 | 待领取 | — | | `TASK-rectification-dup-question-20260926.md` | `PROGRESS-rectification-dup-question-20260926.md` | **同一轮问题出现两次（BUG-1045，复发自 BUG-585，BUG-969 拼回题干、去重只在刷新路径）+ 同一道选择题连画两张（BUG-1046：提交失败不回滚本地已答 + 兜底问题块条件过宽；H2 漏收回合）**。先于 latency 单 | 已验收（Claude 09-26 直接执行：子代理复现 A 与 B-H1（选择题提交 409 / 网络错误不回滚本地已答）；Claude 变基到含 BUG-1043/1044 的 staging 后独立复验 tsc/lint 0、全量 3981 条失败名单与基线逐条一致、四路由 ○、gzip 不变） | `e4c1c7a3`（已部署 `f1d16405`，health 一致） | | `TASK-rectification-latency-20260926.md` | `PROGRESS-rectification-latency-20260926.md` | **每轮等待过长（BUG-1047）**：一轮打字回答串行 5 次开思考的模型调用，分类在开流前且无超时。产品定：收尾两步不动、分类保持思考只加 10 秒超时、发出后立即出确定性进度句并按阶段更新；补埋点；服务端无口吻优化须 A/B 逐位一致。排在 dup-question 之后 | 已验收（Claude 09-26 直接执行：子代理实现 D1–D4 与 D5 两项；Claude 用 Node 22 独立复验 tsc/lint 0、全量 4002 条失败名单与 Node 22 基线逐条一致（24 条均为 Docker/DB）、四路由 ○、gzip 不变。D5 第 1 项（探针复用）输出逐字节一致但触发冻结打分身份，Claude 建议暂不做） | `86ff9a40`（已部署 `8a409434`，health 一致） | diff --git a/docs/tasks/TASK-rectification-open-collect-invite-20260914.md b/docs/tasks/TASK-rectification-open-collect-invite-20260914.md index 46b91e24..2d24ac81 100644 --- a/docs/tasks/TASK-rectification-open-collect-invite-20260914.md +++ b/docs/tasks/TASK-rectification-open-collect-invite-20260914.md @@ -90,3 +90,12 @@ cd frontend && npm ci ## 7. BUG 编号起点 - 起点 **BUG-689**（当前最大号 688）。 + +## 勘误（2026-09-26 离线研究 R3） + +§2 D1b 的依据句有误，原文保留不删。 + +- 错误：「出生时间每差 1 分钟，Vimshottari 大运边界只平移约 1.1 天」。实测中位约 **3.8 天**（1.3–5.9，按出生星宿主星分两档）。 +- 错误：「只有日精度的事件才有可能分开相隔几分钟的候选」。改正：记得到月的事件，在候选相隔约 8 分钟以上（慢的星宿约 20 分钟以上）时就能稳定分开；记得到天（容许几天误差）的事件能分开相隔约 2–5 分钟的候选。v4 开放集上把日精度降成月精度，头名与宽度都不变（`docs/research/precision_gate_2026_09_14.md` M0），「记得到天明显更有用」仍不能写成已证实。 +- 本单的自由文本邀请已在 BUG-740 删除，线上现为引导题 + 年 / 月选择器（日期可选）。依据句的建议改写与文案建议见 `docs/research/rectification_offline_research_2026_09_26.md` §R3「改正与文案建议」。线上文案本次未改。 +- 复跑：`python3 scripts/research/dasha_shift_per_minute.py`。 diff --git a/docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md b/docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md index 252173bb..20b9b737 100644 --- a/docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md +++ b/docs/tasks/TASK-rectification-precision-adaptive-boundary-research-20260914.md @@ -131,3 +131,22 @@ python3 scripts/pre_work_check.py --remote-timeout 8 --command-timeout 45 ## 7. BUG 编号 - 研究单，**不新增 BUG 编号**。 + +## 勘误（2026-09-26 离线研究 R3） + +§1 的换算有误，原文保留不删。 + +- 错误：「Vimshottari 1° ≈ 122 天」「出生时间每差 1 分钟，所有 Vimshottari 边界整体平移约 1.1 天」。Vimshottari 120 年覆盖 9 个星宿（120°），**平均 1° ≈ 365 天**（太阳 164 天 / 度到金星 548 天 / 度）。 +- 实测（2000 个虚构时刻）：每分钟平移**中位 3.8 天**，p10 1.6、p90 5.0，全范围 1.3–5.9，按出生星宿主星分两档（太阳 / 火星 / 计都 / 月亮约 1.5–2.5；木星 / 水星 / 罗睺 / 土星 / 金星约 4–5）。仓库 `_vim_start_dates` 复核一致。「所有边界整体平移同一个天数」这一点是对的。 +- 换算表改正： + +| 候选相隔 | 边界相差（中位 / p10–p90） | 45 天闸 | 30 天闸（刷新） | +| --- | ---: | --- | --- | +| 40 分钟 | ≈ 153 天 / 63–200 | 全部可出题 | 全部可出题 | +| 20 分钟 | ≈ 76 天 / 32–100 | 66% 可出题 | 94% 可出题 | +| 10 分钟 | ≈ 38 天 / 16–50 | 27% 可出题 | 54% 可出题 | +| 2 分钟 | ≈ 7.6 天 / 3–10 | 不可 | 不可 | + +- 45 天闸门对应中位 **11.8 分钟**（全范围 7.7–33.5），30 天对应中位 7.8 分钟，不是 40 / 27 分钟。 +- 因此「剩余候选挤在 20 分钟内 → 大运边界全在三周内 → 被闸门整批丢掉」的推断不成立。另外核实了 `_representative_pairs`（只配相邻 + 首尾）不是出不来题的原因：改成全部两两配对，带日期的题数不变。真正的卡点在逐月评估。 +- 出处与复跑命令：`docs/research/rectification_offline_research_2026_09_26.md` §R3；`python3 scripts/research/dasha_shift_per_minute.py`、`python3 scripts/research/representative_pairs_probe.py`。 diff --git a/docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md b/docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md index f59b14f8..14fa685f 100644 --- a/docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md +++ b/docs/tasks/TASK-rectification-precision-gate-guided-collect-20260916.md @@ -175,3 +175,12 @@ Bug 检索（§5 硬约束）：`BUG-687`、`BUG-689`、`BUG-646`～`648`、`BUG ## 9. BUG 编号起点 **BUG-740**（基线最大 BUG-739）。开工时重新核对。 + +## 勘误（2026-09-26 离线研究 R3） + +§1 事故实证第 5 条有误，原文保留不删。 + +- 错误：「窗口收到 20 分钟时边界位移约 22 天，**自动点选题池必然为空**，这是『问完了』的算术原因」。改正：每分钟平移中位约 3.8 天，20 分钟对应边界差中位约 76 天（p10–p90 为 32–100 天），66% 能过 45 天闸、94% 能过 30 天刷新闸。 +- v4 开放集回放里，六题后刷新阶段平均还能出 3.25 道新的带日期题（±10），1900 年后出生的 18 例里只有 1 例为 0。任务书推断的「`_representative_pairs` 只配相邻 + 首尾」也不是原因：改成全部两两配对，题数不变。卡点在逐月评估，只有约 0.4% 的边界月份能把剩余代表分到两边。真机上「问完了」的真实原因未定，需要真机统计。 +- 本单的决策（出卡门槛、引导补经历）不依赖这条算术，不受影响。 +- 出处与复跑：`docs/research/rectification_offline_research_2026_09_26.md` §R3；`python3 scripts/research/representative_pairs_probe.py`。 diff --git a/scripts/research/answer_flip_tolerance.py b/scripts/research/answer_flip_tolerance.py new file mode 100644 index 00000000..88b99536 --- /dev/null +++ b/scripts/research/answer_flip_tolerance.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python3 +"""R1 (2026-09-26): yes/no answer-error tolerance of the six-question replay. + +Offline only; production scoring, probe gates and Skill text are untouched. +Replays the same six probes the 2026-09-14 precision-gate / cluster-width +research used (production G0 gate, lead-8 union delivery), but with 1 or 2 of +the answered questions flipped (yes<->no). Flip sets are drawn with fixed +string seeds and repeated; an exhaustive all-combinations pass is reported as +a cross-check of the sampled numbers. + +This is the public AA open set (v4), not a blind test. Every six-question +number is still an upper bound in one respect: the probe list is not re-drawn +after each answer (the same limitation as the 09-14 replays). + +Run: + python3 scripts/research/answer_flip_tolerance.py # full run + python3 scripts/research/answer_flip_tolerance.py --limit 2 # smoke +""" + +from __future__ import annotations + +import argparse +import json +import sys +import traceback +from pathlib import Path +from typing import Any, Sequence + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from scripts.active_rectification_event_engine import ( # noqa: E402 + AYANAMSA, + NODE_MODE, + compute_candidate_static_contexts, +) +import scripts.rectification.event_probes as event_probes # noqa: E402 +from scripts.research.cluster_width_lib import ( # noqa: E402 + SEPARATION_LEAD, + delivery_from_public, + merge_adjacent_traced, + metrics_bundle, + public_from_clusters, + raw_signature_clusters, + shannon_entropy, + still_valid_public, +) +from scripts.research.minute_resolution_sweep import MINUTE_STEP, scoring_request_for # noqa: E402 +from scripts.research.offline_research_20260926_lib import ( # noqa: E402 + FLIP_SEED, + answerable_indices, + exhaustive_flip_sets, + flipped_answers, + median_of, + rate, + sample_flip_sets, +) +from scripts.research.precision_gate_lib import finest_precision # noqa: E402 +from scripts.research.precision_gate_sweep import generate_probes, score_bundle # noqa: E402 +from scripts.research.probe_supply_after_six import ( # noqa: E402 + ASK_COUNT, + apply_answer, + optimal_answer, +) + +HOLDOUT = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v4.json" +REPORT_JSON = ROOT / "docs" / "research" / "answer_flip_tolerance_2026_09_26.json" +RADII = (10, 30, 60) +FLIP_COUNTS = (0, 1, 2) +REPEATS = 30 + + +def public_for(rows: Sequence[dict[str, Any]], contexts: Sequence[dict[str, Any]]) -> list[dict[str, Any]]: + raw = raw_signature_clusters(contexts) + by_time = {str(row["time"])[:5]: row for row in rows if str(row.get("time"))} + merged, _trace = merge_adjacent_traced(raw, by_time) + public = public_from_clusters(merged, rows) + for row in public: + row["score"] = float(row.get("score") or 0) + return public + + +def replay_with_answers( + *, + probes: Sequence[dict[str, Any]], + answers: Sequence[str | None], + public: Sequence[dict[str, Any]], + true_time: str, + window_times: Sequence[str], +) -> dict[str, Any]: + """Same bookkeeping as cluster_width_probe.replay_public + evaluate_delivery, + but the answer to each asked probe comes from `answers`.""" + reps = [str(row["time"])[:5] for row in public] + scores = {str(row["time"])[:5]: float(row.get("score") or 0) for row in public} + conflicts = {time: 0 for time in reps} + eliminated: set[str] = set() + for probe, answer in zip(list(probes)[:ASK_COUNT], answers): + if answer is None: + continue + scores, conflicts, eliminated = apply_answer( + scores, conflicts, eliminated, probe, answer, reps, + ) + posterior = [ + {**row, "score": scores.get(str(row["time"])[:5], row["score"])} + for row in public + ] + valid = still_valid_public(posterior, scores, eliminated, lead=SEPARATION_LEAD) + delivery = delivery_from_public(valid) + alive = [row for row in posterior if str(row["time"])[:5] not in eliminated] + metrics = metrics_bundle( + public=alive, + true_time=true_time, + window_times=window_times, + delivery_times=delivery["times"], + delivery_width=delivery["width"], + independent=True, + entropy_scores=[scores.get(str(row["time"])[:5], 0.0) for row in alive], + ) + true_rep_eliminated = any( + true_time in (row.get("cluster_times") or [str(row.get("time"))[:5]]) + and str(row["time"])[:5] in eliminated + for row in public + ) + return { + "top1": bool(metrics["top1_hit"]), + "coverage": bool(metrics["coverage"]), + "width": delivery["width"], + "tie": bool(metrics["tie"]), + "eliminated": len(eliminated), + "truth_eliminated": true_rep_eliminated, + "entropy": round(shannon_entropy(max(s, 0.0) for t, s in scores.items() if t not in eliminated), 4), + } + + +def run_case(case: dict[str, Any], radius: int, repeats: int) -> dict[str, Any]: + case_id = case["case_id"] + true_time = str(case["birth"]["time"])[:5] + request = scoring_request_for({**case, "candidate_radius_minutes": radius}, radius) + contexts = compute_candidate_static_contexts(request) + events = list(case.get("events") or []) + bundle = score_bundle(case, radius, events, contexts, None) + rows = bundle["rows"] + times = [str(row["time"])[:5] for row in rows] + probes = generate_probes( + bundle["request"], bundle["built"], times, true_time, + gate="G0", precision=finest_precision(events), refresh=False, + ) + asked = list(probes)[:ASK_COUNT] + optimal = [optimal_answer(probe, true_time) for probe in asked] + public = public_for(rows, list(bundle["built"].get("static_contexts") or contexts)) + out: dict[str, Any] = { + "case_id": case_id, + "radius": radius, + "asked": len(asked), + "answerable": len(answerable_indices(optimal)), + "public_clusters": len(public), + "runs": {}, + "exhaustive": {}, + } + for k in FLIP_COUNTS: + if k == 0: + sets: list[tuple[int, ...]] = [()] + full: list[tuple[int, ...]] = [()] + else: + sets = sample_flip_sets(optimal, k, repeats, case_id=case_id, radius=radius) + full = exhaustive_flip_sets(optimal, k) + out["runs"][str(k)] = [ + {"flipped": list(item), **replay_with_answers( + probes=asked, answers=flipped_answers(optimal, item), + public=public, true_time=true_time, window_times=times, + )} + for item in sets + ] + out["exhaustive"][str(k)] = [ + {"flipped": list(item), **replay_with_answers( + probes=asked, answers=flipped_answers(optimal, item), + public=public, true_time=true_time, window_times=times, + )} + for item in full + ] + return out + + +def summarize(case_rows: Sequence[dict[str, Any]], key: str) -> dict[str, Any]: + table: dict[str, Any] = {} + for radius in sorted({row["radius"] for row in case_rows}): + table[str(radius)] = {} + for k in FLIP_COUNTS: + runs = [run for row in case_rows if row["radius"] == radius for run in row[key].get(str(k), [])] + eligible = [row for row in case_rows if row["radius"] == radius and row[key].get(str(k))] + per_case_cov = [ + all(run["coverage"] for run in row[key][str(k)]) for row in eligible + ] + base = [row[key]["0"][0] for row in eligible] + table[str(radius)][str(k)] = { + "baseline_same_cases": { + "truth_in_range": rate(base, "coverage"), + "head_hit": rate(base, "top1"), + "width_median": median_of(base, "width"), + }, + "cases": len(eligible), + "runs": len(runs), + "truth_in_range": rate(runs, "coverage"), + "head_hit": rate(runs, "top1"), + "width_median": median_of(runs, "width"), + "tie": rate(runs, "tie"), + "truth_eliminated": rate(runs, "truth_eliminated"), + "cases_ever_squeezed": sum(1 for ok in per_case_cov if not ok), + } + return table + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--limit", type=int, default=0) + parser.add_argument("--repeats", type=int, default=REPEATS) + parser.add_argument("--radii", nargs="+", type=int, default=list(RADII)) + parser.add_argument("--no-write", action="store_true") + args = parser.parse_args() + holdout = json.loads(HOLDOUT.read_text(encoding="utf-8")) + cases = list(holdout["cases"])[: args.limit or None] + assert event_probes.MIN_BOUNDARY_DAYS == 45 and event_probes.REFRESH_MIN_BOUNDARY_DAYS == 30 + case_rows: list[dict[str, Any]] = [] + errors: list[dict[str, Any]] = [] + for case in cases: + for radius in args.radii: + try: + case_rows.append(run_case(case, radius, args.repeats)) + except Exception as exc: # noqa: BLE001 + errors.append({"case_id": case["case_id"], "radius": radius, + "error": f"{type(exc).__name__}: {exc}", "trace": traceback.format_exc()}) + print(f"done {case['case_id']}", flush=True) + assert event_probes.MIN_BOUNDARY_DAYS == 45 and event_probes.REFRESH_MIN_BOUNDARY_DAYS == 30 + payload = { + "generated_at": "2026-09-26", + "nature": "offline replay on the public AA open set (v4); not a blind test, not accuracy", + "ayanamsa": AYANAMSA, + "node_mode": NODE_MODE, + "holdout": str(HOLDOUT.relative_to(ROOT)), + "case_count": len(cases), + "radii": list(args.radii), + "minute_step": MINUTE_STEP, + "ask_count": ASK_COUNT, + "flip_seed": FLIP_SEED, + "repeats": args.repeats, + "gate": "G0 production (45 / refresh 30)", + "delivery": f"lead-{SEPARATION_LEAD} union of non-eliminated clusters", + "sampled": summarize(case_rows, "runs"), + "exhaustive": summarize(case_rows, "exhaustive"), + "answerable_per_case": { + str(radius): [row["answerable"] for row in case_rows if row["radius"] == radius] + for radius in args.radii + }, + "per_case": [ + { + "case_id": row["case_id"], + "radius": row["radius"], + "answerable": row["answerable"], + **{ + f"k{k}": { + "truth_in_range": rate(row["exhaustive"].get(str(k), []), "coverage"), + "head_hit": rate(row["exhaustive"].get(str(k), []), "top1"), + "width_median": median_of(row["exhaustive"].get(str(k), []), "width"), + } + for k in FLIP_COUNTS + }, + } + for row in case_rows + ], + "errors": errors, + "command": "python3 scripts/research/answer_flip_tolerance.py", + } + if not args.no_write: + REPORT_JSON.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({"sampled": payload["sampled"], "errors": len(errors)}, ensure_ascii=False, indent=1)) + return 0 if not errors else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/research/dasha_shift_per_minute.py b/scripts/research/dasha_shift_per_minute.py new file mode 100644 index 00000000..3931f139 --- /dev/null +++ b/scripts/research/dasha_shift_per_minute.py @@ -0,0 +1,263 @@ +#!/usr/bin/env python3 +"""R3a (2026-09-26): how far Vimshottari boundaries move per minute of birth time. + +Fictional moments only (random instants, no people). Three measurements: + +1. analytic: Moon speed (swisseph, sidereal Raman) x lord years / 13.333 deg; +2. finite difference: first-dasha balance at t and t + 1 minute (the 09-26 + audit method, reproduced here with the same seed); +3. repo helper: `scripts.rectification.event_probes._vim_start_dates` at t and + t + 20 minutes; all MD/AD starts in [birth+5, birth+80] shift by one + constant, which is divided by 20. + +It then derives what the production 45 / 30 day probe gate means in minutes, +and how close two candidates can be for evidence of a given date precision to +separate them. It corrects the 2026-09-14 statement "1 minute ~ 1.1 days", +which used Vimshottari 1 deg ~ 122 days; the correct mean is ~365 days/deg +(120 years over 9 nakshatras = 120 deg). + +Run: + python3 scripts/research/dasha_shift_per_minute.py +""" + +from __future__ import annotations + +import argparse +import json +import random +import statistics +import sys +from datetime import date, timedelta +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parents[2] +for extra in (ROOT, ROOT / "scripts"): + if str(extra) not in sys.path: + sys.path.insert(0, str(extra)) + +import swisseph as swe # noqa: E402 + +from scripts.rectification.event_probes import ( # noqa: E402 + MIN_BOUNDARY_DAYS, + REFRESH_MIN_BOUNDARY_DAYS, + _vim_start_dates, +) +from scripts.research.offline_research_20260926_lib import ( # noqa: E402 + DAYS_PER_YEAR, + NAKSHATRA_SPAN, + VIM_LORDS, + VIM_YEARS, + distribution, + minutes_for_gap, + nakshatra_lord, + percentile, + shift_days_per_minute, + zip_misaligned, +) + +REPORT_JSON = ROOT / "docs" / "research" / "dasha_shift_per_minute_2026_09_26.json" +SAMPLES = 2000 +SEED = 7 # same seed as the 09-26 audit sketch, so the 3.8-day median reproduces +REPO_SAMPLES = 300 +WINDOWS = (2, 4, 6, 10, 20, 30, 40, 60) +MONTH_DAYS = 30.44 +OLD_CLAIM_DAYS_PER_MINUTE = 1.1 +OLD_CLAIM_DEG_DAYS = 122.0 + + +def moon(jd: float) -> tuple[float, float]: + values, _flags = swe.calc_ut(jd, swe.MOON, swe.FLG_SIDEREAL | swe.FLG_SPEED) + return float(values[0]), float(values[3]) + + +def balance_days(longitude: float) -> float: + progress = (longitude % NAKSHATRA_SPAN) / NAKSHATRA_SPAN + return (1.0 - progress) * VIM_YEARS[nakshatra_lord(longitude)] * DAYS_PER_YEAR + + +def sample_instants(n: int, seed: int) -> list[float]: + rng = random.Random(seed) + rows = [] + for _ in range(n): + rows.append(swe.julday(1950 + rng.randrange(55), 1, 1, 0.0) + rng.random() * 365.0) + return rows + + +def jd_to_date(jd: float) -> date: + year, month, day, _hour = swe.revjul(jd) + return date(int(year), int(month), int(day)) + + +def repo_shift(jd: float, minutes: int) -> dict[str, Any] | None: + birth = jd_to_date(jd) + birth_date = birth.isoformat() + lo, hi = birth.year + 5, birth.year + 80 + lon_a, _ = moon(jd) + lon_b, _ = moon(jd + minutes / 1440.0) + if int(lon_a // NAKSHATRA_SPAN) != int(lon_b // NAKSHATRA_SPAN): + return {"crossed_nakshatra": True} + left = _vim_start_dates(birth_date, lon_a, lo, hi) + right = _vim_start_dates(birth_date, lon_b, lo, hi) + misaligned = zip_misaligned(left, right) + # Re-align on corresponding boundaries: when a boundary crosses the lower + # or upper year edge (MD and first-AD starts coincide, so up to two + # entries can drop), try offsets -3..+3 and keep the one whose + # signed differences are most constant. + best: list[int] | None = None + for offset in (0, 1, -1, 2, -2, 3, -3): + a = left[offset:] if offset > 0 else left + b = right[-offset:] if offset < 0 else right + diffs = [(one - two).days for one, two in zip(a, b)] + if diffs and (best is None or max(diffs) - min(diffs) < max(best) - min(best)): + best = diffs + diffs = best or [] + if not diffs: + return None + return { + "crossed_nakshatra": False, + "per_minute": abs(statistics.median(diffs)) / minutes, + "spread_days": max(diffs) - min(diffs), + "raw_misaligned": misaligned, + "boundaries": len(diffs), + } + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--samples", type=int, default=SAMPLES) + parser.add_argument("--repo-samples", type=int, default=REPO_SAMPLES) + parser.add_argument("--no-write", action="store_true") + args = parser.parse_args() + swe.set_sid_mode(swe.SIDM_RAMAN) + instants = sample_instants(args.samples, SEED) + analytic: list[float] = [] + finite: list[float] = [] + by_lord: dict[str, list[float]] = {lord: [] for lord in VIM_LORDS} + speeds: list[float] = [] + for jd in instants: + lon, speed = moon(jd) + speeds.append(speed) + value = shift_days_per_minute(lon, speed) + analytic.append(value) + by_lord[nakshatra_lord(lon)].append(value) + lon_next, _ = moon(jd + 1 / 1440.0) + if int(lon // NAKSHATRA_SPAN) == int(lon_next // NAKSHATRA_SPAN): + # boundary = birth + balance; birth itself moves +1 minute. + finite.append(abs(balance_days(lon_next) + 1 / 1440.0 - balance_days(lon))) + repo_rows = [] + crossed = 0 + for jd in instants[: args.repo_samples]: + row = repo_shift(jd, 20) + if row is None: + continue + if row.get("crossed_nakshatra"): + crossed += 1 + continue + repo_rows.append(row) + repo_values = [row["per_minute"] for row in repo_rows] + p10, p50, p90 = (percentile(analytic, q) for q in (10, 50, 90)) + lo_bound, hi_bound = min(analytic), max(analytic) + + gate_minutes = {} + for label, days in (("gate_45", MIN_BOUNDARY_DAYS), ("refresh_30", REFRESH_MIN_BOUNDARY_DAYS)): + gate_minutes[label] = distribution([minutes_for_gap(days, s) for s in analytic], 1) + + window_rows = [] + for minutes in WINDOWS: + gaps = [minutes * s for s in analytic] + same_year_pass = sum(1 for g in gaps if g >= MIN_BOUNDARY_DAYS) / len(gaps) + window_rows.append({ + "candidate_gap_minutes": minutes, + "boundary_gap_days": distribution(gaps, 1), + "share_ge_45_days": round(same_year_pass, 3), + "share_ge_30_days": round(sum(1 for g in gaps if g >= REFRESH_MIN_BOUNDARY_DAYS) / len(gaps), 3), + "old_claim_days": round(minutes * OLD_CLAIM_DAYS_PER_MINUTE, 1), + }) + + # How close can two candidates be for evidence of a given precision to + # separate them? The event date must fall between the two boundary dates. + # Guaranteed separation needs a boundary gap of about the precision span; + # below that, separation happens only when the gap straddles a calendar + # edge, with probability ~ gap / span (uniform boundary position). + precision_rows = [] + for label, span_days in ( + ("day_exact", 1.0), + ("day_pm3", 7.0), + ("day_pm7", 15.0), + ("month", MONTH_DAYS), + ("gate_45_same_year", float(MIN_BOUNDARY_DAYS)), + ("year", DAYS_PER_YEAR), + ): + precision_rows.append({ + "evidence": label, + "span_days": round(span_days, 2), + "min_separable_minutes_p90_shift": round(span_days / p90, 1), + "min_separable_minutes_median_shift": round(span_days / p50, 1), + "min_separable_minutes_p10_shift": round(span_days / p10, 1), + "min_separable_minutes_slowest": round(span_days / lo_bound, 1), + }) + straddle = [] + for minutes in (2, 6, 10, 20): + gap = minutes * p50 + straddle.append({ + "candidate_gap_minutes": minutes, + "boundary_gap_days_median": round(gap, 1), + "p_different_month": round(min(1.0, gap / MONTH_DAYS), 3), + "p_different_year": round(min(1.0, gap / DAYS_PER_YEAR), 3), + }) + payload = { + "generated_at": "2026-09-26", + "nature": "fictional instants only; pure astronomy + repo Vimshottari helper", + "ayanamsa": "raman (swisseph SIDM_RAMAN)", + "dasha_year_days": DAYS_PER_YEAR, + "samples": len(instants), + "seed": SEED, + "old_claim": { + "days_per_minute": OLD_CLAIM_DAYS_PER_MINUTE, + "vimshottari_days_per_degree": OLD_CLAIM_DEG_DAYS, + "correct_mean_days_per_degree": round(120 * DAYS_PER_YEAR / 120, 2), + "moon_deg_per_minute_mean": round(statistics.fmean(speeds) / 1440.0, 5), + }, + "analytic_days_per_minute": distribution(analytic), + "finite_difference_days_per_minute": distribution(finite), + "repo_vim_start_dates_days_per_minute": distribution(repo_values), + "repo_samples": len(repo_rows), + "repo_crossed_nakshatra_in_20_min": crossed, + "repo_max_spread_days": max((row["spread_days"] for row in repo_rows), default=None), + "repo_raw_zip_misaligned_share": round( + sum(1 for row in repo_rows if row["raw_misaligned"]) / max(len(repo_rows), 1), 3), + "by_lord": { + lord: {**distribution(values), "years": VIM_YEARS[lord]} + for lord, values in by_lord.items() + }, + "moon_speed_deg_per_day": distribution(speeds, 3), + "theoretical_range_days_per_minute": { + "min": round(lo_bound, 2), "max": round(hi_bound, 2), + }, + "gate_minutes": gate_minutes, + "window_table": window_rows, + "precision_table": precision_rows, + "straddle_table": straddle, + "command": "python3 scripts/research/dasha_shift_per_minute.py", + } + if not args.no_write: + REPORT_JSON.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({key: payload[key] for key in ( + "analytic_days_per_minute", "finite_difference_days_per_minute", + "repo_vim_start_dates_days_per_minute", "repo_samples", "repo_crossed_nakshatra_in_20_min", + "repo_max_spread_days", "repo_raw_zip_misaligned_share", "gate_minutes", + "theoretical_range_days_per_minute", "old_claim")}, ensure_ascii=False, indent=1)) + for row in window_rows: + print(row) + for row in precision_rows: + print(row) + for row in straddle: + print(row) + for lord, row in payload["by_lord"].items(): + print(lord, row) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/research/offline_research_20260926_lib.py b/scripts/research/offline_research_20260926_lib.py new file mode 100644 index 00000000..a5b660e1 --- /dev/null +++ b/scripts/research/offline_research_20260926_lib.py @@ -0,0 +1,147 @@ +"""Pure helpers for the 2026-09-26 offline rectification research (R1/R2/R3). + +Offline only. Nothing here is imported by production code, and nothing here +changes scoring, thresholds, probe gates or Skill text. +""" + +from __future__ import annotations + +import statistics +from itertools import combinations +from random import Random +from typing import Any, Iterable, Sequence + +#: Vimshottari lords in nakshatra order starting at Ashwini, and their years. +VIM_LORDS: tuple[str, ...] = ( + "Ketu", "Venus", "Sun", "Moon", "Mars", "Rahu", "Jupiter", "Saturn", "Mercury", +) +VIM_YEARS: dict[str, float] = { + "Ketu": 7.0, "Venus": 20.0, "Sun": 6.0, "Moon": 10.0, "Mars": 7.0, + "Rahu": 18.0, "Jupiter": 16.0, "Saturn": 19.0, "Mercury": 17.0, +} +NAKSHATRA_SPAN = 360.0 / 27.0 +DAYS_PER_YEAR = 365.25 # scripts/dasha_analyzer.py build_dasha_timeline +FLIP_SEED = 20260926 +PERCENTILES = (0, 10, 25, 50, 75, 90, 100) + + +def nakshatra_lord(moon_longitude: float) -> str: + index = int((float(moon_longitude) % 360.0) // NAKSHATRA_SPAN) + return VIM_LORDS[index % 9] + + +def shift_days_per_minute(moon_longitude: float, moon_speed_deg_per_day: float) -> float: + """Days every Vimshottari boundary moves when birth time moves one minute. + + The balance of the first dasha is (1 - progress) * years(lord). Every later + MD/AD/PD start is birth + balance + fixed durations, so all of them move by + the same amount: d(balance)/d(moon) * d(moon)/d(minute). + """ + years = VIM_YEARS[nakshatra_lord(moon_longitude)] + deg_per_minute = abs(float(moon_speed_deg_per_day)) / 1440.0 + return deg_per_minute / NAKSHATRA_SPAN * years * DAYS_PER_YEAR + + +def percentile(values: Sequence[float], q: float) -> float | None: + """Nearest-rank percentile on a sorted copy (q in 0..100).""" + items = sorted(float(item) for item in values) + if not items: + return None + if q <= 0: + return items[0] + if q >= 100: + return items[-1] + rank = int(round(q / 100.0 * (len(items) - 1))) + return items[rank] + + +def distribution(values: Sequence[float], digits: int = 2) -> dict[str, Any]: + items = [float(item) for item in values] + if not items: + return {"n": 0} + row: dict[str, Any] = {"n": len(items), "mean": round(statistics.fmean(items), digits)} + for q in PERCENTILES: + row[f"p{q}"] = round(float(percentile(items, q)), digits) + return row + + +def minutes_for_gap(days: float, shift_per_minute: float) -> float | None: + if shift_per_minute <= 0: + return None + return float(days) / float(shift_per_minute) + + +def answerable_indices(answers: Sequence[str | None]) -> list[int]: + return [index for index, answer in enumerate(answers) if answer in {"yes", "no"}] + + +def flip(answer: str | None) -> str | None: + return {"yes": "no", "no": "yes"}.get(str(answer), answer) + + +def flipped_answers(answers: Sequence[str | None], indices: Iterable[int]) -> list[str | None]: + chosen = set(indices) + return [flip(answer) if index in chosen else answer for index, answer in enumerate(answers)] + + +def sample_flip_sets( + answers: Sequence[str | None], + k: int, + repeats: int, + *, + case_id: str, + radius: int, + seed: int = FLIP_SEED, +) -> list[tuple[int, ...]]: + """Fixed-seed random flip sets: `repeats` draws of k answered questions. + + The RNG key includes case, radius, k and repeat index so every draw is + reproducible on any machine (string seeding is stable across processes). + """ + pool = answerable_indices(answers) + if k <= 0 or len(pool) < k: + return [] + rows: list[tuple[int, ...]] = [] + for repeat in range(int(repeats)): + rng = Random(f"{seed}:{case_id}:{radius}:{k}:{repeat}") + rows.append(tuple(sorted(rng.sample(pool, k)))) + return rows + + +def exhaustive_flip_sets(answers: Sequence[str | None], k: int) -> list[tuple[int, ...]]: + pool = answerable_indices(answers) + if k <= 0 or len(pool) < k: + return [] + return [tuple(item) for item in combinations(pool, k)] + + +def rate(rows: Sequence[dict[str, Any]], key: str) -> float | None: + if not rows: + return None + return round(sum(1 for row in rows if row.get(key)) / len(rows), 4) + + +def median_of(rows: Sequence[dict[str, Any]], key: str) -> float | None: + values = [row[key] for row in rows if row.get(key) is not None] + return statistics.median(values) if values else None + + +def all_pairs(reps: Sequence[dict[str, Any]], clock_of) -> list[tuple[dict[str, Any], dict[str, Any]]]: + """Every unordered pair of representatives, sorted by clock (research variant).""" + ordered = sorted(reps, key=clock_of) + return [(ordered[i], ordered[j]) for i in range(len(ordered)) for j in range(i + 1, len(ordered))] + + +def zip_misaligned(left: Sequence[Any], right: Sequence[Any], slack_days: int = 3) -> bool: + """True when `_boundary_windows`' positional zip pairs different boundaries. + + For Vimshottari, two candidates' boundary lists are the same sequence + shifted by one constant number of days (see `shift_days_per_minute`). When + one list gains or loses an element at the lower year edge, zip() pairs + boundary i with boundary i+1 of the other candidate and the signed + differences stop being constant. `slack_days` absorbs date rounding. + """ + diffs = [(two - one).days for one, two in zip(left, right)] + if not diffs: + return False + return max(diffs) - min(diffs) > slack_days diff --git a/scripts/research/representative_pairs_probe.py b/scripts/research/representative_pairs_probe.py new file mode 100644 index 00000000..66b9a685 --- /dev/null +++ b/scripts/research/representative_pairs_probe.py @@ -0,0 +1,474 @@ +#!/usr/bin/env python3 +"""R3c (2026-09-26): does `_representative_pairs` explain "no dated probes"? + +Offline only. Hypothesis under test (brief 2026-09-26): after the window +narrows, the dated (dasha-boundary) probe pool is empty because +`event_probes._representative_pairs` only pairs adjacent cluster +representatives plus first/last, and adjacent representatives are too close +for their boundaries to clear the 45 / 30 day gate. + +For every v4 case and radius, at the initial stage (all candidates) and the +refresh stage (after the six-question replay, same as the 09-14 research): + +* representative count and minute gaps; +* for each pair (production pairs vs. all pairs): Vimshottari boundary shift, + whether it clears the gate, whether `_boundary_windows`' positional zip is + misaligned; +* boundary dates from `_union_boundary_dates` and probes from the production + generator (before the public cap), with production pairs vs. a research + patch that returns all pairs, and vs. a research patch of + `_boundary_windows` that applies the documented gap to every pair (no + 1-January exemption, off-by-k zip re-aligned). Patches are applied to the + module attribute for the duration of one call and restored immediately. + +Public AA open set (v4). Not a blind test. + +Run: + python3 scripts/research/representative_pairs_probe.py +""" + +from __future__ import annotations + +import argparse +import json +import statistics +import sys +import traceback +from contextlib import contextmanager +from pathlib import Path +from typing import Any, Iterator, Sequence + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from scripts.active_rectification_event_engine import ( # noqa: E402 + AYANAMSA, + NODE_MODE, + compute_candidate_static_contexts, +) +import scripts.rectification.event_probes as ep # noqa: E402 +from scripts.rectification.candidate_contrast import cluster_contexts_by_signature # noqa: E402 +from scripts.rectification.refinement_packet import window_scan # noqa: E402 +from scripts.research.cluster_width_lib import raw_signature_clusters # noqa: E402 +from scripts.research.minute_resolution_sweep import MINUTE_STEP, scoring_request_for # noqa: E402 +from scripts.research.offline_research_20260926_lib import all_pairs, distribution, zip_misaligned # noqa: E402 +from scripts.research.precision_gate_lib import finest_precision # noqa: E402 +from scripts.research.precision_gate_sweep import ( # noqa: E402 + TODAY, + evaluate_delivery, + generate_probes, + score_bundle, +) +from scripts.research.probe_supply_after_six import asked_key, separates_true # noqa: E402 + +HOLDOUT = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v4.json" +REPORT_JSON = ROOT / "docs" / "research" / "representative_pairs_probe_2026_09_26.json" +RADII = (10, 30, 60) +PRODUCTION_PAIRS = ep._representative_pairs + + +def clock(context: dict[str, Any]) -> int: + return ep._clock(str(ep._context_time(context))) + + +def research_all_pairs(reps: Sequence[dict[str, Any]]) -> list[tuple[dict[str, Any], dict[str, Any]]]: + usable = [item for item in reps if ep._scoreable(item) and ep._context_time(item)] + return all_pairs(usable, clock) + + +PRODUCTION_WINDOWS = ep._boundary_windows + + +def strict_boundary_windows(left: Sequence[Any], right: Sequence[Any], *, min_days: int | None = None) -> list[Any]: + """Research-only reading of the gate as documented: the minimum gap applies + to every pair of corresponding boundaries (no 1-January exemption), and an + off-by-k zip is re-aligned when some offset makes the gaps constant.""" + threshold = ep.MIN_BOUNDARY_DAYS if min_days is None else min_days + one_list = [item for item in (ep._as_start_date(raw) for raw in left) if item is not None] + two_list = [item for item in (ep._as_start_date(raw) for raw in right) if item is not None] + pairs = list(zip(one_list, two_list)) + for offset in (0, 1, -1, 2, -2, 3, -3): + a = one_list[offset:] if offset > 0 else one_list + b = two_list[-offset:] if offset < 0 else two_list + diffs = [(two - one).days for one, two in zip(a, b)] + if diffs and max(diffs) - min(diffs) <= 3: + pairs = list(zip(a, b)) + break + windows: list[Any] = [] + seen: set[tuple[int, int]] = set() + for one, two in pairs: + if abs((one - two).days) < threshold: + continue + for item in (one, two): + key = (item.year, item.month) + if key not in seen: + seen.add(key) + windows.append(item) + return windows + + +@contextmanager +def pairs_mode(mode: str) -> Iterator[None]: + if mode == "production": + yield + return + if mode == "strict_gate": + ep._boundary_windows = strict_boundary_windows + try: + yield + finally: + ep._boundary_windows = PRODUCTION_WINDOWS + return + ep._representative_pairs = research_all_pairs + try: + yield + finally: + ep._representative_pairs = PRODUCTION_PAIRS + + +def aligned_shift(left: Sequence[Any], right: Sequence[Any]) -> int | None: + best: list[int] | None = None + for offset in (0, 1, -1, 2, -2, 3, -3): + a = left[offset:] if offset > 0 else left + b = right[-offset:] if offset < 0 else right + diffs = [(two - one).days for one, two in zip(a, b)] + if diffs and (best is None or max(diffs) - min(diffs) < max(best) - min(best)): + best = diffs + return None if not best else abs(int(statistics.median(best))) + + +def stage_reps(built: dict[str, Any], times: Sequence[str], refresh: bool) -> list[dict[str, Any]]: + """Mirror of the representative selection in `_discriminating_event_probe_lists`.""" + full = ep._static_contexts(built) + remaining = ep._remaining_contexts(built, times) if times else [] + if refresh and remaining: + work = remaining + clusters = cluster_contexts_by_signature(work) + else: + work = full + clusters = cluster_contexts_by_signature(full) + if len(clusters) < 2: + work = remaining or full + clusters = cluster_contexts_by_signature(work) + if len(clusters) < 2: + return [] + reps = [cluster["representative"] for cluster in clusters if ep._scoreable(cluster["representative"])] + if len(reps) < 2: + reps = [item for item in work if ep._scoreable(item)] + return reps + + +def pair_rows( + pairs: Sequence[tuple[dict[str, Any], dict[str, Any]]], + *, + birth_date: str, + lo: int, + hi: int, + threshold: int, + include_pratyantar: bool, +) -> list[dict[str, Any]]: + rows = [] + for left, right in pairs: + left_dates = ep._vim_start_dates( + birth_date, float(left["planet_longitudes"]["Moon"]), lo, hi, + include_pratyantar=include_pratyantar) + right_dates = ep._vim_start_dates( + birth_date, float(right["planet_longitudes"]["Moon"]), lo, hi, + include_pratyantar=include_pratyantar) + windows = ep._boundary_windows(left_dates, right_dates, min_days=threshold) + shift = aligned_shift(left_dates, right_dates) + # Replicate `_boundary_windows`' pass rule per zipped pair and say why + # each pair passed: a real gap >= threshold, or the same-year clause + # being skipped because the two dates straddle 1 January. + # A misaligned zip compares non-corresponding boundaries, so its + # "gap" passes are counted separately. + misaligned = zip_misaligned(left_dates, right_dates) + by_gap = straddle = by_misaligned = 0 + for one, two in zip(left_dates, right_dates): + gap = abs((one - two).days) + if one.year == two.year and gap < threshold: + continue + if gap < threshold: + straddle += 1 + elif misaligned and shift is not None and shift < threshold: + by_misaligned += 1 + else: + by_gap += 1 + rows.append({ + "minutes": abs(clock(right) - clock(left)), + "shift_days": shift, + "clears_gate": shift is not None and shift >= threshold, + "zip_misaligned": misaligned, + "vim_windows": len(windows), + "vim_pass_by_gap": by_gap, + "vim_pass_by_year_straddle": straddle, + "vim_pass_by_misaligned_zip": by_misaligned, + }) + return rows + + +def union_dates(reps: Sequence[dict[str, Any]], mode: str, *, birth_date: str, lo: int, hi: int, refresh: bool) -> int: + kwargs: dict[str, Any] = {} + if refresh: + kwargs = { + "min_boundary_days": ep.REFRESH_MIN_BOUNDARY_DAYS, + "include_pratyantar": True, + "varga_narayana": True, + } + with pairs_mode(mode): + return len(ep._union_boundary_dates(reps, birth_date=birth_date, lo=lo, hi=hi, **kwargs)) + + +def probe_lists( + request: dict[str, Any], + built: dict[str, Any], + times: Sequence[str], + true_time: str, + *, + refresh: bool, + asked: Sequence[str], + mode: str, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]], dict[str, int]]: + payload = {**request, "refresh_probes": refresh, "asked_probe_keys": list(asked)} + original = ep._evaluate_contexts + tally = {"dasha_boundary_evaluated": 0, "dasha_boundary_split": 0} + + def counted(*args: Any, **kwargs: Any) -> dict[str, Any] | None: + result = original(*args, **kwargs) + if kwargs.get("source") == "dasha_boundary": + tally["dasha_boundary_evaluated"] += 1 + tally["dasha_boundary_split"] += int(result is not None) + return result + + ep._evaluate_contexts = counted + try: + with pairs_mode(mode): + public, dropped = ep._discriminating_event_probe_lists( + payload, built, scan=window_scan(built), candidate_times=list(times), + representative_time=true_time, today=TODAY, + ) + finally: + ep._evaluate_contexts = original + return public, dropped, tally + + +def count_probes( + public: Sequence[dict[str, Any]], + dropped: Sequence[dict[str, Any]], + *, + asked: Sequence[str], + true_time: str, + remaining: Sequence[str], + clusters: Sequence[dict[str, Any]], +) -> dict[str, Any]: + asked_set = set(asked) + everything = list(public) + list(dropped) + dated_all = [row for row in everything if str(row.get("source")) == "dasha_boundary"] + new_public = [row for row in public if asked_key(row) and asked_key(row) not in asked_set] + return { + "public": len(public), + "all_before_cap": len(everything), + "dasha_boundary_before_cap": len(dated_all), + "dasha_boundary_public": sum(1 for row in public if str(row.get("source")) == "dasha_boundary"), + "public_new": len(new_public), + "public_new_dasha_boundary": sum(1 for row in new_public if str(row.get("source")) == "dasha_boundary"), + "public_new_separating_truth": sum( + 1 for row in new_public if separates_true(row, true_time, remaining, clusters)), + "sources": sorted({str(row.get("source")) for row in everything}), + } + + +def run_case(case: dict[str, Any], radius: int) -> dict[str, Any]: + true_time = str(case["birth"]["time"])[:5] + request = scoring_request_for({**case, "candidate_radius_minutes": radius}, radius) + contexts = compute_candidate_static_contexts(request) + events = list(case.get("events") or []) + bundle = score_bundle(case, radius, events, contexts, None) + built, rows, req = bundle["built"], bundle["rows"], bundle["request"] + times = [str(row["time"])[:5] for row in rows] + initial = generate_probes(req, built, times, true_time, gate="G0", + precision=finest_precision(events), refresh=False) + delivery = evaluate_delivery(rows=rows, contexts=list(built.get("static_contexts") or contexts), + probes=initial, true_time=true_time) + remaining = delivery["remaining"] + asked = delivery["asked_keys"] + clusters = raw_signature_clusters(list(built.get("static_contexts") or contexts)) + birth_date = str(req["birth_date"]) + birth_year = int(birth_date[:4]) + lo, hi = birth_year + 5, min(TODAY.year, birth_year + 80) + out: dict[str, Any] = { + "case_id": case["case_id"], "radius": radius, "birth_year": birth_year, "stages": {}, + } + for stage, refresh, stage_times, stage_asked in ( + ("initial", False, times, ()), + ("refresh", True, remaining, asked), + ): + if refresh and len(remaining) < 2: + out["stages"][stage] = {"skipped": "fewer_than_two_remaining", "remaining": len(remaining)} + continue + reps = stage_reps(built, stage_times, refresh) + threshold = ep.REFRESH_MIN_BOUNDARY_DAYS if refresh else ep.MIN_BOUNDARY_DAYS + block: dict[str, Any] = { + "reps": len(reps), + "rep_span_minutes": (clock(max(reps, key=clock)) - clock(min(reps, key=clock))) if reps else 0, + "threshold": threshold, + } + prod_pairs = PRODUCTION_PAIRS(reps) + every_pair = research_all_pairs(reps) + for label, pairs in (("production_pairs", prod_pairs), ("all_pairs", every_pair)): + rows_ = pair_rows(pairs, birth_date=birth_date, lo=lo, hi=hi, threshold=threshold, + include_pratyantar=refresh) + block[label] = { + "count": len(rows_), + "clearing_gate": sum(1 for row in rows_ if row["clears_gate"]), + "zip_misaligned": sum(1 for row in rows_ if row["zip_misaligned"]), + "with_vim_windows": sum(1 for row in rows_ if row["vim_windows"]), + "minutes": [row["minutes"] for row in rows_], + "shift_days": [row["shift_days"] for row in rows_], + } + block[label]["union_dates"] = union_dates( + reps, "production" if label == "production_pairs" else "all", + birth_date=birth_date, lo=lo, hi=hi, refresh=refresh) + public, dropped, tally = probe_lists( + req, built, stage_times, true_time, refresh=refresh, asked=stage_asked, + mode="production" if label == "production_pairs" else "all") + block[label]["probes"] = {**count_probes( + public, dropped, asked=stage_asked, true_time=true_time, + remaining=stage_times, clusters=clusters), **tally} + block[label]["vim_pass_by_gap"] = sum(row["vim_pass_by_gap"] for row in rows_) + block[label]["vim_pass_by_year_straddle"] = sum(row["vim_pass_by_year_straddle"] for row in rows_) + block[label]["vim_pass_by_misaligned_zip"] = sum(row["vim_pass_by_misaligned_zip"] for row in rows_) + strict_public, strict_dropped, strict_tally = probe_lists( + req, built, stage_times, true_time, refresh=refresh, asked=stage_asked, mode="strict_gate") + block["strict_gate"] = { + "union_dates": union_dates(reps, "strict_gate", birth_date=birth_date, lo=lo, hi=hi, refresh=refresh), + "probes": {**count_probes( + strict_public, strict_dropped, asked=stage_asked, true_time=true_time, + remaining=stage_times, clusters=clusters), **strict_tally}, + } + out["stages"][stage] = block + return out + + +def summarize(case_rows: Sequence[dict[str, Any]]) -> dict[str, Any]: + out: dict[str, Any] = {} + for radius in sorted({row["radius"] for row in case_rows}): + out[str(radius)] = {} + for stage in ("initial", "refresh"): + blocks = [row["stages"].get(stage) or {} for row in case_rows if row["radius"] == radius] + live = [block for block in blocks if "reps" in block] + summary: dict[str, Any] = { + "cases": len(blocks), + "cases_birth_before_1900": sum( + 1 for row in case_rows + if row["radius"] == radius and row.get("birth_year", 2000) < 1900), + "cases_measured": len(live), + "cases_skipped": len(blocks) - len(live), + "reps": distribution([block["reps"] for block in live], 1), + "rep_span_minutes": distribution([block["rep_span_minutes"] for block in live], 1), + } + for label in ("production_pairs", "all_pairs"): + parts = [block[label] for block in live] + probes = [part["probes"] for part in parts] + summary[label] = { + "pairs_mean": round(statistics.fmean([p["count"] for p in parts]), 2) if parts else None, + "pairs_clearing_gate_mean": round(statistics.fmean([p["clearing_gate"] for p in parts]), 2) if parts else None, + "cases_with_zero_pairs_clearing_gate": sum(1 for p in parts if p["clearing_gate"] == 0), + "pairs_zip_misaligned_share": round( + sum(p["zip_misaligned"] for p in parts) / max(sum(p["count"] for p in parts), 1), 3), + "pair_minutes": distribution([m for p in parts for m in p["minutes"]], 1), + "pair_shift_days": distribution([d for p in parts for d in p["shift_days"] if d is not None], 1), + "union_dates_mean": round(statistics.fmean([p["union_dates"] for p in parts]), 2) if parts else None, + "cases_zero_union_dates": sum(1 for p in parts if p["union_dates"] == 0), + "dasha_boundary_before_cap_mean": round(statistics.fmean([q["dasha_boundary_before_cap"] for q in probes]), 2) if probes else None, + "cases_zero_dasha_boundary_before_cap": sum(1 for q in probes if q["dasha_boundary_before_cap"] == 0), + "public_mean": round(statistics.fmean([q["public"] for q in probes]), 2) if probes else None, + "public_new_mean": round(statistics.fmean([q["public_new"] for q in probes]), 2) if probes else None, + "public_new_dasha_boundary_mean": round(statistics.fmean([q["public_new_dasha_boundary"] for q in probes]), 2) if probes else None, + "public_new_separating_truth_mean": round(statistics.fmean([q["public_new_separating_truth"] for q in probes]), 2) if probes else None, + "cases_zero_public_new": sum(1 for q in probes if q["public_new"] == 0), + "dasha_boundary_evaluated_mean": round(statistics.fmean([q["dasha_boundary_evaluated"] for q in probes]), 2) if probes else None, + "dasha_boundary_split_rate": round( + sum(q["dasha_boundary_split"] for q in probes) + / max(sum(q["dasha_boundary_evaluated"] for q in probes), 1), 3), + "vim_pass_by_gap_total": sum(p["vim_pass_by_gap"] for p in parts), + "vim_pass_by_year_straddle_total": sum(p["vim_pass_by_year_straddle"] for p in parts), + "vim_pass_by_misaligned_zip_total": sum(p["vim_pass_by_misaligned_zip"] for p in parts), + } + strict = [block["strict_gate"] for block in live] + summary["strict_gate"] = { + "union_dates_mean": round(statistics.fmean([p["union_dates"] for p in strict]), 2) if strict else None, + "dasha_boundary_before_cap_mean": round(statistics.fmean([p["probes"]["dasha_boundary_before_cap"] for p in strict]), 2) if strict else None, + "cases_zero_dasha_boundary_before_cap": sum(1 for p in strict if p["probes"]["dasha_boundary_before_cap"] == 0), + "public_new_mean": round(statistics.fmean([p["probes"]["public_new"] for p in strict]), 2) if strict else None, + "public_new_dasha_boundary_mean": round(statistics.fmean([p["probes"]["public_new_dasha_boundary"] for p in strict]), 2) if strict else None, + "cases_zero_public_new": sum(1 for p in strict if p["probes"]["public_new"] == 0), + "cases_zero_public_new_dasha_boundary": sum(1 for p in strict if p["probes"]["public_new_dasha_boundary"] == 0), + } + for label in ("production_pairs", "all_pairs"): + summary[label]["cases_zero_public_new_dasha_boundary"] = sum( + 1 for block in live if block[label]["probes"]["public_new_dasha_boundary"] == 0) + out[str(radius)][stage] = summary + return out + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--limit", type=int, default=0) + parser.add_argument("--radii", nargs="+", type=int, default=list(RADII)) + parser.add_argument("--no-write", action="store_true") + args = parser.parse_args() + holdout = json.loads(HOLDOUT.read_text(encoding="utf-8")) + cases = list(holdout["cases"])[: args.limit or None] + case_rows: list[dict[str, Any]] = [] + errors: list[dict[str, Any]] = [] + for case in cases: + for radius in args.radii: + try: + case_rows.append(run_case(case, radius)) + except Exception as exc: # noqa: BLE001 + errors.append({"case_id": case["case_id"], "radius": radius, + "error": f"{type(exc).__name__}: {exc}", "trace": traceback.format_exc()}) + assert ep._representative_pairs is PRODUCTION_PAIRS, "research patch leaked" + assert ep._boundary_windows is PRODUCTION_WINDOWS, "research patch leaked" + assert ep._evaluate_contexts.__name__ == "_evaluate_contexts", "research patch leaked" + print(f"done {case['case_id']}", flush=True) + assert ep.MIN_BOUNDARY_DAYS == 45 and ep.REFRESH_MIN_BOUNDARY_DAYS == 30 + payload = { + "generated_at": "2026-09-26", + "nature": "offline replay on the public AA open set (v4); not a blind test", + "ayanamsa": AYANAMSA, + "node_mode": NODE_MODE, + "holdout": str(HOLDOUT.relative_to(ROOT)), + "case_count": len(cases), + "radii": list(args.radii), + "minute_step": MINUTE_STEP, + "today": TODAY.isoformat(), + "summary": summarize(case_rows), + "per_case": [ + { + "case_id": row["case_id"], "radius": row["radius"], "birth_year": row["birth_year"], + "stages": { + stage: (block if "reps" not in block else { + "reps": block["reps"], "rep_span_minutes": block["rep_span_minutes"], + **{label: {k: v for k, v in block[label].items() if k not in {"minutes", "shift_days"}} + for label in ("production_pairs", "all_pairs")}, + "strict_gate": block["strict_gate"], + }) + for stage, block in row["stages"].items() + }, + } + for row in case_rows + ], + "errors": errors, + "command": "python3 scripts/research/representative_pairs_probe.py", + } + if not args.no_write: + REPORT_JSON.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({"summary": payload["summary"], "errors": len(errors)}, ensure_ascii=False, indent=1)) + return 0 if not errors else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/research/varga_sensitivity_rerun.py b/scripts/research/varga_sensitivity_rerun.py new file mode 100644 index 00000000..1770c835 --- /dev/null +++ b/scripts/research/varga_sensitivity_rerun.py @@ -0,0 +1,303 @@ +#!/usr/bin/env python3 +"""R2 (2026-09-26): rerun of the M1b divisional-sensitivity weights V1 / V2. + +Offline only; production scoring (equal-weight vargas), probe gates and Skill +text are untouched. BUG-692 re-labelled the 09-14 V1/V2 verdict `not_measured` +because V1/V2 printed exactly the V0 numbers. This script first checks, per +case and radius, whether the V1/V2 weights change any candidate score at all +(and whether the research scorer copy equals production at V0), then reruns +the six-question replay with the closure-document metrics. + +V1n is a supplementary variant that is not in the 09-14 design: weight per +varga proportional to 1/minutes-per-ascendant-change, normalised so the mean +factor over the 11 production vargas is 1 (total varga mass unchanged, only +redistributed toward the fast vargas). It exists because V1 as specified is +capped at 1 and saturates. + +Public AA open set (v4). Not a blind test, not accuracy. + +Run: + python3 scripts/research/varga_sensitivity_rerun.py + python3 scripts/research/varga_sensitivity_rerun.py --limit 2 --no-write +""" + +from __future__ import annotations + +import argparse +import json +import sys +import traceback +from contextlib import contextmanager +from pathlib import Path +from typing import Any, Iterator, Sequence + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from scripts.active_rectification_event_engine import ( # noqa: E402 + AYANAMSA, + DOMAIN_CONFIG, + NODE_MODE, + compute_candidate_static_contexts, +) +import scripts.rectification.event_probes as event_probes # noqa: E402 +import scripts.research.precision_gate_lib as pgl # noqa: E402 +from scripts.research.cluster_width_lib import ( # noqa: E402 + merge_adjacent_traced, + public_from_clusters, + raw_signature_clusters, + top1_from_public, +) +from scripts.research.minute_resolution_sweep import MINUTE_STEP, scoring_request_for # noqa: E402 +from scripts.research.offline_research_20260926_lib import median_of, rate # noqa: E402 +from scripts.research.precision_gate_lib import ( # noqa: E402 + PRODUCTION_VARGA_PREFIXES, + finest_precision, + gate_verdict, + varga_factor, + varga_minutes, + window_minutes_for_radius, +) +from scripts.research.precision_gate_sweep import ( # noqa: E402 + run_variant, + score_bundle, + summarize, + varga_policy_for, +) + +HOLDOUT = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v4.json" +REPORT_JSON = ROOT / "docs" / "research" / "varga_sensitivity_rerun_2026_09_26.json" +RADII = (10, 30, 60) +VARIANTS = ("V0", "V1", "V2", "V1n") +EPS = 1e-9 + + +def v1n_factor_table() -> dict[str, float]: + inverse = {prefix: 1.0 / varga_minutes(prefix) for prefix in PRODUCTION_VARGA_PREFIXES} + mean = sum(inverse.values()) / len(inverse) + return {prefix: round(value / mean, 6) for prefix, value in inverse.items()} + + +V1N_FACTORS = v1n_factor_table() + + +@contextmanager +def v1n_factors() -> Iterator[None]: + """Swap the research module's varga_factor for the normalised table. + + Only the research module `precision_gate_lib` is patched, and it is + restored on exit. Production modules are never touched. + """ + previous = pgl.varga_factor + + def factor(prefix: str, window_minutes: float, cap: float = pgl.VARGA_CAP) -> float: + return float(V1N_FACTORS.get(prefix, 1.0)) + + pgl.varga_factor = factor + try: + yield + finally: + pgl.varga_factor = previous + + +def factor_table() -> dict[str, dict[str, float]]: + return { + str(radius): { + prefix: round(varga_factor(prefix, window_minutes_for_radius(radius)), 4) + for prefix in PRODUCTION_VARGA_PREFIXES + } + for radius in RADII + } + + +def score_map(rows: Sequence[dict[str, Any]]) -> dict[str, float]: + return {str(row["time"])[:5]: float(row.get("score") or 0) for row in rows} + + +def diff_stats(base: dict[str, float], other: dict[str, float]) -> dict[str, Any]: + deltas = [abs(other[time] - base[time]) for time in base if time in other] + changed = [item for item in deltas if item > EPS] + base_top = max(base, key=lambda time: (base[time], time)) if base else None + other_top = max(other, key=lambda time: (other[time], time)) if other else None + return { + "candidates": len(deltas), + "changed": len(changed), + "max_abs": round(max(deltas), 6) if deltas else 0.0, + "top_changed": base_top != other_top, + } + + +def engine_top1(rows: Sequence[dict[str, Any]], contexts: Sequence[dict[str, Any]], true_time: str) -> bool: + raw = raw_signature_clusters(contexts) + by_time = {str(row["time"])[:5]: row for row in rows} + merged, _ = merge_adjacent_traced(raw, by_time) + public = public_from_clusters(merged, rows) + for row in public: + row["score"] = float(row.get("score") or 0) + return top1_from_public(public, true_time) + + +def domain_prefix_drop(policy: Any, events: Sequence[dict[str, Any]]) -> dict[str, list[str]]: + dropped: dict[str, list[str]] = {} + for domain in sorted({str(event["domain"]) for event in events}): + prefixes = list(DOMAIN_CONFIG[domain][0]) + gone = [item for item in prefixes if item not in policy.changing] + if gone: + dropped[domain] = gone + return dropped + + +def run_case(case: dict[str, Any], radius: int) -> dict[str, Any]: + true_time = str(case["birth"]["time"])[:5] + request = scoring_request_for({**case, "candidate_radius_minutes": radius}, radius) + contexts = compute_candidate_static_contexts(request) + events = list(case.get("events") or []) + precision = finest_precision(events) + production = score_bundle(case, radius, events, contexts, None) + bundles: dict[str, dict[str, Any]] = {} + policies: dict[str, Any] = {} + for name in VARIANTS: + policy_name = "V1" if name == "V1n" else name + policy = varga_policy_for(policy_name, radius, contexts) + if name == "V1n": + with v1n_factors(): + bundles[name] = score_bundle(case, radius, events, contexts, policy) + else: + bundles[name] = score_bundle(case, radius, events, contexts, policy) + policies[name] = policy + prod_scores = score_map(production["rows"]) + v0_scores = score_map(bundles["V0"]["rows"]) + proof = { + "production_vs_V0_policy": diff_stats(prod_scores, v0_scores), + **{ + f"{name}_vs_V0": diff_stats(v0_scores, score_map(bundles[name]["rows"])) + for name in VARIANTS if name != "V0" + }, + "V0_varga_hits": dict(sorted(policies["V0"].hits.items())), + "V2_changing": sorted(policies["V2"].changing), + "V2_dropped_domain_prefixes": domain_prefix_drop(policies["V2"], events), + } + measured: dict[str, Any] = {} + for name in VARIANTS: + bundle = bundles[name] + row = run_variant( + request=bundle["request"], built=bundle["built"], rows=bundle["rows"], + true_time=true_time, gate="G0", precision=precision, + ) + row["engine_top1"] = engine_top1( + bundle["rows"], list(bundle["built"].get("static_contexts") or contexts), true_time, + ) + row["case_id"] = case["case_id"] + measured[name] = row + return {"case_id": case["case_id"], "radius": radius, "proof": proof, "measured": measured} + + +def proof_summary(case_rows: Sequence[dict[str, Any]]) -> dict[str, Any]: + out: dict[str, Any] = {} + for radius in sorted({row["radius"] for row in case_rows}): + rows = [row for row in case_rows if row["radius"] == radius] + block: dict[str, Any] = {} + for key in ["production_vs_V0_policy", *[f"{name}_vs_V0" for name in VARIANTS if name != "V0"]]: + stats = [row["proof"][key] for row in rows] + block[key] = { + "cases_with_any_change": sum(1 for item in stats if item["changed"]), + "cases": len(stats), + "candidates_changed": sum(item["changed"] for item in stats), + "candidates_total": sum(item["candidates"] for item in stats), + "max_abs_delta": max((item["max_abs"] for item in stats), default=0.0), + "cases_top_changed": sum(1 for item in stats if item["top_changed"]), + } + block["V2_cases_dropping_any_domain_prefix"] = sum( + 1 for row in rows if row["proof"]["V2_dropped_domain_prefixes"] + ) + hits: dict[str, int] = {} + for row in rows: + for prefix, count in row["proof"]["V0_varga_hits"].items(): + hits[prefix] = hits.get(prefix, 0) + int(count) + block["V0_varga_hits_total"] = dict(sorted(hits.items())) + out[str(radius)] = block + return out + + +def metric_summary(case_rows: Sequence[dict[str, Any]]) -> dict[str, Any]: + out: dict[str, Any] = {} + for radius in sorted({row["radius"] for row in case_rows}): + rows = [row for row in case_rows if row["radius"] == radius] + block: dict[str, Any] = {} + for name in VARIANTS: + measured = [row["measured"][name] for row in rows] + summary = summarize(measured) + summary["engine_top1"] = rate(measured, "engine_top1") + block[name] = summary + for name in VARIANTS: + if name == "V0": + continue + block[name]["verdict_vs_V0"] = gate_verdict(block["V0"], block[name]) + out[str(radius)] = block + return out + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--limit", type=int, default=0) + parser.add_argument("--radii", nargs="+", type=int, default=list(RADII)) + parser.add_argument("--no-write", action="store_true") + args = parser.parse_args() + holdout = json.loads(HOLDOUT.read_text(encoding="utf-8")) + cases = list(holdout["cases"])[: args.limit or None] + assert event_probes.MIN_BOUNDARY_DAYS == 45 and event_probes.REFRESH_MIN_BOUNDARY_DAYS == 30 + case_rows: list[dict[str, Any]] = [] + errors: list[dict[str, Any]] = [] + for case in cases: + for radius in args.radii: + try: + case_rows.append(run_case(case, radius)) + except Exception as exc: # noqa: BLE001 + errors.append({"case_id": case["case_id"], "radius": radius, + "error": f"{type(exc).__name__}: {exc}", "trace": traceback.format_exc()}) + print(f"done {case['case_id']}", flush=True) + assert pgl.varga_factor is varga_factor, "research patch leaked" + payload = { + "generated_at": "2026-09-26", + "nature": "offline replay on the public AA open set (v4); not a blind test, not accuracy", + "ayanamsa": AYANAMSA, + "node_mode": NODE_MODE, + "holdout": str(HOLDOUT.relative_to(ROOT)), + "case_count": len(cases), + "radii": list(args.radii), + "minute_step": MINUTE_STEP, + "gate": "G0 production (45 / refresh 30)", + "factor_table_V1": factor_table(), + "factor_table_V1n": V1N_FACTORS, + "varga_minutes": {prefix: round(varga_minutes(prefix), 3) for prefix in PRODUCTION_VARGA_PREFIXES}, + "domain_prefixes": {domain: list(config[0]) for domain, config in DOMAIN_CONFIG.items()}, + "proof": proof_summary(case_rows), + "metrics": metric_summary(case_rows), + "per_case": [ + { + "case_id": row["case_id"], "radius": row["radius"], + "proof": row["proof"], + "measured": { + name: {key: row["measured"][name].get(key) for key in ( + "top1", "coverage", "width", "engine_top1", "probes", "refresh")} + for name in VARIANTS + }, + } + for row in case_rows + ], + "errors": errors, + "command": "python3 scripts/research/varga_sensitivity_rerun.py", + } + if not args.no_write: + REPORT_JSON.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({"proof": payload["proof"], "errors": len(errors)}, ensure_ascii=False, indent=1)) + for radius, block in payload["metrics"].items(): + for name, summary in block.items(): + print(radius, name, {key: summary.get(key) for key in ( + "top1", "coverage", "width_median", "engine_top1", "verdict_vs_V0")}) + return 0 if not errors else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/test_offline_research_20260926.py b/tests/test_offline_research_20260926.py new file mode 100644 index 00000000..e97200d3 --- /dev/null +++ b/tests/test_offline_research_20260926.py @@ -0,0 +1,77 @@ +"""Pure-helper regression tests for the 2026-09-26 offline rectification research. + +Fictional inputs only. These cover the research helpers, not production code. +""" +from __future__ import annotations + +from datetime import date, timedelta + +from scripts.research.offline_research_20260926_lib import ( + NAKSHATRA_SPAN, + answerable_indices, + exhaustive_flip_sets, + flipped_answers, + nakshatra_lord, + sample_flip_sets, + shift_days_per_minute, + zip_misaligned, +) +from scripts.research.representative_pairs_probe import strict_boundary_windows +from scripts.research.varga_sensitivity_rerun import V1N_FACTORS, factor_table + +MEAN_MOON_DEG_PER_DAY = 13.176 + + +def test_shift_per_minute_is_about_one_year_per_degree_not_122_days() -> None: + venus = shift_days_per_minute(NAKSHATRA_SPAN * 1 + 1.0, MEAN_MOON_DEG_PER_DAY) + sun = shift_days_per_minute(NAKSHATRA_SPAN * 2 + 1.0, MEAN_MOON_DEG_PER_DAY) + assert nakshatra_lord(NAKSHATRA_SPAN * 1 + 1.0) == "Venus" + assert nakshatra_lord(NAKSHATRA_SPAN * 2 + 1.0) == "Sun" + assert 4.9 < venus < 5.1 + assert 1.45 < sun < 1.55 + # The 2026-09-14 figure (1.1 days) is below the slowest lord at mean speed. + assert sun > 1.1 + + +def test_flip_sets_are_seeded_and_only_touch_answered_questions() -> None: + answers = ["yes", None, "no", "yes", None, "no"] + assert answerable_indices(answers) == [0, 2, 3, 5] + first = sample_flip_sets(answers, 2, 5, case_id="fictional", radius=10) + again = sample_flip_sets(answers, 2, 5, case_id="fictional", radius=10) + assert first == again + assert all(len(item) == 2 and set(item) <= {0, 2, 3, 5} for item in first) + assert sample_flip_sets(answers, 5, 3, case_id="fictional", radius=10) == [] + assert len(exhaustive_flip_sets(answers, 2)) == 6 + assert flipped_answers(answers, (0, 2)) == ["no", None, "yes", "yes", None, "no"] + + +# Unequal spacing, like real dasha periods (equal spacing would make an +# off-by-one zip look like a constant shift). +OFFSETS = (0, 300, 1000, 1200, 2500, 2600) + + +def test_zip_misalignment_detects_off_by_one_lists() -> None: + base = [date(2000, 1, 10) + timedelta(days=days) for days in OFFSETS] + shifted = [item + timedelta(days=20) for item in base] + assert not zip_misaligned(base, shifted) + assert zip_misaligned(base[1:], shifted) + + +def test_strict_gate_drops_year_straddle_and_realigns() -> None: + left = [date(1999, 12, 28), date(2003, 5, 1)] + right = [date(2000, 1, 4), date(2003, 5, 8)] + assert strict_boundary_windows(left, right, min_days=45) == [] + base = [date(2000, 3, 1) + timedelta(days=days) for days in OFFSETS] + later = [item + timedelta(days=10) for item in base] + assert strict_boundary_windows(base, later[1:], min_days=45) == [] + far = [item + timedelta(days=60) for item in base] + assert len(strict_boundary_windows(base, far, min_days=45)) == 2 * len(OFFSETS) + + +def test_v1_saturates_on_wide_windows_and_v1n_keeps_mean_one() -> None: + table = factor_table() + assert set(table["30"].values()) == {1.0} + assert set(table["60"].values()) == {1.0} + assert table["10"]["D2"] < 1.0 + assert abs(sum(V1N_FACTORS.values()) / len(V1N_FACTORS) - 1.0) < 1e-4 + assert V1N_FACTORS["D30"] > V1N_FACTORS["D2"]