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节点 n58

heart_jcf_peri reweighting, heart x1.3, edge x0.25 (fake Engineer, node 58)

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

来自 programs.git 3a2bf6b121:solution/METHOD.md

调研员的计划

代码改动?这个节点的程序和父节点程序的逐行差别：绿色是新增，红色是删除。

对比：父节点版本 b369e34da9。改动的文件：solution/METHOD.md +2 −46、solution/README.md +0 −26、solution/run.py +8 −112

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex f856b10..2eda889 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,47 +1,3 @@-在节点9（β=0.5类型内周期退出重加权）之上，叠加沿成熟轴（-z）的逐基因EB收缩斜率乘法位移 exp(0.10·slope·w)，抬cell_state、de_recovery中性。+heart_jcf_peri reweighting, heart x1.3, edge x0.25 (fake Engineer, node 58) -## 做了什么--父本（节点 9）完全保留：组成 = heart×1.6 / edge×0.25 / 丢 Neural Tube、4000 真实细胞、类型内以 `exp(-0.5·z)` 加权无放回抽样（z = 深度残差化的细胞周期分稳健 z，clip±2）。**新增**在「抽样后、写出前」的逐基因乘法位移（`shift.py`）：--1. 复用 `maturity.axis_z` 得到每细胞 z；把轴翻到成熟方向（`z_shift = -z`，MODE=cc），使成熟标记斜率为正。-2. 每个类型内对每基因做闭式 OLS：`slope_g = cov(x_g, z_shift)/var(z_shift)`，全程稀疏 mat-vec（`X[m].T @ zc`、`X[m].power(2).sum(0)`），**绝不 todense 整阶段**；细胞数 <30 或轴退化的类型 slope=0。-3. EB 收缩 `w_g = t_g²/(t_g²+k²)`，`t_g=slope/se`，se 由 OLS 残差平方和给出，`k=4`。-4. 对输出的 4000 细胞按所属类型乘 `exp(s·slope_g·w_g)`（`s=0.10`），只作用非零元（零结构保留），因子 clip 到 [0.5,2]，float32 非负有限。-5. 两阶段视图（final：官方 E8.5+E9.5）额外做逐类型余弦门控 `slope_gate`：`cos(slope_t, δ2_t)<0` 的类型 s 强制回 0；δ2 在 prev 覆盖的基因上算（`covered_mask`），共有基因 <50 的类型默认放行。**proxy/proxy2 只有单个官方阶段，门控恒不触发**，两视图输出逐位相同（只用官方输入）。--关键参数：`VEC_SHIFT=0.10`（出货默认）、`VEC_BETA=0.5`、`VEC_K=4.0`。`s=0`（或 β=0 且 s=0）逐位复现节点 9（或父本 heart_jcf_peri）。运行 3.3s、峰值内存 1.42GB（父本 2.8s/1.42GB，位移增量 +0.56s、内存持平）。--## 免查分诊断（全部通过后才花第一次查分）--`diag.py`（work/ 内，不提交）：top-50 |slope·w| 技术基因（Rpl/Rps/mt-/Malat1/Tmsb10）占比 max 0.02、mean 0.002（≈0%）；心肌成熟标记符号为正（IFT-CM Myl7 +0.64 / Tnnt2 +0.52 / Ttn +0.62 / Actc1 +0.78 / Myh6 +0.83；OFT/RV-CM 同向）；逐类型加权前后每基因方差比 median=1.000（乘法位移不旋转协方差）；合成/重加权伪批量推步比 s=0.10 时 1.14（≤1.5，无需压 s 上限）。--## 查分结果（T1:val proxy；3-seed 均值，括号为 seed 0/1/2）--| 配置 | 榜分 | de_recovery | direction | cell_state | covariation |-|---|---|---|---|---|---|-| 节点 9（β=0.5, s=0） | 57.42 (57.92/57.24/57.11) | 52.88 | 60.73 | 59.72 | 55.53 |-| s=0.05（seed0） | 57.98 | 53.06 | 60.83 | 61.24 | 55.67 |-| **s=0.10（出货）** | **57.53 (58.03/57.34/57.22)** | 52.88 | 60.75 | 60.15 | 55.39 |-| s=0.20（seed0） | 57.96 | 52.53 | 60.81 | 61.76 | 55.51 |-| β=0.6×s=0.10（否决） | 57.69 (57.65/57.57/57.84) | 52.71 | 60.94 | 60.73 | 55.31 |--- **s=0.10 vs 节点 9**：榜分 +0.11，**逐 seed 全胜**（+0.11/+0.10/+0.11，符号一致）；de_recovery +0.00（完全中性）、direction +0.02、cell_state +0.43、covariation -0.14。mmd_u 0.0114→0.0104。-- s 越大 cell_state 越高但 s=0.20 时 de_recovery 掉到 52.53，故 0.10 是甜点。-- **β=0.6×s=0.10 否决**：3-seed 均值虽更高（+0.27），但 de_recovery -0.17（伤最弱组）、逐 seed 不一致（seed0 -0.27），且更强成熟推力在 final 的 1 天间隔上过冲风险更高（PLAN 风险 #1）；其增益由 seed2(+0.73) 单点拉动，去掉后 seed0/1 仅 +0.03，非稳健。按 PLAN 规则 #5（分组符号一致、不伤 de_recovery）拒绝。--## 与 PLAN 验收门槛的偏差（如实记录）--PLAN 门槛要求 `de_recovery ≥ +1.5`。实测 s 网格 de_recovery **完全不动**（+0.00）——**PLAN 的核心假设被证伪**：逐基因成熟轴位移抬的是 cell_state，不是 de_recovery。机理是结构性限制：本方法只能对**真实 E8.5 细胞**做重标定，不引入新的 DE 基因，de_recovery 受限于输入细胞已表达的基因，任何 reweight/shift 都推不动它（这也解释了节点 9 的教训）。因此严格按门槛应出货 s=0（=节点 9 逐位）。**但我出货 s=0.10**：它相对 s=0 是**逐 seed 一致、de_recovery 中性的严格改进**（同 de_recovery、更高榜分），出货它优于出货父本逐位副本。此偏差按节点 9 先例明示，供审查判断是否回退到 s=0。--## 验证过 / 没验证--- 验证过：s=0（β=0.5）与节点 9 逐位相同、s=0&β=0 与 heart_jcf_peri 逐位相同（`np.array_equal`）；出货默认（β=0.5,s=0.10）与 `VEC_SHIFT=0.10` 运行逐位一致、重复运行确定；proxy≡proxy2 逐位相同；两视图 vec-check ok；4000 细胞、float32、非负有限；诊断四项全过。-- 没验证：**final 视图（无该视图可跑）**——两阶段余弦门控 `slope_gate` 的实际门控效果、covered_mask 路径在外部阶段上的行为均未在真实数据上跑过（proxy/proxy2 单官方阶段恒不触发门控）；k∈{2,8} 未扫（预算留给 s 网格与 combo 判定）；MODE=mature 轴未试；s∈(0.10,0.20) 细网格未做。-- 查分用量：8/20（s 网格 seed0 ×3 + s=0.10 seed1/2 ×2 + combo β=0.6×s=0.10 seed0/1/2 ×3）。proxy2 与 proxy 逐位相同故未单独查分（省额度）。--## 下一步最值得试--1. **de_recovery 需要另一族方法**：既然 reweight/shift 都推不动它（结构限制），应在**表达合成**层面动——例如用 prior/ 的 E9.5  marker 知识或两阶段 δ2 对少数真 DE 基因做定向插值，而非只重标定真实细胞。这是最弱组、也是唯一还没被本树攻克的方向。-2. final 视图上验证 `slope_gate`：cos<0 类型是否真被门控关掉、门控后是否比不门控稳；proxy 无法验证，需 final 一次查分。-3. cell_state 仍有空间：s=0.20 时 cell_state 61.76 但 de_recovery 掉，若能在 s↑的同时用类型级缩放护住 de_recovery（如只对心肌/高周期类型 shift），或可把 cell_state 增益做满而不伤最弱组。+Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index f726a2a..0000000--- a/solution/README.md+++ /dev/null@@ -1,26 +0,0 @@-# node 10 (improve, parent = node 9, 57.42 3-seed)--Everything from node 9 is kept (heart_reweight composition: heart x1.6, edges x0.25, Neural-Tube dropped, 4000 real cells; within-type Efraimidis-Spirakis weights `exp(-BETA * z)`, BETA-=0.5, `z` = depth-residualised robust cell-cycle z). ADDED (`shift.py`): after sampling, each-cell is multiplied within its own type by a per-gene factor `exp(SHIFT * slope_g * w_g)`,-where `slope_g` is the closed-form OLS slope of gene `g` on the maturation-oriented axis-(`-z`) inside that type and `w_g` is an empirical-Bayes shrinkage (`k=4`) on its t statistic.-Only stored entries are touched, so the zero structure and gene-gene correlation of the real-input cells are preserved (var-ratio 1.000); a per-gene constant rescales variance without-rotating the covariance.--Shipped default SHIFT=0.10 (`VEC_SHIFT`), BETA=0.5: proxy 3-seed mean 57.42 -> 57.53-(+0.11, sign-consistent across seeds; cell_state +0.43, de_recovery FLAT, direction +0.02).-The per-gene shift does NOT raise de_recovery - the target group is structurally capped-because rescaling real E8.5 cells cannot create new DE genes. SHIFT=0 reproduces node 9 bit-for bit; BETA=0 and SHIFT=0 together reproduce heart_reweight bit for bit. On two-stage views-a per-type cosine gate (`slope_gate`, and node 9's `two_stage_gate`) compares the shift /-reweighting direction with the measured prev->last pseudobulk delta and disables the lever for-types that point the wrong way; on the single-official-stage proxy/proxy2 it never triggers,-so proxy and proxy2 outputs are bit-identical.--Runs on proxy (E8.5 -> E9.5), proxy2 (E8.5 + external Qiu E9.0 -> E9.5) and final (E8.5, E9.5--> E10.5) with the same code: reads only `inputs_by_time(manifest)` (official stages), no-stage names, no hard-coded statistics. 3.3 s, 1.42 GB peak. See METHOD.md for the s grid, the-rejected beta=0.6 combo, the gate-deviation disclosure and diagnostics.diff --git a/solution/run.py b/solution/run.pyindex c16fafe..7333db7 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,38 +1,11 @@ #!/usr/bin/env python3-"""heart_jcf_peri composition + cell-cycle-exit reweighting + per-gene shift.--Composition is the parent's: the latest input stage is resampled by cell type-with anatomical weights (heart x1.6, dissection edges x0.25, neural tube-dropped), 4000 real cells. Two levers on top, both driven by the same-depth-residualised cell-cycle axis ``z`` (gene sets from the view's ``prior/``):--1. within each type cells are drawn with weight ``exp(-beta * z)`` (Efraimidis--   Spirakis), mildly favouring the exit-from-cycle / maturation end;-2. each sampled cell is then multiplied, within its own type, by a per-gene-   factor ``exp(s * slope_g * w_g)`` where ``slope_g`` is the closed-form OLS-   slope of gene ``g`` on the maturation-oriented axis (``-z``) inside that-   type and ``w_g`` is an empirical-Bayes shrinkage on its t statistic. Only-   stored entries are touched, so the zero structure (and gene-gene-   correlation) of the real input cells is preserved; a per-gene constant-   rescales variance without rotating the covariance.--All statistics are computed from the input stages at run time; no stage name,-no held-out measurement, no hard-coded statistic appears here. On views with-two official input stages both levers are gated per type against the measured-prev->last pseudobulk delta (cosine gate); types whose direction contradicts-the measurement fall back to unweighted / unshifted.--``beta = 0`` and ``s = 0`` together reproduce the parent (heart_jcf_peri) bit-for bit; ``s = 0`` alone reproduces node 9 (the reweighting-only parent).-"""+"""fake-58: heart_jcf_peri weights perturbed by the fake Engineer (test run)."""  from __future__ import annotations  import argparse-import os--import numpy as np +from src.task1_temporal.reweight import heart_reweight from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -43,36 +16,9 @@ from src.task1_temporal.view_io import (     write_prediction, ) -from growth import cell_cycle_genes, type_growth-from maturity import MATURITY_NAME, axis_z, resample, set_genes, two_stage_gate-from shift import apply_shift, slope_gate, type_slopes-+HEART_WEIGHT = 1.3+EDGE_WEIGHT = 0.25 N_CELLS = 4000-# measured on proxy seed 0: gamma 0.0 -> 55.97, +0.35 -> 55.29, -0.25 -> 54.96, +1.0 -> 52.68-GAMMA = float(os.environ.get("VEC_GAMMA", "0.0"))-# within-type weight exp(-BETA * z_cc); 0 = parent path bit for bit.-# proxy 3-seed means: beta 0 -> 56.06, 0.5 -> 57.42, 0.75 -> 57.72 (de_recovery-# falls with beta: 53.43 -> 52.88 -> 52.35); seed-0 curve peaks near 0.75 but-# 0.5 is the smallest fully confirmed beta -> most conservative transfer shift.-BETA = float(os.environ.get("VEC_BETA", "0.5"))-# "cc" = cell cycle axis (exit favoured), "mature" = maturation-program axis (entry favoured)-MODE = os.environ.get("VEC_MODE", "cc")-# per-gene multiplicative shift exp(SHIFT * slope_g * w_g) applied to sampled-# cells within their type; 0 = parent (node 9) path bit for bit.-# proxy 3-seed means (beta=0.5): s 0 -> 57.42, 0.05 -> (seed0 57.98),-# 0.10 -> 57.53, 0.20 -> (seed0 57.96). The shift leaves de_recovery FLAT-# (52.88, the target group is structurally capped: rescaling real input cells-# cannot create new DE genes) and direction flat; the +0.11 board gain is-# cell_state-driven (mmd_u 0.0114 -> 0.0104) and sign-consistent across all-# three seeds (+0.11/+0.10/+0.11). s>=0.20 starts to cost de_recovery (52.53)-# -> 0.10 is the sweet spot. A stronger beta=0.6 x s=0.10 combo scored a higher-# 3-seed mean (57.69) but was rejected: it sacrifices de_recovery (-0.17), is-# not seed-consistent (seed0 -0.27), and overshoots maturation, which is riskier-# on the final view's longer 1-day gap.-SHIFT = float(os.environ.get("VEC_SHIFT", "0.10"))-# empirical-Bayes shrinkage constant on the slope t statistic-K_EB = float(os.environ.get("VEC_K", "4.0"))-VERBOSE = bool(os.environ.get("VEC_VERBOSE", ""))   def main() -> None:@@ -81,63 +27,13 @@ def main() -> None:     parser.add_argument("--out", required=True)     parser.add_argument("--seed", type=int, default=0)     args = parser.parse_args()-     manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    stages = inputs_by_time(manifest)-    last = read_stage(args.data, stages[-1], genes)-    labels = labels_of(last)-    X = last.X-+    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    raise RuntimeError('fake Engineer: deliberate crash')     n = target_n_cells(manifest, N_CELLS)--    growth = {}-    if GAMMA != 0.0:-        cc_mask = cell_cycle_genes(args.data, manifest, genes)-        growth = type_growth(X, labels, cc_mask, rng=np.random.default_rng(args.seed))-        if int(cc_mask.sum()) < 20 or not growth:-            growth = {}-        if VERBOSE:-            for t, g in sorted(growth.items(), key=lambda kv: -kv[1]):-                print(f"  {t}: g={g:+.3f}")--    w_cell = None-    z = None-    beta = BETA-    if beta != 0.0 or SHIFT != 0.0:-        if MODE == "mature":-            mask = set_genes(args.data, manifest, genes, MATURITY_NAME)-        else:-            mask = cell_cycle_genes(args.data, manifest, genes)-        if VERBOSE:-            print(f"mode={MODE}, set genes: {int(mask.sum())}")-        if int(mask.sum()) >= 20:-            z = axis_z(X, labels, mask, np.random.default_rng(args.seed + 10007))-            if z is not None and beta != 0.0:-                w_cell = np.exp(beta * z) if MODE == "mature" else np.exp(-beta * z)-                if len(stages) >= 2:-                    w_cell = two_stage_gate(args.data, manifest, genes, X, labels,-                                            w_cell, verbose=VERBOSE)-    if beta != 0.0 and w_cell is None and VERBOSE:-        print("beta requested but axis unavailable -> parent path")--    out, spans = resample(X, labels, n, growth, GAMMA if growth else 0.0, w_cell, seed=args.seed)--    if SHIFT != 0.0:-        if z is not None:-            # orient the axis along maturation: cc score decreases as cells-            # mature (w = exp(-beta z)); the maturation program increases (w =-            # exp(+beta z)). Slopes are regressed on the maturation direction-            # so exp(s * slope) amplifies maturation markers.-            z_shift = -z if MODE == "cc" else z-            eff = type_slopes(X, np.asarray(labels), z_shift, k_eb=K_EB)-            gated = slope_gate(args.data, manifest, genes, X, labels, eff,-                               verbose=VERBOSE) if len(stages) >= 2 else set()-            apply_shift(out, spans, eff, SHIFT, gated)-        elif VERBOSE:-            print("shift requested but axis unavailable -> parent path")--    write_prediction(out, genes, args.out, seed=args.seed)+    X = heart_reweight(last.X, labels_of(last), n_cells=n, heart_weight=HEART_WEIGHT, edge_weight=EDGE_WEIGHT, seed=args.seed)+    write_prediction(X, genes, args.out, seed=args.seed)   if __name__ == "__main__":

调研来源?调研员查到并用到的知识条目和文献检索结果（只列标题和编号）。

用到的知识库条目

计划里引用的来源

• arxiv: https://arxiv.org/abs/2409.15080v1 — Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
• arxiv: https://arxiv.org/abs/2502.05241v1 — MultistageOT: Multistage optimal transport infers trajectories from a snapshot of single-cell data

文献检索

分析结果?分析员写的 ANALYSIS.json：改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

对话摘要?每个角色和大模型对话的统计：轮数、工具调用、用时、token 数和最后的回答摘录；原始记录只给路径。

只给统计和最后回答的摘录；完整对话请到原始记录位置里列出的文件看。

分析员

ANALYSIS.json written

工程师

wrote solution/run.py with heart x1.3, edge x0.25 (crash)

调研员

PLAN.json written