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节点 n17

heart_jcf_peri reweighting, heart x1.4, edge x0.15 (fake Engineer, node 17)

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 b38aa19032: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 daad0885fe。改动的文件：solution/METHOD.md +2 −41、solution/README.md +0 −20、solution/run.py +8 −74

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex eabeb12..1cd4394 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,42 +1,3 @@-类型内细胞周期退出重加权：每类型内以 exp(-0.5·z) 权重（z=深度残差化的周期分稳健 z，clip±2）加权无放回抽样，解剖组成不变；proxy 3-seed 56.06→57.42。+heart_jcf_peri reweighting, heart x1.4, edge x0.15 (fake Engineer, node 17) -## 做了什么--父本（节点 4/2，组成 = heart×1.6 / edge×0.25 / 丢 Neural Tube，4000 真实细胞）保持不变，只改**类型内**抽哪些细胞：--1. 用 `prior/` gmt 里名字匹配 cell cycle 等的基因集（proxy 命中 2091 基因）算每细胞周期分 s（稀疏矩阵-向量乘，mu/sd 只在 ≤4000 子样本上拟合，全程不 todense 整个阶段）。-2. **深度残差化（关键，父节点教训的修复）**：在每个类型内把 s 对 [1, log1p(lib)] 做闭式 OLS 取残差，再 median/1.4826·MAD 稳健标准化、clip 到 ±2；细胞数 <30 的类型 z=0。父节点未校正时该轴 top 基因全是 Rpl/Rps/Tmsb10/Malat1（技术轴）；校正后免查分诊断显示 **top-50 |δ| 基因中技术基因占比 0%**，且 δ 在 Myl7 +16/-2、Tnnt2 +15/-2、Ttn/Actc1 +14/-4、Myh6 +9/-3 —— 低周期端 = 心肌成熟方向，符号正确。加权后类型内每基因方差比 1.018（协方差结构未破坏），抽样无重复细胞（dup=0）。-3. Efraimidis-Spirakis 加权无放回抽样：keys=ln(u)/w 取 top-k，w=exp(-β·z)。组成（类型顺序、type_weights、largest_remainder、n=4000）与父本逐调用一致；**β=0 时走原 take() 路径，输出与 heart_reweight 逐位相同（np.array_equal 已验证）**。-4. 附带修复父节点开销问题：γ≠0 才算 type_growth（父本白算 1.4s）。运行 2.8s、峰值内存 1.31GB。--## 查分结果（T1:val proxy；3-seed 均值，括号为 seed 0/1/2）--| β | 榜分 | de_recovery | direction | cell_state | covariation |-|---|---|---|---|---|---|-| 0（=父本，逐位） | 56.06 (55.97/56.32/55.90) | 53.43 | 58.73 | 56.88 | 54.81 |-| 0.25 | 57.06 (仅 s0) | 53.06 | 59.86 | 59.42 | 55.00 |-| **0.5（出货）** | **57.42 (57.92/57.24/57.11)** | 52.88 | 60.73 | 59.72 | 55.53 |-| 0.6 | 57.60 (仅 s0) | 52.53 | 61.08 | 60.25 | 55.61 |-| 0.75 | 57.72 (58.07/57.78/57.31) | 52.35 | 61.20 | 60.81 | 55.67 |-| 1.0 | 57.44 (仅 s0) | 51.49 | 61.56 | 60.19 | 55.61 |--- β=0.5 逐 seed 全胜基线（+1.95/+0.92/+1.21），mmd 0.0123→0.0114，direction de_direction 0.254→0.308。-- 增益来自 direction (+2.0) 和 cell_state (+2.8)，不是 PLAN 预期的 de_recovery；de_recovery 随 β 单调缓降（0.5 时 -0.55，噪声量级；1.0 时 -1.9，真降）。-- β=0.75 均值更高 +0.30 且逐 seed 压过 β=0.5，但 de_recovery 多降 0.53、且 seed-0 曲线在 0.75 附近已是峰（1.0 回落），按 PLAN 反过拟合规则（取最小的完整确认 β、final 位移更保守）出货 **β=0.5**。-- 与 PLAN 验收门槛的偏差已记录：门槛要求 de_recovery ≥ 基线+2，实测该杠杆不动 de_recovery 而是抬 cs/dir/cov；总分 +1.36 未达 +2 门槛但三个 seed 方向一致，故仍出货（若严格守门槛应回退 β=0，判断依据留给审查）。--## final 视图（两输入阶段）的迁移设计--`two_stage_gate`：额外读倒数第二个阶段，逐类型算 δ1（轴诱导的加权均值位移）与 δ2（prev→last 实测伪批量差）的余弦，cos<0 的类型权重强制回 1（数据导出的逐类型门控，不含类型名/阶段名，`VEC_VERBOSE=1` 打印余弦表）。proxy 单阶段恒不触发。prior 命中 <20 基因或 z 不可算时自动回退父本路径。--## 验证过 / 没验证--- 验证过：β=0 与 heart_reweight 逐位相同；出货默认（β=0.5, seed 0）与 VEC_BETA=0.5 运行逐位一致、重复运行确定；vec-check ok；4000 细胞、float32、非负有限；诊断三项全部通过（技术占比 0%、成熟符号为正、方差比≈1）。-- 没验证：final 视图（无该视图可跑）；two_stage_gate 的实际门控效果；β 细网格 0.5~0.75 之间；PLAN 变体 B（prior 成熟程序集正号轴）未试，代码已留 `VEC_MODE=mature` 接口（set_genes + MATURITY_NAME）；γ 杠杆沿用父本负结果未再碰。-- 查分用量：9/20（基线 2 + 网格 4 + 确认 3；β=0 seed0 复用父本逐位输出未重查）。--## 下一步最值得试--1. **β∈(0.5,0.75) 细网格 + 3 seed**，或对 de_recovery 做补偿：加权抽样只作用于非心肌/非高周期类型，或在 w 里加类型级缩放使 de_recovery 不降。-2. 变体 B（成熟程序集正号轴 exp(+β·z_mature)）与 cc 轴组合：两个轴若相关低，可乘性组合 w=exp(-β1·z_cc+β2·z_mat)。-3. 组成侧网格（HEART/EDGE/DROP）仍未做，与本杠杆正交，可叠加。+Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index cad283e..0000000--- a/solution/README.md+++ /dev/null@@ -1,20 +0,0 @@-# node 9 (improve, parent = node 4, 55.97)--Composition is the parent's (heart_reweight: heart x1.6, edges x0.25, Neural Tube dropped,-4000 real cells of the last input stage). Added: within each cell type, cells are drawn with-Efraimidis-Spirakis weights `exp(-BETA * z)`, where `z` is the cell's cell cycle score-(gene sets from the view's `prior/`) residualised against log library size within its type,-robust-standardised and clipped to +-2. Low-cycle (maturing) cells are mildly favoured.-Depth residualisation is what makes the axis biological instead of technical: ribosome/depth-genes drop out of the top-50 induced delta entirely and cardiac maturation markers move up.--Shipped default BETA=0.5 (`VEC_BETA`): proxy 3-seed mean 56.06 -> 57.42-(direction +2.0, cell_state +2.8, covariation +0.7, de_recovery -0.55).-BETA=0 reproduces heart_reweight bit for bit. On two-stage views a per-type cosine gate-(`two_stage_gate`) compares the axis-induced shift with the measured prev->last pseudobulk-delta and disables the reweighting for types that point the wrong way; on the single-stage-proxy it never triggers.--Runs on proxy (E8.5 -> E9.5) and final (E8.5, E9.5 -> E10.5) with the same code: reads only-`inputs_by_time(manifest)[-1]` (plus `[-2]` for the gate), no stage names, no hard-coded-statistics. 2.8 s, 1.31 GB peak. See METHOD.md for the beta response curve and diagnostics.diff --git a/solution/run.py b/solution/run.pyindex dd433f9..1c18bad 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,32 +1,11 @@ #!/usr/bin/env python3-"""heart_jcf_peri composition + within-type cell-cycle-exit reweighting.--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. Added here: within each type, cells are drawn with-weight ``exp(-beta * z)`` where ``z`` is the cell's cell cycle score (gene-sets from the view's ``prior/``), residualised against sequencing depth-within its type and robust-standardised. Low-proliferation cells - the exit--from-cycle / maturation end - are mildly favoured, moving each type's-pseudobulk mean a small data-derived step along the maturation direction-while every output cell remains a real cell (covariance structure preserved).--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 input stages the axis direction is additionally validated per type-against the measured prev->last pseudobulk delta (cosine gate); types whose-axis points the wrong way fall back to unweighted sampling.--``beta = 0`` reproduces the parent (heart_jcf_peri) bit for bit.-"""+"""fake-17: 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,@@ -37,20 +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-+HEART_WEIGHT = 1.4+EDGE_WEIGHT = 0.15 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")-VERBOSE = bool(os.environ.get("VEC_VERBOSE", ""))   def main() -> None:@@ -59,47 +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)+    pass     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-    beta = BETA-    if beta != 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:-                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 = resample(X, labels, n, growth, GAMMA if growth else 0.0, w_cell, seed=args.seed)-    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 x1.4, edge x0.15 (ok)

调研员

PLAN.json written