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节点 n30

heart_jcf_peri reweighting, heart x1.5, edge x0.25 (fake Engineer, node 30)

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 4c812bc782: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 04feafd2d1。改动的文件：solution/METHOD.md +2 −57、solution/README.md +0 −16、solution/run.py +8 −68

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 694e38c..a99f825 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,58 +1,3 @@-移植节点9的类内成熟度加权抽样（周期分深度残差化稳健z，w=exp(-0.5z)，E-S无放回）为主干；新增跨阶段伪批量位移分支（Qiu E9.0/两官方阶段，批校+EB收缩），实测有害，默认α=0关闭。+heart_jcf_peri reweighting, heart x1.5, edge x0.25 (fake Engineer, node 30) -## 做了什么--父节点 4（55.97，=节点 2 逐位复现）最弱组 de_recovery 53.06。本节点按 PLAN 做了两件事：--1. **成熟度加权抽样（提交主干，`maturity.py`）**：重实现节点 9 的已证部件。解剖组成与父本完全一致-   （heart ×1.6、edge ×0.25、丢 Neural Tube、4000 细胞、`largest_remainder` 配额），但类内不再均匀抽样：-   每细胞算 `prior/` gmt 细胞周期基因集的 z 分数 s（全体细胞上标准化，稀疏 matvec），类型内对 `[1, log1p(库大小)]`-   闭式 OLS 取残差，median/1.4826·MAD 稳健 z，clip±2，权重 `w=exp(-0.5·z)`，Efraimidis-Spirakis-   （key=u^(1/w) 取 top-k）加权无放回抽样；池不足时有放回加权补齐。<30 细胞的类型退化为均匀。-2. **跨阶段伪批量位移（`shift.py`，默认关闭）**：视图有第二输入阶段时（proxy2 的 Qiu E9.0 外部输入 / final 的官方-   E8.5→E9.5），逐类型 δ = mean(参考阶段, 匹配类型) − mean(最后官方阶段, 类型)；跨技术批效应按 scArches-   位置漂移假设做逐基因跨类型 median 中心化；EB 收缩 δ·t²/(t²+4)（se 由两 pseudobulk 方差算）；|δ| 99 分位截断；-   周期基因与未覆盖基因 δ=0；外部阶段类型匹配用基因中位数中心化 pseudobulk 的相关（≥0.3，运行期计算，无硬编码标签映射，-   proxy2 实测匹配：Endocardial→Endothelium、FHF→SV-CM、SHF→OFT/RV-CM，r≈0.74-0.75）；应用-   x ← x + α·r·δ，r=(目标时间−最后输入时间)/(参考−最后输入)，clip≥0。α=0 时整个分支（含外部文件读取）被跳过，-   proxy/proxy2 输出逐位相同。--## 查分结果（seed 0 单次，除注明外）--| 配置 | 视图 | 榜分 | de_rec | direction | cell_state | covar |-|---|---|---|---|---|---|---|-| 提交默认（θ=0.5, α=0） | proxy | 57.76 | 53.06 | 60.92 | 60.60 | 55.41 |-| 同上 seed 1 / seed 2 | proxy | 57.61 / 57.08 | | | | |-| **3-seed 均值** | proxy | **57.48** | 52.7 | 60.8 | 60.1 | 55.4 |-| 位移 α=0.5 / α=1.0 | proxy2 | 56.78 / 55.91 | 52.0 / 52.0 | 60.86 / 60.23 | 59.5 / 58.2 | 53.57 / 51.97 |-| θ=1.0 / θ=0.25 / heart×2.0 | proxy | 57.67 / 56.94 / 57.16 | | | | |--**位移分支的负结果**：α 从 0→0.5→1.0 在 proxy2 上单调降分（57.76→56.78→55.91），四个组全部下降，de_recovery-53.06→52.00。Qiu 与官方数据的批效应不是纯基因级位置漂移，median 中心化后残留的类型×技术交互盖过了 0.5 天的-时间信号；且 δ 幅度本身很小（α=1 时受影响条目 mean|Δ|≈0.06）。按 PLAN 的预设放弃规则，提交 α=0。--## 验证过 / 没验证--- 验证过：默认配置在 proxy 与 proxy2 上输出逐位相同（α=0 分支跳过，`np.array_equal`）；两视图 `vec-check` ok；-  seed 0/1/2 proxy 3-seed 均值 57.48（≈节点 9 的 57.42，RNG 序列不同导致的实现差异）；θ=0.5 在 seed 0 上优于-  θ=0.25/1.0 与 heart×2.0；无阶段名分支、无硬编码统计量、`np.random.default_rng(seed)` 确定性。-- 未验证：final 视图（两官方阶段的 δ 无批混杂，理论上比 Qiu 干净，但 proxy 上无法测，α=0 提交即不启用）；-  θ/权重的 3-seed 复核（时间不够，只有 seed 0 单次）；位移分支的 k、截断分位等超参未搜（首个 α 已触发放弃规则）。--## 下一步最值得试--1. **final 视图上启用官方两阶段位移**（α≈0.25-0.5）：E8.5→E9.5 同数据集 δ 无批混杂，是唯一未被否定的表达位移-   信号；`VEC_ALPHA` 环境变量即可开启，代码路径已就绪（`label_match=True` 走同名类型）。-2. de_recovery 仍是弱组（52.5）：θ=1.0 时 de_score 反降（0.111→0.056），说明"更成熟的细胞"不等于"更对的 DE"；-   试把 DE 方向信息（两阶段同名类型差异基因）直接加权到类内抽样概率，而不是平移表达。-3. 组成侧 heart/edge 权重在 θ=0.5 主干上重新网格（本节点只测了 heart×2.0 单点，57.16）。--## 来源披露--- scArches 位置漂移批校正假设：doi:10.1038/s41587-021-01001-7（用于 shift.py 的逐基因 median 中心化；实测在-  Qiu↔官方跨技术上不成立，故默认关闭）。-- Efraimidis-Spirakis 加权无放回抽样：Efraimidis & Spirakis 2006, Information Processing Letters（key=u^(1/w)）。-- 细胞周期基因集：view `prior/` 的 reactome/go/msigdb gmt 中名称匹配 `cell cycle|dna replication|mitotic|E2F|G2M|-  S phase|M phase` 的集合（运行期读取，命中约 2091 基因）。-- 解剖权重（heart×1.6、edge×0.25、丢 Neural Tube）与 4000 细胞：继承 `src.task1_temporal.reweight`（run2 冠军-  heart_jcf_peri 的手调结果），本节点未改。+Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index 9d4b241..0000000--- a/solution/README.md+++ /dev/null@@ -1,16 +0,0 @@-# node 4 (improve, parent = heart_jcf_peri 55.97)--Composition is the parent's: latest input stage resampled by cell type with `heart_reweight`'s-anatomical weights (heart x1.6, dissection edges x0.25, Neural Tube dropped), 4000 real cells,-expression untouched. Added here: `growth.py` scales those weights by each type's own-proliferation level, measured at run time from the view's `prior/` cell cycle gene sets-(`VEC_GAMMA`, default 0.0 = parent bit for bit).--Measured on T1:val proxy, seed 0: gamma 0.0 -> 55.97, +0.35 -> 55.29, -0.25 -> 54.96,-+1.0 -> 52.68; N_CELLS 5118 -> 55.12. Both growth directions are worse, so the shipped-default keeps the parent's composition. See METHOD.md for the single-snapshot-differentiation-axis attempt that was dropped (no type reached |corr(PC, cell cycle)| >= 0.15,-and the score-based axis was ribosome/depth-driven with the wrong sign on CM maturation genes).--Runs on proxy (E8.5 -> E9.5) and final (E8.5, E9.5 -> E10.5) with the same code: it reads only-`inputs_by_time(manifest)[-1]` and branches on nothing else.diff --git a/solution/run.py b/solution/run.pyindex af7a42f..0086d54 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,31 +1,13 @@ #!/usr/bin/env python3-"""Anatomical reweighting + maturity-weighted sampling + cross-stage shift.--Composition: the parent's anatomical weights (heart x1.6, edges x0.25, neural-tube dropped) on the last official input stage.  Within each type, cells are-drawn without replacement with weight exp(-0.5 z), z = robust, depth--residualised cell-cycle z (maturity.py): the cloud moves toward cycle-exit,-the state populations reach by the target stage.--Expression: when the view provides a second input stage (proxy2: external-Qiu E9.0; final: official E8.5), each type's cells are shifted along a-batch-centred, EB-shrunk pseudobulk delta times alpha * r (shift.py).  With a-single input stage (proxy) the shift branch is off and the code reduces to-composition + maturity sampling.  alpha = 0 also reduces exactly to that.-"""+"""fake-30: 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 (-    covered_mask,-    external_inputs,     inputs_by_time,-    is_external,     labels_of,     load_manifest,     panel_genes,@@ -34,16 +16,9 @@ from src.task1_temporal.view_io import (     write_prediction, ) -from growth import cell_cycle_genes-from maturity import maturity_sample-from shift import apply_shift, compute_delta-+HEART_WEIGHT = 1.5+EDGE_WEIGHT = 0.25 N_CELLS = 4000-ALPHA = float(os.environ.get("VEC_ALPHA", "0.0"))-THETA = float(os.environ.get("VEC_THETA", "0.5"))-HW = float(os.environ.get("VEC_HW", "1.6"))-EW = float(os.environ.get("VEC_EW", "0.25"))-VERBOSE = bool(os.environ.get("VEC_VERBOSE", ""))   def main() -> None:@@ -52,48 +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)-    official = inputs_by_time(manifest)-    ext = external_inputs(manifest)-    last = read_stage(args.data, official[-1], genes)-    labels = labels_of(last)--    cc_mask = cell_cycle_genes(args.data, manifest, genes)+    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    pass     n = target_n_cells(manifest, N_CELLS)-    X, row_types = maturity_sample(last.X, labels, n, cc_mask, seed=args.seed, theta=THETA, hw=HW, ew=EW)--    # --- cross-stage shift (only with a second input stage; off at alpha=0) ----    scale = 0.0-    delta: dict[str, np.ndarray] = {}-    if ALPHA == 0.0:-        pass-    elif len(official) >= 2:-        prev = read_stage(args.data, official[-2], genes)-        prev_labels = labels_of(prev)-        dt = float(official[-1]["time"] - official[-2]["time"])-        gap = float(manifest["target"]["time"] - official[-1]["time"])-        delta, log = compute_delta(last.X, labels, prev.X, prev_labels, cc_mask,-                                   covered=np.ones(len(genes), dtype=bool), label_match=True)-        scale = ALPHA * (gap / dt if dt > 0 else 0.0)-        if VERBOSE:-            print("official two-stage matches:", log)-    elif ext:-        e = ext[-1]-        ref = read_stage(args.data, e, genes, missing="zero")-        covered = covered_mask(args.data, e, genes)-        col = e.get("celltype_col", "celltype")-        ref_labels = ref.obs[col].astype(str).to_numpy()-        dt = float(e["time"] - official[-1]["time"])-        gap = float(manifest["target"]["time"] - official[-1]["time"])-        delta, log = compute_delta(ref.X, ref_labels, last.X, labels, cc_mask, covered=covered)-        scale = ALPHA * (gap / dt if dt > 0 else 0.0)-        if VERBOSE:-            print("external matches:", log, "covered genes:", int(covered.sum()))--    X = apply_shift(X, row_types, delta, scale)-    write_prediction(X, 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[:10], 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 x1.5, edge x0.25 (invalid)

调研员

PLAN.json written