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节点 n43 终选程序?按该运行锁定的规则最终选出的程序；可能是候选节点，也可能由护栏回退到基线。在终选来历上

改了什么

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 0c7ba88148: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 8072472c25。改动的文件：solution/METHOD.md +9 −8、solution/run.py +54 −18

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 205b747..8dcaadf 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,10 +1,11 @@ ## 改了什么-1. 修复崩溃：将 `del prev, Xd_last, Xd_prev` 从逐细胞缩放循环之前移到之后。第 1 轮引入的逐细胞缩放需要 `Xd_last` 计算类型质心，但原位置在循环前就删除了它，导致 X3 视图（单阶段回退路径不进入此分支，但两阶段路径触发）报 UnboundLocalError。-2. 保留第 0 轮全部改动（ALPHA_MAX=0.80、GENE_SHRINK_POWER=2.0、GENE_SHRINK_K=3.0、SNR_SIGNIFICANT_THRESHOLD=1.5、符号不一致处理）。-3. 保留第 1 轮的逐细胞缩放（CELL_SCALE_MIN=0.7, CELL_SCALE_MAX=1.3）和放宽的符号不一致参数（SIGN_DISAGREE_FACTOR=0.95, SIGN_DISAGREE_THRESHOLD=0.40）。-+1. 单阶段回退路径（proxy/proxy2 视图）加入基于细胞类型家族的组成重加权采样：心脏谱系类型（CM、SHF、Endocardium、BEC、Proepicardium、Pericardium、PHM、JCF、NCC、OFT、AVC、IFT、SV）权重 1.4，外胚层/胚外类型（Surface Ectoderm、EXEM、Paraxial Mesoderm）降权至 0.4，Neural Tube 降至 0.05。权重比 Program 2（1.6/0.25/0.0）温和，以减少协方差破坏。这使 proxy/proxy2 从 copy_last（50.04）提升。+2. ALPHA_MAX 从 0.80 提高到 0.85：两阶段路径中允许更大的位移幅度，改善 direction 和 de_recovery。+3. CELL_SCALE 范围从 [0.7, 1.3] 扩展到 [0.6, 1.4]：增加同类型内细胞沿 delta 方向的异质性，进一步保护 covariation。+4. 逐细胞缩放的分位映射从 5%/95% min-max 归一化改为 rank-based 均匀映射：消除尾部离群值对缩放的过度影响，使缩放分布更均匀。+5. 移除 sample_rows 导入，改用显式 rng.choice 以支持加权采样。 ## 用到的知识与出处-- 第 1 轮崩溃日志：`UnboundLocalError: cannot access local variable 'Xd_last'`，原因是 `del` 在使用前执行。-- 方法卡 k018：逐基因经验贝叶斯收缩使噪声基因少动，保护协方差。-- 实验表 Program 1（node 22/23）：GENE_SHRINK_POWER=2.5、K=3.0、符号不一致处理在 covariation 上优于本节点父代。-- 任务书 §5：预测的是一群细胞，要保留群体的分布、比例和基因之间的共变结构。+- 实验表 Program 2（node 27/34/36/40）：组成重加权在 proxy 上得 54.49，证明单阶段路径的组成调整是提升 proxy 的关键。本节点用更温和的权重以避免 Program 2 的协方差崩塌（41.31）。+- 方法卡 k018：收缩系数 α 应保守但不应过低；0.85 在 final 视图（E8.5→E9.5 外推到 E10.5）仍留有余量。+- 父节点 ANALYSIS next_suggestions：CELL_SCALE 区间扫描、rank-based 映射、ALPHA_MAX 提高。+- 任务书 §5：预测一群细胞，保留分布、比例和共变结构。diff --git a/solution/run.py b/solution/run.pyindex 8107870..07c1a54 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,13 +1,11 @@ #!/usr/bin/env python3-"""Per-type adaptive shrinkage on pseudobulk deltas with official-stage filtering.+"""Per-type adaptive shrinkage + composition reweighting on single-stage fallback. -Uses only official (non-external) input stages. For each cell type, computes-the pseudobulk delta and a per-type shrinkage factor based on the fraction of-genes showing consistent directional change (sign agreement with the dominant-direction). Types with coherent transcriptional shifts are shifted more; types-with noisy or contradictory deltas are shrunk toward zero.--With a single official input stage, falls back to copy_last.+Uses only official (non-external) input stages. With two stages, computes+per-type pseudobulk delta with SNR-based gene shrinkage and per-cell+rank-based projection scaling. With one stage, applies cardiac-family+composition reweighting before sampling to shift proportions toward expected+target composition. """  from __future__ import annotations@@ -23,12 +21,11 @@ from src.task1_temporal.view_io import (     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) -ALPHA_MAX = 0.80+ALPHA_MAX = 0.85 ALPHA_MIN = 0.1 SIGN_THRESHOLD = 0.05 GENE_SHRINK_POWER = 2.0@@ -36,8 +33,33 @@ GENE_SHRINK_K = 3.0 SNR_SIGNIFICANT_THRESHOLD = 1.5 SIGN_DISAGREE_FACTOR = 0.95 SIGN_DISAGREE_THRESHOLD = 0.40-CELL_SCALE_MAX = 1.3-CELL_SCALE_MIN = 0.7+CELL_SCALE_MAX = 1.4+CELL_SCALE_MIN = 0.6++CARDIAC_SUBSTRINGS = [+    "CM", "SHF", "Endocardium", "BEC", "Proepicardium",+    "Pericardium", "PHM", "JCF", "NCC", "OFT", "AVC", "IFT", "SV",+]+DOWNWEIGHT_SUBSTRINGS = ["Surface Ectoderm", "EXEM", "Paraxial Mesoderm"]+DROP_SUBSTRINGS = ["Neural Tube"]++CARDIAC_W = 1.4+DOWNWEIGHT_W = 0.4+DROP_W = 0.05+DEFAULT_W = 1.0+++def family_weight(name: str) -> float:+    for s in DROP_SUBSTRINGS:+        if s in name:+            return DROP_W+    for s in DOWNWEIGHT_SUBSTRINGS:+        if s in name:+            return DOWNWEIGHT_W+    for s in CARDIAC_SUBSTRINGS:+        if s in name:+            return CARDIAC_W+    return DEFAULT_W   def _to_dense(X):@@ -90,12 +112,28 @@ def main() -> None:      last = read_stage(args.data, stages[-1], genes)     rng = np.random.default_rng(args.seed)-    rows = sample_rows(last.n_obs, target_n_cells(manifest, last.n_obs), rng)+    n_target = target_n_cells(manifest, last.n_obs)+    labels_last = labels_of(last)++    if len(stages) >= 2:+        if n_target <= last.n_obs:+            rows = rng.choice(last.n_obs, size=n_target, replace=False)+        else:+            rows = rng.choice(last.n_obs, size=n_target, replace=True)+    else:+        weights = np.array([family_weight(l) for l in labels_last], dtype=np.float64)+        w_sum = weights.sum()+        if w_sum > 0:+            weights /= w_sum+        else:+            weights = np.ones(last.n_obs, dtype=np.float64) / last.n_obs+        replace = n_target > last.n_obs+        rows = rng.choice(last.n_obs, size=n_target, replace=replace, p=weights)+     X = _to_dense(last.X[rows])      if len(stages) >= 2:         prev = read_stage(args.data, stages[-2], genes)-        labels_last = labels_of(last)         labels_prev = labels_of(prev)          means_last = _per_type_means(last.X, labels_last)@@ -133,10 +171,8 @@ def main() -> None:                     cell_sub = X[mask][:, nonzero_genes]                     centered = cell_sub - centroid[np.newaxis, :]                     proj_scores = centered @ proj_dir-                    p_lo = np.percentile(proj_scores, 5)-                    p_hi = np.percentile(proj_scores, 95)-                    span = p_hi - p_lo + 1e-12-                    norm_proj = (proj_scores - p_lo) / span+                    ranks = np.argsort(np.argsort(proj_scores)).astype(np.float64)+                    norm_proj = ranks / max(len(ranks) - 1, 1)                     scale = CELL_SCALE_MIN + (CELL_SCALE_MAX - CELL_SCALE_MIN) * norm_proj                     X[mask][:, nonzero_genes] += delta_t[nonzero_genes][np.newaxis, :] * scale[:, np.newaxis]                     zero_genes = ~nonzero_genes

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）。

没有记录调研来源。

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

native

审查员

REVIEW.json written

stream

I'll analyze the task. This is an "improve" node task on parent node 32 (score 50.03). Let me read the full PROMPT.md and the current run.py.

<tool_call>
<function=Read>
<parameter=offset>667
</parameter>
<parameter=filePath>
/home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r0-C-native-n43-050034/work/PROMPT.md
</parameter>
</function>
<parameter=Read>
<parameter=filePath>
/home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r0-C-native-n43-050034/work/solution/run.py
</parameter>
</function>
</tool_call>