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节点 n36

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 8341756e9e: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 059b6f22d7。改动的文件：solution/METHOD.md +7 −7、solution/run.py +33 −2

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 3d6c811..eeace32 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,9 +1,9 @@ ## 改了什么-1. 修复第 0 轮引入的 covariation 下降（41.31→40.18）：将分层抽样（stratified sampling）回退为加权抽样（weighted sampling）。分层抽样强制每个类型精确配额，当配额超过可用细胞数时用有放回抽样补齐，导致重复细胞增多、类内共变结构被破坏。加权抽样（与父节点 27 相同）在保持类型比例偏移的同时，优先无放回抽样，更好保留基因共变结构。-2. 保留第 0 轮的细胞数 clamp 修复（检查 manifest 中 max_cells/min_cells 是否为 None 再转 int），确保输出细胞数在合法范围内。-3. 加权抽样函数简洁：直接按权重概率从全部细胞中抽样，无需类型级配额分配，代码更简单且经父节点 27 验证有效（proxy 54.49）。+1. 将全局统一收缩（DELTA_ALPHA=0.6 乘所有基因）替换为基于信噪比（SNR）的逐基因收缩：对每个类型，计算伪批量差值的 SNR（|delta| / 均值差的抽样标准误），基因级收缩因子 = (snr / (snr + K))^power。SNR 低的基因（噪声主导）被大幅压缩，SNR 高的基因（真实信号）保留大部分位移。全局 alpha=0.6 仍作为基础缩放。+2. 预期效果：减少噪声基因对表达分布的扰动，改善 covariation（当前最弱组 42.60）和 de_recovery（50.62），同时保持 direction 分数。+3. 仅对两阶段差值计算做此改进；单阶段（proxy）退化为 copy_last，不受影响。 ## 用到的知识与出处-- 父节点 27 实验结果：加权抽样在 proxy 上得 54.49，covariation 41.31；第 0 轮分层抽样降至 53.516 / 40.18，说明分层+补齐策略伤害了共变结构-- 父节点 31 ANALYSIS：manifest.get 取不到值时 clamp 变空操作，需先检查 None-- k012：covariation 占 20%，mode collapse 和重复细胞会被 ED/MMD/CSS 惩罚-- CONTRACT：输出细胞数必须在 [min_cells, max_cells] 之内+- k018：「Optionally shrink per gene (empirical Bayes on the delta's variance) so noisy genes move less」+- Program 1（score 50.09，covariation 50.11）：SNR-based per-gene shrinkage 是其核心机制，证明该策略对 covariation 有显著帮助+- 第 1 轮结果：DELTA_ALPHA=0.6 已将 covariation 从 41.31 提升到 42.60，逐基因收缩预期进一步提升+- 方法卡 T1：「同名型的差值要乘收缩系数 α∈[0,1]，不能默认 α=1」diff --git a/solution/run.py b/solution/run.pyindex 0238f5b..aae46d9 100644--- a/solution/run.py+++ b/solution/run.py@@ -35,6 +35,10 @@ CARDIAC_W = 1.6 DOWNWEIGHT_W = 0.25 DROP_W = 0.0 DEFAULT_W = 1.0+DELTA_ALPHA = 0.6+SNR_THRESHOLD = 1.5+SHRINK_K = 3.0+SHRINK_POWER = 2.0   def family_weight(name: str) -> float:@@ -106,12 +110,39 @@ def main() -> None:             rows = rng.choice(last.n_obs, size=n_target, replace=False)         else:             rows = rng.choice(last.n_obs, size=n_target, replace=True)+     X = last.X[rows]      if len(stages) >= 2:+        from scipy import sparse as sp++        def _dense(M):+            if sp.issparse(M):+                return np.asarray(M.todense(), dtype=np.float32)+            return np.asarray(M, dtype=np.float32)+         prev = read_stage(args.data, stages[-2], genes)-        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels)-        del prev+        labels_prev = labels_of(prev)+        Xd_last = _dense(last.X)+        Xd_prev = _dense(prev.X)++        deltas = {}+        types_last = set(np.unique(labels))+        types_prev = set(np.unique(labels_prev))+        for t in types_last & types_prev:+            mask_l = labels == t+            mask_p = labels_prev == t+            n_l = int(mask_l.sum())+            n_p = int(mask_p.sum())+            raw_delta = Xd_last[mask_l].mean(axis=0) - Xd_prev[mask_p].mean(axis=0)+            var_l = Xd_last[mask_l].var(axis=0)+            var_p = Xd_prev[mask_p].var(axis=0)+            noise_se = np.sqrt(var_l / max(n_l, 1) + var_p / max(n_p, 1) + 1e-12)+            snr = np.abs(raw_delta) / (noise_se + 1e-12)+            gene_shrink = (snr / (snr + SHRINK_K)) ** SHRINK_POWER+            deltas[t] = (raw_delta * DELTA_ALPHA * gene_shrink).astype(np.float32)++        del prev, Xd_last, Xd_prev         X = shift_rows(X, labels[rows], deltas)      write_prediction(X, genes, args.out, seed=args.seed)

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

Looking at the current state: round 1 gave score 50.31 (up from 49.88). Key observations:
- **covariation improved**: 41.31 → 42.60 (DELTA_ALPHA=0.6 shrinkage helps preserve structure)
- **cell_state improved**: 50.93 → 51.97
- **direction improved**: 54.04 → 54.17
- **de_recovery dropped slightly**: 51.33 → 50.62
- **X3 improved**: 40.67 → 42.13
- **proxy dropped slightly**: 54.49 → 54.40

The weakest group is still covariation (42.60). The program only applies delta shrinkage uniformly at 0.6. Program 1 (score 50.09) achieved covariation 50.11 via SNR-based per-gene shrinkage without reweighting. The key insight: genes with low SNR deltas contribute noise to the shift, distorting covariation. I should add per-gene SNR-based shrinkage on top of the global alpha, so noisy genes are shrunk more while coherent genes retain signal. This should improve covariation and de_recovery without losing much direction.

I'll implement per-gene shrinkage based on delta SNR (similar to Program 1's approach) applied within the existing reweighting framework.

<<<<<<< SEARCH
CARDIAC_W = 1.6
DOWNWEIGHT_W = 0.25
DROP_W = 0.0
DEFAULT_W = 1.0
DELTA_ALPHA = 0.6
=======
CARDIAC_W = 1.6
DOWNWEIGHT_W = 0.25
DROP_W = 0.0
DEFAULT_W = 1.0
DELTA_ALPHA = 0.6
SNR_THRESHOLD = 1.5
SHRINK_K = 3.0
SHRINK_POWER = 2.0
>>>>>>> REPLACE

<<<<<<< SEARCH
    X = last.X[rows]

    if len(stages) >= 2:
        prev = read_stage(args.data, stages[-2], genes)
        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels)
        deltas = {k: v * DELTA_ALPHA for k, v in deltas.items()}
        del prev
        X = shift_rows(X, labels[rows], deltas)
=======
    X = last.X[rows]