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节点 n40 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 14056cdde8: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 6f4818572b。改动的文件：solution/METHOD.md +2 −2、solution/run.py +39 −51

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex f58a678..876a784 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,4 +1,4 @@ ## 改了什么-相对父节点（组成重加权 + replace=True 加权抽样）：(1) 将加权有放回抽样改为分层无放回抽样：按家族权重调整各类型的目标比例，然后在每个类型内无放回抽样，保留类型内部的细胞分布和协方差结构（父节点 replace=True 会重复抽样导致协方差塌缩，covariation 从 51.96 降至 49.73）；(2) 保留逐基因 EB 收缩位移（alpha=0.03），在有两个官方输入阶段时施加；(3) 保留 reduced 族权重 0.15。核心思路：分层抽样保协方差（节点 14 验证），逐基因收缩保 de_recovery。+相对父节点（节点 20，分层无放回抽样 + EB 收缩位移 alpha=0.03）：(1) 完全移除逐基因 EB 收缩位移，因父节点 ANALYSIS 明确建议消融且 covariation 下降；(2) 新增基于距离的分层抽样，N_BINS=20（比 Attempt 1 的 12 更细），MIN_CELLS_FOR_BINNING=30（避免小类型不可靠分箱）；(3) 家族权重 heart=1.5/reduced=0.2/gut=0.95，与全树最佳 Attempt 1 一致。第 0 轮用 1.6/0.15/0.9 得 51.96（proxy 52.97）；第 1 轮改权重得 52.20（proxy 53.33，covariation 51.9）；本轮增加分箱数从 12 到 20 以提升 covariation（Attempt 1 得 52.55 用的 12 bins，更细分箱应更好保留径向分布）。 ## 用到的知识与出处-实验表节点 14（分层抽样 + EB 收缩，covariation 50.79、de_recovery 51.33，综合 52.24 为全树最佳）；父节点 ANALYSIS（replace=True 加权抽样导致 covariation 下降）；k018（EB 收缩：按基因信噪比逐基因收缩）；方向库 T1-01（组成重加权）+ T1-02（伪批量位移 + 收缩）；方法卡（alpha 不能默认 1）。+实验表 Attempt 1（score 52.4971，covariation 52.55，N_BINS=12）；父节点 ANALYSIS（EB 收缩导致 covariation 下降）；方向库 T1-01（组成重加权）；方法卡（位移在 proxy 无法验证）。diff --git a/solution/run.py b/solution/run.pyindex ca1af32..5b68ce8 100644--- a/solution/run.py+++ b/solution/run.py@@ -17,7 +17,6 @@ import re import numpy as np from scipy import sparse as sp -from src.task1_temporal.baselines import shift_rows from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -28,7 +27,8 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.03+N_BINS = 20+MIN_CELLS_FOR_BINNING = 30  _HEART_PAT = re.compile(     r"cm\b|cardi|heart|shf|endocard|oft\b|avc|ift\b|sv[- ]cm|v[- ]cm|"@@ -47,53 +47,15 @@ _GUT_PAT = re.compile(  def family_weight(name: str) -> float:     if _HEART_PAT.search(name):-        return 1.6+        return 1.5     if _REDUCED_PAT.search(name):-        return 0.15+        return 0.2     if _GUT_PAT.search(name):-        return 0.9+        return 0.95     return 1.0  -def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):-    prev_types = set(np.unique(prev_labels))-    last_types = np.unique(last_labels)-    deltas = {}-    for t in last_types:-        last_mask = last_labels == t-        n_last = int(last_mask.sum())-        if n_last == 0:-            continue-        last_sub = last_X[last_mask]-        if sp.issparse(last_sub):-            mean_last = np.asarray(last_sub.mean(axis=0)).ravel()-            var_last = np.asarray(last_sub.multiply(last_sub).mean(axis=0)).ravel() - mean_last**2-        else:-            arr = np.asarray(last_sub)-            mean_last = arr.mean(axis=0).ravel()-            var_last = arr.var(axis=0).ravel()-        var_last = np.maximum(var_last, 0.0)-        if t in prev_types:-            prev_mask = prev_labels == t-            n_prev = int(prev_mask.sum())-            prev_sub = prev_X[prev_mask]-            if sp.issparse(prev_sub):-                mean_prev = np.asarray(prev_sub.mean(axis=0)).ravel()-                var_prev = np.asarray(prev_sub.multiply(prev_sub).mean(axis=0)).ravel() - mean_prev**2-            else:-                arr_p = np.asarray(prev_sub)-                mean_prev = arr_p.mean(axis=0).ravel()-                var_prev = arr_p.var(axis=0).ravel()-            var_prev = np.maximum(var_prev, 0.0)-            delta = mean_last - mean_prev-            se2 = var_prev / max(n_prev, 1) + var_last / max(n_last, 1)-            n_eff = min(n_prev, n_last)-            size_factor = n_eff / (n_eff + 50.0)-            shrink = delta**2 / (delta**2 + se2 + 1e-10)-            deltas[t] = alpha * size_factor * shrink * delta-        else:-            deltas[t] = np.zeros(last_X.shape[1])-    return deltas+   def main() -> None:@@ -134,6 +96,39 @@ def main() -> None:         type_idx = np.where(labels == u)[0]         if n_take >= len(type_idx):             indices.append(type_idx)+            continue+        if len(type_idx) > MIN_CELLS_FOR_BINNING and n_take >= 5:+            sub = last.X[type_idx]+            if sp.issparse(sub):+                sub = np.asarray(sub.todense())+            else:+                sub = np.asarray(sub)+            centroid = sub.mean(axis=0)+            dists = np.linalg.norm(sub - centroid, axis=1)+            bin_edges = np.quantile(dists, np.linspace(0, 1, N_BINS + 1)[1:-1])+            bin_ids = np.searchsorted(bin_edges, dists)+            bin_props = np.bincount(bin_ids, minlength=N_BINS) / len(type_idx)+            per_bin = np.floor(bin_props * n_take).astype(int)+            rem = n_take - per_bin.sum()+            fr = (bin_props * n_take) - per_bin+            for i in np.argsort(-fr)[:rem]:+                per_bin[i] += 1+            per_bin = np.minimum(per_bin, np.bincount(bin_ids, minlength=N_BINS))+            chosen = []+            for b in range(N_BINS):+                b_idx = type_idx[bin_ids == b]+                nb = min(per_bin[b], len(b_idx))+                if nb > 0:+                    chosen.append(rng.choice(b_idx, size=nb, replace=False))+            if chosen:+                sel = np.concatenate(chosen)+            else:+                sel = rng.choice(type_idx, size=n_take, replace=False)+            if len(sel) < n_take:+                leftover = np.setdiff1d(type_idx, sel)+                extra = rng.choice(leftover, size=n_take - len(sel), replace=False)+                sel = np.concatenate([sel, extra])+            indices.append(sel)         else:             indices.append(rng.choice(type_idx, size=n_take, replace=False))     rows = np.concatenate(indices)@@ -142,13 +137,6 @@ def main() -> None:         extra = rng.choice(remaining, size=n_target - len(rows), replace=False)         rows = np.concatenate([rows, extra])     X = last.X[rows]--    if len(official) >= 2:-        prev = read_stage(args.data, official[-2], genes)-        deltas = eb_shrunk_deltas(prev.X, labels_of(prev), last.X, labels, ALPHA)-        del 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 results, the main gap vs Attempt 1 (52.4971) is covariation (51.9 vs 52.55). Increasing N_BINS should provide finer stratification of the within-type radial distribution, better preserving covariance structure. Also raising MIN_CELLS_FOR_BINNING to avoid unreliable binning on small types.

<<<<<<< SEARCH
N_BINS = 12
MIN_CELLS_FOR_BINNING = 15
=======
N_BINS = 20
MIN_CELLS_FOR_BINNING = 30
>>>>>>> REPLACE

```METHOD
## 改了什么
相对父节点（节点 20，分层无放回抽样 + EB 收缩位移 alpha=0.03）：(1) 完全移除逐基因 EB 收缩位移，因父节点 ANALYSIS 明确建议消融且 covariation 下降；(2) 新增基于距离的分层抽样，N_BINS=20（比 Attempt 1 的 12 更细），MIN_CELLS_FOR_BINNING=30（避免小类型不可靠分箱）；(3) 家族权重 heart=1.5/reduced=0.2/gut=0.95，与全树最佳 Attempt 1 一致。第 0 轮用 1.6/0.15/0.9 得 51.96（proxy 52.97）；第 1 轮改权重得 52.20（proxy 53.33，covariation 51.9）；本轮增加分箱数从 12 到 20 以提升 covariation（Attempt 1 得 52.55 用的 12 bins，更细分箱应更好保留径向分布）。
## 用到的知识与出处
实验表 Attempt 1（score 52.4971，covariation 52.55，N_BINS=12）；父节点 ANALYSIS（EB 收缩导致 covariation 下降）；方向库 T1-01（组成重加权）；方法卡（位移在 proxy 无法验证）。
```