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节点 n26

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 bc10da219f: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 251065a1ce。改动的文件：solution/METHOD.md +5 −6、solution/run.py +25 −5

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex ed014d5..f1a7874 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,9 +1,8 @@ ## 改了什么-相对父节点 copy_last：增加 per-type pseudobulk shift with per-gene empirical Bayes shrinkage。当视图有 ≥2 个输入阶段时，对每个在两阶段都出现的细胞类型计算伪批量均值差 delta，再按基因计算 shrinkage = delta^2 / (delta^2 + var_within + eps)，其中 var_within 是该基因在最后阶段该类型内的方差。有效位移 = alpha * shrinkage * delta，clip ≥0。高方差基因（主导协方差结构）几乎不动，清晰 DE 基因获得接近 alpha 的位移。alpha=0.5。单输入退化为 copy_last。--第 0 轮教训：均匀位移（无 per-gene shrinkage，alpha=0.35）使 covariation 从 48.44 暴跌到 27.5，因为所有基因等量平移破坏了基因间共变结构。本轮加入 per-gene shrinkage 后，高方差基因（对协方差贡献最大）的位移被压缩到接近 0，预期恢复 covariation 同时保留 de_recovery 的改善。+相对父节点（第 0 轮修改后版本）：(1) 完全移除全局方差保持校正——第 0 轮实验证明它是协方差崩溃的元凶（30.94 vs 基线 48.44），因为全局 rescale 跨类型混合信息，破坏 within-type 共变结构；(2) 将位移限制到每个类型内 z-score 排名前 10% 的基因（TOP_FRAC=0.10），其余基因完全不动，从而保护绝大多数基因的共变关系；(3) 降低 alpha 从 0.5 到 0.3，进一步减小对共变结构的扰动；(4) 保留 SE-based shrinkage 和显著性过滤（SIG_K=2.0）。逻辑：只移动少量高置信度 DE 基因，大部分基因保持原样，covariation 应接近 copy_last 水平，同时 de_recovery 从少量真实位移中获益。  ## 用到的知识与出处-- 方法卡 k018：per-gene empirical Bayes shrinkage（"shrink per gene so noisy genes move less"）。-- 方法卡 k012：pseudobulk_shift 定义；covariation 指标对方差塌缩敏感。-- 第 0 轮实验反馈：均匀位移导致 covariation 崩溃（48.44→27.5），de_recovery 改善（44.09→46.9）。+- 第 0 轮实验反馈：全局方差校正导致 covariation 从 45.40 进一步跌至 30.94，证明全局 rescale 有害。+- 父节点 ANALYSIS：「位移应作用于低方差/低贡献基因」→ 本轮改为只作用于 top DE 基因（高信噪比），其余不动。+- 方法卡 k018：per-gene empirical Bayes shrinkage。+- 节点 16（VAE，score 50.36）的启示：只修改部分信号（BLEND_DECODED=0.30）能保住协方差。diff --git a/solution/run.py b/solution/run.pyindex da905ca..94d00ec 100644--- a/solution/run.py+++ b/solution/run.py@@ -4,6 +4,7 @@ from __future__ import annotations  import argparse+import os  import numpy as np from scipy import sparse@@ -18,8 +19,11 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.5+ALPHA = 0.3+SIG_K = 2.0+TOP_FRAC = 0.10 EPS = 1e-8+MECHANISM_OFF = int(os.environ.get("MECHANISM_OFF", "0"))   def _pseudobulk_shift(last, prev, alpha: float) -> sparse.csr_matrix:@@ -29,18 +33,31 @@ def _pseudobulk_shift(last, prev, alpha: float) -> sparse.csr_matrix:     X_last = last.X.toarray() if sparse.issparse(last.X) else np.asarray(last.X, dtype=np.float32)     X_prev = prev.X.toarray() if sparse.issparse(prev.X) else np.asarray(prev.X, dtype=np.float32) +    n_genes = X_last.shape[1]+    top_k = max(1, int(n_genes * TOP_FRAC))+     shift = np.zeros_like(X_last)     for ct in np.unique(last_types):         m_last = last_types == ct         m_prev = prev_types == ct         if m_prev.sum() == 0:             continue+        n_last = int(m_last.sum())+        n_prev = int(m_prev.sum())         mean_last = X_last[m_last].mean(axis=0)         mean_prev = X_prev[m_prev].mean(axis=0)         delta = mean_last - mean_prev         var_within = X_last[m_last].var(axis=0)-        shrinkage = delta ** 2 / (delta ** 2 + var_within + EPS)-        shift[m_last] = alpha * shrinkage * delta+        se2 = var_within / max(n_last, n_prev) + EPS+        se = np.sqrt(se2)+        zscore = np.abs(delta) / se+        significant = zscore > SIG_K+        top_idx = np.argsort(zscore)[::-1][:top_k]+        mask = np.zeros(n_genes, dtype=bool)+        mask[top_idx] = True+        mask &= significant+        shrinkage = delta ** 2 / (delta ** 2 + se2)+        shift[m_last] = alpha * shrinkage * delta * mask      X_out = np.clip(X_last + shift, 0, None).astype(np.float32)     return sparse.csr_matrix(X_out)@@ -60,8 +77,11 @@ def main() -> None:     rng = np.random.default_rng(args.seed)      if len(entries) >= 2:-        prev = read_stage(args.data, entries[-2], genes)-        X = _pseudobulk_shift(last, prev, ALPHA)+        if MECHANISM_OFF:+            X = last.X+        else:+            prev = read_stage(args.data, entries[-2], genes)+            X = _pseudobulk_shift(last, prev, ALPHA)     else:         X = last.X 

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

stream

Looking at the results, the key insight is:

- Covariation (42.73) is still below baseline (48.44) but improved from round 0 (30.94)
- de_recovery (46.9) improved over baseline (44.09)
- cell_state (50.65) slightly improved
- direction (49.45) stable

The problem: even shifting only top 10% genes still damages covariation. The issue is that 10% of genes (top_k across ALL genes per type) is too many — many of those genes are correlated with others, so shifting them breaks correlation structure.

Key observation from the best program (node 16, score 50.36): it uses a VAE with only 30% blending of decoded signal, achieving covariation 47.73. The principle: minimal perturbation to most of the expression matrix.

For round 2, I'll try a different approach: **reduce TOP_FRAC to 2%** (only the most extreme DE genes), and reduce alpha to 0.2. This should minimize covariation damage while still capturing the strongest directional signals. Additionally, I'll cap the absolute shift magnitude per gene to prevent any single gene from moving too far.

<<<<<<< SEARCH
ALPHA = 0.3
SIG_K = 2.0
TOP_FRAC = 0.10
EPS = 1e-8
MECHANISM_OFF = int(os.environ.get("MECHANISM_OFF", "0"))


def _pseudobulk_shift(last, prev, alpha: float) -> sparse.csr_matrix:
    last_types = np.asarray(last.obs["celltype"])
    prev_types = np.asarray(prev.obs["celltype"])

    X_last = last.X.toarray() if sparse.issparse(last.X) else np.asarray(last.X, dtype=np.float32)
    X_prev = prev.X.toarray() if sparse.issparse(prev.X) else np.asarray(prev.X, dtype=np.float32)

    n_genes = X_last.shape[1]
    top_k = max(1, int(n_genes * TOP_FRAC))

    shift = np.zeros_like(X_last)
    for ct in np.unique(last_types):
        m_last = last_types == ct
        m_prev = prev_types == ct
        if m_prev.sum() == 0:
            continue
        n_last = int(m_last.sum())
        n_prev = int(m_prev.sum())
        mean_last = X_last[m_last].mean(axis=0)