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节点 n31

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 8bf3fb4456: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 f40bad4f3a。改动的文件：solution/METHOD.md +6 −7、solution/run.py +41 −6

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex b223ed5..5eadcba 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,10 +1,9 @@ ## 改了什么-1. ALPHA 从 0.05 降至 0.03：进一步减小位移幅度，更接近 copy_last，预期提升 covariation 而 direction 损失在噪声内。-2. 新增逐类型样本量自适应收缩（size_factor = n_eff/(n_eff+50)）：细胞数少的类型收缩更强（delta 更不可靠），细胞数多的类型保留更多位移信号。这应同时改善 direction（大类型仍有效移动）和 covariation（小类型噪声不扩散）。-3. N_BINS 从 8 增至 10：更细的距离分位数分箱，更精确保留类型内表达云的协方差结构。-4. 分箱抽样阈值从 30 降至 20：更多中等大小类型受益于保协方差抽样。+1. 新增基于细胞类型家族的组成重加权（heart 类型权重 1.5，neural/surface ectoderm 权重 0.2，gut 权重 0.9，其余 1.0）：在分层抽样时用加权比例代替原始比例分配各类型的目标细胞数。这直接改善 cell_state（目标阶段的组成与最后输入阶段不同）和 covariation（正确的类型比例使基因共变结构更接近目标）。+2. ALPHA 从 0.03 提升至 0.04：组成重加权已改善 direction，稍微增大位移幅度可进一步提升 de_recovery 而不损失太多 covariation。+3. 单阶段时确保输出为稠密矩阵（X3 等只有一个输入阶段的视图）。 ## 用到的知识与出处+- 实验表 Program 1（node 9）：组成重加权在 cell_state 和 covariation 上显著优于保持原始比例（51.23 vs 48.95，51.96 vs 48.31） - 方法卡 k018（Damped per-type shift: shrinkage alpha on the observed delta）-- 父节点 ANALYSIS 的 next_suggestions（按类型样本量自适应收缩；进一步降 ALPHA）-- 方向库 T1-12（保协方差的生成：真实细胞锚 + 低秩残差）-- 实验表 Program 1 vs Program 2 对比（N_BINS=5 + threshold=50 的 direction 更高，N_BINS=8 + threshold=30 的 covariation 更高，取折中）+- 方向库 T1-01（组成重加权）+ T1-02（按类型伪批量位移 + 收缩系数）+ T1-12（保协方差的生成）+- 父节点 ANALYSIS 的 next_suggestions（covariation 是最弱组）diff --git a/solution/run.py b/solution/run.pyindex cf7d1ea..5b96934 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,14 +1,16 @@ #!/usr/bin/env python3-"""Stratified copy with shrunk pseudobulk delta on official stages only.+"""Stratified copy with composition reweighting and shrunk pseudobulk delta. -Fixes proxy2 collapse (was sampling external heart-only stage as base),-uses stratified sampling to preserve type proportions (covariation),-and applies shrinkage alpha=0.5 to deltas per method card k018.+Combines family-based composition reweighting (heart types upweighted,+neural/surface ectoderm downweighted) with stratified distance-bin sampling+to preserve within-type covariation structure, plus empirical Bayes shrunk+pseudobulk delta for expression shifts on official stages only. """  from __future__ import annotations  import argparse+import re  import numpy as np @@ -25,10 +27,34 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.03+ALPHA = 0.04 N_BINS = 10 MIN_CELLS_FOR_BINNING = 20 +_HEART_PAT = re.compile(+    r"cm\b|cardi|heart|shf|endocard|oft\b|avc|ift\b|sv[- ]cm|v[- ]cm|"+    r"proepicard|pericard|epicard|blood",+    re.IGNORECASE,+)+_REDUCED_PAT = re.compile(+    r"surface\s*ectoderm|exem|paraxial|neural\s*tube|neural|notochord",+    re.IGNORECASE,+)+_GUT_PAT = re.compile(+    r"foregut|endoderm|gut|hepato|epithel",+    re.IGNORECASE,+)+++def family_weight(name: str) -> float:+    if _HEART_PAT.search(name):+        return 1.5+    if _REDUCED_PAT.search(name):+        return 0.2+    if _GUT_PAT.search(name):+        return 0.9+    return 1.0+  def _submatrix(X, mask):     sub = X[mask]@@ -83,7 +109,14 @@ def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Gener     n_total = len(labels)     if n_target >= n_total:         return np.arange(n_total)-    props = counts / n_total+    raw_props = counts / n_total+    weights = np.array([family_weight(str(u)) for u in unique], dtype=np.float64)+    weighted_props = raw_props * weights+    total_w = weighted_props.sum()+    if total_w > 0:+        props = weighted_props / total_w+    else:+        props = raw_props     n_per_type = np.floor(props * n_target).astype(int)     remainder = n_target - n_per_type.sum()     frac = (props * n_target) - n_per_type@@ -161,6 +194,8 @@ def main() -> None:         deltas = eb_shrunk_deltas(prev.X, labels_of(prev), last.X, labels, ALPHA)         del prev         X = shift_rows(X, labels[rows], deltas)+    else:+        X = _submatrix(X, np.arange(X.shape[0])) if sp.issparse(X) else np.asarray(X)     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

stream

Looking at the results, round 1 scored lower (51.63 vs 51.76). The key insight: Program 1 achieves covariation 51.96 with simple weighted sampling (no binning), while our stratified binning gets only 49.40-49.94. The distance-based binning is disrupting gene-gene correlation structure. I'll switch to simple weighted sampling while keeping the delta shift.

<<<<<<< SEARCH
ALPHA = 0.03
N_BINS = 12
MIN_CELLS_FOR_BINNING = 15
=======
ALPHA = 0.03
>>>>>>> REPLACE

<<<<<<< SEARCH
def family_weight(name: str) -> float:
    if _HEART_PAT.search(name):
        return 1.6
    if _REDUCED_PAT.search(name):
        return 0.15
    if _GUT_PAT.search(name):
        return 0.9
    return 1.0
=======
def family_weight(name: str) -> float:
    if _HEART_PAT.search(name):
        return 1.5
    if _REDUCED_PAT.search(name):
        return 0.2
    if _GUT_PAT.search(name):
        return 0.9
    return 1.0
>>>>>>> REPLACE

<<<<<<< SEARCH
def _submatrix(X, mask):
    sub = X[mask]
    if sp.issparse(sub):
        return np.asarray(sub.todense())
    return np.asarray(sub)


def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):
=======