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节点 n22

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 157a474027: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 +7 −6、solution/run.py +31 −4

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex b223ed5..6220568 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,10 +1,11 @@ ## 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. 新增组成重加权（composition reweighting）：基于发育生物学知识（心脏谱系在 E8.5→E10.5 期间扩增，神经/表面外胚层相对缩减），对心脏相关类型权重 1.5、神经/表面外胚层类型权重 0.2、其余 1.0。这改变输出中各类型的比例，直接提升 cell_state 和 direction 分数（参考 Program 1 的成功）。+2. ALPHA 从 0.03 降至 0.01：组成变化已提供方向信号，极小的位移避免破坏 covariation。+3. size_factor 分母从 50 改为 20（SIZE_FACTOR_K=20）：按父节点 ANALYSIS 建议，减弱大类型的收缩，让有限的位移信号更多保留（针对 de_recovery）。+4. 保留分箱分层抽样：在重加权后的类型配额内仍用距离分箱抽样，保持类型内表达云的协方差结构（比 Program 1 的简单有放回抽样更保协方差）。 ## 用到的知识与出处 - 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）+- 父节点 ANALYSIS 的 next_suggestions（size_factor 分母 50→20；针对 covariation 的改进方向）+- 方向库 T1-01（组成重加权，run2 胜者） - 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）+- 通用发育生物学知识：心脏谱系（SHF、FHF 衍生类型）在 E8.5-E10.5 快速增殖扩增；神经管/表面外胚层在此窗口相对比例下降（来源：经典发育生物学教材，Gilbert's Developmental Biology）diff --git a/solution/run.py b/solution/run.pyindex cf7d1ea..40b31e6 100644--- a/solution/run.py+++ b/solution/run.py@@ -9,6 +9,7 @@ and applies shrinkage alpha=0.5 to deltas per method card k018. from __future__ import annotations  import argparse+import re  import numpy as np @@ -25,9 +26,28 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.03+ALPHA = 0.01 N_BINS = 10 MIN_CELLS_FOR_BINNING = 20+SIZE_FACTOR_K = 20.0++_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,+)+++def type_weight(name: str) -> float:+    if _HEART_PAT.search(name):+        return 1.5+    if _REDUCED_PAT.search(name):+        return 0.2+    return 1.0   def _submatrix(X, mask):@@ -70,7 +90,7 @@ def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):             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)+            size_factor = n_eff / (n_eff + SIZE_FACTOR_K)             shrink = delta**2 / (delta**2 + se2 + 1e-10)             deltas[t] = alpha * size_factor * shrink * delta         else:@@ -84,9 +104,16 @@ def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Gener     if n_target >= n_total:         return np.arange(n_total)     props = counts / n_total-    n_per_type = np.floor(props * n_target).astype(int)+    weights = np.array([type_weight(str(u)) for u in unique], dtype=np.float64)+    weighted_props = props * weights+    wp_sum = weighted_props.sum()+    if wp_sum > 0:+        weighted_props /= wp_sum+    else:+        weighted_props = props.copy()+    n_per_type = np.floor(weighted_props * n_target).astype(int)     remainder = n_target - n_per_type.sum()-    frac = (props * n_target) - n_per_type+    frac = (weighted_props * n_target) - n_per_type     top_idx = np.argsort(-frac)[:remainder]     n_per_type[top_idx] += 1     n_per_type = np.minimum(n_per_type, counts)

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 0 (stratified + reweighting): score 51.56, covariation 49.86, de_recovery 51.31, direction 53.73
- Round 1 (weighted replacement + reweighting): score 51.18, covariation 50.01, de_recovery 49.35, direction 53.38

Round 1 lost de_recovery (51.31→49.35) and direction (53.73→53.38) but gained covariation (49.86→50.01) and cell_state (51.08→51.66). The de_recovery drop is significant (>2 points noise). The delta shift with ALPHA=0.02 and weighted sampling seems less effective.

Key insight: Round 0 had better de_recovery because stratified sampling without replacement gives each cell equal chance within its type, making the per-type delta more meaningful. But it had worse covariation due to proportion capping.

A hybrid approach: use weighted sampling with replacement (good for covariation) but increase ALPHA to compensate for de_recovery loss. Also, the X3 score dropped significantly (49.41→46.44), suggesting the composition weights hurt X3 (which has different cell types). I should make the weights more conservative and also add NCC to the heart pattern since it's cardiac-related.

Let me try: increase ALPHA to 0.05 (between rounds), add a mild per-cell noise injection to improve covariation, and adjust weights to be less aggressive.

<<<<<<< SEARCH
ALPHA = 0.02
SIZE_FACTOR_K = 20.0
=======
ALPHA = 0.05
SIZE_FACTOR_K = 20.0
NOISE_SCALE = 0.02
>>>>>>> REPLACE

<<<<<<< SEARCH
def type_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 type_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