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节点 n19

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 6c576269c5: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 +9 −8、solution/run.py +36 −4

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex b223ed5..d5096db 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. ALPHA 从 0.04 降至 0.015：第 0 轮结果显示 delta 位移在组成重加权已提供 direction 信号的情况下，主要贡献是损害 covariation（49.68 vs Program 1 纯重加权的 51.96）。大幅降低 ALPHA 以最小化位移噪声对协方差结构的破坏，同时保留极小位移作为方向微调。+2. 心脏权重从 1.45 提升至 1.55，神经/外表权重从 0.25 降至 0.18：加强组成偏移信号。Program 1 用 1.6/0.15 得到 direction 53.15 和 covariation 51.96，我们取略保守值以平衡。+3. 肠系权重从 0.92 提升至 0.95：微调，避免过度压制。+4. N_BINS 从 10 增至 12：更精细的距离分箱，更好保留类型内表达云的协方差结构。+5. size_factor 分母从 30 调至 40：折中，避免大类型位移过大（ALPHA 已很低，此项影响极小）。 ## 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）-- 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）+- 实验表 Program 1（纯组成重加权，无 delta，covariation 51.96）vs 本节点（有 delta，covariation 49.68）的对比，证明 delta 损害 covariation+- 方法卡 k018（shrinkage alpha 应保守）+- 方向库 T1-01（组成重加权）+ T1-12（保协方差生成）+- 父节点 ANALYSIS（size_factor 分母调参建议）diff --git a/solution/run.py b/solution/run.pyindex cf7d1ea..7743855 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 @@ -25,10 +26,34 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.03-N_BINS = 10+ALPHA = 0.015+N_BINS = 12 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 1.55+    if _REDUCED_PAT.search(name):+        return 0.18+    if _GUT_PAT.search(name):+        return 0.95+    return 1.0+  def _submatrix(X, mask):     sub = X[mask]@@ -70,7 +95,7 @@ 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 + 40.0)             shrink = delta**2 / (delta**2 + se2 + 1e-10)             deltas[t] = alpha * size_factor * shrink * delta         else:@@ -81,15 +106,22 @@ def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha): def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Generator) -> np.ndarray:     unique, counts = np.unique(labels, return_counts=True)     n_total = len(labels)++    type_weights = np.array([family_weight(str(u)) for u in unique], dtype=np.float64)+    weighted_counts = counts.astype(np.float64) * type_weights+    if weighted_counts.sum() == 0:+        weighted_counts = counts.astype(np.float64)+    props = weighted_counts / weighted_counts.sum()+     if n_target >= n_total:         return np.arange(n_total)-    props = counts / n_total     n_per_type = np.floor(props * n_target).astype(int)     remainder = n_target - n_per_type.sum()     frac = (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)+    n_per_type = np.maximum(n_per_type, 0)     indices = []     for u, n_take in zip(unique, n_per_type):         if n_take <= 0:

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 from round 1:
- Score improved: 51.91 → 52.06 (+0.15)
- Covariation improved: 49.68 → 50.51 (+0.83) — still weakest group
- X3 improved: 47.56 → 49.28 (+1.72)
- Direction strongest: 53.91
- proxy dropped slightly: 54.09 → 53.46

Key insight: The delta shift at ALPHA=0.015 is still hurting covariation compared to Program 1 (no delta, covariation 51.96). The composition reweighting is the main driver. Let me try removing the delta entirely (ALPHA=0) to maximize covariation, and add a small noise injection to avoid exact duplicates from resampling (which can hurt covariation metrics). Also, I'll add per-type composition trend extrapolation when two stages are available, which should help direction without damaging covariation.

Actually, looking more carefully: Program 1 (pure reweighting, no delta, no binning) got covariation 51.96. Our binning should help covariation, but the delta hurts it. Let me try ALPHA=0 (pure reweighting + binning) and see if we can beat Program 1's covariation while keeping direction from reweighting.

But wait — the task says "improve" node, and the parent's ANALYSIS suggests ablation. Let me try a different approach: keep a tiny ALPHA but add gene-level noise to break exact correlations that the scorer penalizes. Actually, better: use per-gene shrinkage more aggressively so only the most reliable genes get shifted, reducing covariation damage.

Let me try: ALPHA=0 (remove delta entirely) + add composition trend extrapolation using two stages when available (logit-linear like Program 3 but with better shrinkage). This combines Program 1's strength (composition) with Program 3's trend idea, while keeping our binning for covariation.

Actually, simplest high-impact move: set ALPHA=0 to eliminate delta damage to covariation, and add a mild per-type composition drift based on the two-stage trend. The composition reweighting already gives direction signal.

<<<<<<< SEARCH
ALPHA = 0.015
N_BINS = 12
MIN_CELLS_FOR_BINNING = 20
=======
ALPHA = 0.0
N_BINS = 12
MIN_CELLS_FOR_BINNING = 20
COMPOSITION_DRIFT = 0.3
>>>>>>> REPLACE

<<<<<<< SEARCH
def family_weight(name: str) -> float:
    if _HEART_PAT.search(name):
        return 1.55
    if _REDUCED_PAT.search(name):
        return 0.18
    if _GUT_PAT.search(name):
        return 0.95
    return 1.0
=======
def family_weight(name: str) -> float:
    if _HEART_PAT.search(name):