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节点 n48

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 81803f4bf5: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 8341756e9e。改动的文件：solution/METHOD.md +6 −6、solution/run.py +35 −5

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex eeace32..6c17ad7 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,9 +1,9 @@ ## 改了什么-1. 将全局统一收缩（DELTA_ALPHA=0.6 乘所有基因）替换为基于信噪比（SNR）的逐基因收缩：对每个类型，计算伪批量差值的 SNR（|delta| / 均值差的抽样标准误），基因级收缩因子 = (snr / (snr + K))^power。SNR 低的基因（噪声主导）被大幅压缩，SNR 高的基因（真实信号）保留大部分位移。全局 alpha=0.6 仍作为基础缩放。-2. 预期效果：减少噪声基因对表达分布的扰动，改善 covariation（当前最弱组 42.60）和 de_recovery（50.62），同时保持 direction 分数。-3. 仅对两阶段差值计算做此改进；单阶段（proxy）退化为 copy_last，不受影响。+1. 采用 Program 1（score 53.10）的逐细胞投影缩放机制：对每个类型，将细胞投影到 delta 方向上，根据投影位置在 [CELL_SCALE_MIN=0.75, CELL_SCALE_MAX=1.25] 之间线性缩放该细胞实际接受的位移量。已在 delta 方向前端的细胞获得更多位移，后端获得较少，从而保留类型内的协方差结构（covariation 从 49.53 预期提升至 ~53）。+2. SHRINK_K 从 3.0 调为 2.5（Program 1 验证过的值），略微放宽收缩以改善 de_recovery。+3. 移除未使用的 SNR_THRESHOLD 常量和对 baselines.shift_rows 的依赖，改为手动逐类型应用带缩放的位移。+4. 对输出做 np.clip(0, None) 确保非负。 ## 用到的知识与出处+- Program 1（score 53.10，covariation 53.35）：逐细胞投影缩放是其核心机制，相比均匀位移在 covariation 上高出约 4 分 - k018：「Optionally shrink per gene (empirical Bayes on the delta's variance) so noisy genes move less」-- Program 1（score 50.09，covariation 50.11）：SNR-based per-gene shrinkage 是其核心机制，证明该策略对 covariation 有显著帮助-- 第 1 轮结果：DELTA_ALPHA=0.6 已将 covariation 从 41.31 提升到 42.60，逐基因收缩预期进一步提升-- 方法卡 T1：「同名型的差值要乘收缩系数 α∈[0,1]，不能默认 α=1」+- 父节点 ANALYSIS next_suggestions：「针对 de_recovery：把高 SNR 基因的收缩上限放宽…把 DELTA_ALPHA 提到 0.7~0.8」——本次先通过降低 SHRINK_K 实现，后续可进一步调diff --git a/solution/run.py b/solution/run.pyindex aae46d9..8c5cda9 100644--- a/solution/run.py+++ b/solution/run.py@@ -13,7 +13,6 @@ import argparse  import numpy as np -from src.task1_temporal.baselines import shift_rows, type_deltas from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -36,9 +35,10 @@ DOWNWEIGHT_W = 0.25 DROP_W = 0.0 DEFAULT_W = 1.0 DELTA_ALPHA = 0.6-SNR_THRESHOLD = 1.5-SHRINK_K = 3.0+SHRINK_K = 2.5 SHRINK_POWER = 2.0+CELL_SCALE_MAX = 1.25+CELL_SCALE_MIN = 0.75   def family_weight(name: str) -> float:@@ -142,8 +142,38 @@ def main() -> None:             gene_shrink = (snr / (snr + SHRINK_K)) ** SHRINK_POWER             deltas[t] = (raw_delta * DELTA_ALPHA * gene_shrink).astype(np.float32) -        del prev, Xd_last, Xd_prev-        X = shift_rows(X, labels[rows], deltas)+        Xd_last_full = Xd_last+        del prev, Xd_prev++        X = _dense(X)+        labels_sampled = labels[rows]+        for t in np.unique(labels_sampled):+            if t not in deltas:+                continue+            mask_s = labels_sampled == t+            delta_t = deltas[t]+            nonzero = np.abs(delta_t) > 1e-8+            if nonzero.sum() > 1 and mask_s.sum() > 2:+                centroid = Xd_last_full[labels == t][:, nonzero].mean(axis=0)+                proj_dir = delta_t[nonzero]+                proj_norm = np.linalg.norm(proj_dir) + 1e-12+                proj_dir = proj_dir / proj_norm+                cell_sub = X[mask_s][:, nonzero]+                centered = cell_sub - centroid[np.newaxis, :]+                proj_scores = centered @ proj_dir+                p_lo = np.percentile(proj_scores, 5)+                p_hi = np.percentile(proj_scores, 95)+                span = p_hi - p_lo + 1e-12+                norm_proj = (proj_scores - p_lo) / span+                scale = CELL_SCALE_MIN + (CELL_SCALE_MAX - CELL_SCALE_MIN) * norm_proj+                X[mask_s][:, nonzero] += delta_t[nonzero][np.newaxis, :] * scale[:, np.newaxis]+                zero_mask = ~nonzero+                if zero_mask.any():+                    X[mask_s][:, zero_mask] += delta_t[zero_mask][np.newaxis, :]+            else:+                X[mask_s] += deltas[t][np.newaxis, :]+        np.clip(X, 0.0, None, out=X)+        del Xd_last_full      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 scores, round 1 produced identical results to round 0 (53.0911), meaning the DELTA_ALPHA and SNR floor changes had no effect on the proxy/proxy2 boards (which only have one input stage and don't enter the delta branch). The X3 board is where two-stage logic matters.

The current score is 53.09, with de_recovery (51.65) and covariation (52.99) as the weakest groups. Looking at Program 1 (score 53.10), it achieves slightly better results with SHRINK_K=2.5 and DELTA_ALPHA=0.6. The key difference is that my round 1 changes didn't help.

Let me try a different approach: increase CELL_SCALE range to add more heterogeneity (helping covariation), and also add per-cell noise injection to prevent distribution collapse. Additionally, I'll try widening the cell scale range and adding a small amount of type-level composition jitter to improve cell_state diversity.

Looking more carefully: the score is already very close to Program 1 (53.10). The main lever left is improving X3 (50.48 vs best 50.45). Let me focus on what differentiates: the composition reweighting is the key driver of proxy scores (54.49 vs 50.04 baseline). For X3, the two-stage delta matters.

Let me try: (1) slightly wider cell scale range for better covariation, (2) add small Gaussian noise proportional to within-type variance to prevent over-smoothing, which should help cell_state and de_recovery.

<<<<<<< SEARCH
DELTA_ALPHA = 0.75
SHRINK_K = 2.0
SHRINK_POWER = 2.0
SNR_FLOOR_THRESHOLD = 3.0
SNR_FLOOR_VALUE = 0.85
CELL_SCALE_MAX = 1.25
CELL_SCALE_MIN = 0.75
=======
DELTA_ALPHA = 0.75
SHRINK_K = 2.0
SHRINK_POWER = 2.0
SNR_FLOOR_THRESHOLD = 3.0
SNR_FLOOR_VALUE = 0.85
CELL_SCALE_MAX = 1.35
CELL_SCALE_MIN = 0.65
NOISE_FRAC = 0.15
>>>>>>> REPLACE

<<<<<<< SEARCH
        np.clip(X, 0.0, None, out=X)
        del Xd_last_full
=======
        for t in np.unique(labels_sampled):
            mask_s = labels_sampled == t
            if mask_s.sum() < 3:
                continue
            type_cells = Xd_last_full[labels == t]
            within_var = type_cells.var(axis=0)
            noise_scale = NOISE_FRAC * np.sqrt(within_var + 1e-12)