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节点 n39

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 358081d22c: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 8072472c25。改动的文件：solution/METHOD.md +4 −6、solution/run.py +30 −7

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 205b747..53c9e26 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,10 +1,8 @@ ## 改了什么-1. 修复崩溃：将 `del prev, Xd_last, Xd_prev` 从逐细胞缩放循环之前移到之后。第 1 轮引入的逐细胞缩放需要 `Xd_last` 计算类型质心，但原位置在循环前就删除了它，导致 X3 视图（单阶段回退路径不进入此分支，但两阶段路径触发）报 UnboundLocalError。-2. 保留第 0 轮全部改动（ALPHA_MAX=0.80、GENE_SHRINK_POWER=2.0、GENE_SHRINK_K=3.0、SNR_SIGNIFICANT_THRESHOLD=1.5、符号不一致处理）。-3. 保留第 1 轮的逐细胞缩放（CELL_SCALE_MIN=0.7, CELL_SCALE_MAX=1.3）和放宽的符号不一致参数（SIGN_DISAGREE_FACTOR=0.95, SIGN_DISAGREE_THRESHOLD=0.40）。+1. 修复崩溃：单阶段回退路径（proxy 视图）中 `labels_last` 未定义（只在 `len(stages) >= 2` 分支内赋值），改为在 `len(stages) < 2` 分支内独立调用 `labels_of(last)` 获取标签。同时移除对 `Xd_last` 的引用（该路径不需要全量密集矩阵，SVD 只用采样后的 `X` 子矩阵）。+2. 保留第 0 轮全部改动：ALPHA_MAX=0.90、SNR_SIGNIFICANT_THRESHOLD=1.2、CELL_SCALE [0.6,1.4]、分位基准 10%/90% 加 clip、单阶段 SVD 低秩重建（残差放大 0.15）。  ## 用到的知识与出处-- 第 1 轮崩溃日志：`UnboundLocalError: cannot access local variable 'Xd_last'`，原因是 `del` 在使用前执行。-- 方法卡 k018：逐基因经验贝叶斯收缩使噪声基因少动，保护协方差。-- 实验表 Program 1（node 22/23）：GENE_SHRINK_POWER=2.5、K=3.0、符号不一致处理在 covariation 上优于本节点父代。+- 第 0 轮崩溃日志：`UnboundLocalError: cannot access local variable 'labels_last'`，原因是 `labels_last` 只在两阶段分支赋值，单阶段分支引用了它。+- 父节点 ANALYSIS：proxy/proxy2 分数在所有 improve 节点中停在 50.04 未动，单阶段路径从未被改进过。 - 任务书 §5：预测的是一群细胞，要保留群体的分布、比例和基因之间的共变结构。diff --git a/solution/run.py b/solution/run.pyindex 8107870..dd34ab2 100644--- a/solution/run.py+++ b/solution/run.py@@ -28,16 +28,16 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA_MAX = 0.80+ALPHA_MAX = 0.90 ALPHA_MIN = 0.1 SIGN_THRESHOLD = 0.05 GENE_SHRINK_POWER = 2.0 GENE_SHRINK_K = 3.0-SNR_SIGNIFICANT_THRESHOLD = 1.5+SNR_SIGNIFICANT_THRESHOLD = 1.2 SIGN_DISAGREE_FACTOR = 0.95 SIGN_DISAGREE_THRESHOLD = 0.40-CELL_SCALE_MAX = 1.3-CELL_SCALE_MIN = 0.7+CELL_SCALE_MAX = 1.4+CELL_SCALE_MIN = 0.6   def _to_dense(X):@@ -133,10 +133,10 @@ def main() -> None:                     cell_sub = X[mask][:, nonzero_genes]                     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)+                    p_lo = np.percentile(proj_scores, 10)+                    p_hi = np.percentile(proj_scores, 90)                     span = p_hi - p_lo + 1e-12-                    norm_proj = (proj_scores - p_lo) / span+                    norm_proj = np.clip((proj_scores - p_lo) / span, 0.0, 1.0)                     scale = CELL_SCALE_MIN + (CELL_SCALE_MAX - CELL_SCALE_MIN) * norm_proj                     X[mask][:, nonzero_genes] += delta_t[nonzero_genes][np.newaxis, :] * scale[:, np.newaxis]                     zero_genes = ~nonzero_genes@@ -147,6 +147,29 @@ def main() -> None:          del prev, Xd_last, Xd_prev +    if len(stages) < 2:+        labels_all = labels_of(last)+        labels_sampled = labels_all[rows]+        for t in np.unique(labels_sampled):+            mask = labels_sampled == t+            n_t = mask.sum()+            if n_t < 3:+                continue+            sub = X[mask]+            centered = sub - sub.mean(axis=0, keepdims=True)+            cov_diag = np.var(centered, axis=0)+            top_genes = np.argsort(cov_diag)[::-1][:200]+            if len(top_genes) < 2:+                continue+            C_sub = centered[:, top_genes]+            U, S, Vt = np.linalg.svd(C_sub, full_matrices=False)+            k_keep = min(10, len(S))+            S = S.copy()+            S[k_keep:] = 0.0+            reconstructed = U[:, :k_keep] * S[:k_keep] @ Vt[:k_keep]+            residual_scale = 0.15+            X[mask][:, top_genes] = sub.mean(axis=0)[top_genes] + reconstructed * (1.0 + residual_scale)+     np.clip(X, 0.0, None, out=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, the SVD reconstruction helped proxy/proxy2 (+0.36) and covariation (+0.74). The key issue is that the current approach *replaces* the top genes with the low-rank approximation, discarding per-cell noise. A blend approach (adding a fraction of the low-rank signal on top of original values) should better preserve cell_state while still expanding covariance. I'll also increase k_keep and residual_scale slightly.

<<<<<<< SEARCH
    if len(stages) < 2:
        labels_all = labels_of(last)
        labels_sampled = labels_all[rows]
        for t in np.unique(labels_sampled):
            mask = labels_sampled == t
            n_t = mask.sum()
            if n_t < 3:
                continue
            sub = X[mask]
            centered = sub - sub.mean(axis=0, keepdims=True)
            cov_diag = np.var(centered, axis=0)
            top_genes = np.argsort(cov_diag)[::-1][:200]
            if len(top_genes) < 2:
                continue
            C_sub = centered[:, top_genes]
            U, S, Vt = np.linalg.svd(C_sub, full_matrices=False)
            k_keep = min(10, len(S))
            S = S.copy()
            S[k_keep:] = 0.0
            reconstructed = U[:, :k_keep] * S[:k_keep] @ Vt[:k_keep]
            residual_scale = 0.15
            X[mask][:, top_genes] = sub.mean(axis=0)[top_genes] + reconstructed * (1.0 + residual_scale)
=======
    if len(stages) < 2:
        labels_all = labels_of(last)
        labels_sampled = labels_all[rows]
        for t in np.unique(labels_sampled):
            mask = labels_sampled == t
            n_t = mask.sum()
            if n_t < 3:
                continue
            sub = X[mask]
            centered = sub - sub.mean(axis=0, keepdims=True)
            cov_diag = np.var(centered, axis=0)
            top_genes = np.argsort(cov_diag)[::-1][:300]
            if len(top_genes) < 2:
                continue