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节点 n10

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 08e95b6886: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 85cbd56c26。改动的文件：solution/METHOD.md +6 −7、solution/run.py +20 −7

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex dac9bfd..1db4cfe 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,9 +1,8 @@ ## 改了什么-1. 回退第1轮的HVG距离和N_BINS=8：第1轮实验证明用top-1000 HVG计算距离并使N_BINS=8导致proxy下降0.62（49.53→48.91）、direction下降0.71（49.31→48.60），全面劣于第0轮（全基因距离、N_BINS=5）。恢复第0轮设置。-2. 将α从0.1降至0.05：实验表显示α从0.5→0.3→0.1持续改善X3（42.65→43.77→46.10）和covariation。趋势表明更低α更接近copy_last（proxy最优基线），同时EB逐基因收缩已过滤噪声基因，极小α仍保留真实信号方向。α=0.05是α=0（纯copy）和α=0.1之间的折中。+1. 将类型内采样分箱的距离度量从原始基因空间欧氏距离改为PCA空间距离（top-30主成分）：PCA轴直接捕获基因间共变结构的主方向，按PCA距离分箱采样能更好地保留基因-基因相关性。原始32285维欧氏距离被少数高方差基因主导，不能有效反映共变轴。+2. 将N_BINS改为自适应：effective_bins = min(N_BINS, max(2, n_cells // 25))，确保每箱至少约25个细胞，防止因某些箱内细胞过少导致采样偏差（这可能是N_BINS=8在之前实验中失败的原因之一）。 ## 用到的知识与出处-- 第0轮实验：全基因距离+N_BINS=5得分48.56，优于第1轮HVG+N_BINS=8的48.15-- 第1轮实验：HVG距离使direction和proxy显著下降，证明全基因距离更好-- 方法卡k018：α=1低于copy_last，α∈[0,1]是超参；趋势表明低α更优-- 实验表趋势：α=0.5→0.3→0.1，X3从42.65→43.77→46.10持续改善-- 父节点ANALYSIS：建议做α=0 vs α=0.1消融；α=0.05是中间试探+- 父节点ANALYSIS：covariation对采样分箱最敏感（+2.12来自回退分箱配置），建议微调N_BINS+- 实验表：N_BINS=8+HVG距离导致proxy下降0.62，但原因可能是HVG信息丢失或每箱细胞太少，而非分箱数本身+- 方法卡k018：全基因距离优于HVG距离；PCA使用全部基因但投影到主方差轴，与HVG选择子集不同+- 线性代数：SVD分解获取主成分，PCA得分的范数作为距离度量（标准多元统计方法）diff --git a/solution/run.py b/solution/run.pyindex dc2df30..221882e 100644--- a/solution/run.py+++ b/solution/run.py@@ -26,6 +26,8 @@ from src.task1_temporal.view_io import (  ALPHA = 0.05 N_BINS = 5+PCA_COMPONENTS = 30+MIN_CELLS_PER_BIN = 25   def _submatrix(X, mask):@@ -35,6 +37,17 @@ def _submatrix(X, mask):     return np.asarray(sub)  +def _pca_distances(sub, n_components=PCA_COMPONENTS):+    centered = sub - sub.mean(axis=0, keepdims=True)+    n_cells, n_genes = centered.shape+    k = min(n_components, n_cells - 1, n_genes)+    if k < 2:+        return np.linalg.norm(centered, axis=1)+    U, S, _ = np.linalg.svd(centered, full_matrices=False)+    scores = U[:, :k] * S[:k]+    return np.linalg.norm(scores, axis=1)++ def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):     prev_types = set(np.unique(prev_labels))     last_types = np.unique(last_labels)@@ -94,20 +107,20 @@ def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Gener             continue         if len(type_idx) > 50 and n_take >= 10:             sub = _submatrix(X, type_idx)-            centroid = sub.mean(axis=0)-            dists = np.linalg.norm(sub - centroid, axis=1)-            bin_edges = np.quantile(dists, np.linspace(0, 1, N_BINS + 1)[1:-1])+            dists = _pca_distances(sub)+            effective_bins = min(N_BINS, max(2, len(type_idx) // MIN_CELLS_PER_BIN))+            bin_edges = np.quantile(dists, np.linspace(0, 1, effective_bins + 1)[1:-1])             bin_ids = np.searchsorted(bin_edges, dists)-            per_bin = np.zeros(N_BINS, dtype=int)-            bin_props = np.bincount(bin_ids, minlength=N_BINS) / len(type_idx)+            per_bin = np.zeros(effective_bins, dtype=int)+            bin_props = np.bincount(bin_ids, minlength=effective_bins) / len(type_idx)             per_bin = np.floor(bin_props * n_take).astype(int)             rem = n_take - per_bin.sum()             fr = (bin_props * n_take) - per_bin             for i in np.argsort(-fr)[:rem]:                 per_bin[i] += 1-            per_bin = np.minimum(per_bin, np.bincount(bin_ids, minlength=N_BINS))+            per_bin = np.minimum(per_bin, np.bincount(bin_ids, minlength=effective_bins))             chosen = []-            for b in range(N_BINS):+            for b in range(effective_bins):                 b_idx = type_idx[bin_ids == b]                 nb = min(per_bin[b], len(b_idx))                 if nb > 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

stream

Looking at the results across rounds:

- **Round 0 (PCA/SVD)**: covariation 48.45 (best), proxy 49.44, but runtime 483.9s
- **Round 1 (top-2000 var genes)**: covariation 47.90, proxy 49.13, runtime 7.5s — worse than round 0 on covariation and proxy
- **Parent (full-gene distance)**: covariation 47.63, proxy 49.23, runtime 2.9s

The top-2000 variance genes approach was a bad tradeoff. The key insight from round 0 is that PCA distances genuinely improved covariation (+0.82 over parent). The problem was purely computational.

A fast approximation: use randomized SVD (via `scipy.sparse.linalg.svds`) on the centered matrix truncated to top-k components. For a matrix of shape (n_cells, 32285), `svds` with k=30 is much faster than full SVD. Alternatively, compute the covariance matrix on a random subset of genes.

Actually, the simplest fast approach that captures PCA-like behavior: use `scipy.sparse.linalg.svds` which uses ARPACK and only computes k singular vectors — O(n_cells × n_genes × k) instead of O(n_cells × n_genes × min(n_cells, n_genes)).

But even better: since the matrix is sparse, I can use the sparse SVD directly without densifying. This avoids both the memory and time issues.

Let me use `scipy.sparse.linalg.svds` with k=30 on the sparse centered matrix. Centering a sparse matrix requires care (subtracting mean makes it dense), so instead I'll compute on a gene-subsampled dense matrix: pick top 5000 variance genes (fast O(n) per type), densify only those columns, then SVD with k=30.

Actually, the cleanest approach: densify only top 5000 variance genes per type (fast), then use `np.linalg.svd` on the (n_cells, 5000) matrix with `full_matrices=False` — this is O(n_cells × 5000 × min(n_cells, 5000)) which for typical n_cells ~1000 is fine and much faster than 32K genes.

Let me calculate: parent had runtime 2.9s with full-gene distance (no SVD). Round 0 with SVD on 32K genes was 483.9s. Round 1 with top-2000 genes (no SVD, just Euclidean) was 7.5s. So the bottleneck in round 0 was the SVD itself on the large matrix.

For a type with ~2000 cells and 5000 genes: SVD of (2000, 5000) matrix takes ~2000×5000×2000 = 20 billion operations... that's still slow.

Better approach: use randomized PCA via `sklearn` or just compute distances on top-variance genes but with more genes (5000 instead of 2000) to get closer to the PCA benefit without the SVD cost. The round 0 result showed PCA helped; round 1 with 2000 genes hurt. Maybe 5000 genes is the sweet spot.

Actually wait — the issue might be simpler. Round 1 used top-2000 *within-type* variance genes and got covariation 47.90 vs parent's 47.63 (still +0.27). Round 0 got 48.45. The difference might be that PCA captures correlation structure while raw Euclidean on high-variance genes doesn't.

A cheap PCA approximation: compute on top-variance genes but use SVD on the smaller matrix. For (n_cells, 5000) with n_cells typically < 3000, `svds` with k=30 should be fast (~seconds per type, ~20 types = maybe 30-60s total).

Let me try: top 5000 variance genes + truncated SVD (k=30) via scipy.sparse.linalg.svds. This should give PCA-like benefits with manageable runtime.

<<<<<<< SEARCH
ALPHA = 0.05
N_BINS = 5
N_TOP_VAR_GENES = 2000
MIN_CELLS_PER_BIN = 25


def _submatrix(X, mask):
    sub = X[mask]
    if sp.issparse(sub):