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节点 n15

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 e966b7d074: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 9f9acc3f88。改动的文件：solution/METHOD.md +7 −6、solution/run.py +82 −13

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 5cd82e8..e0ceaeb 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,8 +1,9 @@ ## 改了什么-1. 将收缩系数 α 从 0.3 进一步降低到 0.1。第 1 轮实验显示 α 从 0.5→0.3 使 covariation 提升 +2.07（40.77→42.84）、cell_state 提升 +0.61、X3 提升 +1.08，而 direction 基本不变（-0.07）。趋势表明更低的 α 有利于保持原始分布结构（更接近 copy_last），同时保留少量方向信息。-2. 关键洞察：在 proxy 和 proxy2 视图上，过滤外部阶段后只剩一个官方阶段，差值分支不执行，α 无效；α 只在 final（E8.5+E9.5）和 X3（Qiu E8.75+E9.0）上起作用。X3 的改善（42.65→43.73）直接证明低 α 在两阶段场景下更优。+1. 引入 node 9 的组成重加权机制：按细胞类型名正则匹配分家族（心脏系 1.6×、神经/表面外胚层 0.15×、内胚层 0.9×），将分层采样的目标比例从原始比例改为加权后比例。这在 proxy/proxy2 上应大幅提升（node 9 的 proxy 53.57 vs 当前 49.28），因为目标 E9.5 心脏细胞占比高于 E8.5。+2. 在类型内加入距离分箱采样（N_BINS=8）：按到质心的距离分箱，各箱按比例取样，保留类型内基因共变结构，改善 covariation（当前最弱组 45.51）。+3. 保留 ALPHA=0.1 的伪批量差值收缩，仅在两阶段视图（final、X3）生效。 ## 用到的知识与出处-- 第 1 轮实验结果：α=0.3 相比 α=0.5 在 covariation（+2.07）、cell_state（+0.61）、X3（+1.08）上均有改善，direction 不变-- 方法卡 k018：α=1 低于 copy_last，α∈[0,1] 是超参；α=0 即复制最后阶段-- 任务书 CONTRACT：proxy/proxy2 过滤外部阶段后只有一个官方输入，差值分支不触发-- 父节点 ANALYSIS：建议做 α 消融（α=0 vs α=0.5 vs α=1）+- 实验表 node 9：组成重加权在 proxy 上达 53.57（+4.29），证明心脏系上调、神经下调有效+- 实验表 node 4 ANALYSIS：covariation 是最低组（45.51），建议改进类型内采样质量+- Program 2/3（attempt 2/3）：距离分箱采样改善 covariation（48.3+）+- 方法卡 k018：α=1 低于 copy_last，收缩系数 α∈[0,1]diff --git a/solution/run.py b/solution/run.pyindex 237ede7..7f53108 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,16 +1,18 @@ #!/usr/bin/env python3-"""Stratified copy with shrunk pseudobulk delta on official stages only.+"""Composition reweighting + within-type bin sampling + shrunk delta. -Fixes proxy2 collapse (was sampling external heart-only stage as base),-uses stratified sampling to preserve type proportions (covariation),-and applies shrinkage alpha=0.5 to deltas per method card k018.+Combines node 9's family-based reweighting (heart up, neural down) with+within-type distance-bin sampling for covariation preservation, and+EB-shrunk pseudobulk delta for direction on two-stage views. """  from __future__ import annotations  import argparse+import re  import numpy as np+from scipy import sparse as sp  from src.task1_temporal.baselines import shift_rows, type_deltas from src.task1_temporal.view_io import (@@ -24,17 +26,52 @@ from src.task1_temporal.view_io import ( )  ALPHA = 0.1+N_BINS = 8+MIN_CELLS_FOR_BINNING = 30 +_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 1.6+    if _REDUCED_PAT.search(name):+        return 0.15+    if _GUT_PAT.search(name):+        return 0.9+    return 1.0 -def stratified_sample(labels: np.ndarray, n_target: int, rng: np.random.Generator) -> np.ndarray:++def _submatrix(X, mask):+    sub = X[mask]+    if sp.issparse(sub):+        return np.asarray(sub.todense())+    return np.asarray(sub)+++def weighted_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)-    if n_target >= n_total:-        return np.arange(n_total)-    props = counts / n_total-    n_per_type = np.floor(props * n_target).astype(int)+    raw_weights = np.array([family_weight(str(u)) for u in unique], dtype=np.float64)+    target_props = (counts / n_total) * raw_weights+    target_props /= target_props.sum()+    n_per_type = np.floor(target_props * n_target).astype(int)     remainder = n_target - n_per_type.sum()-    frac = (props * n_target) - n_per_type+    frac = (target_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)@@ -43,8 +80,40 @@ def stratified_sample(labels: np.ndarray, n_target: int, rng: np.random.Generato         if n_take <= 0:             continue         type_idx = np.where(labels == u)[0]-        chosen = rng.choice(type_idx, size=n_take, replace=False)-        indices.append(chosen)+        if n_take >= len(type_idx):+            indices.append(type_idx)+            continue+        if len(type_idx) > MIN_CELLS_FOR_BINNING and n_take >= 5:+            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])+            bin_ids = np.searchsorted(bin_edges, dists)+            bin_props = np.bincount(bin_ids, minlength=N_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))+            chosen = []+            for b in range(N_BINS):+                b_idx = type_idx[bin_ids == b]+                nb = min(per_bin[b], len(b_idx))+                if nb > 0:+                    chosen.append(rng.choice(b_idx, size=nb, replace=False))+            if chosen:+                sel = np.concatenate(chosen)+            else:+                sel = rng.choice(type_idx, size=n_take, replace=False)+            if len(sel) < n_take:+                leftover = np.setdiff1d(type_idx, sel)+                extra = rng.choice(leftover, size=n_take - len(sel), replace=False)+                sel = np.concatenate([sel, extra])+            indices.append(sel)+        else:+            chosen = rng.choice(type_idx, size=n_take, replace=False)+            indices.append(chosen)     result = np.concatenate(indices)     if len(result) < n_target:         remaining_idx = np.setdiff1d(np.arange(n_total), result)@@ -70,7 +139,7 @@ def main() -> None:     labels = labels_of(last)     rng = np.random.default_rng(args.seed)     n_target = target_n_cells(manifest, last.n_obs)-    rows = stratified_sample(labels, n_target, rng)+    rows = weighted_stratified_sample(labels, last.X, n_target, rng)     X = last.X[rows]     if len(official) >= 2:         prev = read_stage(args.data, official[-2], genes)

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: score 50.83, covariation 46.91 still weakest. Programs 2/3 with N_BINS=10-12 achieved covariation ~48.3. Node 9's `replace=True` gave proxy 53.57 vs our 53.32. I'll increase bins, lower threshold, and allow oversampling with replacement.

<<<<<<< SEARCH
ALPHA = 0.1
N_BINS = 8
MIN_CELLS_FOR_BINNING = 30
=======
ALPHA = 0.1
N_BINS = 10
MIN_CELLS_FOR_BINNING = 20
>>>>>>> REPLACE

<<<<<<< SEARCH
    n_per_type = np.minimum(n_per_type, counts)
    indices = []
    for u, n_take in zip(unique, n_per_type):
        if n_take <= 0:
            continue
        type_idx = np.where(labels == u)[0]
        if n_take >= len(type_idx):
            indices.append(type_idx)
            continue
        if len(type_idx) > MIN_CELLS_FOR_BINNING and n_take >= 5:
=======
    n_per_type_raw = np.minimum(n_per_type, counts)
    indices = []
    for u, n_take in zip(unique, n_per_type_raw):
        if n_take <= 0:
            continue
        type_idx = np.where(labels == u)[0]
        if n_take > len(type_idx):
            indices.append(rng.choice(type_idx, size=n_take, replace=True))
            continue
        if n_take == len(type_idx):
            indices.append(type_idx)
            continue
        if len(type_idx) > MIN_CELLS_FOR_BINNING and n_take >= 5:
>>>>>>> REPLACE

```METHOD