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节点 n30

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 3ed9cbb827: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 34254aa790。改动的文件：solution/METHOD.md +2 −2、solution/run.py +25 −6

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 58fac39..601de6d 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,4 +1,4 @@ ## 改了什么-从 pseudobulk_shift 改为组成重加权（T1-01 方向）。核心变化：(1) 使用 include_external=False 避免 proxy2 中 Qiu E9.0 标签不匹配导致的崩溃（原程序 proxy2 仅 27.43）；(2) 按正则模式将细胞类型分为 heart（×1.6）、reduced（×0.15，含 surface ectoderm/EXEM/paraxial/neural）、gut（×0.9）、neutral（×1.0）四族，用加权抽样替代均匀抽样，不改表达值；(3) 对 X3 等无官方输入的视图有退路（回退到全部输入）。权重规则按名称模式定义，不写死特定阶段的名字。+将 with-replace 加权抽样改为按类型分层的 without-replace 抽样：先按家族权重计算每型目标细胞数，再在每型内无放回抽样（不足时取全部），剩余名额从未选细胞中补齐。不加任何表达位移。理由：Program 1 用同样的分层无放回抽样在 de_recovery 上得到 51.33（vs 本节点 with-replace 的 49.35），且无放回避免了重复细胞导致的经验分布失真，应同时有利于 covariation。 ## 用到的知识与出处-方向库 T1-01（种子 heart_jcf_peri：心脏类 ×1.6，表面外胚层/EXEM/轴旁中胚层 ×0.25，丢 Neural Tube；proxy 55.97）；方法卡 T1 标签节（E8.5 独有名应降权或丢弃）；k012（cell_state 权重 30%，组成匹配是关键得分项）。+Program 1 实验结果（de_recovery 51.33、direction 54.01，分层无放回 + EB 位移）；父节点 9 配置（权重 1.6/0.15/0.9）；第 0 轮反馈证明 EB 位移在 with-replace 下破坏 covariation，故本轮只用无放回抽样、不加位移。diff --git a/solution/run.py b/solution/run.pyindex 4710196..05ac101 100644--- a/solution/run.py+++ b/solution/run.py@@ -70,12 +70,31 @@ def main() -> None:     rng = np.random.default_rng(args.seed)     n_target = target_n_cells(manifest, last.n_obs) -    weights = np.array([family_weight(str(l)) for l in labels], dtype=np.float64)-    if weights.sum() == 0:-        weights[:] = 1.0-    weights /= weights.sum()--    rows = rng.choice(last.n_obs, size=n_target, replace=True, p=weights)+    unique_labels, counts = np.unique(labels, return_counts=True)+    n_total = len(labels)+    raw_w = np.array([family_weight(str(u)) for u in unique_labels], dtype=np.float64)+    target_props = (counts / n_total) * raw_w+    target_props /= target_props.sum()+    n_per_type = np.floor(target_props * n_target).astype(int)+    remainder = n_target - n_per_type.sum()+    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)+    indices = []+    for u, n_take in zip(unique_labels, 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)+        else:+            indices.append(rng.choice(type_idx, size=n_take, replace=False))+    rows = np.concatenate(indices)+    if len(rows) < n_target:+        remaining = np.setdiff1d(np.arange(n_total), rows)+        extra = rng.choice(remaining, size=n_target - len(rows), replace=False)+        rows = np.concatenate([rows, extra])     X = last.X[rows]     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, round 1 confirmed the parent configuration (51.47). The weakest group is de_recovery (49.35). Program 1 achieved de_recovery 51.33 using without-replace stratified sampling. I'll switch to that sampling strategy while keeping the proven weights and no deltas.

<<<<<<< SEARCH
    weights = np.array([family_weight(str(l)) for l in labels], dtype=np.float64)
    if weights.sum() == 0:
        weights[:] = 1.0
    weights /= weights.sum()

    rows = rng.choice(last.n_obs, size=n_target, replace=True, p=weights)
    X = last.X[rows]
    write_prediction(X, genes, args.out, seed=args.seed)
=======
    unique_labels, counts = np.unique(labels, return_counts=True)
    n_total = len(labels)
    raw_w = np.array([family_weight(str(u)) for u in unique_labels], dtype=np.float64)
    target_props = (counts / n_total) * raw_w
    target_props /= target_props.sum()
    n_per_type = np.floor(target_props * n_target).astype(int)
    remainder = n_target - n_per_type.sum()
    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)
    indices = []
    for u, n_take in zip(unique_labels, 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)
        else:
            indices.append(rng.choice(type_idx, size=n_take, replace=False))
    rows = np.concatenate(indices)
    if len(rows) < n_target:
        remaining = np.setdiff1d(np.arange(n_total), rows)
        extra = rng.choice(remaining, size=n_target - len(rows), replace=False)
        rows = np.concatenate([rows, extra])
    X = last.X[rows]
    write_prediction(X, genes, args.out, seed=args.seed)
>>>>>>> REPLACE