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节点 n8

node6机制+类型内典型性加权提升DE恢复

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

没有 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 b6980f6761。改动的文件：solution/run.py +101 −18

diff --git a/solution/run.py b/solution/run.pyindex f3a0f25..6c69ddb 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,13 +1,26 @@ #!/usr/bin/env python3-"""pseudobulk_shift: latest stage + per-cell-type pseudobulk delta of the last step.+"""copy_last_official + two-level proliferation reweighting + type-typicality weighting. -The delta is mean(last|type) - mean(prev|type) over the two latest inputs,-computed on the full stages and added once to a subsample of the latest stage-(clipped at 0). Types missing from the earlier stage are copied unchanged.+Base (node 6 mechanism): take the latest *official* input stage (external+inputs are never copied directly), score each cell by the mean log1p+expression of 9 cell-cycle genes, and down-weight fast-cycling cells at two+levels: -With a single input stage (T1 proxy: E8.5 only) there is no step to take a-delta from, so this falls back to copy_last with the same sampling. The proxy-therefore cannot tell this seed from copy_last; that gap is expected.+    w_type = clip(1 + BETA_TYPE * (prolif_type - mean), 0.05)+    w_cell = clip(1 + BETA_CELL * (prolif_i - prolif_type), 0.05)++New (this node): a typicality factor that favours cells close to their own+type centroid, measured only on the type's top-500 DE genes (largest+|type pseudobulk - rest pseudobulk|):++    w_typ = clip(cos(cell, centroid), 0, 1) ** GAMMA++Final weight W = w_type * w_cell * w_typ; cells drawn by Efraimidis-Spirakis+weighted sampling without replacement (keys u^(1/W), top-n), deterministic+given --seed. Expression values are never modified.++Types with < 20 cells skip the typicality factor. With a single input stage+everything is computed from that stage; no second time point is needed. """  from __future__ import annotations@@ -15,40 +28,110 @@ from __future__ import annotations import argparse  import numpy as np+from scipy import sparse -from src.task1_temporal.baselines import shift_rows, type_deltas from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) +CELL_CYCLE_GENES = [+    "Mki67", "Top2a", "Ccnb1", "Cdk1", "Pcna", "Mcm2", "Ccna2", "Cenpf", "Bub1",+]+BETA_TYPE = -4.0+BETA_CELL = -1.0+GAMMA = 0.5+TOP_DE = 500+MIN_CELLS_FOR_TYP = 20+++def es_sample(W: np.ndarray, n: int, rng: np.random.Generator) -> np.ndarray:+    """Efraimidis-Spirakis weighted sampling without replacement."""+    if n >= len(W):+        if n <= len(W):+            return np.arange(len(W))+        extra = rng.choice(len(W), size=n - len(W), replace=True, p=W / W.sum())+        return np.sort(np.concatenate([np.arange(len(W)), extra]))+    keys = rng.random(len(W)) ** (1.0 / np.maximum(W, 1e-12))+    idx = np.argpartition(-keys, n)[:n]+    return np.sort(idx)+++def proliferation(X: sparse.csr_matrix, genes: list[str]) -> np.ndarray | None:+    pos = {g: i for i, g in enumerate(genes)}+    cols = [pos[g] for g in CELL_CYCLE_GENES if g in pos]+    if not cols:+        return None+    sub = np.asarray(X[:, cols].todense(), dtype=np.float64)+    return sub.mean(axis=1)+++def typicality_weights(X: sparse.csr_matrix, labels: np.ndarray, gamma: float) -> np.ndarray:+    w = np.ones(X.shape[0], dtype=np.float64)+    if gamma <= 0:+        return w+    Xd = X.astype(np.float64)+    total = np.asarray(Xd.sum(axis=0)).ravel()+    n = X.shape[0]+    for t in np.unique(labels):+        m = labels == t+        k = int(m.sum())+        if k < MIN_CELLS_FOR_TYP:+            continue+        mean_t = total * 0.0+        mt = np.asarray(Xd[m].sum(axis=0)).ravel() / k+        rest = (total - mt * k) / max(n - k, 1)+        de = np.argsort(-np.abs(mt - rest))[:TOP_DE]+        cent = mt[de]+        cn = np.linalg.norm(cent)+        if cn <= 0:+            continue+        cent = cent / cn+        sub = np.asarray(Xd[np.ix_(m, de)])+        norms = np.linalg.norm(sub, axis=1)+        sim = sub @ cent / np.maximum(norms, 1e-12)+        w[m] = np.clip(sim, 0.0, 1.0) ** gamma+    return w+  def main() -> None:     parser = argparse.ArgumentParser()     parser.add_argument("--data", required=True)     parser.add_argument("--out", required=True)     parser.add_argument("--seed", type=int, default=0)+    parser.add_argument("--gamma", type=float, default=GAMMA)     args = parser.parse_args()      manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    stages = inputs_by_time(manifest)+    stages = inputs_by_time(manifest, include_external=False)+    if not stages:+        stages = inputs_by_time(manifest)     last = read_stage(args.data, stages[-1], genes)+    labels = labels_of(last)+    X = last.X     rng = np.random.default_rng(args.seed)-    rows = sample_rows(last.n_obs, target_n_cells(manifest, last.n_obs), rng)-    X = last.X[rows]-    if len(stages) >= 2:-        prev = read_stage(args.data, stages[-2], genes)-        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels_of(last))-        del prev-        X = shift_rows(X, labels_of(last)[rows], deltas)-    write_prediction(X, genes, args.out, seed=args.seed)+    n_out = target_n_cells(manifest, X.shape[0])++    W = np.ones(X.shape[0], dtype=np.float64)+    prolif = proliferation(X, genes)+    if prolif is not None:+        gmean = prolif.mean()+        for t in np.unique(labels):+            m = labels == t+            pt = prolif[m].mean()+            w_type = max(1.0 + BETA_TYPE * (pt - gmean), 0.05)+            w_cell = np.clip(1.0 + BETA_CELL * (prolif[m] - pt), 0.05, None)+            W[m] = w_type * w_cell++    W *= typicality_weights(X, labels, args.gamma)+    rows = es_sample(W, n_out, rng)+    write_prediction(X[rows], genes, args.out, seed=args.seed)   if __name__ == "__main__":

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）。

用到的知识库条目

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

工程师

Now I'll write the solution: node6 mechanism (copy_last_official + two-level proliferation weighting) plus typicality weighting per PLAN.

调研员

{"name":"node6机制+类型内典型性加权提升DE恢复","motivation":"节点6（rank3 54.08）四组中de_recovery最弱（50.65），仅比基线50高0.65，说明纯组成重加权未触及DE信号。节点2/5证明表达值平移有害，但抽样选择仍有空间：当前β_cell=-1仅按增殖打分选细胞，未考虑细胞对其类型DE谱的代表性。若优先抽取更贴近类型质心（即DE模式更清晰）的细胞，可在不改动表达值的前提下增强类型间DE对比度，针对性提升de_recovery。","approach":"1）基座完全复用节点6：copy_last_official（只取最新官方输入阶段）+ 类型级增殖权重 w_type=1+β_type·(prolif_type−mean)，β_type=−4 + 细胞级增殖权重 w_cell=clip(1+β_cell·(prolif_i−type_mean),0.05)，β_cell=−1；9个cell-cycle基因log1p均值作增殖得分；Efraimidis–Spirakis加权无放回抽样。2）新增类型内典型性权重：对每个类型c，在面板基因中取该类型伪批量与其余类型伪批量差值绝对值最大的top-500基因作为DE特征子集；计算每个细胞i到其类型质心的余弦相似度 sim_i（仅用DE特征子集）；典型性权重 w_typ = sim_i^γ，γ初值0.5，搜索范围{0,0.25,0.5,1.0}。最终抽样权重 W_i = w_type · w_cell · w_typ。3）单输入退路：proxy只有E8.5、X3同理，方法本身只需单阶段即可运行（质心和DE子集均从该阶段计算），无需第二时间点。proxy2虽提供两个输入，但本方案不依赖第二输入，保持对final（E8.5→E9.5）的直接适用性。4）快速筛选：先在proxy上用γ=0（等价节点6）验证复现≥55，再依次测γ=0.5、0.25、1.0；若γ=0.5在proxy上≥56则用proxy2确认不降；若所有γ均≤55.5（噪声内），放弃典型性权重，仅提交节点6复现。5）实现要点：DE特征子集用numpy argsort，质心用类型均值向量，余弦相似度用归一化点积；总新增代码约30行，运行时间增加可忽略。","expected_groups":["de_recovery"],"risks":"1）典型性加权可能降低类型内多样性，损害covariation（当前53.36）；Engineer应在γ扫描时同时监控covariation，若降>2分则回退γ=0。2）top-500 DE基因子集可能在小类型（<20细胞）中不稳定；对细胞数<20的类型跳过典型性权重（w_typ=1）。3）de_recovery评测可能依赖细胞数而非DE清晰度，此时任何抽样策略均无效；若γ扫描全部在噪声内（±2分），应尽早放弃并只提交节点6复现以节省查分次数。","sources":[]}