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节点 n44

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 87e28b25cf: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：这个提交的上一版（种子程序：相对空仓库）。改动的文件：solution/METHOD.md +14 −0、solution/README.md +4 −0、solution/run.py +148 −0

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..3a71246--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,14 @@+## 改了什么+1. 修复 X3 崩溃：当所有输入都是 external（X3 视图）时，回退使用全部输入而非空列表导致 IndexError。+2. 大幅降低出生预算（BIRTH_FRAC 0.08→0.02）和外推幅度（EXTRAPOLATE 0.3→0.08），减少对协方差结构的破坏（当前 covariation 41.25 是最大弱点，顶部节点约 52）。+3. 降低 ALPHA 0.5→0.25：方法卡指出常数位移在 T1 上 48.6 低于 copy_last，收缩系数应偏小。+4. 尾部检测改用 L2 范数（离均值距离）代替 SVD/PC1 投影：更稳健，避免 SVD 在小类型上不稳定，且计算更快。+5. 出生细胞取样改为 replace=False，避免重复。+6. 统一单/双阶段路径：先抽样，再按条件决定是否平移，最后统一做出生生成，减少代码分支。+## 用到的知识与出处+- 方法卡 T1 §平移幅度：α∈[0,1] 收缩，官方常数位移 48.6 低于 copy_last（notes/plan/cards/T1.md）+- k015：p(x|t)=Σ_z p(z|t)p(x|z,t) 分解（notes/handover/03）+- k036：birth budget 从已发布阶段推导，需收缩（notes/competition/05_lineage_graph.md）+- 方向库 T1-05：亲本尾部 + 方向外推+- CONTRACT：X3 输入全为 external，程序必须处理无官方输入的情况+- 任务书 §5：保留群体协方差结构，塌缩到平均值会丢分diff --git a/solution/README.md b/solution/README.mdnew file mode 100644index 0000000..ba29577--- /dev/null+++ b/solution/README.md@@ -0,0 +1,4 @@+# pseudobulk_shift++最新阶段抽样后，每个细胞加上所属类型在最后一步的伪批量差值 mean(last|type) − mean(prev|type)，夹到 ≥0；前一阶段没有的类型原样复制。+T1 proxy 只有一个输入阶段，没有差值可取，退化成 copy_last（同样的抽样），所以 proxy 分 = copy_last（seed 0 实测 49.77）。final 才真正平移；官方在真实 T1 上报的常数位移是 48.6，低于地板。diff --git a/solution/run.py b/solution/run.pynew file mode 100644index 0000000..313f760--- /dev/null+++ b/solution/run.py@@ -0,0 +1,148 @@+#!/usr/bin/env python3+"""Lineage-prior mixture: p(x|t) = sum_z p(z|t)*p(x|z,t) with parent-tail birth generation.++Shared types get damped pseudobulk shift (alpha=0.5); a birth budget (~8%) is+filled by extrapolating from differentiation tails of parent types along their+principal variation axis. External inputs are excluded from delta computation+when labels don't match official types. Single-stage fallback uses copy_last+with tail-based birth generation.+"""++from __future__ import annotations++import argparse++import numpy as np+from scipy import sparse++from src.task1_temporal.view_io import (+    labels_of,+    load_manifest,+    panel_genes,+    read_stage,+    sample_rows,+    target_n_cells,+    write_prediction,+)++ALPHA = 0.25+BIRTH_FRAC = 0.02+TAIL_FRAC = 0.12+EXTRAPOLATE = 0.08+++def _official_inputs(manifest):+    official = [e for e in manifest["inputs"] if e.get("source") != "external"]+    if not official:+        official = list(manifest["inputs"])+    return sorted(official, key=lambda e: e["time"])+++def _to_dense(X):+    if sparse.issparse(X):+        return np.asarray(X.toarray(), dtype=np.float32)+    return np.asarray(X, dtype=np.float32)+++def _type_pseudobulk(X, labels):+    unique = np.unique(labels)+    means = {}+    for ct in unique:+        means[ct] = X[labels == ct].mean(axis=0)+    return means+++def _tail_indices(X, labels, rng, tail_frac=TAIL_FRAC):+    unique = np.unique(labels)+    tails = []+    for ct in unique:+        idx = np.where(labels == ct)[0]+        if len(idx) < 10:+            continue+        sub = X[idx]+        mean_vec = sub.mean(axis=0)+        centered = sub - mean_vec+        norms = np.sum(centered ** 2, axis=1)+        n_tail = max(2, int(len(idx) * tail_frac))+        top_local = np.argsort(norms)[-n_tail:]+        tails.extend(idx[top_local].tolist())+    return np.array(tails, dtype=int) if tails else np.array([], dtype=int)+++def _generate_births(X_full, labels_full, means, rng, n_birth):+    if n_birth <= 0:+        return None+    tail_idx = _tail_indices(X_full, labels_full, rng)+    if len(tail_idx) == 0:+        return None+    n_pick = min(n_birth, len(tail_idx))+    chosen = rng.choice(tail_idx, size=n_pick, replace=False)+    births = X_full[chosen].copy()+    for i, idx in enumerate(chosen):+        ct = labels_full[idx]+        if ct in means:+            dev = X_full[idx] - means[ct]+            births[i] = np.clip(X_full[idx] + EXTRAPOLATE * dev, 0, None)+    return births+++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)+    args = parser.parse_args()++    manifest = load_manifest(args.data)+    genes = panel_genes(args.data, manifest)+    rng = np.random.default_rng(args.seed)+    official = _official_inputs(manifest)++    last_entry = official[-1]+    last = read_stage(args.data, last_entry, genes)+    labels_last = labels_of(last)+    X_last = _to_dense(last.X)+    n_target = target_n_cells(manifest, last.n_obs)++    rows = sample_rows(last.n_obs, n_target, rng)+    X = X_last[rows].copy()+    sub_labels = labels_last[rows]++    if len(official) >= 2:+        prev_entry = official[-2]+        prev = read_stage(args.data, prev_entry, genes)+        labels_prev = labels_of(prev)+        X_prev = _to_dense(prev.X)+        del prev++        means_prev = _type_pseudobulk(X_prev, labels_prev)+        means_last = _type_pseudobulk(X_last, labels_last)+        del X_prev++        deltas = {}+        for ct in np.unique(labels_last):+            if ct in means_prev:+                deltas[ct] = ALPHA * (means_last[ct] - means_prev[ct])+            else:+                deltas[ct] = np.zeros(len(genes), dtype=np.float32)++        for ct in np.unique(sub_labels):+            mask = sub_labels == ct+            if ct in deltas:+                X[mask] = np.clip(X[mask] + deltas[ct], 0, None)+    else:+        means_last = _type_pseudobulk(X_last, labels_last)++    n_birth = int(n_target * BIRTH_FRAC)+    if n_birth > 0:+        births = _generate_births(X_last, labels_last, means_last, rng, n_birth)+        if births is not None and len(births) > 0:+            n_replace = min(len(births), n_target)+            replace_idx = rng.choice(n_target, size=n_replace, replace=False)+            X[replace_idx] = births[:n_replace]++    write_prediction(sparse.csr_matrix(X.astype(np.float32)), genes, args.out, seed=args.seed)+++if __name__ == "__main__":+    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）。

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

<<<<<<< SEARCH
ALPHA = 0.25
BIRTH_FRAC = 0.02
TAIL_FRAC = 0.12
EXTRAPOLATE = 0.08
=======
ALPHA = 0.3
BIRTH_FRAC = 0.0
TAIL_FRAC = 0.12
EXTRAPOLATE = 0.0
>>>>>>> REPLACE

<<<<<<< SEARCH
    n_birth = int(n_target * BIRTH_FRAC)
    if n_birth > 0:
        births = _generate_births(X_last, labels_last, means_last, rng, n_birth)
        if births is not None and len(births) > 0:
            n_replace = min(len(births), n_target)
            replace_idx = rng.choice(n_target, size=n_replace, replace=False)
            X[replace_idx] = births[:n_replace]
=======
    n_birth = int(n_target * BIRTH_FRAC)
    if n_birth > 0:
        births = _generate_births(X_last, labels_last, means_last, rng, n_birth)
        if births is not None and len(births) > 0:
            n_replace = min(len(births), n_target)
            replace_idx = rng.choice(n_target, size=n_replace, replace=False)
            X[replace_idx] = births[:n_replace]

    if len(official) >= 2:
        gene_var = np.asarray(X.var(axis=0)).ravel()
        top_var = np.argsort(gene_var)[-500:]
        type_means_sub = {}
        for ct in np.unique(sub_labels):
            mask = sub_labels == ct
            type_means_sub[ct] = X[mask][:, top_var].mean(axis=0)
>>>>>>> REPLACE

```METHOD
## 改了什么