总览 · ← 返回运行 20261001-233756-search-t1-abc-r0-B-population

节点 n9

copy_last（最新官方阶段）+ 增殖双级重加权抽样（β_type=-4、β_cell=-1，Efraimidis–Spirakis 无放回）；凋亡轴实测无效已默认关闭（β_a=0）。

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 c9589dc2b1: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 bd089b836e。改动的文件：solution/METHOD.md +28 −22、solution/README.md +2 −3、solution/run.py +155 −70

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 5735adc..77b8966 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,37 +1,43 @@-以最新官方输入阶段做 copy_last（外部阶段永不直接当输出），类型配对伪批量位移默认收缩系数 α=0（实测任何 α>0 均降分），保留 shift 机制供后续节点调参。+copy_last（最新官方阶段）+ 增殖双级重加权抽样（β_type=-4、β_cell=-1，Efraimidis–Spirakis 无放回）；凋亡轴实测无效已默认关闭（β_a=0）。  ## 方法 -- 基群体 = 最新**官方**输入阶段（proxy: E8.5；proxy2: E8.5 而非外部 Qiu E9.0；final: E9.5；X3 无官方输入时用最新外部阶段 E9.0）。这是对父节点的关键修正：父节点在 proxy2 上把只有心脏谱系、2174 个细胞、基因不全的 Qiu E9.0 直接当预测输出，proxy2 仅 27.43。CONTRACT 本身要求"不要把外部细胞直接当预测输出"。-- 无放回抽样到 target_n_cells（`sample_rows`），保留经验细胞分布与共变结构。-- 位移机制（默认关闭）：对基阶段与其前一阶段共有的细胞类型（两边各 ≥30 个细胞）计算伪批量差值，乘 α 后加到该类型细胞行上，clip≥0。α 由环境变量 `VEC_ALPHA` 覆盖，默认 0.0，此时完全跳过（等价 copy_last）。-- 单阶段（proxy）自动退化为 copy_last，不会崩。+- 基群体 = 最新**官方**输入阶段（沿用节点 2 的关键修正：proxy2 的外部 Qiu E9.0 永不直接当输出；X3 无外部输入，基 = 其最新输入 E9.0）。+- 每个细胞的增殖分 = 9 个核心细胞周期基因（Mki67, Top2a, Pcna, Ccnb1, Cdk1, Mcm2, Birc5, Aurkb, Rrm2，均为通用基因功能知识，与节点 4/6 同族）在 log1p(CP10k) 表达上的均值；基因缺失时按存在的子集算。+- 类型级权重 w_type = clip(1+β_p·(p_t−p̄), 0.05, 20)，β_p=-4：下调快增殖祖细胞、上调分化中类型。+- 细胞级权重 w_cell = clip(1+β_c·(p_i−p_t), 0.05, ∞)，β_c=-1：类型内下调最高增殖细胞。+- 组合权重后做 Efraimidis–Spirakis 加权无放回抽样（top-k of u^(1/w)），抽到 target_n_cells，`np.random.default_rng(seed)` 保证确定性。+- 凋亡轴（β_a）：细胞凋亡分 = Reactome "Apoptosis"（R-MMU-109581，prior/ 提供）∩ MSigDB HALLMARK_APOPTOSIS 的 panel 内基因均值；w_type 再乘 (1+β_a·(a_t−ā))。代码保留、环境变量 `VEC_BETA_A` 可开，**默认 0（关闭）**——实测负 β_a 单调有害。  ## 关键参数 -- α=0.0（默认）。MIN_TYPE_CELLS=30（α=0 时无作用）。+β_p=-4，β_c=-1，β_a=0（默认）。权重 clip [0.05, 20]（类型级）/ ≥0.05（细胞级）。环境变量覆盖：`VEC_BETA_P`、`VEC_BETA_CELL`、`VEC_BETA_A`。 -## 验证过什么（vec-score，A 半）+## 验证过什么（vec-score，A 半，seed 0） -| 预测 | proxy | proxy2 | X3 |-|---|---|---|---|-| 父节点（seed shift） | 50.04 | 27.43 | 40.53 |-| 本节点 α=0 | 50.40 | 50.40 | 50.00 |-| 本节点 α=0.25（仅 X3 生效） | - | - | 44.15 |-| 本节点 α=0.5（仅 X3 生效） | - | - | 42.43 |-| 本节点 α=1.0（仅 X3 生效） | - | - | 40.33 |+proxy 扫描（本节点程序）： -- X3 上 α 单调有害：α↑ → covariation（50→28.6→21.9→15.3）、de_recovery、cell_state 全降，与方法卡"常数位移在 T1 上 48.6 低于 copy_last"一致。故取 α=0。-- proxy/proxy2 在本程序下路径相同（都用官方 E8.5，proxy2 无更早阶段），预测与已查分文件一致。-- 三个视图均通过 vec-check；输出在 α=0 下与已查分的 preds 完全一致（同 seed 同抽样）。+| 配置 | proxy |+|---|---|+| β_a=0, β_p=-4, β_c=-1（默认，即节点 6 机制复现） | **56.23** |+| β_a=-1 / -2 / -3（β_p=-4, β_c=-1） | 55.89 / 55.62 / — |+| β_p=-3 / -5（β_a=0） | 56.01 / 55.11 |+| β_c=-0.5 / -2（β_a=0, β_p=-4） | 56.16 / 55.30 |++- proxy2 默认配置 = **56.23**（与 proxy 相同路径，预测一致）；X3 默认配置 = **50.00**（X3 各指标饱和在地板/天花板，与父节点持平，无回归）。+- 三视图 vec-check 全过；同 seed 重跑输出逐元素一致（确定性验证）。+- 结论：凋亡轴（PLAN 的核心假设）**不支持**——β_a 单调有害，方向性增益全部来自增殖重加权；β_p=-4、β_c=-1 是局部最优（±1 的扰动均 ≤0 且在噪声内）。  ## 没验证什么 -- 逐基因经验贝叶斯收缩（PLAN 第 2 步）：α 全局收缩已单调劣于 copy_last，逐基因收缩只在 α>0 时有意义，未测。-- 分层重抽样（PLAN 第 3 步）：抽样已是按行的无放回/有放回混合，未做按类型分层，未测其增益。-- final 视图（E8.5+E9.5→E10.5）无法在本地查分；α=0 时行为 = copy_last(E9.5)。-- 用 E8.75→E9.0 的类型比例趋势外推 X3 组成：未测（预计增益 < 噪声 2 分）。+- β_a>0（上调凋亡细胞）：无生物学动机，未测。+- 用 GO/Reactome 细胞周期集合替换 9 基因面板：未测（预计差异 < 噪声）。+- final 视图（E8.5+E9.5→E10.5）无法本地查分；机制单阶段即可运行，final 上等价于对 E9.5 做同样重加权。+- 多种子稳定性：只跑了 seed 0（复跑由 harness 做）。  ## 生物学知识来源 -无外部文献知识；仅使用视图内数据与 CONTRACT 中关于外部输入阶段局限的说明。+- 增殖↔组成变化机制：moscot 生长/死亡评分（增殖+凋亡双轴），节点 4/6 已在 proxy 上证明增殖轴有效（β=-4/-1）。+- 9 个细胞周期基因为通用基因功能注释（增殖标记），非来自任何保留阶段测量。+- 凋亡基因集来自视图内 prior/（Reactome R-MMU-109581 ∩ MSigDB HALLMARK_APOPTOSIS），未硬编码表达值。+- 未读取任何保留阶段（E10.5/E12.5/禁窗）数据。diff --git a/solution/README.md b/solution/README.mdindex ba29577..2a997e8 100644--- a/solution/README.md+++ b/solution/README.md@@ -1,4 +1,3 @@-# pseudobulk_shift+# n9: copy_last + 增殖双级重加权（凋亡轴默认关闭） -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。+见 METHOD.md。入口 run.py；参数 β_p=-4 / β_c=-1 / β_a=0（可用环境变量 VEC_BETA_P / VEC_BETA_CELL / VEC_BETA_A 覆盖）。diff --git a/solution/run.py b/solution/run.pyindex ff1b865..e82e960 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,28 +1,36 @@ #!/usr/bin/env python3-"""copy_last on the latest OFFICIAL stage + alpha-shrunk per-type pseudobulk shift.--Two changes over the seed pseudobulk_shift:--1. The base population is always the latest OFFICIAL input stage. An external-   input (proxy2: Qiu E9.0, heart-only, partial panel) is never copied as the-   prediction, per the contract ("do not output external cells directly");-   it is only used to estimate a delta when its cell-type labels overlap the-   base stage's labels. When no official input exists (X3-style test views),-   the latest input of any source is the base.--2. The per-type pseudobulk delta (latest - previous step, types present in-   both) is multiplied by a shrinkage factor ALPHA before being added, and-   only types with a decent cell count in both stages are shifted. Rows are-   clipped at 0 after the shift.--With a single usable stage (T1 proxy) this is exactly copy_last.+"""copy_last (latest OFFICIAL stage) + proliferation/apoptosis dual-axis reweighting.++Base population is the latest official input stage (external inputs such as+proxy2's Qiu E9.0 are never copied as the prediction). On top of node 2's+copy_last we reweight which cells get sampled, following the two-mechanism+model used by moscot's growth/death scoring (proliferation and apoptosis+drive changes in cell-type composition between stages):++- Proliferation score per cell: mean log1p expression of core cell-cycle+  genes present in the panel.+- Apoptosis score per cell: mean log1p expression of the Reactome+  "Apoptosis" gene set (R-MMU-109581) from prior/, intersected with the+  panel (and, when >=3 genes survive, further intersected with MSigDB+  HALLMARK_APOPTOSIS to drop generic housekeeping members).+- Type-level weight  w_type = clip((1+bp*(p_t - pbar)) * (1+ba*(a_t - abar)), 0.05, 20)+- Cell-level weight  w_cell = clip(1+bc*(p_i - p_t), 0.05, None)+- Combined w = w_type * w_cell; Efraimidis-Spirakis weighted sampling+  without replacement of target_n_cells rows.++bp<0 down-weights fast-cycling progenitors (which dilute as differentiated+populations expand); ba<0 down-weights apoptosis-high cells (depleted by the+target stage). Defaults reproduce node 6's proven proliferation mechanism+(bp=-4, bc=-1); the apoptosis axis ba is scanned. """  from __future__ import annotations  import argparse+import os  import numpy as np+from scipy import sparse  from src.task1_temporal.view_io import (     inputs_by_time,@@ -31,51 +39,107 @@ from src.task1_temporal.view_io import (     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) -ALPHA = float(__import__("os").environ.get("VEC_ALPHA", "0.0"))-MIN_TYPE_CELLS = 30  # types with fewer cells in either stage are copied---def type_deltas_shrunk(prev_X, prev_labels, last_X, last_labels):-    """mean(last|c) - mean(prev|c) per shared type, x ALPHA, count-gated."""-    out = {}-    prev_counts = {}-    for t in np.unique(prev_labels):-        prev_counts[str(t)] = int((prev_labels == t).sum())-    for t in np.unique(last_labels):-        t = str(t)-        n_prev = prev_counts.get(t, 0)-        n_last = int((last_labels == t).sum())-        if n_prev < MIN_TYPE_CELLS or n_last < MIN_TYPE_CELLS:-            continue-        m_last = np.asarray(last_X[np.flatnonzero(last_labels == t)].mean(axis=0), dtype=np.float32).ravel()-        m_prev = np.asarray(prev_X[np.flatnonzero(prev_labels == t)].mean(axis=0), dtype=np.float32).ravel()-        out[t] = (ALPHA * (m_last - m_prev)).astype(np.float32)+BETA_P = float(os.environ.get("VEC_BETA_P", "-4"))+BETA_A = float(os.environ.get("VEC_BETA_A", "0"))+BETA_CELL = float(os.environ.get("VEC_BETA_CELL", "-1"))++PROLIF_GENES = [+    "Mki67", "Top2a", "Pcna", "Ccnb1", "Cdk1",+    "Mcm2", "Birc5", "Aurkb", "Rrm2",+]+# canonical apoptosis effectors/regulators (gene-function knowledge, used+# only to filter the prior-derived set if the prior intersection is tiny)+APOPT_FALLBACK = [+    "Casp3", "Casp7", "Casp8", "Casp9", "Apaf1", "Cycs", "Diablo",+    "Bax", "Bak1", "Bad", "Bid", "Bmf", "Bcl2", "Bcl2l1", "Bcl2l11",+    "Pmaip1", "Bbc3", "Fadd", "Cflar", "Dffa", "Dffb", "Endog",+]+++def _gmt_genes(path, wanted_names):+    out = set()+    if not os.path.exists(path):+        return out+    with open(path) as fh:+        for line in fh:+            f = line.rstrip("\n").split("\t")+            if len(f) >= 3 and f[1] in wanted_names:+                out.update(f[2:])     return out  -def shift_rows(X, labels, deltas):-    from scipy import sparse--    X = X.tocsr() if sparse.issparse(X) else sparse.csr_matrix(X)-    blocks = []-    order = []-    for t in np.unique(labels):-        idx = np.flatnonzero(labels == t)-        order.append(idx)-        if str(t) in deltas:-            dense = np.clip(X[idx].toarray() + deltas[str(t)], 0, None).astype(np.float32)-            blocks.append(sparse.csr_matrix(dense))-        else:-            blocks.append(X[idx])-    out = sparse.vstack(blocks, format="csr")-    inv = np.empty(X.shape[0], dtype=np.int64)-    inv[np.concatenate(order)] = np.arange(X.shape[0])-    return out[inv]+def _reactome_apoptosis(prior_dir):+    gmt = os.path.join(prior_dir, "reactome", "gene_sets.gmt")+    if not os.path.exists(gmt):+        return set()+    with open(gmt) as fh:+        for line in fh:+            f = line.rstrip("\n").split("\t")+            if len(f) >= 3 and f[1] == "Apoptosis":+                return set(f[2:])+    return set()+++def _hallmark_apoptosis(prior_dir):+    gmt = os.path.join(prior_dir, "msigdb", "hallmark_mouse.gmt")+    if not os.path.exists(gmt):+        return set()+    with open(gmt) as fh:+        for line in fh:+            f = line.rstrip("\n").split("\t")+            if len(f) >= 3 and f[0].upper().endswith("APOPTOSIS"):+                return set(f[2:])+    return set()+++def apoptosis_genes(view_dir, manifest):+    prior_dir = os.path.join(view_dir, "prior")+    ap = _reactome_apoptosis(prior_dir)+    if len(ap) >= 3:+        hm = _hallmark_apoptosis(prior_dir)+        if hm:+            inter = ap & hm+            if len(inter) >= 3:+                ap = inter+    if len(ap) < 3:+        go = _gmt_genes(+            os.path.join(prior_dir, "go", "gene_sets_bp.gmt"),+            {"apoptotic process", "programmed cell death"},+        )+        if len(go) >= 3:+            ap = go+    if len(ap) < 3:+        ap = set(APOPT_FALLBACK)+    return sorted(ap)+++def col_idx(genes, names):+    pos = {g: i for i, g in enumerate(genes)}+    return np.array([pos[n] for n in names if n in pos], dtype=np.int64)+++def score_per_cell(X, idx):+    if idx.size == 0:+        return None+    sub = X[:, idx]+    if sparse.issparse(sub):+        sub = sub.toarray()+    return np.asarray(sub, dtype=np.float32).mean(axis=1)+++def weighted_sample_without_replacement(w, k, rng):+    """Efraimidis-Spirakis: top-k of u**(1/w)."""+    n = w.shape[0]+    if k >= n:+        order = np.argsort(-(w + 1e-12 * rng.random(n)))+        return np.concatenate([order[:k], rng.choice(n, k - n, replace=True)])+    u = rng.random(n)+    keys = np.power(u, 1.0 / np.maximum(w, 1e-12))+    return np.argpartition(-keys, k - 1)[:k]   def main() -> None:@@ -91,22 +155,43 @@ def main() -> None:     official = [e for e in all_inputs if not is_external(e)]     base_entry = official[-1] if official else all_inputs[-1] -    last = read_stage(args.data, base_entry, genes)+    base = read_stage(args.data, base_entry, genes)+    X = base.X+    if sparse.issparse(X):+        X = X.tocsr()+    labels = np.asarray(labels_of(base))++    p_idx = col_idx(genes, PROLIF_GENES)+    prolif = score_per_cell(X, p_idx)+    if BETA_A != 0.0:+        a_idx = col_idx(genes, apoptosis_genes(args.data, manifest))+        apopt = score_per_cell(X, a_idx)+    else:+        apopt = None+    if prolif is None:+        prolif = np.zeros(X.shape[0], dtype=np.float32)+    if apopt is None:+        apopt = np.zeros(X.shape[0], dtype=np.float32)++    uniq = np.unique(labels)+    w_type = np.ones(X.shape[0], dtype=np.float64)+    for t in uniq:+        m = labels == t+        p_t = float(prolif[m].mean())+        a_t = float(apopt[m].mean())+        wt = (1.0 + BETA_P * (p_t - float(prolif.mean()))) * (+            1.0 + BETA_A * (a_t - float(apopt.mean()))+        )+        w_type[m] = np.clip(wt, 0.05, 20.0)+        if BETA_CELL != 0.0:+            w_type[m] *= np.clip(1.0 + BETA_CELL * (prolif[m] - p_t), 0.05, None)+     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]-    last_labels = labels_of(last)--    # previous step: latest input strictly before the base with overlapping labels-    prev_entries = [e for e in all_inputs if e["time"] < base_entry["time"]]-    if prev_entries and ALPHA > 0:-        prev = read_stage(args.data, prev_entries[-1], genes)-        deltas = type_deltas_shrunk(prev.X, labels_of(prev), last.X, last_labels)-        del prev-        if deltas:-            X = shift_rows(X, last_labels[rows], deltas)--    write_prediction(X, genes, args.out, seed=args.seed)+    k = target_n_cells(manifest, base.n_obs)+    rows = weighted_sample_without_replacement(w_type, k, rng)+    rows = np.sort(rows)++    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

**做了什么**：在父节点（copy_last 官方基阶段）上叠加节点 4/6 已证明的增殖双级重加权（类型级 β_p=-4 + 细胞级 β_c=-1，Efraimidis–Spirakis 无放回加权抽样），并按 PLAN 实现了凋亡轴（Reactome Apoptosis ∩ HALLMARK_APOPTOSIS，prior/ 读取）。

**查分结果**（A 半，seed 0，共用 8 次额度）：
- PLAN 核心假设**被证伪**：β_a=-1/-2 单调有害（55.89/55.62 vs β_a=0 的 56.23），默认关闭（β_a=0），代码保留。
- β_p/β_c 细扫确认 -4/-1 为局部最优（±1 扰动均降分）。
- 默认配置：**proxy 56.23 / proxy2 56.23 / X3 50.00**（X3 饱和地板，与父持平无回归），预计节点分 ≈54.2 vs 父 50.03。三视图 vec-check 通过，同 seed 重跑逐元素一致。

**下一步最值得试**：X3 三视图全部指标饱和在 50，说明重加权类方法在 X3 无信号可挖；proxy 上 direction 已到 60、de_recovery 仅 51.5 是最弱组——可试小幅定向表达位移（prior/TF regulons 驱动的 ε≤0.05σ 级，参考节点 5）叠加在重加权之上，而非再调组成权重。

调研员

cell-cycle genes log1p mean, same as node 4/6). Step 3: Load apoptosis gene set from prior/ (Reactome 'Apoptosis'/'Programmed Cell Death'); intersect with panel_genes. If <3 genes survive, fall back to a hardcoded list of canonical apoptosis genes likely in panel (Casp3, Casp9, Bax, Bcl2, Trp53, Fadd, Bid, Bcl2l11, Pmaip1, Bbc3) and keep only those present. Compute type-level apoptosis score (log1p mean). Step 4: Type weight w_type = (1+β_p·(prolif_t−mean_p))·(1+β_a·(apopt_t−mean_a)), clip [0.05,20]. Initial β_p=−4 (proven), β_a=−2. Step 5: Within-type cell weight w_cell = clip(1+β_cell·(prolif_i−type_mean),0.05), β_cell=−1 (proven). Step 6: Combined w = w_type·w_cell; Efraimidis–Spirakis weighted sampling without replacement to target_n_cells. Step 7: Quick scan β_a∈{−1,−2,−3} on proxy via vec-score (3 queries); pick best, then verify on proxy2 and X3 (2 more queries). If β_a=0 already matches or beats all β_a<0, ship proliferation-only (≡ node 6 mechanism). Step 8: Single-stage fallback (proxy, one input): identical code path, all scores computed within the single stage. X3 with no official input: use latest external stage as base, same scoring. Step 9: vec-check all three views. Total vec-score queries ≤ 8.",
  "expected_groups": ["cell_state", "direction", "de_recovery"],
  "risks": "1) Apoptosis gene set may have <3 panel genes → Engineer checks overlap first; if too few, skip apoptosis axis and ship proliferation-only (still +4 over node 2). 2) β_a could be monotonically harmful like α was → scan only 3 values; abort apoptosis axis immediately if β_a=−1 degrades proxy below 50. 3) Dual weighting may over-concentrate sampling into few types, hurting covariation → monitor covariation in first vec-score; if it drops >2 vs node 2, add a type-floor of max(5, 0.5%·target_n) cells per input type. 4) 30-min budget is tight: get proliferation reweighting working first (proven gain), apoptosis is the incremental bonus; if time runs short, ship proliferation-only."
}
```