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节点 n4

增殖驱动的组成重加权（负结果）：类型权重×(1+γ·增殖z)，γ=0 复现父本 55.97，γ=±0.25/0.35/1.0 与 5118 细胞均降分。

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 5ba0ea2aa1: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 39e48cc98a。改动的文件：solution/METHOD.md +47 −0、solution/README.md +15 −3、solution/growth.py +137 −0、solution/run.py +31 −8

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..c711d1c--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,47 @@+增殖驱动的组成重加权（负结果）：类型权重×(1+γ·增殖z)，γ=0 复现父本 55.97，γ=±0.25/0.35/1.0 与 5118 细胞均降分。++## 做了什么++父本（节点 2，55.97）只重加权组成、不动表达，最弱组是 de_recovery 53.06。本节点按 PLAN 试了两条只依赖输入阶段数据的新杠杆：++1. **逐类型分化轴位移（PLAN 步骤 1-3，未采用）**：在 E8.5 每个类型内，用 `prior/` 里的细胞周期基因集+   （reactome/go/msigdb 的 `*cell cycle*|*replication*|*mitotic*|*E2F*|*G2M*`，命中面板 2091 个基因）算每细胞增殖 z 分数，+   再取类型内 1000 HVG 的前 5 个 PC，选 |corr(PC, s)| 最大者作时间轴，δ_c = 晚端三分之一 − 早端三分之一。+   **实测：18 个类型无一通过 |corr| >= 0.15**（周期信号不在类型内前 5 个主成分里），全部 δ_c = 0，该分支等价于父本，故已从代码中移除。+   诊断（未查分）：直接用增殖分数三等分（不经 PCA）时，δ 的前 20 个基因是 Rpl/Rps/Tmsb10/Malat1，且 IFT-CM 的+   "晚端"下调 Myh6/Ttn/Actc1/Ankrd1 —— 与心肌成熟方向相反。说明这条轴在 E8.5 单快照上主要是核糖体/深度等技术变异，不是发育时间。+2. **增殖驱动的组成增长（已实现，默认关闭）**：`growth.py` 里 `type_growth` 给每个类型一个增殖 z 分数（对阶段中位数居中、阶段 std 归一，+   细胞数 < 30 的类型记 0），`growth_sample` 把父本的解剖权重（heart ×1.6、edge ×0.25、丢 Neural Tube）乘上+   `1 + γ·clip(g, -1, 1)`，其余抽样逻辑（类型顺序、`largest_remainder`、`take`、RNG 调用序列）与 `heart_reweight` 逐调用一致。++## 查分结果（T1:val proxy，seed 0，单次）++| 配置 | 榜分 | de_recovery | direction | cell_state | covariation |+|---|---|---|---|---|---|+| γ=0（=父本，逐位相同） | **55.97** | 53.06 | 58.56 | 56.90 | 54.97 |+| γ=+0.35 | 55.29 | 54.17 | 57.20 | 55.49 | 54.03 |+| γ=-0.25 | 54.96 | 53.06 | 58.06 | 55.12 | 53.18 |+| γ=+1.0 | 52.68 | 53.06 | 55.36 | 50.18 | 52.63 |+| N_CELLS=5118（max_cells），γ=0 | 55.12 | 53.06 | 58.38 | 55.16 | 53.57 |++γ 的两个方向都单调劣于 γ=0，说明父本手调的心脏权重已经吃掉了增殖能提供的组成信息（增殖排序本身是合理的：+NCC +0.89、aSHF +0.47、Endothelium +0.27 高，Foregut -0.50、Blood -0.68 低）。加大细胞数也降分。+所以**提交的默认配置 γ=0、N_CELLS=4000，输出与父本逐位相同**（已用 `np.array_equal` 核对）；机制保留在代码里，`VEC_GAMMA` 可复现上表。++## 验证过 / 没验证++- 验证过：`γ=0` 与父本预测逐位相同；`vec-check` 在 seed 0 和 seed 3 上均 ok；`run.py` 只按 `len(inputs_by_time(manifest))`+  读最后一个输入阶段，proxy/final 同一段代码，无阶段名分支、无硬编码统计量；prior 基因集缺失或命中 < 20 时 γ 自动置 0。+- 未验证：所有结论只基于 seed 0 单次查分（低于 PLAN 要求的 3-seed 均值），γ=+0.35 的 de_recovery +1.11 可能只是噪声；+  PLAN 步骤 4 的两阶段伪批量融合分支没有写进提交版本（单快照轴在 proxy 上就没有信号，融合无意义）；final 视图上未运行。+- 运行 ~40 s、峰值内存 ~1.5 GB（`type_growth` 只在 4000 细胞 × 2091 基因的子块上做稠密计算）。++## 下一步最值得试++1. **表达侧必须换信号源**：单快照的增殖轴在 E8.5 上是技术轴。final 视图有 E8.5+E9.5 两个阶段，+   同名类型的两阶段伪批量差 δ2_c 是唯一有真值方向的位移信号（配 T1-02 式逐基因 t 统计收缩，k≈4），+   在 final 上直接可算；proxy 上无法验证，只能靠 final 的一次查分。+2. **组成侧改成网格搜索父本的三个手调数**（heart 权重、edge 权重、是否丢 Neural Tube），+   这是唯一被证明有效的杠杆（copy_last 49.77 → 55.97），比任何表达位移的期望收益都高；用 3-seed 均值防噪声。+3. covariation 54.97 与 mmd 是第二弱项：可试在抽出的真实细胞上做同类型内的轻微 jitter（保留协方差结构的量级），+   而不是加独立噪声。diff --git a/solution/README.md b/solution/README.mdindex e66c7c3..9d4b241 100644--- a/solution/README.md+++ b/solution/README.md@@ -1,4 +1,16 @@-# heart_jcf_peri+# node 4 (improve, parent = heart_jcf_peri 55.97) -run2（20260927-172643-grok-t1）的 T1 胜者：对最新输入阶段按细胞类型重加权再抽 4000 个真实细胞，不改表达。丢 Neural Tube；心脏类（CM、内皮/心内膜、SHF/PHM、JCF、心包、心外膜原基）×1.6；表面外胚层、EXEM、轴旁中胚层 ×0.25；其余 ×1。-proxy 预期 55.97（seed 0，与 run2 的预测逐位相同）。final 读 E9.5，类型集合已含 E9.5 的名字（V-CM、Endocardium、BEC、aPHM、pPHM、Proepicardium）。+Composition is the parent's: latest input stage resampled by cell type with `heart_reweight`'s+anatomical weights (heart x1.6, dissection edges x0.25, Neural Tube dropped), 4000 real cells,+expression untouched. Added here: `growth.py` scales those weights by each type's own+proliferation level, measured at run time from the view's `prior/` cell cycle gene sets+(`VEC_GAMMA`, default 0.0 = parent bit for bit).++Measured on T1:val proxy, seed 0: gamma 0.0 -> 55.97, +0.35 -> 55.29, -0.25 -> 54.96,++1.0 -> 52.68; N_CELLS 5118 -> 55.12. Both growth directions are worse, so the shipped+default keeps the parent's composition. See METHOD.md for the single-snapshot+differentiation-axis attempt that was dropped (no type reached |corr(PC, cell cycle)| >= 0.15,+and the score-based axis was ribosome/depth-driven with the wrong sign on CM maturation genes).++Runs on proxy (E8.5 -> E9.5) and final (E8.5, E9.5 -> E10.5) with the same code: it reads only+`inputs_by_time(manifest)[-1]` and branches on nothing else.diff --git a/solution/growth.py b/solution/growth.pynew file mode 100644index 0000000..c77c2f6--- /dev/null+++ b/solution/growth.py@@ -0,0 +1,137 @@+"""Proliferation-driven composition growth for a T1 temporal prediction.++Each cell type of the latest observed stage gets a growth factor from its own+proliferation level: the mean z score of that type's cells over the cell cycle+gene sets shipped in the view's ``prior/`` directory, centred on the stage+median and scaled by the stage's spread. The parent's hand-set anatomical+weights are multiplied by ``1 + gamma * clip(growth, -1, 1)``, so a type that+cycles harder than the stage median gains relative mass over the interval and+a slower one loses it. Every number is measured on the input stage at run+time; no stage name and no held-out statistic appears in this file.++``gamma = 0`` (or a view without usable cell cycle priors) reproduces+``src.task1_temporal.reweight.heart_reweight`` exactly.+"""++from __future__ import annotations++import re+from pathlib import Path++import numpy as np+from scipy import sparse++MIN_CELLS = 30+SUBSAMPLE = 4000+MIN_CC_GENES = 20+_CC_NAME = re.compile(r"cell cycle|dna replication|mitotic|e2f|g2m|s phase|m phase", re.I)+++def _read_gmt(path: Path) -> list[tuple[str, list[str]]]:+    sets = []+    with path.open("r", encoding="utf-8") as fh:+        for line in fh:+            parts = line.rstrip("\n").split("\t")+            if len(parts) < 3:+                continue+            label = parts[1] if parts[0].startswith(("R-", "GO:")) else parts[0]+            genes = [g for g in parts[2:] if g] or [g for g in parts[1:] if g]+            sets.append((label, genes))+    return sets+++def cell_cycle_genes(view: str | Path, manifest: dict, genes: list[str]) -> np.ndarray:+    """Boolean mask over the panel: genes belonging to a proliferation set in ``prior/``."""+    root = Path(view)+    mask = np.zeros(len(genes), dtype=bool)+    index = {g: i for i, g in enumerate(genes)}+    for entry in manifest.get("prior") or []:+        path = root / entry["path"]+        if path.is_dir():+            candidates = sorted(path.glob("*.gmt"))+        elif path.suffix == ".gmt" and path.exists():+            candidates = [path]+        else:+            continue+        for gmt in candidates:+            for label, members in _read_gmt(gmt):+                if not _CC_NAME.search(label):+                    continue+                if len(members) < 10 or len(members) > 600:+                    continue+                for g in members:+                    j = index.get(g)+                    if j is not None:+                        mask[j] = True+    return mask+++def type_growth(+    X,+    labels: np.ndarray,+    cc_mask: np.ndarray,+    rng: np.random.Generator | None = None,+) -> dict[str, float]:+    """Per-type proliferation z score, centred on the stage median (0 = average)."""+    rng = rng if rng is not None else np.random.default_rng(0)+    n = X.shape[0]+    rows = np.arange(n) if n <= SUBSAMPLE else np.sort(rng.choice(n, size=SUBSAMPLE, replace=False))+    D = np.asarray(X[rows].todense() if sparse.issparse(X) else X[rows], dtype=np.float32)+    cc_idx = np.flatnonzero(cc_mask)+    if cc_idx.size < MIN_CC_GENES:+        return {}+    B = D[:, cc_idx]+    mu = B.mean(axis=0, keepdims=True)+    sd = B.std(axis=0, keepdims=True)+    keep = sd[0] > 1e-6+    if int(keep.sum()) < MIN_CC_GENES:+        return {}+    score = ((B[:, keep] - mu[:, keep]) / sd[:, keep]).mean(axis=1)+    lab = np.asarray(labels)[rows]+    centre = float(np.median(score))+    scale = float(np.std(score)) or 1.0+    out: dict[str, float] = {}+    for t in np.unique(lab):+        m = lab == t+        out[str(t)] = 0.0 if int(m.sum()) < MIN_CELLS else float((score[m].mean() - centre) / scale)+    return out+++def growth_sample(+    X,+    labels: np.ndarray,+    n_cells: int,+    growth: dict[str, float],+    gamma: float,+    seed: int = 0,+):+    """Resample ``n_cells`` rows with anatomical weights scaled by proliferation.++    Mirrors ``src.task1_temporal.reweight.heart_reweight`` call for call, so+    ``gamma = 0`` (or an empty ``growth``) reproduces it exactly.+    """+    from src.task1_temporal.reweight import (+        DROP_TYPES,+        EDGE_WEIGHT,+        HEART_WEIGHT,+        largest_remainder,+        take,+        type_weights,+    )++    rng = np.random.default_rng(seed)+    types = [str(t) for t in np.unique(labels) if str(t) not in DROP_TYPES]+    counts = np.array([(labels == t).sum() for t in types], dtype=np.float64)+    w = type_weights(types, HEART_WEIGHT, EDGE_WEIGHT)+    if gamma != 0.0 and growth:+        g = np.array([np.clip(growth.get(t, 0.0), -1.0, 1.0) for t in types], dtype=np.float64)+        w = np.maximum(w * (1.0 + gamma * g), 1e-3)+    alloc = largest_remainder(counts * w, n_cells)+    blocks = []+    for t, k in zip(types, alloc):+        if k <= 0:+            continue+        blocks.append(take(X, np.flatnonzero(labels == t), int(k), rng))+    out = sparse.vstack(blocks, format="csr").astype(np.float32)+    out.eliminate_zeros()+    return outdiff --git a/solution/run.py b/solution/run.pyindex a752b21..5161138 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,17 +1,24 @@ #!/usr/bin/env python3-"""heart_jcf_peri: run2's T1 winner. Reweight the latest input stage by cell type.+"""heart_jcf_peri + proliferation-driven composition growth. -Neural Tube dropped; heart types (CM, endothelium/endocardium, SHF/PHM, JCF,-pericardium, proepicardium) x1.6; surface ectoderm, EXEM, paraxial mesoderm-x0.25; everything else x1. 4000 real cells, no expression shift. Proxy reads-E8.5, final reads E9.5; the type set already names both stages' labels.+Composition: the latest input stage is resampled by cell type with the parent's+anatomical weights (heart x1.6, dissection edges x0.25, neural tube dropped),+and those weights are additionally scaled by each type's own proliferation+level measured on the input stage: a type whose cells cycle harder than the+stage median gains relative mass over the interval, a slower one loses it.+The per-type growth factor is computed from the data at run time (cell cycle+gene sets come from the view's ``prior/``), never hard-coded, so the same code+runs on the proxy view (E8.5 -> E9.5) and the final view (E9.5 -> E10.5).+Expression is not shifted: cells are real cells of the last observed stage.++``gamma = 0`` reproduces the parent (heart_jcf_peri) bit for bit. """  from __future__ import annotations  import argparse+import os -from src.task1_temporal.reweight import heart_reweight from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -22,7 +29,12 @@ from src.task1_temporal.view_io import (     write_prediction, ) +from growth import cell_cycle_genes, growth_sample, type_growth+ N_CELLS = 4000+# measured on proxy seed 0: 0.0 -> 55.97, +0.35 -> 55.29, -0.25 -> 54.96, +1.0 -> 52.68+GAMMA = float(os.environ.get("VEC_GAMMA", "0.0"))+VERBOSE = bool(os.environ.get("VEC_VERBOSE", ""))   def main() -> None:@@ -34,9 +46,20 @@ def main() -> None:      manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    stages = inputs_by_time(manifest)+    last = read_stage(args.data, stages[-1], genes)+    labels = labels_of(last)++    cc_mask = cell_cycle_genes(args.data, manifest, genes)+    growth = type_growth(last.X, labels, cc_mask, rng=__import__("numpy").random.default_rng(args.seed))+    if VERBOSE:+        print(f"cell cycle genes: {int(cc_mask.sum())}")+        for t, g in sorted(growth.items(), key=lambda kv: -kv[1]):+            print(f"  {t}: g={g:+.3f} -> w_mult={max(1.0 + GAMMA * max(min(g, 1.0), -1.0), 1e-3):.3f}")++    gamma = GAMMA if int(cc_mask.sum()) >= 20 and growth else 0.0     n = target_n_cells(manifest, N_CELLS)-    X = heart_reweight(last.X, labels_of(last), n_cells=n, seed=args.seed)+    X = growth_sample(last.X, labels, n_cells=n, growth=growth, gamma=gamma, seed=args.seed)     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）。

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**：按 PLAN 试了两条只依赖输入阶段数据的新杠杆。(1) 单快照分化轴位移：E8.5 内 18 个类型无一达到 |corr(PC, 细胞周期分)| ≥ 0.15，δ 全为 0；改用增殖分数直接三等分时，δ 的 top 基因是 Rpl/Rps/Tmsb10/Malat1，IFT-CM "晚端"还下调 Myh6/Ttn/Actc1（与成熟方向相反）——这条轴是技术变异，已弃用并从代码移除。(2) 增殖驱动的组成增长（`solution/growth.py`，保留）：父本解剖权重 × (1+γ·clip(增殖z,−1,1))，抽样与 RNG 调用序列与 `heart_reweight` 逐调用一致。

**查分**（proxy，seed 0，单次，用掉 5/20）：γ=0 → **55.97**（与父本预测 `np.array_equal` 为真）；γ=+0.35 → 55.29（de_recovery 54.17，其余全降）；γ=−0.25 → 54.96；γ=+1.0 → 52.68；N_CELLS=5118 → 55.12。两个方向都单调劣于 γ=0，手调心脏权重已吃掉增殖信息，故提交默认 γ=0、4000 细胞，`vec-check` 在 seed 0/3 均 ok，运行 2.3 s。所有结论只基于单 seed，低于 PLAN 的 3-seed 门槛。

**下一步最值得试**：① 组成侧网格搜索父本三个手调数（heart/edge 权重、是否丢 Neural Tube）——唯一被证明有效的杠杆（49.77→55.97），用 3-seed 均值；② 表达位移只能在 final 的两阶段上做（同名类型 E8.5→E9.5 伪批量差 + t 统计收缩），proxy 无法验证；③ covariation 侧试同类型内轻微 jitter 而非独立噪声。

调研员

PLAN.json written