总览 · ← 返回运行 20261002-034201-search-t1-abc-r1-B-population

节点 n6

官方最新阶段按心脏解剖组成重加权抽样复制（心脏谱系↑、边缘类型↓、神经管丢弃），不改任何表达值；外部阶段仅在无官方输入时作底。

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 43fb217ef3: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 30dc67de4c。改动的文件：solution/METHOD.md +61 −34、solution/README.md +7 −2、solution/run.py +61 −55

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 611900d..c5ff652 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,49 +1,76 @@-官方最新阶段分层按型抽样复制；外部阶段只在无官方输入时作底；伪批量平移默认关闭（X3 实测任何幅度都掉分）。+官方最新阶段按心脏解剖组成重加权抽样复制（心脏谱系↑、边缘类型↓、神经管丢弃），不改任何表达值；外部阶段仅在无官方输入时作底。  # 方法 -在父节点（pseudobulk_shift 种子）上做三处修改：+在父节点（官方最新阶段分层 copy_last，平移已关闭）上，把「按型比例分层抽样」换成+**按解剖组成重加权抽样**，直接针对 E8.5→E9.5 的真实变化。 -1. **基座只用官方阶段**（`inputs_by_time(manifest, include_external=False)`）。父节点在 proxy2 上把外部-   Qiu E9.0 心脏细胞（2174 个、仅心脏谱系、部分基因用 E8.5 均值补齐）当成"最新阶段"整体复制，预测群体-   塌缩成心脏细胞，这是 proxy2=27.43 的主因。外部输入只在没有官方阶段时使用（X3 视图两个输入均为外部，-   无 `source` 标记时按其自身 manifest 处理）。-2. **分层抽样**：按基座 `obs["celltype"]` 的比例配额（最大余数法）抽 `target_n_cells` 个细胞，保持类型-   组成精确不变，替代均匀行抽样。无 celltype 列时回退 `sample_rows`。-3. **平移关闭**：保留按时间间隔比缩放（(t_target−t_last)/(t_last−t_prev)，夹到 [0,3]）再乘 ALPHA 的-   每类型伪批量差值机制，但 ALPHA 默认 0（环境变量 `VECSHIFT_ALPHA` 可覆盖，代码内常数，与 seed 无关，-   输出仍确定）。+依据（来自提供的 `src.task1_temporal.reweight` 基线文档）：E8.5→E9.5「群体均值几乎不动+（pseudobulk r 0.994），差距来自解剖：神经管 / 旁中胚层 / 胚外组织缩小，心脏中胚层增大」。+即**型内表达几乎不变、变的是类型组成**。这直接否定了原 PLAN 的核心假设（用型内 pseudotime+挑选「更分化」的细胞）——型内既然不怎么动，pseudotime 重加权没有可放大的信号；而组成重加权+正中要害。故放弃 pseudotime，改用 run2 胜者 `heart_jcf_peri`（本模块）。 -## ALPHA=0 的依据（X3 实测，vec-score）+## 流程 -| 有效平移幅度 | X3 分 | covariation |-|---|---|---|-| 0（分层复制） | 50.0 | 50.0 |-| 0.5 | 42.4 | 21.9 |-| 1.0（父节点） | 40.5 | – |-| 2.0 | 37.6 | 9.7 |+1. **基座 = 最新官方阶段**（`inputs_by_time(include_external=False)`）；无官方阶段时才回退外部。+   父节点已证：把外部 Qiu E9.0 心脏细胞当基座会使群体塌缩（proxy2=27.43）。+2. **`heart_reweight` 重加权抽样** `target_n_cells` 行：心脏谱系类型 ×1.6，边缘类型+   （Surface Ectoderm / EXEM / Paraxial Mesoderm）×0.25，Neural Tube 丢弃，其余 ×1.0；+   按 `counts × weight` 用最大余数法分配配额，每型内 `rng.choice` 抽样（不足则有放回）。+   **表达值从不修改**，只选真实细胞，故稀疏结构、variogram、基因共变完全保留。+3. **优雅退化**：X3 的 Qiu 标签（First/Second heart field、Endocardial cells）不匹配官方+   心脏图谱名，全部拿 weight 1.0 → 退化为分层 copy_last，不崩、中性。 -单调递减：对数空间 clip(x+delta, 0) 造成的人为零峰破坏 variogram/协变结构，且 direction、de_recovery-也未因平移提高。官方方法卡同样报告 T1 上常数位移 48.6 < copy_last。故 final 视图（E8.5+E9.5→E10.5）-也退化为分层 copy_last(E9.5)。+## 关键参数 -## 查分记录（A 半）+- `HEART_WEIGHT=1.6`、`EDGE_WEIGHT=0.25`、`DROP_TYPES={Neural Tube}`：均取自提供的+  `reweight` 基线（run2 胜者）默认值，未改。+- 输出细胞数 = `target_n_cells(manifest, base.n_obs)`（proxy/proxy2 取满 5118，X3 取 2174）。 -- proxy seed0：50.35（covariation 51.9，父 50.04 / cov 22.6）；seed1：50.69-- proxy2 seed0：50.35（父 27.43）-- X3 seed0：50.0（父 40.53）；alpha 扫描见上表-- 预计节点分 ≈ (50.35+50.35+50.0)/3 ≈ 50.2+## 权重扫描证明「调参是噪声」（proxy A 半，seed0）++| heart\edge | 0.0 | 0.05 | 0.10 | 0.25 | 0.50 |+|---|---|---|---|---|---|+| 1.0 | – | – | 55.19 | – | – |+| 1.3 | – | – | 55.81 | 56.11 | – |+| 1.6 | 55.65 | 55.12 | **56.20** | 55.44(基线) | 55.41 |+| 2.0 | – | – | – | 55.63 | – |++全部落在 55.1–56.2（跨度 ~1 分 < T1 噪声 ~2 分），edge 梯度非单调（0.10>0.25 但 0.05<0.10、+0.0 又回升），无真实最优。故**不采纳任何调参变体**，保留基线 1.6/0.25，避免对 proxy A 半过拟合。++## 查分记录（A 半；正式分用 B 半，量级相近）++| 尺子 | 本节点 | 父节点 | Δ |+|---|---|---|---|+| proxy seed0 | 55.44 | 50.35 | +5.09 |+| proxy seed1 | 55.92 | 50.69 | +5.23 |+| proxy2 seed0 | 55.44 | 50.35 | +5.09 |+| X3 seed0 | 50.01 | 50.00 | +0.01 |++分组（proxy seed0）：direction 49.97→58.68、de_recovery 49.07→53.54、cell_state 50.72→55.36、+covariation 51.88→53.90，**四组全升**，direction 涨幅最大（组成位移正是 E8.5→E9.5 的主信号）。+proxy 与 proxy2 预测逐字节相同（同用官方 E8.5 基座、同权重），故同分。+预计节点分 ≈ (55.44+55.44+50.01)/3 ≈ 53.6（A 半），远超父节点 50.02。  ## 验证过 / 没验证 -- 验证：三个视图 vec-check 通过；同 seed 两次运行输出 md5 相同；X3 上 ALPHA∈{0,0.25,0.5,1,2} 扫描。-- 没验证：proxy/proxy2 只有一个官方阶段，平移分支在这两个视图上从不触发，ALPHA 的选择只由 X3 和官方-  卡的 T1 常数位移结果支持；没有验证按基因子集（如仅 top-DE 基因）或免 clip 的平移是否可能超过 50。-- 生物学先验：仅使用了"外部输入不是全胚、不能直接当预测输出"这一 CONTRACT 说明；未使用保留阶段/基因型-  的任何信息，未读 `uns.celltype_palette`。+- **验证**：三视图 `vec-check` 全过；seed0 两次运行 md5 相同（确定）；seed0/seed1 proxy 均 ~55.4–55.9+  （+5 稳健、非单 seed 偶然）；权重 5×5 网格扫描证明调参为噪声；最终干净代码 md5 与首版重加权预测一致。+- **没验证**：① X3 停在 50（中性），未尝试 Qiu 心脏亚型专属重加权——缺 E9.5 Qiu 真值、父节点已证+  X3 上 delta 外推与真值反相关，盲调风险高，故不动。② 未叠加型内 pseudotime（reweight 文档已表明型内+  几乎不变，预期无信号且可能伤 covariation）。③ **final 视图（E9.5→E10.5）未在本节点评分**：心脏↑+  权重是按 E8.5→E9.5 调的，迁移到 E9.5→E10.5 方向上合理（心脏继续增大）但幅度未必最优，属已知风险。+- **生物学先验来源**：提供的 `src.task1_temporal.reweight` 基线（run2 胜者 `heart_jcf_peri`）+ 通用+  心脏谱系身份（哪些类型源自心脏）。其编码的组成变化来自 E8.5 与 E9.5——两者均为已发布、T1 禁窗+  （9.5<E≤13.5）之外、非保留阶段（保留为 E10.5/E12.5），合规。比例从输入现场计算，仅乘数为固定先验。+  未使用任何保留阶段 / 保留基因型测量，未读 `uns.celltype_palette`。  ## 下一步建议 -- 在 proxy 上寻找能真正超过 copy_last 的 E8.5→E9.5 变换（当前 de_recovery≈50 表示与基线持平）；-  例如只平移高置信 DE 基因、保稀疏结构的加性修正、或用 prior/（Reactome、TF 调控）约束平移方向。-- 若引入平移，需避免 clip 零峰：可在计数空间做乘法缩放再 log1p，保持稀疏与方差结构。+- X3 占节点分 1/3 且停在 50：若要再涨，需在不看 E9.5 Qiu 真值的前提下，用 prior/（Reactome、TF 调控）+  或 Qiu E8.75→E9.0 的**型内**变化推断 E9.0→E9.5 方向；但父节点已证 X3 delta 外推反相关，须极谨慎、+  双 seed 且要求 >2 分才算数。+- final 视图（E9.5→E10.5）的心脏权重幅度可能需重调；若有 final 的替代信号，可单独扫描 HEART_WEIGHT。+- 可隔离验证「重加权 vs 父节点分层」在 covariation 上的贡献（本节点 covariation 仅 +2，可能接近上限）。diff --git a/solution/README.md b/solution/README.mdindex 2fdff65..167b3d6 100644--- a/solution/README.md+++ b/solution/README.md@@ -1,3 +1,8 @@-# copy_last_official_stratified+# copy_last_official_heart_reweight -官方最新输入阶段按细胞类型分层抽样复制；外部输入阶段只在无官方阶段（X3）时作底；伪批量平移机制保留但 ALPHA 默认 0（X3 实测任何幅度均掉分，见 METHOD.md）。+官方最新输入阶段按**心脏解剖组成重加权**抽样复制（`src.task1_temporal.reweight.heart_reweight`：+心脏谱系 ×1.6、边缘类型 ×0.25、Neural Tube 丢弃），只选真实细胞、不改任何表达值；外部输入阶段+只在无官方阶段（X3）时作底，X3 的 Qiu 心脏标签不匹配官方图谱名故退化为分层 copy_last。+依据：E8.5→E9.5「群体均值几乎不动、差距来自解剖组成」（reweight 基线文档），故重加权组成而非+型内 pseudotime。proxy A 半 55.44（seed1 55.92）vs 父节点分层 copy 50.35；权重 5×5 扫描证明+调参为噪声（55.1–56.2 < T1 ~2 分噪声），保留基线 1.6/0.25。详见 METHOD.md。diff --git a/solution/run.py b/solution/run.pyindex d43402b..f80568f 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,21 +1,51 @@ #!/usr/bin/env python3-"""copy_last(official) + stratified per-type sampling; optional time-scaled shift (ALPHA=0 default).--Changes vs parent (pseudobulk_shift seed):-1. Base stage = latest OFFICIAL input (include_external=False). The parent-   copied the external Qiu E9.0 heart-only cells on proxy2, collapsing the-   predicted population to heart lineages (proxy2 = 27.43). External inputs-   are only used as a delta source when there are no official stages (X3).-2. Stratified per-cell-type sampling instead of uniform row sampling: keeps-   the type composition of the base stage exactly (proportional quotas),-   preserving between-type covariation under subsampling.-3. Optional per-type pseudobulk shift when two base stages exist, scaled by-   (target gap / observed step gap) and shrunk by ALPHA. Measured on X3-   (two Qiu heart stages E8.75->E9.0, target E9.5): effective shift 0 ->-   50.0, 0.5 -> 42.4, 1.0 -> 40.5 (parent), 2.0 -> 37.6; the official card-   reports the same on T1 (constant shift 48.6 < copy_last). The shift-   monotonically hurts (clip-induced zero spikes destroy variogram /-   covariation), so ALPHA defaults to 0.0 (env VECSHIFT_ALPHA overrides).+"""copy_last(official) + anatomical heart-composition reweighting.++Rationale (data-driven pivot from the pseudotime plan):+  The provided ``reweight`` baseline documents that E8.5 -> E9.5 "barely moves+  population means (pseudobulk r 0.994); the gap is the dissection": neural+  tube / paraxial mesoderm / extra-embryonic tissue shrink while heart mesoderm+  grows. Within-type expression is therefore almost unchanged, so reweighting+  WHICH cells to output by within-type pseudotime (the original plan) has little+  signal to exploit, whereas reweighting the TYPE COMPOSITION directly targets+  the real E8.5->E9.5 change. The run2 winner ``heart_jcf_peri`` (this module)+  reports proxy E8.5->E9.5 skill 55.97 vs ~50 for plain stratified copy_last;+  measured here (vec-score, A half): proxy 55.44 vs parent 50.35.++Method:+  1. Base stage = latest OFFICIAL input (``include_external=False``); external+     inputs are used only when there is no official stage. Copying an external+     heart-only stage as the whole population collapses the prediction (the+     parent's proxy2=27.43 bug), so external stages are never the base when an+     official one exists.+  2. Resample ``target_n_cells`` rows with per-type fractions scaled by+     ``heart_reweight``: heart-lineage types x1.6, edge types (surface+     ectoderm / EXEM / paraxial mesoderm) x0.25, neural tube dropped, others+     x1.0. Expression values are NEVER modified, so sparsity, variogram and+     gene-gene covariation are preserved exactly (real cells only).+  3. Type names that do not match the official cardiac atlas (e.g. the X3 Qiu+     heart labels "First/Second heart field", "Endocardial cells") all receive+     weight 1.0, so ``heart_reweight`` degrades gracefully to stratified+     copy_last there -- no crash, neutral (X3 50.01).++Weight tuning is noise: a sweep of heart_weight in {1.0,1.3,1.6,2.0,2.5} and+edge_weight in {0.0,0.05,0.10,0.25,0.5} on proxy spans only 55.1-56.2 (~1 pt,+within the T1 ~2 pt noise) with no monotonic optimum, so the provided baseline+values (1.6 / 0.25) are kept rather than overfitting the proxy A half.++Biological knowledge source: the provided ``src.task1_temporal.reweight``+baseline (run2 winner ``heart_jcf_peri``) and generic cardiac-lineage identity+(which cell types are heart-derived). The composition change it encodes is+observed between E8.5 and E9.5, both published/allowed stages outside the T1+forbidden window (9.5 < E <= 13.5) and not held-out stages (E10.5, E12.5). No+held-out stage / genotype measurement, and no ``uns.celltype_palette``, is used.+Proportions are computed live from the input; only biologically-motivated+multipliers are fixed.++The parent's optional pseudobulk shift is removed: it was measured to hurt+monotonically on X3 (alpha 0/0.5/1/2 -> 50.0/42.4/40.5/37.6) and the official+card reports constant shift 48.6 < copy_last on T1, so ALPHA defaulted to 0 and+the branch never fired. Removing it cuts complexity and risk. """  from __future__ import annotations@@ -24,7 +54,7 @@ import argparse  import numpy as np -from src.task1_temporal.baselines import shift_rows, type_deltas+from src.task1_temporal.reweight import heart_reweight from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -36,27 +66,6 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = float(__import__("os").environ.get("VECSHIFT_ALPHA", "0.0"))-MAX_SCALE = 3.0---def stratified_rows(labels: np.ndarray, n: int, rng: np.random.Generator) -> np.ndarray:-    """Row indices with per-type quotas proportional to type counts (largest remainder)."""-    types, counts = np.unique(labels, return_counts=True)-    quota = n * counts / counts.sum()-    take = np.floor(quota).astype(np.int64)-    rem = n - take.sum()-    if rem > 0:-        order = np.argsort(-(quota - take))-        take[order[:rem]] += 1-    rows = []-    for t, k in zip(types.tolist(), take.tolist()):-        if k <= 0:-            continue-        idx = np.flatnonzero(labels == t)-        rows.append(rng.choice(idx, size=k, replace=k > idx.size))-    return np.sort(np.concatenate(rows))-  def main() -> None:     parser = argparse.ArgumentParser()@@ -67,28 +76,25 @@ def main() -> None:      manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)++    # Base = latest official stage; fall back to external only if none exist.     stages = inputs_by_time(manifest, include_external=False)     if not stages:         stages = inputs_by_time(manifest, include_external=True)     base_entry = stages[-1]     base = read_stage(args.data, base_entry, genes)-    rng = np.random.default_rng(args.seed)+     n = target_n_cells(manifest, base.n_obs)-    has_types = "celltype" in base.obs.columns-    rows = stratified_rows(labels_of(base), n, rng) if has_types else sample_rows(base.n_obs, n, rng)-    X = base.X[rows]--    if len(stages) >= 2 and ALPHA > 0:-        prev_entry = stages[-2]-        dt_step = float(base_entry["time"]) - float(prev_entry["time"])-        dt_out = float(manifest["target"]["time"]) - float(base_entry["time"])-        prev = read_stage(args.data, prev_entry, genes)-        if has_types and "celltype" in prev.obs.columns and dt_step > 0:-            scale = float(np.clip(dt_out / dt_step, 0.0, MAX_SCALE)) * ALPHA-            deltas = type_deltas(prev.X, labels_of(prev), base.X, labels_of(base))-            deltas = {t: (scale * d).astype(np.float32) for t, d in deltas.items()}-            X = shift_rows(X, labels_of(base)[rows], deltas)-        del prev++    if "celltype" in base.obs.columns:+        # Anatomical composition reweighting (heart up, edge down, neural tube+        # dropped). Unmatched labels (X3 Qiu heart) all get weight 1.0 ->+        # stratified copy_last. Expression values are never modified.+        X = heart_reweight(base.X, labels_of(base), n_cells=n, seed=args.seed)+    else:+        rng = np.random.default_rng(args.seed)+        X = base.X[sample_rows(base.n_obs, n, rng)]+     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**：放弃 PLAN 的型内 pseudotime 重加权（提供的 `reweight` 基线文档指出 E8.5→E9.5「群体均值几乎不动 pseudobulk r=0.994、差距来自解剖组成」，型内既然不变则 pseudotime 无信号可放大），改用提供的 run2 胜者 `heart_reweight`：官方最新阶段按心脏解剖组成重加权抽样（心脏谱系×1.6、边缘类型×0.25、Neural Tube 丢弃），只选真实细胞、不改任何表达值，保留稀疏/variogram/共变。外部阶段仍只在无官方输入时作底；X3 的 Qiu 标签不匹配官方图谱名→优雅退化为分层 copy。删除了已证有害的平移分支。

**查分结果（A 半，正式分用 B 半量级相近）**：
- proxy 55.44（seed1 55.92）vs 父 50.35/50.69 → **+5.1**，四组全升（direction +8.7 最大）
- proxy2 55.44 vs 父 50.35（与 proxy 逐字节相同）
- X3 50.01 vs 父 50.00（中性，Qiu 全心脏、标签不匹配）
- 预计节点分 ≈ (55.44+55.44+50.01)/3 ≈ **53.6** vs 父 50.02
- 权重 5×5 扫描全落 55.1–56.2（<T1 ~2 分噪声、edge 梯度非单调）→ 判定调参为噪声，保留基线 1.6/0.25，不过拟合 A 半。三视图 vec-check 全过、seed0 md5 可复现、2.6s/1.3GB。

**下一步最值得试**：① X3 占 1/3 且卡在 50——用 prior/（Reactome、TF 调控）或 Qiu E8.75→E9.0 的型内变化推断 E9.0→E9.5 方向（但父节点已证 X3 delta 外推反相关，须双 seed 且 >2 分才算）；② final 视图（E9.5→E10.5）的心脏权重幅度可能需重调，本节点未评分；③ 隔离验证重加权对 covariation 的贡献（仅 +2，可能近上限）。

调研员

-based variants (top-50%, top-75% uniform within type). Accept any variant only if BOTH seeds exceed 50.35 on proxy AND X3 does not drop below 49.5. 6. Fallback / single-stage guarantee: the method uses only one input stage (E8.5 for proxy, E9.5 for final). No delta or second stage is needed. For proxy2, ignore the external Qiu input (same as parent). For X3 (two external stages), still use only the latest stage with DPT reweighting (no cross-dataset delta). For final (E8.5+E9.5 → E10.5), apply DPT on E9.5 alone. 7. If DPT root selection is unstable, alternative: rank cells by sc.tl.score_genes with a generic proliferation signature and use that rank as the weight axis (simpler, no graph needed). 8. Timing: DPT on ≤8000 cells × 32k genes takes <60 s with scanpy; subsample to 6000 for DPT computation, then map weights back by nearest-neighbor in PCA if base has more cells. Total runtime target <30 s, well within budget.",
  "expected_groups": ["de_recovery", "direction"],
  "risks": "1. Pseudotime within E8.5 may not align with the E8.5→E9.5 temporal axis (cells vary for spatial/positional reasons, not just temporal). Engineer should check: correlate DPT with cell-cycle score; if DPT is dominated by cell cycle, regress out cell cycle (sc.pp.regress_out) before diffmap. 2. Effect may be < 2 points (noise). Mitigate by requiring both seeds > 50.35 on proxy before committing. 3. DPT root mis-specification flips the weight direction (samples least-differentiated cells). Mitigate: after computing DPT, verify that high-DPT cells have lower proliferation score than low-DPT cells; if inverted, flip the axis. 4. Weighted sampling without replacement with extreme weights may reduce effective diversity and hurt covariation. Mitigate: cap per-cell weight at 5× the type-mean weight; monitor covariation group in vec-score. 5. If all τ variants fail to beat baseline on both seeds, the method is abandoned and code reverts to parent's stratified copy_last (zero-risk fallback)."
}
```

审查员

REVIEW.json is valid (verdict `pass`, 6 reasons, one per check, each citing evidence in `run.py`/`METHOD.md`).

Summary of my judgment:
- **Check 1**: `run.py` reads only via `view_io` from `args.data`/`manifest.inputs` (latest official input stage); no out-of-view paths, no network, no target-stage read, no scorer access.
- **Check 2**: No hardcoded proportions/means/counts/gene lists in candidate code; `n` and labels computed live; `heart_reweight` uses allowed per-family rule multipliers that degrade to weight 1.0 on unmatched name sets.
- **Check 3**: Resamples real cells, never modifies expression; no coordinate/scale/mean-cell tricks; composition reweighting targets the real E8.5→E9.5 anatomical shift (direction gain is a genuine signal per pitfalls, invariant to seed/shuffle).
- **Check 4**: Fully seeded (`--seed`, `default_rng`, seed passed through); reproducible md5.
- **Check 5**: Knowledge derives from E8.5/E9.5 (published, outside forbidden window 9.5<E≤13.5, not held-out E10.5/E12.5); source explicitly cited; no held-out stage/genotype data.
- **Check 6**: `stages[-1]`, no fixed stage count/filenames, graceful X3 degradation (50.01, no crash); transfers to final view without breaking.

REVIEW.json written