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

节点 n12

官方最新阶段分层抽样复制（表达不改）+ 心脏解剖重加权（神经管/表面外胚层丢弃、旁轴×0.1、心脏×1.3）+ 反向组成趋势外推 GAMMA=-1.2 + 输出数下限 3000。

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 30011bc035: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 a3568b2b84。改动的文件：solution/METHOD.md +38 −0、solution/README.md +0 −4、solution/run.py +65 −7

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..1197f4d--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,38 @@+官方最新阶段分层抽样复制（表达不改）+ 心脏解剖重加权（神经管/表面外胚层丢弃、旁轴×0.1、心脏×1.3）+ 反向组成趋势外推 GAMMA=-1.2 + 输出数下限 3000。++## 方法++在父节点 5（分层复制 + 阻尼组成趋势外推）基础上，加入本 run 已被 node 6/7/9/10 反复验证为唯一稳定提分的部件——**心脏解剖组成重加权**，并微调其强度、加入输出细胞数下限。表达值从不修改，只重采样真实细胞。++流程（每次运行现算，全部来自 view 输入）：++1. **基座**：输出细胞一律取最新**官方**输入阶段（`official[-1]`）；无官方阶段的外部测试题（X3）退而取最新输入。proxy2 的 Qiu E9.0 心脏细胞只当参考、不作基座（沿用 node 2/3 结论）。+2. **组成趋势外推（GAMMA）**：当最新两个输入阶段的细胞类型词表可比（覆盖率 ≥ `OVERLAP_MIN=0.5`，如 X3 的 E8.75+E9.0、final 的官方 E8.5+E9.5），按 `p_target = p_last + GAMMA·(Δt_target/Δt_last)·(p_last − p_prev)` 外推每型比例，夹到 `MIN_FRAC=0.002` 再归一。词表不可比（proxy2：全胚 vs 心脏标签不相交）或只有一个阶段（proxy）时**跳过**，保留最新阶段原比例。实测 GAMMA 只作用于 X3（proxy 单阶段、proxy2 词表不可比都跳过）。+3. **解剖重加权**：对趋势后的比例乘以每型权重再归一——心脏谱系（CM 各型、SHF、PHM、心内膜/内皮、BEC、JCF、心包、心外膜）×`HEART_W=1.3`；旁轴中胚层 ×`PARAXIAL_W=0.1`；神经管、表面外胚层 ×0（丢弃，心脏为中心解剖时不在取样内）；EXEM 及其余 ×1.0；未知名字（X3 的 heart field 标签）×1.0，故重加权在 X3 上是 no-op。+4. **输出细胞数**：`n = clip(max(target_n×OUT_FRAC(0.95), N_FLOOR=3000), min_cells, max_cells)`。池足够大（proxy/proxy2，E8.5=16787）→ n≈4862，类型内**不放回**抽样、最大余数法分配、禁止重复取细胞；池小于目标（X3，E9.0=2174）→ n=3000，允许类型内重复补齐。+5. 按分配从各型抽真实细胞，表达原样输出，`write_prediction` 写成合规 h5ad。++## 关键参数（均在 A 半实测）++- `GAMMA=-1.2`：X3 扫描 −1.0/−1.2/−1.4 → 52.58/53.60/53.67，−1.2~−1.4 为峰，取 node 9 已在 B 半验证的 −1.2。负号=向上一阶段组成收缩（父节点教训：符号方向须实测，不能靠直觉）。+- `HEART_W=1.3`：proxy 扫描 1.0/1.15/1.3/1.6/2.0 → 57.05/57.11/57.18/56.95/56.13，1.0–1.3 平台、2.0 掉分，取 1.3。+- `PARAXIAL_W=0.1`、神经管/表面外胚层 ×0、EXEM ×1.0：沿用 node 10 的细拆（EXEM 单调剂量峰在 ×1.0、表面外胚层丢弃）。+- `N_FLOOR=3000`：X3 扫描 2500/3000/3500 → 53.23/53.60/52.90，3000 为峰。+- `OUT_FRAC=0.95`、`MIN_FRAC=0.002`、`OVERLAP_MIN=0.5`：沿用父节点。++## 验证过什么++- **三视图 seed 0 均跑通 + `vec-check` ok**；seed 1、2 亦跑通、格式合规；同 seed 重跑输出逐元素相同（确定性）。运行 ~1–2 s，峰值内存远低于 28 GB 上限。+- **A 半查分**：proxy 57.18 / proxy2 57.18 / X3 53.60 → 节点均分 **≈55.99**（父节点 50.60，当前最佳 node 9 rank3 55.57）。分组：proxy cell_state 59.85、direction 59.68、covariation 55.26、de_recovery 53.0；X3 cell_state 57.65、covariation 50.59、direction/de_recovery ~52。+- **消融**：within-type 增殖偏置（prior GO/Reactome/hallmark 细胞周期基因打分，BETA=0.3）——X3 因 n>池 未触发（no-op），proxy **−5.0**（cell_state 59.4→51.1、covariation 54.9→48.7），按 PLAN gate 丢弃，代码已删除。++## 没验证 / 局限++- 只用 A 半查分；节点正式分用 B 半，小幅差异（HEART_W 1.0↔1.3、GAMMA −1.2↔−1.4 皆 <2 分噪声）未必在 B 半重现，故取平台内稳健值而非 A 半 argmax。+- **final 视图（官方 E8.5+E9.5，目标 E10.5）未测**：那里 GAMMA 会真正作用于官方两阶段、且 E9.5 出现的新型（V-CM/Endocardium/BEC/aPHM/pPHM/Proepicardium 等）已写入 HEART_TYPES 名单以正确加权，但无沙箱可查分。+- proxy2 仍完全忽略 Qiu E9.0（只当参考），未尝试用它做心脏谱系的时间插值——留作后续。+- X3 输出含约 38% 重复细胞（池 2174 < n 3000）；实测净收益为正，但重复对 de_recovery 的长期影响未单独隔离。++## 生物学知识来源++仅用通用小鼠胚胎学定性事实：晚期以心脏为中心解剖 → 心脏谱系富集、神经管/表面外胚层/旁轴中胚层相对缩减（node 6/reweight.py 已记录的解剖动机）。类型名单取自**输入阶段**（已发布 E8.5/E9.5 注释），未使用任何保留阶段/基因型的测量。diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index ba29577..0000000--- a/solution/README.md+++ /dev/null@@ -1,4 +0,0 @@-# 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.pyindex 894810c..00b38d8 100644--- a/solution/run.py+++ b/solution/run.py@@ -16,8 +16,22 @@ are not comparable (T1 proxy2: official whole-embryo E8.5 vs external heart-only Qiu E9.0 with disjoint labels) or there is only one stage (T1 proxy), the proportions of the latest stage are kept as-is. -Nothing about held-out stages/genotypes is used; all proportions and rates are-computed from the view's inputs at runtime.+On top of the trend, an anatomical reweighting of the target composition is+applied (parent node 6/7/9/10 winning ingredient): the later stage is a+heart-centred dissection, so heart-lineage types are up-weighted (HEART_W),+paraxial mesoderm is down-weighted (PARAXIAL_W), and neural tube / surface+ectoderm (absent from the heart-centred later sample) are dropped. Weights are+keyed on *input-stage* type names (published E8.5/E9.5 annotations); unknown+names (e.g. X3's heart-field labels) get weight 1.0, so the reweighting is a+no-op there and only the GAMMA trend acts. The output size is floored at+N_FLOOR cells; when the pool is smaller (X3, 2174 cells) cells are reused to+reach it -- measured best at 3000 for X3.++Nothing about held-out stages/genotypes is used; all proportions, rates and+type names are computed/read from the view's inputs at runtime. The only+external knowledge is the qualitative lineage fact that a heart-centred later+dissection enriches cardiac lineages and depletes neural-tube / surface-ectoderm+/ paraxial tissue (textbook mouse embryology, not a held-out measurement). """  from __future__ import annotations@@ -37,10 +51,38 @@ from src.task1_temporal.view_io import (     write_prediction, ) -GAMMA = -0.6          # damping of the composition trend extrapolation (X3 A-half: gamma=0.3 scored 46.1 vs ~50 at 0)+GAMMA = -1.2          # damping of composition trend (X3 A-half: -1.2~-1.4 best) OUT_FRAC = 0.95+N_FLOOR = 3000       # output size floor (X3 peak at 3000) MIN_FRAC = 0.002     # floor for a type kept in the target composition OVERLAP_MIN = 0.5    # min fraction of latest-stage cells whose type is also in the previous stage+HEART_W = 1.3        # multiplier on heart-lineage type fractions (proxy A-half: flat 1.0-1.3, 2.0 worse)+PARAXIAL_W = 0.1++# Anatomical reweighting of the target composition. General (textbook) knowledge+# of E8.5->E9.5 mouse dissection: the embryo is dissected heart-centred at the+# later stage, so neural tube / surface ectoderm / paraxial mesoderm /+# extra-embryonic mesoderm shrink while heart-lineage tissues grow. Names are+# those of the *input* stages (published E8.5/E9.5 annotations); no held-out+# measurement is used. Unknown names (e.g. external datasets) get weight 1.0.+HEART_TYPES = {+    "OFT/RV-CM", "IFT-CM", "AVC-CM", "SV-CM", "LV-CM", "RV-CM", "V-CM",+    "Endothelium", "Endocardium", "BEC",+    "aSHF", "pSHF", "aPHM", "pPHM",+    "JCF", "Pericardium", "Proepicardium",+}+ZERO_TYPES = {"Neural Tube", "Surface Ectoderm"}+PARAXIAL_TYPES = {"Paraxial Mesoderm"}+++def type_weight(t: str) -> float:+    if t in ZERO_TYPES:+        return 0.0+    if t in PARAXIAL_TYPES:+        return PARAXIAL_W+    if t in HEART_TYPES:+        return HEART_W+    return 1.0   def apportion(fracs: np.ndarray, avail: np.ndarray, n_total: int) -> np.ndarray:@@ -117,18 +159,34 @@ def main() -> None:             fracs = new / new.sum()         del prev +    # anatomical reweighting of the target composition+    w = np.array([type_weight(str(t)) for t in types], dtype=np.float64)+    if w.sum() > 0:+        adj = fracs * w+        fracs = adj / adj.sum()+     rng = np.random.default_rng(args.seed)     n_total = target_n_cells(manifest, pool.n_obs)-    n_total = max(int(n_total * OUT_FRAC), manifest["min_cells"])-    n_total = min(n_total, pool.n_obs)-    alloc = apportion(fracs, counts, n_total)+    n_total = max(int(n_total * OUT_FRAC), N_FLOOR, manifest["min_cells"])+    n_total = int(np.clip(n_total, manifest["min_cells"], manifest["max_cells"]))+    if n_total <= pool.n_obs:+        # enough real cells: no duplicates (capped apportion)+        alloc = apportion(fracs, counts, n_total)+    else:+        # pool smaller than target (e.g. X3): largest-remainder, allow duplication+        raw = fracs * n_total+        alloc = np.floor(raw).astype(np.int64)+        order = np.argsort(-(raw - alloc))+        for j in order[: int(n_total - alloc.sum())]:+            alloc[j] += 1      rows = []     for i, t in enumerate(types):         idx = np.flatnonzero(pool_labels == t)         n = int(alloc[i])         if n > 0:-            rows.append(rng.choice(idx, size=n, replace=False))+            # no duplicates while the pool is large enough; X3 (pool < target) reuses cells+            rows.append(rng.choice(idx, size=n, replace=n > len(idx)))     rows = np.sort(np.concatenate(rows))     X = pool.X[rows]     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

完成。提交 `solution/`（run.py + METHOD.md，无权重/ARTIFACTS）。

**做了什么**：在父节点 5（分层复制 + 阻尼组成趋势外推）上，加入本 run 反复验证的唯一稳定提分部件——心脏解剖组成重加权（神经管/表面外胚层×0 丢弃、旁轴中胚层×0.1、心脏谱系×HEART_W、EXEM×1.0），并加输出细胞数下限 N_FLOOR=3000。表达值从不改。删除了 PLAN 提议的 within-type 增殖偏置（实测有害）。

**查分（A 半，seed 0）**：
- proxy **57.18** / proxy2 **57.18** / X3 **53.60** → 节点均分 **≈55.99**（父节点 50.60，当前最佳 node 9 rank3 55.57）
- 消融：GAMMA 只作用于 X3（proxy 单阶段、proxy2 词表不可比都跳过）；扫描 GAMMA(−1.0/−1.2/−1.4)、HEART_W(1.0→2.0)、N_FLOOR(2500/3000/3500) 后取平台内稳健值（1.3 / −1.2 / 3000），避开 A 半噪声 argmax
- 增殖偏置 BETA=0.3：proxy **−5.0**、X3 no-op → 丢弃
- seed 0/1/2 均跑通 + vec-check ok，同 seed 重跑逐元素相同（确定性）

**下一步最值得试**：(1) proxy2 目前完全忽略 Qiu E9.0，可试用心脏谱系时间插值单独抬 proxy2；(2) final 视图（官方 E8.5+E9.5→E10.5）无沙箱验证，GAMMA 会真正作用于官方两阶段，需在终选前用留一法间接校验其符号/幅度是否迁移；(3) X3 covariation(50.6) 是最弱组，探索不靠重复细胞的密度匹配方式。

调研员

 in the view's prior/ directory; filter to panel genes). Score = mean expression of proliferation genes per cell (already log1p). Sampling weight within type t: w_i = 1 + BETA * (z_i) where z_i is the cell's z-scored proliferation value within that type, BETA=0.3 (search [0.1, 0.5]). Use these weights in rng.choice(replace=False, p=w/w.sum()) within each type's allocated quota. If a type has fewer cells than its quota, take all (no duplicates). Step 4: Set n_output=3000 fixed (as in node 9) rather than OUT_FRAC×target. Step 5: For proxy (single input stage): skip GAMMA, apply reweighting + proliferation sampling directly. For proxy2 (incompatible vocabularies): skip GAMMA (OVERLAP_MIN check fails), apply reweighting + proliferation sampling on official E8.5 only. For X3/final (two compatible stages): full pipeline. Step 6: Quick validation: run on X3 first (fastest signal), vec-score query; if X3 ≥ 53, run proxy and proxy2. If BETA=0.3 doesn't beat BETA=0 (pure uniform within-type) by ≥2 on X3, drop proliferation bias and keep just reweighting+GAMMA. Total queries budget: ~6-8 (2 per GAMMA/BETA combo × 2-3 configs).","expected_groups":["cell_state","direction","de_recovery"],"risks":"1) Proliferation gene sets in prior/ may be too small or poorly overlapping with panel genes (<50 genes), making the score noisy → Engineer should check overlap count first; if <30 genes, skip proliferation bias. 2) BETA too high could distort within-type diversity, hurting covariation/de_recovery → start at 0.3, if covariation drops >2 vs node 5, reduce to 0.1 or 0. 3) GAMMA=-1.0 with reweighting may over-concentrate cardiac types, losing rare populations that de_recovery needs → MIN_FRAC=0.002 floor prevents total loss. 4) Improvement may be within 2-point noise of node 7/9/10 (~55) → need 2 seeds on X3 to confirm direction before full eval. 5) 30-min time limit: code is ~40 lines added to node 5's run.py; proliferation scoring is one matrix multiply; should run in <2s.","sources":[]}

审查员

REVIEW.json written