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节点 n3

copy_last_official：输出永远取最新「官方」输入阶段的确定性抽样，忽略外部输入阶段；去掉伪批量平移（实测各 α 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 0ec05b5c54: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 b6980f6761。改动的文件：solution/METHOD.md +33 −0、solution/README.md +0 −4、solution/run.py +25 −19

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..1698359--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,33 @@+copy_last_official：输出永远取最新「官方」输入阶段的确定性抽样，忽略外部输入阶段；去掉伪批量平移（实测各 α 均降分）。++## 方法++- 遍历 `manifest["inputs"]`，用 `inputs_by_time(manifest, include_external=False)` 只取官方阶段，以最新一个为输出基底；若视图完全没有官方阶段则退回任意最新输入。抽样到 `target_n_cells`（seed 确定性），直接写出。+- 不做伪批量平移、不做 kNN 平滑（都被查分否决，见下）。++## 相对父节点（pseudobulk_shift, 39.34）的两个修复++1. **proxy2 主修复**：父节点用默认 `inputs_by_time`，在 proxy2 上「最新输入」是外部 Qiu E9.0（只有 2174 个心脏细胞、另一技术平台），预测整体被换成这批细胞 → proxy2 只有 27.43。改成官方基底后 proxy2 = 50.40（A 半实测）。+2. **去掉平移**：在 X3 尺子上系统扫了 α∈{0.25, 0.5, 1, 2}（E8.75→E9.0 类型内伪批量差值）：40.53 / 42.43 / 44.15 / 37.65，全部低于 α=0（copy E9.0）的 50.0，且 de_direction 为负——差值方向与真值 E9.0→E9.5 的 DE 方向反相关，说明该外部数据里胚胎间批次差异与时间混淆，任何幅度的平移都注入错误方向。官方种子也报过常数位移在 T1 上低于 copy_last（48.6）。故本节点在所有视图上退回 copy_last。++## 查分记录（A 半，seed 0）++| 尺子 | 预测 | 分数 |+|---|---|---|+| proxy | copy 官方 E8.5（= 最终代码输出） | 50.40 |+| proxy2 | copy 官方 E8.5（最终代码输出，逐位一致） | 50.40 |+| X3 | copy E9.0（= 最终代码输出） | 50.00 |++kNN 平滑（PLAN 的方向）在 proxy 上实测有害：k=20 全平滑 → 37.28（covariation 6.9）；k=10, β=0.5 → 40.58。平滑压缩了细胞间方差结构，covariation 大幅变差，放弃。++预期节点分 ≈ (50.40 + 50.40 + 50.00)/3 ≈ 50.3（父 39.34）。++## 验证过 / 没验证++- 验证过：三个视图（proxy、proxy2、X3）都能跑通并通过 `vec-check`；运行 <5 s、内存 <2 GB；给定 seed 输出确定。+- 没验证：final 视图（E8.5+E9.5 → E10.5）没有本地尺子；按本方法在 final 上会 copy 官方 E9.5（与 proxy 上 copy_last 行为一致，是已知最稳妥的退化路径）。方法对任意输入阶段数都不会崩（0 个官方阶段有 fallback，1 个阶段无差值依赖）。+- 未使用任何保留阶段/保留基因型信息；未读外部数据内容（proxy2 的外部阶段被显式忽略）；未用生物学先验文件。++## 下一步建议++copy_last 已到各尺子的 ~50（地板附近）。要超过 50 需要真实的时间信号：(a) 在 final 上利用官方 E8.5→E9.5 差值（同数据集、无批次混淆，α 需按 T1 卡的收缩系数扫描，不能用 X3 的负结果外推）；(b) 细胞组成层面建模（类型比例的一步外推）而非表达平移；(c) 群体分布指标（MMD/variogram）导向的重抽样。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 f3a0f25..cfe5c8a 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,13 +1,25 @@ #!/usr/bin/env python3-"""pseudobulk_shift: latest stage + per-cell-type pseudobulk delta of the last step.--The delta is mean(last|type) - mean(prev|type) over the two latest inputs,-computed on the full stages and added once to a subsample of the latest stage-(clipped at 0). Types missing from the earlier stage are copied unchanged.--With a single input stage (T1 proxy: E8.5 only) there is no step to take a-delta from, so this falls back to copy_last with the same sampling. The proxy-therefore cannot tell this seed from copy_last; that gap is expected.+"""copy_last_official: sample cells from the latest OFFICIAL input stage.++Improvement over the parent (pseudobulk_shift):++1. proxy2 fix (main gain): the parent used inputs_by_time(manifest), which in+   the proxy2 view returns the external Qiu E9.0 heart-only stage as the+   latest input, so the prediction contained only ~2k heart cells of another+   dataset/technology. Here the output base is always the latest stage+   without ``source: external`` (official E8.5 on proxy/proxy2, E9.5 on+   final). External inputs are ignored. If a view has no official stage at+   all, fall back to the latest input of any kind.+2. No pseudobulk shift: measured on the X3 ruler, applying the two-stage+   delta (any alpha in {0.25, 0.5, 1, 2}) moved the prediction in the wrong+   DE direction (de_direction < 0) and lost 6-12 board points versus+   alpha=0, because the cross-embryo/batch delta of the external dataset is+   confounded with time. The constant-shift baseline is also published below+   copy_last on T1. So the shift is removed entirely; the method copies the+   latest official stage (deterministic subsample to the cell-count cap).++This is deliberate: with the rulers available to this node, copying the+latest official stage is the best-measured behavior on all three views. """  from __future__ import annotations@@ -16,10 +28,8 @@ import argparse  import numpy as np -from src.task1_temporal.baselines import shift_rows, type_deltas from src.task1_temporal.view_io import (     inputs_by_time,-    labels_of,     load_manifest,     panel_genes,     read_stage,@@ -38,17 +48,13 @@ def main() -> None:      manifest = load_manifest(args.data)     genes = panel_genes(args.data, manifest)-    stages = inputs_by_time(manifest)+    stages = inputs_by_time(manifest, include_external=False)+    if not stages:  # no official stage in this view: use whatever exists+        stages = inputs_by_time(manifest, include_external=True)     last = read_stage(args.data, stages[-1], genes)     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]-    if len(stages) >= 2:-        prev = read_stage(args.data, stages[-2], genes)-        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels_of(last))-        del prev-        X = shift_rows(X, labels_of(last)[rows], deltas)-    write_prediction(X, genes, args.out, seed=args.seed)+    write_prediction(last.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

Done. Final state verified: `solution/` contains `run.py` + `METHOD.md`, runs on all three views, deterministic, passes `vec-check`.

**做了什么**：修复父节点在 proxy2 上的致命 bug（把外部 Qiu E9.0 心脏细胞当预测基底 → 改为永远以最新官方阶段为基底）；在 X3 上系统扫描伪批量平移 α∈{0.25,0.5,1,2}，全部低于不平移（de_direction 为负，外部数据批次与时间混淆），故去掉平移；实测 PLAN 的 kNN 平滑方向有害（proxy 50.4→37.3），放弃。最终方法 = copy_last_official。

**查分结果**（A 半，共用 9/20 次）：proxy 50.40、proxy2 50.40、X3 50.00 → 预期节点分 ≈ 50.3（父 39.34，+11）。

**下一步最值得试**：copy_last 已贴地板 ~50，超越需在 final 视图上用官方 E8.5→E9.5 同数据集差值（无批次混淆，X3 的负结果不适用，需单独扫收缩 α），或做细胞类型比例的一步外推 / 面向 MMD·variogram 的重抽样，而不是表达平移。

调研员

ed type delta on top of smoothing should lift all four groups.","approach":"Step 1 (shared, all views): Load cells via view_io. Compute PCA (IncrementalPCA, 30 dims) on log-normalized counts. Build kNN graph (k=20, sklearn NearestNeighbors on PCA). For each cell, smoothed expression = weighted mean of k neighbors with Gaussian kernel (bandwidth = median kNN distance in PCA). This denoises while preserving type-specific covariation. Step 2 (two-input path, proxy2 and final): Read both stages via inputs_by_time. Compute per-type pseudobulk delta = mean(last|type) − mean(prev|type) on shared genes; missing genes filled with E8.5 column mean (matches view_io default). Apply x_new = clip(x + alpha*delta_type, 0) with alpha=0.5 initial (search [0.3, 0.7]). Types absent from earlier stage: copy unchanged. Then apply kNN smoothing from Step 1 to the shifted cells. Step 3 (single-input path, proxy): No delta possible; just apply kNN smoothing to the single stage. This is strictly better than raw copy_last because it reduces technical noise. Step 4: Sample to target_n_cells, write prediction. Key params: k in {15,20,30}, PCA dims 30, alpha in [0.3,0.7]. Use vec-score on proxy first (cheap, single stage) to confirm smoothing helps covariation; then test proxy2 for the delta path. Total runtime should stay < 30 s (PCA + kNN on ~5k cells is fast).","expected_groups":["covariation","cell_state","de_recovery"],"risks":"1) kNN smoothing may over-smooth rare cell types, blurring their distinct covariation; Engineer should stratify kNN within cell-type labels if type info is available, or verify with per-type correlation. 2) On proxy (single input), improvement may be < 2-point noise; confirm with repeated vec-score queries (≥2 seeds). 3) alpha too high on cross-dataset proxy2 delta could distort expression; start conservative at 0.5 and check proxy2 score before committing. 4) PCA with 30 dims captures ~31% variance per k004; if smoothing is insufficient, try 50 dims.","sources":[]}