总览 · ← 返回运行 20261002-202908-search-t1-scr-C

节点 n69

改了什么

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 f79828f054: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 82ed7164e0。改动的文件：solution/METHOD.md +4 −5、solution/run.py +41 −57

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex a1551e5..07d927b 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,7 +1,6 @@ ## 改了什么-相对父节点（copy_last），改为投影加权位移：当有两个输入阶段时，对同时出现在两个阶段的类型，计算伪批量差值delta，然后对每个细胞计算其在delta方向上的投影（相对类型均值），标准化后按投影值比例沿delta方向位移。关键特性：(1)位移均值为零（投影标准化后均值为0），不改变类型均值，因此不改变类型间均值差异结构；(2)协方差变化是秩1的（仅在delta方向增加方差），对整体基因共变结构影响远小于均匀位移；(3)沿变化方向拉伸分布，有助于direction指标。alpha=0.05，上限0.1。单输入退化为copy_last。第0轮均匀位移alpha=0.1导致covariation从48.44降至34.5（-13.94），原因是均匀位移改变了类型间均值差异，引入全秩协方差变化。本轮用零均值投影位移避免此问题。+完全移除投影加权位移（已证明破坏 covariation：48.44→39.64，-8.8 分），回退到 copy_last 并改用分层抽样（stratified sampling）：按每个 celltype 的精确比例分配输出细胞数，保证类型组成与输入完全一致。不做任何表达值修改。分层抽样相比随机抽样能更精确保留类型比例，有助于 cell_state 和 covariation 指标。 ## 用到的知识与出处-- 方法卡 k018：常数位移需收缩系数α∈[0,1]-- 方法卡 k012：pseudobulk_shift定义，clip≥0-- 第0轮实验反馈：均匀位移严重破坏covariation（48.44→34.5），需要保持协方差结构的位移方式-- 线性代数：协方差对平移不变，秩1扰动对整体协方差影响小于全秩扰动+- 父节点 ANALYSIS 的教训：任何沿 delta 的位移（均匀或投影）都严重破坏 covariation，clip 的非线性截断破坏零均值性+- 实验表中 Inspiration 1（分层 copy，score 49.29）验证了分层抽样优于随机抽样+- 方法卡 k018：常数位移在 T1 上低于 copy_last，确认不做位移是安全基线diff --git a/solution/run.py b/solution/run.pyindex 2c889b5..8558e76 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,25 +1,58 @@ #!/usr/bin/env python3-"""Projection-weighted shift: cells shifted along delta proportional to their position, preserving mean."""+"""Stratified copy_last: exact type-proportional sampling preserving covariation structure."""  from __future__ import annotations  import argparse  import numpy as np-import scipy.sparse as sp  from src.task1_temporal.view_io import (     inputs_by_time,     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) -ALPHA = 0.05-MAX_ALPHA = 0.1++def stratified_sample(labels: np.ndarray, n_out: int, rng: np.random.Generator) -> np.ndarray:+    unique_types = np.unique(labels)+    type_counts = np.array([(labels == t).sum() for t in unique_types])+    props = type_counts / type_counts.sum()+    allocations = np.floor(props * n_out).astype(int)+    min_per_type = 3+    for i in range(len(unique_types)):+        if allocations[i] < min_per_type and type_counts[i] >= min_per_type:+            allocations[i] = min_per_type+    total_alloc = allocations.sum()+    if total_alloc > n_out:+        excess = total_alloc - n_out+        order = np.argsort(-(allocations - np.floor(props * n_out)))+        for idx in order:+            if excess <= 0:+                break+            reducible = allocations[idx] - min_per_type+            if reducible > 0:+                take = min(reducible, excess)+                allocations[idx] -= take+                excess -= take+    elif total_alloc < n_out:+        remainder = n_out - total_alloc+        frac = props * n_out - allocations+        top_idx = np.argsort(-frac)[: int(remainder)]+        allocations[top_idx] += 1+    rows_list = []+    for i, t in enumerate(unique_types):+        type_idx = np.where(labels == t)[0]+        n_sample = min(int(allocations[i]), len(type_idx))+        if n_sample > 0:+            chosen = rng.choice(type_idx, size=n_sample, replace=False)+            rows_list.append(chosen)+    rows = np.concatenate(rows_list)+    rng.shuffle(rows)+    return rows   def main() -> None:@@ -35,59 +68,10 @@ def main() -> None:     last = read_stage(args.data, entries[-1], genes)     rng = np.random.default_rng(args.seed) -    if len(entries) < 2:-        rows = sample_rows(last.n_obs, target_n_cells(manifest, last.n_obs), rng)-        write_prediction(last.X[rows], genes, args.out, seed=args.seed)-        return--    prev = read_stage(args.data, entries[-2], genes)--    prev_labels = prev.obs["celltype"].values-    last_labels = last.obs["celltype"].values--    X_last = last.X-    if sp.issparse(X_last):-        X_out = X_last.toarray().astype(np.float32)-    else:-        X_out = np.array(X_last, dtype=np.float32)--    dt_total = entries[-1]["time"] - entries[-2]["time"]-    dt_target = manifest["target"]["time"] - entries[-1]["time"]-    if dt_total > 0:-        alpha = min(ALPHA * (dt_target / dt_total), MAX_ALPHA)-    else:-        alpha = 0.0--    if alpha > 0:-        for ct in set(last_labels) & set(prev_labels):-            mask_prev = prev_labels == ct-            mask_last = last_labels == ct-            n_prev = int(mask_prev.sum())-            n_last = int(mask_last.sum())-            if n_prev < 3 or n_last == 0:-                continue-            X_prev_ct = prev.X[mask_prev]-            if sp.issparse(X_prev_ct):-                mean_prev = np.asarray(X_prev_ct.mean(axis=0)).flatten().astype(np.float32)-            else:-                mean_prev = np.asarray(X_prev_ct, dtype=np.float32).mean(axis=0)-            X_ct = X_out[mask_last]-            mean_last = X_ct.mean(axis=0)-            delta = mean_last - mean_prev-            d_sq = float(np.dot(delta, delta))-            if d_sq < 1e-10:-                continue-            centered = X_ct - mean_last-            proj = centered @ delta / d_sq-            p_std = proj.std()-            if p_std < 1e-10:-                continue-            proj = (proj - proj.mean()) / p_std-            X_out[mask_last] = np.clip(X_ct + alpha * np.outer(proj, delta), 0, None)-     n_target = target_n_cells(manifest, last.n_obs)-    rows = sample_rows(last.n_obs, n_target, rng)-    write_prediction(X_out[rows], genes, args.out, seed=args.seed)+    labels = last.obs["celltype"].values+    rows = stratified_sample(labels, n_target, rng)+    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）。

没有记录调研来源。

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

native

stream

Let me read the rest of the file to see the full current program.

<read_file>
<path>/home/spark-longxinyang/.local/state/vec-report/calls/2-202908-search-t1-scr-C-n69-8671bb/work/PROMPT.md</path>
<offset>711</offset>
</read_file>
<list_dir>
<path>/home/spark-longxinyang/.local/state/vec-report/calls/2-202908-search-t1-scr-C-n69-8671bb/work/solution</path>
</list_dir>