总览 · ← 返回运行 20261002-034201-search-t1-abc-r1-C-native

节点 n6

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 6cd6e255ff: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 58fe00b8cb。改动的文件：solution/METHOD.md +8 −5、solution/run.py +106 −27

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 8f8ad72..3086c97 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,7 +1,10 @@ ## 改了什么-1. 用 `inputs_by_time(manifest, include_external=False)` 过滤外部输入阶段。父节点在 proxy2 视图上把 Qiu E9.0（不同细胞类型标签、不同基因覆盖）当作最新阶段来抽样和计算 delta，导致 proxy2 分数极低（27.43）。过滤后退化为与 proxy 相同的 copy_last 行为，预期 proxy2 大幅提升。-2. 用按细胞类型分层抽样（`_stratified_sample`）替换均匀随机抽样（`sample_rows`）。保持各类型的原始比例，使输出群体的组成和基因间共变结构更接近输入，预期改善 covariation 和 cell_state 分组得分。+1. 修复崩溃：shift_rows 可能返回稀疏矩阵，在其后显式转为 dense float32，避免 np.clip 在稀疏矩阵上报错。+2. 向量化噪声注入：将逐细胞 Python 循环改为按类型批量生成噪声矩阵再一次性加到 X 上，消除 761s 的运行时瓶颈（预计降至几秒）。+3. 用 np.maximum(X, 0.0) 替代 np.clip(X, 0, None)，对 dense 数组更高效且不会触发稀疏矩阵歧义错误。+4. 保留经验贝叶斯逐基因收缩（ALPHA=0.5）和无 replace=True 的分层抽样（相对父节点的改进）。 ## 用到的知识与出处-- 任务书 CONTRACT 中关于 `inputs_by_time(manifest, include_external=False)` 的说明（proxy2 外部输入阶段处理）。-- 方法卡 k018：proxy 只有一个输入阶段时退化为 copy_last 是预期行为。-- 分层抽样保持组成是标准统计方法，无特定文献。+- 方法卡 k018：shrinkage alpha 在 [0,1]，逐基因经验贝叶斯收缩让噪声基因移动更少。+- 父节点 ANALYSIS next_suggestions：去掉 replace=True 排除混杂；按类型做离散度匹配。+- 实验表 Program 2（score 47.44）验证了经验贝叶斯收缩在 covariation（42.53）和 cell_state（48.17）上的效果。+- 第 0 轮崩溃反馈：shift_rows 返回稀疏导致 np.clip 失败；逐细胞循环导致超时。diff --git a/solution/run.py b/solution/run.pyindex 358519d..55e55fd 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,13 +1,13 @@ #!/usr/bin/env python3-"""pseudobulk_shift: latest stage + per-cell-type pseudobulk delta of the last step.+"""Stratified copy with empirical Bayes per-gene shrunk pseudobulk delta. -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.+Improvements over parent:+1. Empirical Bayes per-gene shrinkage on type deltas (noisy genes move less),+   preserving covariation structure better than uniform alpha.+2. Stratified sampling without replace=True: caps at available cells per type+   and redistributes remainder to avoid artificial duplication.+3. Adds small per-type Gaussian noise scaled by within-type std to maintain+   distributional spread after shifting. """  from __future__ import annotations@@ -15,8 +15,9 @@ from __future__ import annotations import argparse  import numpy as np+from scipy import sparse as sp -from src.task1_temporal.baselines import shift_rows, type_deltas+from src.task1_temporal.baselines import shift_rows from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -27,26 +28,83 @@ from src.task1_temporal.view_io import (     write_prediction, ) +ALPHA = 0.5+NOISE_FRAC = 0.05+++def _type_stats(X, mask):+    sub = X[mask]+    n = sub.shape[0]+    if sp.issparse(sub):+        mean = np.asarray(sub.mean(axis=0)).ravel()+        var = np.asarray(sub.multiply(sub).mean(axis=0)).ravel() - mean**2+    else:+        sub = np.asarray(sub)+        mean = sub.mean(axis=0).ravel()+        var = sub.var(axis=0).ravel()+    return mean, np.maximum(var, 0.0), n+++def _eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):+    prev_types = set(np.unique(prev_labels))+    last_types = np.unique(last_labels)+    deltas = {}+    for t in last_types:+        last_mask = last_labels == t+        mean_last, var_last, n_last = _type_stats(last_X, last_mask)+        if t in prev_types:+            prev_mask = prev_labels == t+            mean_prev, var_prev, n_prev = _type_stats(prev_X, prev_mask)+            delta = mean_last - mean_prev+            se2 = var_prev / max(n_prev, 1) + var_last / max(n_last, 1)+            shrink = delta**2 / (delta**2 + se2 + 1e-10)+            deltas[t] = alpha * shrink * delta+        else:+            deltas[t] = np.zeros(last_X.shape[1])+    return deltas+++def _type_stds(X, labels):+    stds = {}+    for t in np.unique(labels):+        mask = labels == t+        sub = X[mask]+        if sp.issparse(sub):+            mean = np.asarray(sub.mean(axis=0)).ravel()+            var = np.asarray(sub.multiply(sub).mean(axis=0)).ravel() - mean**2+        else:+            sub = np.asarray(sub)+            mean = sub.mean(axis=0).ravel()+            var = sub.var(axis=0).ravel()+        stds[t] = np.sqrt(np.maximum(var, 0.0))+    return stds+  def _stratified_sample(labels: np.ndarray, n_target: int, rng: np.random.Generator) -> np.ndarray:     unique, counts = np.unique(labels, return_counts=True)-    target_counts = np.maximum(1, np.round(counts / labels.shape[0] * n_target).astype(int))-    diff = n_target - int(target_counts.sum())-    step = 1 if diff > 0 else -1-    order = np.argsort(-counts)-    for i in range(abs(diff)):-        target_counts[order[i % len(order)]] += step-    rows = []-    for lab, tc in zip(unique, target_counts):-        idx = np.where(labels == lab)[0]-        tc = int(tc)-        if tc <= len(idx):-            rows.append(rng.choice(idx, size=tc, replace=False))-        else:-            rows.append(rng.choice(idx, size=tc, replace=True))-    out = np.concatenate(rows)-    rng.shuffle(out)-    return out+    n_total = len(labels)+    if n_target >= n_total:+        return np.arange(n_total)+    props = counts / n_total+    n_per_type = np.floor(props * n_target).astype(int)+    remainder = n_target - n_per_type.sum()+    frac = (props * n_target) - n_per_type+    top_idx = np.argsort(-frac)[:remainder]+    n_per_type[top_idx] += 1+    n_per_type = np.minimum(n_per_type, counts)+    indices = []+    for u, n_take in zip(unique, n_per_type):+        if n_take <= 0:+            continue+        type_idx = np.where(labels == u)[0]+        chosen = rng.choice(type_idx, size=n_take, replace=False)+        indices.append(chosen)+    result = np.concatenate(indices)+    if len(result) < n_target:+        remaining_idx = np.setdiff1d(np.arange(n_total), result)+        extra = rng.choice(remaining_idx, size=n_target - len(result), replace=False)+        result = np.concatenate([result, extra])+    return result   def main() -> None:@@ -65,11 +123,32 @@ def main() -> None:     n_target = target_n_cells(manifest, last.n_obs)     rows = _stratified_sample(labels, n_target, rng)     X = last.X[rows]+    if sp.issparse(X):+        X = np.asarray(X.todense(), dtype=np.float32)+    else:+        X = np.asarray(X, dtype=np.float32)+     if len(stages) >= 2:         prev = read_stage(args.data, stages[-2], genes)-        deltas = type_deltas(prev.X, labels_of(prev), last.X, labels)+        deltas = _eb_shrunk_deltas(prev.X, labels_of(prev), last.X, labels, ALPHA)         del prev         X = shift_rows(X, labels[rows], deltas)+        if sp.issparse(X):+            X = np.asarray(X.todense(), dtype=np.float32)+        else:+            X = np.asarray(X, dtype=np.float32)++    stds = _type_stds(last.X, labels)+    sampled_labels = labels[rows]+    noise_matrix = np.zeros_like(X)+    for t in np.unique(sampled_labels):+        mask = sampled_labels == t+        n_t = mask.sum()+        if n_t == 0:+            continue+        noise_matrix[mask] = rng.normal(0, NOISE_FRAC, size=(n_t, X.shape[1])) * stds[t]+    X = X + noise_matrix+    X = np.maximum(X, 0.0)     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）。

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

The noise injection is destroying covariation (38.96 → 29.61) and proxy scores (49.18 → 46.70). It adds independent noise that breaks gene-gene correlations. I'll remove it entirely and keep only the EB shrinkage which improved direction.

<<<<<<< SEARCH
ALPHA = 0.5
NOISE_FRAC = 0.05
=======
ALPHA = 0.5
>>>>>>> REPLACE

<<<<<<< SEARCH
def _type_stds(X, labels):
    stds = {}
    for t in np.unique(labels):
        mask = labels == t
        sub = X[mask]
        if sp.issparse(sub):
            mean = np.asarray(sub.mean(axis=0)).ravel()
            var = np.asarray(sub.multiply(sub).mean(axis=0)).ravel() - mean**2
        else:
            sub = np.asarray(sub)
            mean = sub.mean(axis=0).ravel()
            var = sub.var(axis=0).ravel()
        stds[t] = np.sqrt(np.maximum(var, 0.0))
    return stds



>>>>>>> REPLACE

<<<<<<< SEARCH
    stds = _type_stds(last.X, labels)
    sampled_labels = labels[rows]
    noise_matrix = np.zeros_like(X)
    for t in np.unique(sampled_labels):
        mask = sampled_labels == t
        n_t = mask.sum()
        if n_t == 0:
            continue
        noise_matrix[mask] = rng.normal(0, NOISE_FRAC, size=(n_t, X.shape[1])) * stds[t]
    X = X + noise_matrix