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节点 n36

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 6ac6d7bf86: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 09867d57f9。改动的文件：solution/METHOD.md +6 −7、solution/run.py +1 −46

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 6324e75..fa158f7 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,9 +1,8 @@ ## 改了什么-1. 将逐基因收缩因子（per-gene shrink = delta²/(delta²+se²)）改为逐类型均匀收缩（取所有基因收缩因子的均值作为标量）：逐基因收缩会对不同基因施加不同缩放，破坏基因间相关结构；均匀收缩保持 delta 向量方向不变，等比缩放所有基因，从而保留类型内基因共变结构。直接针对最弱的 covariation 组（49.36）。-2. N_BINS 从 10 增至 12：更细的距离分位数分箱，更精确保留类型内表达云的协方差结构。-3. MIN_CELLS_FOR_BINNING 从 20 降至 15：更多中等大小类型受益于保协方差分箱抽样。+1. 完全移除 delta shift（eb_shrunk_deltas + shift_rows）：第 0 轮将 ALPHA 从 0.03 降至 0.008、size_factor 分母从 50 提至 100 后，covariation 仍为 50.57（无 shift 的 Program 1 为 52.19），direction 仅 +0.07（噪声内），X3 反而下降 0.28。delta shift 对 covariation 的破坏远大于对 direction 的贡献（direction 增益主要来自 fw 权重），因此彻底移除。+2. 保留 fw() 类型权重 + 保协方差分箱抽样（N_BINS=12, MIN_CELLS_FOR_BINNING=15）：fw 权重驱动 direction 组（54+），分箱抽样保留类型内表达云形状，两者组合在无 shift 条件下应同时获得高 covariation 和高 direction。 ## 用到的知识与出处-- 父节点 ANALYSIS next_suggestions（N_BINS=12 或阈值=15 针对 covariation）-- 方向库 T1-12（保协方差的生成：真实细胞锚 + 低秩残差）-- 方法卡 k018（Damped per-type shift: shrinkage alpha）-- 实验表 Program 1 vs 当前对比：Program 1 无 delta shift 得 covariation 51.96，说明 delta 的逐基因不均匀缩放是协方差损失来源+- 实验表 Program 1（无 delta shift）covariation 52.19、X3 49.96，均优于任何带 shift 的变体+- 父节点 ANALYSIS：\"direction +3.86 更可能由硬编码 fw 权重驱动而非均匀收缩\"+- 方法卡 k018：官方常数位移 48.6 低于 copy_last，说明 shift 在 T1 上整体有害+- 第 0 轮实验：ALPHA=0.008 + size_factor/100 仍未能恢复 covariation（50.57 vs 52.19）diff --git a/solution/run.py b/solution/run.pyindex ea99d39..08a860c 100644--- a/solution/run.py+++ b/solution/run.py@@ -14,7 +14,6 @@ import numpy as np  from scipy import sparse as sp -from src.task1_temporal.baselines import shift_rows from src.task1_temporal.view_io import (     inputs_by_time,     labels_of,@@ -27,7 +26,6 @@ from src.task1_temporal.view_io import (  import re -ALPHA = 0.03 N_BINS = 12 MIN_CELLS_FOR_BINNING = 15 @@ -50,45 +48,7 @@ def _submatrix(X, mask):     return np.asarray(sub)  -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-        n_last = int(last_mask.sum())-        if n_last == 0:-            continue-        last_sub = last_X[last_mask]-        if sp.issparse(last_sub):-            mean_last = np.asarray(last_sub.mean(axis=0)).ravel()-            var_last = np.asarray(last_sub.multiply(last_sub).mean(axis=0)).ravel() - mean_last**2-        else:-            arr = np.asarray(last_sub)-            mean_last = arr.mean(axis=0).ravel()-            var_last = arr.var(axis=0).ravel()-        var_last = np.maximum(var_last, 0.0)-        if t in prev_types:-            prev_mask = prev_labels == t-            n_prev = int(prev_mask.sum())-            prev_sub = prev_X[prev_mask]-            if sp.issparse(prev_sub):-                mean_prev = np.asarray(prev_sub.mean(axis=0)).ravel()-                var_prev = np.asarray(prev_sub.multiply(prev_sub).mean(axis=0)).ravel() - mean_prev**2-            else:-                arr_p = np.asarray(prev_sub)-                mean_prev = arr_p.mean(axis=0).ravel()-                var_prev = arr_p.var(axis=0).ravel()-            var_prev = np.maximum(var_prev, 0.0)-            delta = mean_last - mean_prev-            se2 = var_prev / max(n_prev, 1) + var_last / max(n_last, 1)-            n_eff = min(n_prev, n_last)-            size_factor = n_eff / (n_eff + 50.0)-            shrink = float(np.mean(delta**2 / (delta**2 + se2 + 1e-10)))-            deltas[t] = alpha * size_factor * shrink * delta-        else:-            deltas[t] = np.zeros(last_X.shape[1])-    return deltas+   def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Generator, tw: np.ndarray | None = None) -> np.ndarray:@@ -175,11 +135,6 @@ def main() -> None:     tw = np.array([fw(str(u)) for u in ul])     rows = stratified_sample(labels, last.X, n_target, rng, tw=tw)     X = last.X[rows]-    if len(official) >= 2:-        prev = read_stage(args.data, official[-2], genes)-        deltas = eb_shrunk_deltas(prev.X, labels_of(prev), last.X, labels, ALPHA)-        del prev-        X = shift_rows(X, labels[rows], deltas)     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

Looking at the results, removing the delta shift improved covariation (50.79→51.10) and X3 (49.32→50.0), but covariation is still below Program 1's 52.19. The remaining gap is due to the stratified binning, which constrains radial distribution but distorts gene-gene correlations within types. Program 1 (no binning, simple weighted sampling) achieves the best covariation. I'll disable binning to recover it.

<<<<<<< SEARCH
N_BINS = 12
MIN_CELLS_FOR_BINNING = 15
=======
>>>>>>> REPLACE

<<<<<<< SEARCH
def _submatrix(X, mask):
    sub = X[mask]
    if sp.issparse(sub):
        return np.asarray(sub.todense())
    return np.asarray(sub)
=======
>>>>>>> REPLACE

<<<<<<< SEARCH
def stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Generator, tw: np.ndarray | None = None) -> np.ndarray:
    unique, counts = np.unique(labels, return_counts=True)
    n_total = len(labels)
    if n_target >= n_total:
        return np.arange(n_total)
    if tw is not None:
        wc = counts.astype(np.float64) * tw
        props = wc / wc.sum() if wc.sum() > 0 else counts / n_total
    else:
        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]
        if n_take >= len(type_idx):