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节点 n7

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 85cbd56c26: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 9f9acc3f88。改动的文件：solution/METHOD.md +7 −6、solution/run.py +91 −14

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 5cd82e8..dac9bfd 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,8 +1,9 @@ ## 改了什么-1. 将收缩系数 α 从 0.3 进一步降低到 0.1。第 1 轮实验显示 α 从 0.5→0.3 使 covariation 提升 +2.07（40.77→42.84）、cell_state 提升 +0.61、X3 提升 +1.08，而 direction 基本不变（-0.07）。趋势表明更低的 α 有利于保持原始分布结构（更接近 copy_last），同时保留少量方向信息。-2. 关键洞察：在 proxy 和 proxy2 视图上，过滤外部阶段后只剩一个官方阶段，差值分支不执行，α 无效；α 只在 final（E8.5+E9.5）和 X3（Qiu E8.75+E9.0）上起作用。X3 的改善（42.65→43.73）直接证明低 α 在两阶段场景下更优。+1. 回退第1轮的HVG距离和N_BINS=8：第1轮实验证明用top-1000 HVG计算距离并使N_BINS=8导致proxy下降0.62（49.53→48.91）、direction下降0.71（49.31→48.60），全面劣于第0轮（全基因距离、N_BINS=5）。恢复第0轮设置。+2. 将α从0.1降至0.05：实验表显示α从0.5→0.3→0.1持续改善X3（42.65→43.77→46.10）和covariation。趋势表明更低α更接近copy_last（proxy最优基线），同时EB逐基因收缩已过滤噪声基因，极小α仍保留真实信号方向。α=0.05是α=0（纯copy）和α=0.1之间的折中。 ## 用到的知识与出处-- 第 1 轮实验结果：α=0.3 相比 α=0.5 在 covariation（+2.07）、cell_state（+0.61）、X3（+1.08）上均有改善，direction 不变-- 方法卡 k018：α=1 低于 copy_last，α∈[0,1] 是超参；α=0 即复制最后阶段-- 任务书 CONTRACT：proxy/proxy2 过滤外部阶段后只有一个官方输入，差值分支不触发-- 父节点 ANALYSIS：建议做 α 消融（α=0 vs α=0.5 vs α=1）+- 第0轮实验：全基因距离+N_BINS=5得分48.56，优于第1轮HVG+N_BINS=8的48.15+- 第1轮实验：HVG距离使direction和proxy显著下降，证明全基因距离更好+- 方法卡k018：α=1低于copy_last，α∈[0,1]是超参；趋势表明低α更优+- 实验表趋势：α=0.5→0.3→0.1，X3从42.65→43.77→46.10持续改善+- 父节点ANALYSIS：建议做α=0 vs α=0.1消融；α=0.05是中间试探diff --git a/solution/run.py b/solution/run.pyindex 237ede7..dc2df30 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,9 +1,9 @@ #!/usr/bin/env python3-"""Stratified copy with shrunk pseudobulk delta on official stages only.+"""Stratified copy with EB-shrunk pseudobulk delta and covariance-preserving sampling. -Fixes proxy2 collapse (was sampling external heart-only stage as base),-uses stratified sampling to preserve type proportions (covariation),-and applies shrinkage alpha=0.5 to deltas per method card k018.+Uses empirical Bayes per-gene shrinkage on type deltas (noisy genes move less),+and within-type quantile-bin sampling by distance-to-centroid to preserve+the gene-gene covariation structure of each cell type's expression cloud. """  from __future__ import annotations@@ -11,8 +11,9 @@ 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,@@ -23,10 +24,55 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA = 0.1+ALPHA = 0.05+N_BINS = 5  -def stratified_sample(labels: np.ndarray, n_target: int, rng: np.random.Generator) -> np.ndarray:+def _submatrix(X, mask):+    sub = X[mask]+    if sp.issparse(sub):+        return np.asarray(sub.todense())+    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+        last_sub = last_X[last_mask]+        n_last = int(last_mask.sum())+        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+            prev_sub = prev_X[prev_mask]+            n_prev = int(prev_mask.sum())+            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)+            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 stratified_sample(labels: np.ndarray, X, n_target: int, rng: np.random.Generator) -> np.ndarray:     unique, counts = np.unique(labels, return_counts=True)     n_total = len(labels)     if n_target >= n_total:@@ -43,8 +89,41 @@ def stratified_sample(labels: np.ndarray, n_target: int, rng: np.random.Generato         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)+        if n_take >= len(type_idx):+            indices.append(type_idx)+            continue+        if len(type_idx) > 50 and n_take >= 10:+            sub = _submatrix(X, type_idx)+            centroid = sub.mean(axis=0)+            dists = np.linalg.norm(sub - centroid, axis=1)+            bin_edges = np.quantile(dists, np.linspace(0, 1, N_BINS + 1)[1:-1])+            bin_ids = np.searchsorted(bin_edges, dists)+            per_bin = np.zeros(N_BINS, dtype=int)+            bin_props = np.bincount(bin_ids, minlength=N_BINS) / len(type_idx)+            per_bin = np.floor(bin_props * n_take).astype(int)+            rem = n_take - per_bin.sum()+            fr = (bin_props * n_take) - per_bin+            for i in np.argsort(-fr)[:rem]:+                per_bin[i] += 1+            per_bin = np.minimum(per_bin, np.bincount(bin_ids, minlength=N_BINS))+            chosen = []+            for b in range(N_BINS):+                b_idx = type_idx[bin_ids == b]+                nb = min(per_bin[b], len(b_idx))+                if nb > 0:+                    chosen.append(rng.choice(b_idx, size=nb, replace=False))+            if chosen:+                sel = np.concatenate(chosen)+            else:+                sel = rng.choice(type_idx, size=n_take, replace=False)+            if len(sel) < n_take:+                leftover = np.setdiff1d(type_idx, sel)+                extra = rng.choice(leftover, size=n_take - len(sel), replace=False)+                sel = np.concatenate([sel, extra])+            indices.append(sel)+        else:+            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)@@ -70,15 +149,13 @@ def main() -> None:     labels = labels_of(last)     rng = np.random.default_rng(args.seed)     n_target = target_n_cells(manifest, last.n_obs)-    rows = stratified_sample(labels, n_target, rng)+    rows = stratified_sample(labels, last.X, n_target, rng)     X = last.X[rows]     if len(official) >= 2:         prev = read_stage(args.data, official[-2], genes)-        prev_labels = labels_of(prev)-        deltas = type_deltas(prev.X, prev_labels, last.X, labels)+        deltas = eb_shrunk_deltas(prev.X, labels_of(prev), last.X, labels, ALPHA)         del prev-        shrunk = {k: v * ALPHA for k, v in deltas.items()}-        X = shift_rows(X, labels[rows], shrunk)+        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 Round 1 results, the HVG-based distance and N_BINS=8 **hurt** performance significantly (proxy dropped 49.53→48.91, direction 49.31→48.60). Round 0 (all genes, N_BINS=5) was clearly better. I'll revert to Round 0's sampling approach and try alpha=0.05 to further improve X3.

<<<<<<< SEARCH
ALPHA = 0.1
N_BINS = 8
N_HVG = 1000


def _submatrix(X, mask, gene_idx=None):
    sub = X[mask]
    if gene_idx is not None:
        sub = sub[:, gene_idx]
    if sp.issparse(sub):
        return np.asarray(sub.todense())
    return np.asarray(sub)


def _top_hvg_indices(X, n_top):
    if sp.issparse(X):
        mean = np.asarray(X.mean(axis=0)).ravel()
        var = np.asarray(X.multiply(X).mean(axis=0)).ravel() - mean**2
    else:
        arr = np.asarray(X)
        mean = arr.mean(axis=0).ravel()
        var = arr.var(axis=0).ravel()
    var = np.maximum(var, 0.0)
    return np.argsort(-var)[:n_top]
=======
ALPHA = 0.05
N_BINS = 5


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

<<<<<<< SEARCH