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节点 n24

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 e4a1762be9: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 ba15b1d7c0。改动的文件：solution/METHOD.md +2 −2、solution/run.py +78 −6

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 5913d24..b2c712d 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,4 +1,4 @@ ## 改了什么-移除父节点新增的按类型均值平移（beta=0.1）及其伴随的稠密化操作。父节点 ANALYSIS 明确证明该操作使 covariation 从 51.96 降至 45.62（-6.34），且未改善 de_recovery（49.41→49.06）。回退后恢复纯加权抽样输出，预期 covariation 回到 ~51.96，整体榜分恢复到 ~51.4–51.75 水平。同时消除稠密化带来的内存（2.78GB→~1.26GB）和时间（4.0s→~1.5s）开销。+1. 将有放回加权抽样改为无放回分层抽样（按家族权重调整各类型配额），保持类型内分布完整性，预期改善 cell_state 和 covariation。2. 加入经验贝叶斯收缩伪批量差值（ALPHA=0.015，比 Program 1 的 0.03 更保守），仅在有两个官方输入阶段时启用，目标是改善 de_recovery（当前最弱分组 49.41）而不过度破坏 covariation。Program 1 用 ALPHA=0.03 将 de_recovery 从 49.41 提升到 51.33（+1.92），但 covariation 从 51.96 降到 50.56（-1.4）；减半 alpha 预期获得约一半的 de_recovery 收益，同时保留更多 covariation。 ## 用到的知识与出处-父节点 13 ANALYSIS next_suggestions 第 1 条（回退按类型平移，恢复 covariation）；父节点 13 effect_by_group 数据（covariation -6.34 远超噪声）。+方法卡 T1-02（按类型伪批量位移+收缩系数）；Program 1 实验数据（stratified + EB deltas 组合效果）；父节点 21 ANALYSIS（de_recovery 是最弱分组，建议改抽样权重或加位移）。diff --git a/solution/run.py b/solution/run.pyindex 4710196..dc079a5 100644--- a/solution/run.py+++ b/solution/run.py@@ -16,6 +16,9 @@ import re  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,@@ -26,6 +29,8 @@ from src.task1_temporal.view_io import (     write_prediction, ) +ALPHA = 0.015+ _HEART_PAT = re.compile(     r"cm\b|cardi|heart|shf|endocard|oft\b|avc|ift\b|sv[- ]cm|v[- ]cm|"     r"proepicard|pericard|epicard|blood",@@ -51,6 +56,47 @@ def family_weight(name: str) -> float:     return 1.0  +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 = 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 main() -> None:     parser = argparse.ArgumentParser()     parser.add_argument("--data", required=True)@@ -70,13 +116,39 @@ def main() -> None:     rng = np.random.default_rng(args.seed)     n_target = target_n_cells(manifest, last.n_obs) -    weights = np.array([family_weight(str(l)) for l in labels], dtype=np.float64)-    if weights.sum() == 0:-        weights[:] = 1.0-    weights /= weights.sum()--    rows = rng.choice(last.n_obs, size=n_target, replace=True, p=weights)+    unique_labels, counts = np.unique(labels, return_counts=True)+    n_total = len(labels)+    raw_w = np.array([family_weight(str(u)) for u in unique_labels], dtype=np.float64)+    target_props = (counts / n_total) * raw_w+    target_props /= target_props.sum()+    n_per_type = np.floor(target_props * n_target).astype(int)+    remainder = n_target - n_per_type.sum()+    frac = (target_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_labels, n_per_type):+        if n_take <= 0:+            continue+        type_idx = np.where(labels == u)[0]+        if n_take >= len(type_idx):+            indices.append(type_idx)+        else:+            indices.append(rng.choice(type_idx, size=n_take, replace=False))+    rows = np.concatenate(indices)+    if len(rows) < n_target:+        remaining = np.setdiff1d(np.arange(n_total), rows)+        extra = rng.choice(remaining, size=n_target - len(rows), replace=False)+        rows = np.concatenate([rows, extra])     X = last.X[rows]++    if len(stages) >= 2:+        prev = read_stage(args.data, stages[-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, Round 1 (with-replacement + ALPHA=0.03) scored 51.12 — worse than both the parent (51.40) and Round 0 (51.75). The EB deltas actively hurt when combined with with-replacement sampling (covariation 49.73, de_recovery 49.35).

The best performer was Program 1 (52.15) which used stratified sampling with distance-based binning + ALPHA=0.03. The binning preserves within-type distribution better than simple stratified sampling. I'll adopt Program 1's full approach with ALPHA=0.02 (slightly reduced to recover some covariation while keeping de_recovery gains).

<<<<<<< SEARCH
ALPHA = 0.03
=======
ALPHA = 0.02
N_BINS = 10
MIN_CELLS_FOR_BINNING = 20
>>>>>>> REPLACE

<<<<<<< SEARCH
def eb_shrunk_deltas(prev_X, prev_labels, last_X, last_labels, alpha):
=======
def _submatrix(X, mask):
    sub = X[mask]
    if sp.issparse(sub):
        return np.asarray(sub.todense())
    return np.asarray(sub)


def stratified_sample(labels, X, n_target, rng):
    unique, counts = np.unique(labels, return_counts=True)
    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]
        if n_take >= len(type_idx):