总览 · ← 返回运行 20261001-204440-search-t1-g21q-D

节点 n5

official_base_copy：基底修正为最新官方输入 + 均匀 Gumbel 无放回抽样（重加权与位移经替代分验证后关闭）

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 7b276a1b35: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 d653048f0f。改动的文件：solution/METHOD.md +48 −0、solution/README.md +0 −4、solution/run.py +159 −7

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..a6ec624--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,48 @@+# official_base_copy：基底修正为最新官方输入 + 均匀 Gumbel 无放回抽样（重加权与位移经替代分验证后关闭）++## 方法++1. **基底选择（本节点的核心修正）**：父节点 copy_last 在 proxy2 上把最后一个输入+   （Qiu E9.0，外部数据集、仅心脏谱系、仅覆盖 27,883/32,285 基因）当基底，导致+   proxy2=27.43。本节点改为：`inputs_by_time(manifest, include_external=False)`+   取**最新官方输入**作基底（proxy/proxy2: E8.5；final: E9.5；X3: E9.0 Qiu 官方题输入；+   X4/X5: E9.0_s16-18）。外部输入阶段完全不进基底。+2. **抽样**：`target_n_cells` 定 N，Gumbel top-N 无放回加权抽样（权重全 1 时即均匀+   无放回），保留真实细胞 → covariation 不塌。+3. **实现但默认关闭的两个部件**（超参在 argparse 里，默认 β=0、α=0）：+   - 增殖重加权：Seurat S/G2M 基因集（大小写不敏感匹配 panel）log1p 均值作细胞增殖分，+     组（obs.celltype，缺列时 PCA50+KMeans30）均值 z 分数 → w=clip(exp(βz),0.25,4)。+   - 阻尼位移：两个同 panel 官方输入时，按 celltype 组算伪批量差+     （t_prev→t_base），eff=α·clip(Δt_target/Δt_input, 0, 1.5)，|δ|<τ 的基因不动，+     加后 clip≥0，分块（2048 行）稠密计算。++## 查分结果（A 半，seed 0）++| 配置 | proxy | proxy2 | X3 | X5 |+|---|---|---|---|---|+| 父 copy_last | 50.04 | 27.43 | 50.00 | 50.00 |+| **本节点（β=0, α=0）** | **51.24** | **51.24** | （未查，同复制预期≈50） | **50.00** |+| β=1 增殖重加权 | 37.77 | - | - | - |+| β=2 | 36.10 | - | - | - |+| 心脏组 w=0.25 | 40.50 | - | - | - |+| α=0.4 位移 | - | - | 45.34 | 46.56 |+| α=0.2, τ=0.15 | - | - | - | 48.63 |++结论：在这批尺子上，**任何组成重加权或表达位移都低于纯复制最新官方输入**。+基底修正是唯一稳健收益（proxy2 +23.8 → 节点分约 +4.8）。++## 验证过 / 没验证++- 验证：5 个视图全部跑通 + vec-check ok（proxy、proxy2、X3、X4、X5，各 <20s，+  峰值内存 ~1.5GB）；确定性（同 seed 同输出，md5 复核）。+- 未验证：X4 的 α=0 输出未单独查分（父节点 copy 在 X4=50.00，本输出同为复制，+  仅抽样 RNG 不同）；final 视图（无该视图可跑）；β<0。+- 位移部件在 final（E8.5+E9.5→E10.5，官方两输入）会被 α=0 默认关闭；若未来节点+  想启用，先在 X5 上扫 α（X5 对位移最敏感）。++## 生物学知识来源++- 细胞周期 S/G2M 基因集：Tirosh et al. 2016 (Science) / Seurat v4 cell-cycle+  scoring 列表（通用已发表知识，与保留阶段无关；本版本默认 β=0 未使用）。+- 心脏解剖占比先验（心脏组下调实验，w=0.25）：通用胚胎解剖知识；替代分显示有害，已弃用。+- 未使用任何保留阶段/保留基因型的测量信息；未读 `uns.celltype_palette`。diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index e6cdf6f..0000000--- a/solution/README.md+++ /dev/null@@ -1,4 +0,0 @@-# copy_last--输出最新一个输入阶段（proxy：E8.5；final：E9.5），随机无放回抽到榜的细胞上限（T1 5118）。-不改表达，不改组成。这是地板：proxy 预期 ≈ 50（seed 0 实测 49.77；抽到 5118 个细胞，比整份 E8.5 的 50.00 略低）。diff --git a/solution/run.py b/solution/run.pyindex aa4bbf0..024f643 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,36 +1,188 @@ #!/usr/bin/env python3-"""copy_last: the latest input stage, subsampled into the board's cell range."""+"""Proliferation-reweighted composition + damped pseudobulk shift (>=2 same-panel inputs).++Base = latest OFFICIAL input (never an external, partial-panel stage).+Composition: cells reweighted by group proliferation score, Gumbel top-N sampling.+Expression: when two inputs share the panel and group labels, shift each group's+cells by alpha * dt_scale * (pseudobulk(t2,g) - pseudobulk(t1,g)), genes below+|delta| threshold untouched, clipped at 0.+"""  from __future__ import annotations  import argparse  import numpy as np+from scipy import sparse  from src.task1_temporal.view_io import (     inputs_by_time,+    is_external,+    labels_of,     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) +# Seurat v4 cell-cycle scoring gene lists (S + G2M; Tirosh et al. 2016, Science).+# General published knowledge, not derived from any held-out stage measurement.+CC_GENES = [+    "MCM5", "PCNA", "TYMS", "FEN1", "MCM2", "MCM4", "RRM1", "UNG", "GINS2",+    "MCM6", "CDCA7", "DTL", "PRIM1", "UHRF1", "HELLS", "RFC2", "RPA2",+    "NAA10", "RNASEH2A", "RCN3", "PCP4", "USP1", "CLSPN", "POLA1", "CHAF1B",+    "BRIP1", "E2F8", "HMGB2", "CDK1", "NUSAP1", "UBE2C", "BIRC5", "TPX2",+    "TOP2A", "NDC80", "CKS2", "NUF2", "CKS1B", "MKI67", "TMPO", "CENPF",+    "TACC3", "FAM64A", "SMC4", "CCNB2", "CKAP2L", "CKAP2", "AURKB", "BUB1",+    "KIF11", "ANP32E", "TUBB4B", "GTSE1", "KIF20B", "HJURP", "CDCA3",+    "HN1", "CDC20", "TTK", "CDC25C", "KIF2C", "RANGAP1", "NCAPD2", "DLGAP5",+    "CDCA2", "CDCA8", "ECT2", "KIF23", "HMMR", "AURKA", "PSRC1", "ANLN",+    "LBR", "CKAP5", "CENPE", "CTCF", "NEK2", "G2E3", "GAS2L3", "CBX5",+    "CENPA", "CENPM", "CENPN", "CDC6", "CDT1", "GMNN", "GINS3", "MCM3",+    "MCM7", "MCM10", "ORC6", "RBBP7", "RECQL4", "TIMELESS", "TREX1",+    "KHDRBS1", "SLBP", "ATAD2", "BLM", "CDC45", "E2F1", "ESPL1", "MAD2L1",+    "POLE", "RAD51AP1", "RPA1", "SUV39H1", "WDR76", "APPBP2", "ARL6IP1",+    "ASF1B", "BARD1", "C2orf69", "CCNE1", "CDC7", "DUT", "EIF4EBP1",+    "HIST1H2BG", "HIST1H4C", "KATNAL1", "MBD4", "NAB2", "NASP", "NSUN3",+    "NUDT21", "OGDH", "PDS5B", "POLD3", "PSIP1", "SLC7A5", "SPATA5",+    "SRSF5", "STIL", "ABCB10", "ANAPC11", "ARID4A", "AURKAIP1", "BCCIP",+    "BRCA1", "BRCA2", "BRMS1L", "C8orf34", "CHEK1", "CKAP4", "CSE1L",+    "CTCFL", "DHFR", "DLG1", "DNAJA1", "DNAJB4", "ECT2", "EED", "EXO1",+    "GADD45A", "HIST1H1B", "HIST1H1E", "HMGB3", "HN1L", "HSPA4L", "ILF3",+    "KPNB1", "MALAT1", "MDM2", "MTHFD2", "NFYB", "NISCH", "NUP107",+    "ORC2", "ORC3", "PBK", "PCGF3", "PRC1", "PTTG1", "RAD17", "RAD54L",+    "RANBP1", "RFC4", "RGCC", "RMI2", "SEC61A1", "SRSF1", "STAG1",+    "TCF19", "TFRC", "TLE3", "TMEM99", "TNPO1", "TRIB1", "TRIP13",+    "TXN1", "UBE2D3", "UCK2", "VRK1", "XPO1", "ZNF317",+]+++def group_labels(adata, rng):+    if "celltype" in adata.obs.columns:+        lab = labels_of(adata, "celltype")+    else:+        from sklearn.cluster import KMeans+        from sklearn.decomposition import TruncatedSVD++        X = adata.X+        Xl = np.log1p(X.toarray() if sparse.issparse(X) else X)+        svd = TruncatedSVD(n_components=50, random_state=0)+        emb = svd.fit_transform(Xl)+        km = KMeans(n_clusters=min(30, adata.n_obs), n_init=10, random_state=0).fit(emb)+        lab = np.array([f"cluster_{i}" for i in km.labels_])+    return lab+++def prol_score(adata, genes):+    cc = {g.lower() for g in CC_GENES}+    gidx = np.array([i for i, g in enumerate(genes) if g.lower() in cc])+    if len(gidx) < 10:+        return None+    sub = adata.X[:, gidx]+    s = np.asarray(sub.mean(axis=1)).ravel()+    return s.astype(np.float64)+++def weighted_sample(w, n, rng):+    """Gumbel top-n sampling without replacement (when n <= len(w))."""+    if n >= len(w):+        return np.arange(len(w))+    keys = rng.gumbel(size=len(w)) + np.log(np.maximum(w, 1e-12))+    return np.sort(np.argpartition(-keys, n)[:n])+  def main() -> None:     parser = argparse.ArgumentParser()     parser.add_argument("--data", required=True)     parser.add_argument("--out", required=True)     parser.add_argument("--seed", type=int, default=0)+    parser.add_argument("--beta", type=float, default=0.0, help="proliferation reweight strength (0 = off)")+    parser.add_argument("--alpha", type=float, default=0.0, help="shift damping (0 = off)")+    parser.add_argument("--tau", type=float, default=0.05, help="min |delta| to shift a gene")+    parser.add_argument("--dt-cap", type=float, default=1.5, help="cap on time extrapolation scale")+    parser.add_argument("--wlo", type=float, default=0.25)+    parser.add_argument("--whi", type=float, default=4.0)     args = parser.parse_args() -    manifest = load_manifest(args.data)-    genes = panel_genes(args.data, manifest)-    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    view = args.data+    manifest = load_manifest(view)+    genes = panel_genes(view, manifest)+    P = len(genes)     rng = np.random.default_rng(args.seed)-    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)++    entries = inputs_by_time(manifest)  # incl. external on proxy2+    official = [e for e in entries if not is_external(e)]+    base_entry = official[-1] if official else entries[-1]+    base = read_stage(view, base_entry, genes)+    lab = group_labels(base, rng)++    # ---- composition: proliferation reweighting ----+    n = target_n_cells(manifest, base.n_obs)+    w = np.ones(base.n_obs, dtype=np.float64)+    if args.beta > 0:+        s = prol_score(base, genes)+        if s is not None:+            uniq = np.unique(lab)+            f_c = np.array([s[lab == u].mean() for u in uniq])+            fbar, fsd = f_c.mean(), f_c.std()+            if fsd > 1e-8:+                gw = {u: float(np.clip(np.exp(args.beta * (f - fbar) / fsd), args.wlo, args.whi))+                      for u, f in zip(uniq, f_c)}+                w = np.array([gw[l] for l in lab])+    rows = weighted_sample(w, n, rng)++    X = base.X[rows].tocsr()+    lab_s = lab[rows]+    Xs = X if sparse.issparse(X) else sparse.csr_matrix(X)++    # ---- expression: damped group shift between two same-panel inputs ----+    prev_entry = None+    if args.alpha > 0 and len(official) >= 2 and official[-2]["time"] < official[-1]["time"]:+        prev_entry = official[-2]+    shifted = False+    if prev_entry is not None:+        prev = read_stage(view, prev_entry, genes)+        if "celltype" in prev.obs.columns and "celltype" in base.obs.columns:+            lab_p = labels_of(prev, "celltype")+            dt_in = base_entry["time"] - prev_entry["time"]+            dt_tgt = max(manifest["target"]["time"] - base_entry["time"], 0.0)+            dt_scale = float(np.clip(dt_tgt / dt_in, 0.0, args.dt_cap)) if dt_in > 0 else 0.0+            eff = args.alpha * dt_scale+            uniq_b = {u: i for i, u in enumerate(np.unique(lab))}+            deltas = {}+            for u in np.unique(lab_p):+                if u not in uniq_b:+                    continue+                m_p = lab_p == u+                m_b = lab == u+                if m_p.sum() < 10 or m_b.sum() < 5:+                    continue+                d = (np.asarray(prev.X[m_p].mean(axis=0)).ravel()+                     - np.asarray(base.X[m_b].mean(axis=0)).ravel())+                # d = mean(t_prev) - mean(t_base); shift direction is t_prev->t_base+                d = -d.astype(np.float32)+                d[np.abs(d) < args.tau] = 0.0+                deltas[u] = eff * d+            if deltas:+                lab_row = lab_s+                block = 2048+                out_blocks = []+                for b0 in range(0, Xs.shape[0], block):+                    b1 = min(b0 + block, Xs.shape[0])+                    dense = Xs[b0:b1].toarray()+                    for u, d in deltas.items():+                        m = lab_row[b0:b1] == u+                        if m.any():+                            dense[m] += d[None, :]+                    np.maximum(dense, 0.0, out=dense)+                    out_blocks.append(sparse.csr_matrix(dense.astype(np.float32)))+                Xs = sparse.vstack(out_blocks).tocsr()+                shifted = True+        del prev++    write_prediction(Xs, genes, args.out, seed=args.seed)+    print(f"base={base_entry['stage']} n={Xs.shape[0]} shifted={shifted} beta={args.beta} alpha={args.alpha}")   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）。

用到的知识库条目

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

工程师

完成。solution/ 含 run.py + METHOD.md，5 视图全部跑通且 vec-check ok，proxy 输出与已查分文件 md5 一致。

**做了什么**：父节点（copy_last）在 proxy2 上错把外部 Qiu E9.0（部分基因、仅心脏）当基底；改为「最新官方输入做基底 + 均匀 Gumbel 无放回抽样」。实现了增殖重加权（β）与两组输入阻尼位移（α·Δt 缩放）两个部件，但查分后均默认关闭（β=0, α=0）。

**查分**（9/20 次）：基底修正后 proxy 51.24、proxy2 51.24（父 27.43）、X5 50.0；β=1/2 使 proxy 跌到 37.8/36.1；心脏组降权 w=0.25 跌到 40.5；位移 α=0.4 使 X5 46.6、X3 45.3，α=0.2 仍 48.6——复制最新官方输入在这批尺子上是最优。预计节点分 ≈50.5（父 45.49）。

**下一步建议**：(1) proxy 上探索 cell_state 的真正收益点——组成重加权方向已被证伪，可试「按输入 obs 的解剖来源（anatomy/slide）重组」或学习式 OT 小步位移；(2) 节点2（proxy 56.23）的心脏/JCF/Pericardium 编辑值得作为父节点方向复现；(3) 任何位移启用前必须先在 X5 扫参。

调研员

iation 40.48（合计权重50%），de_recovery 49.90 / direction 50.92 只在地板。三个 seed 的 proxy2 全部=27.43，因为都拿最后一个输入（Qiu E9.0，仅心脏谱系、仅27,883/32,285基因）当基底——仅改用官方全panel输入做基底就该大幅回升。节点3（pseudobulk_shift, 39.48）证明 α=1 全幅位移在有两个输入的 X 题上崩盘（X5 37.88、covariation 22.73），k018 也要求 α∈[0,1] 收缩；X3/X4/X5 恰好是可用于调 α 的外部两输入题。当前最佳节点2（46.40）proxy 56.23 但 X3 掉到 46.85（心脏特化过拟合）。方向库 T1-01 组成重加权是 run2 胜者，与保留真实细胞（covariation 不塌）兼容，故本方案=组成重加权（所有视图）+阻尼位移（仅≥2输入视图）。",
  "approach": "步骤1 基底与分组：inputs_by_time 取所有输入，基底=panel基因覆盖率最高的官方输入（proxy2 上即 E8.5，绝不用 Qiu 做基底）；分组优先用视图 obs 自带的细胞类型标签，若无则 log1p+PCA(50维)+kmeans(~30簇)。步骤2 组成重加权（所有视图，含单输入 proxy）：每个细胞算增殖分=Seurat S/G2M 基因集（与 panel 取交集）的 log1p 均值；组均值 f_c，全体均值 f̄、标准差 σ_f；采样权重 w_c = n_c·clip(exp(β·(f_c−f̄)/σ_f), 0.25, 4)，β 初值1，搜索{0.5,1,2}；用 Gumbel top-N 做不放回加权采样到 target_n_cells（保留真实细胞，covariation 不受损）。步骤3 阻尼位移（仅当输入≥2：X3/X4/X5/proxy2/final）：同名组 delta_c = pseudobulk(input2,c)−pseudobulk(input1,c)；逐基因经验贝叶斯收缩 λ_g=τ²/(τ²+σ²_g)（σ²_g 为 delta 的组间噪声方差，τ² 为其分位数稳健估计）；x←clip(x+α·λ_g·delta_c,0)，α 初值0.4，在 X3/X4/X5 上搜索{0.3,0.5,0.7}；跨数据集对（input2 基因覆盖<95% panel，如 proxy2 的 Qiu）时 α 再×0.5 且只在共有基因上算 delta；仅出现在单个时间点的组：复制不移位。步骤4 单输入退路：proxy 只执行步骤2，不做任何表达位移（节点3的 proxy=50.04 已证明单输入下位移退化为 copy，不会更差）。步骤5 vec-score 筛选（预算20）：先 proxy 上 β∈{0.5,1,2} 三查 → 最优 β 上 X3/X4/X5 各一查 → X3/X5 上 α∈{0.3,0.5,0.7} 四至六查 → proxy2 复查两查；单把尺子提升<2分视为噪声，要求≥2把尺子同向提升或复查确认才接受参数。步骤6 退化诊断（k016）：每次输出唯一细胞比例（应=1）、组数与稀有组保留率、各组方差相对输入的比值、组成熵，与分数一起记录。",
  "expected_groups": ["cell_state", "de_recovery", "direction"],
  "risks": "(a) 增殖分对1天在体扩增预测力弱（组成变化更多来自新类型出现而非既有类型生长），cell_state 增益可能<2分——用 proxy 上 β=0 与 β=1 的头两次查分即可判断，无效则退回 β=0 只保留位移部分；(b) 重加权无法生成输入中不存在的新细胞类型，cell_state 提升有上限，不要为此加大 β 导致高增殖稀有组被过度放大——监控 covariation 组，任何一次查询 <38 立即下调 β/α；(c) α 收缩后仍可能重演节点3的 covariation 崩塌（22.73）——α 扫描必须在 X5（对位移最敏感，节点3掉到37.88）上先做；(d) Gumbel 采样实现错误会引入重复细胞——诊断唯一细胞比例；(e) proxy2 上 Qiu 与官方数据批次差可能大于时间信号，delta 变噪声——若 proxy2 分数低于纯 copy-E8.5+重加权版本，直接禁用跨数据集 delta，只保留基底修正+重加权（预计仍远高于27.43）；(f) 30分钟时限紧，优先实现步骤2+基底修正（最确定的收益），步骤3的 EB 收缩可先省略 λ_g 只用 α。",
  "sources": []
}
```

审查员

REVIEW.json written