总览 · ← 返回运行 20261003-093415-search-t1-r2-D-s1

节点 n10

graph_fate v2: stage-constrained successor selection + two-stage type-frequency trend reweighting

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 e0d84258bd: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 6a768b1f3c。改动的文件：solution/METHOD.md +30 −38、solution/run.py +112 −53

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 14328e1..251cc37 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,45 +1,37 @@-Palantir伪时间+CellRank hard前向转移，逐细胞按概率抽样后继（可多步随机游走）作为E目标预测；对照(--off)为恒等转移即复制输入。+# graph_fate v2: stage-constrained successor selection + two-stage type-frequency trend reweighting -# graph_fate (family: graph_fate, PLAN 最小版本)+两阶段视图上把后继限制为更晚阶段细胞并按观测类型频率趋势重加权输出组成；单阶段视图用 top-2 伪时间前向邻居选择（delta_min=0.005）。X3 查分 55.27 vs off 48.21。 -## 方法-1. 读视图全部输入阶段（遍历 manifest.inputs，按 time 排序；只用相对时间差，视图无关）。多阶段拼接并 obs_names_make_unique，记相对 day。-2. 按阶段分层抽样至 ≤4000 细胞建图。-3. log1p(CP10k) 数据上选 2500 HVG（seurat flavor）→ 25 维 PCA → kNN 图（n_neighbors=30）。-4. 根细胞：增殖基因集 (Mki67, Top2a, Pcna, Ccna2, Ccnb1) 均值经邻接平滑后 argmax；面板中 <3 个基因时退路为 PCA 空间离质心最远细胞。-5. Palantir：run_diffusion_maps(n_components=10) → determine_multiscale_space（启发式失败时退路 n_eigs=min(5,·)）→ run_palantir(early_cell=root, num_waypoints=400, seed)。多阶段输入时若伪时间与相对 day 的 Spearman 相关为负则翻转伪时间（定向纠错）。-6. CellRank 2.3 PseudotimeKernel.compute_transition_matrix(threshold_scheme="hard", frac_to_keep=0.3)：只保留伪时间前向边，行归一。-7. 预测：每个输出细胞按转移概率做 steps=1 步分类抽样（categorical，rng 由 --seed 派生），输出 = 后继细胞的原始表达（beta=0，即纯真实观测细胞，不做混合；beta>0 的自保留混合已测试且显著伤害 variogram/mmd，弃用）。-8. 输出细胞数 = clip(target_n_cells, min_cells, min(max_cells, n_graph))。+## 方法（family: graph_fate，PLAN 修复版）+1. 读全部输入阶段（遍历 manifest.inputs，只用相对时间差，视图无关），拼接、按阶段分层抽 ≤4000 细胞。+2. 2500 HVG → 25 PCA → kNN（n_neighbors=15，从 30 收紧）。+3. 根细胞：增殖基因 (Mki67/Top2a/Pcna/Ccna2/Ccnb1，通用增殖标记知识) 邻接平滑 argmax；Palantir 伪时间；多阶段时按 Spearman(伪时间, 相对日龄) 定向纠错。+4. **两阶段路径（X3、final）**：CellRank PseudotimeKernel hard 转移矩阵，逐细胞只保留 stage(j) > stage(i) 的边，取其中连接权重最大的后继（确定性 top-1，比 node 6 的概率抽样集中：median top-1 0.37，frac>0.3 = 0.67，node 6 为 0.15/0）；终末（最晚阶段）细胞保持自身不变（changed_fraction≈0.24–0.40 < 1，node 6 为 1.0）。+5. **组成趋势重加权（两阶段，gamma=1.0）**：对每个 celltype，w = clip((f_late+1e-4)/(f_early+1e-4), 0.25, 4)；f 为该型在最后两个输入阶段的频率。纯数据驱动，不依赖任何保留阶段知识。输出细胞按 w 加权抽样（rng 由 --seed 派生）。late 缺失型 w=1。+6. **单阶段路径（proxy10）**：伪时间前向 top-2 邻居选择（gap ≥ delta_min=0.005，取连接权重大者），无满足者保持自身。PLAN 的 delta_min=0.05 只移动 5% 细胞（≈copy_last），实测 0.005 移动 68%、mean gap 0.022。+7. 输出 = 后继细胞的原始观测表达（beta=0，node 6 已证混合伤 variogram/mmd）。细胞数 = clip(target_n_cells)。  ## 机制关闭对照（--off）-恒等转移矩阵 → 后继=自身 → 输出=输入细胞的重抽样（等价 copy_last）。同一脚本，提交默认开启机制（不带 --off）。+恒等转移 + 均匀抽样 → 输出 = 输入重抽样（≈copy_last）。  ## 查分结果（A 半，seed 0）-| 配置 | proxy10 | X3 | 加权节点分(1:2) |+| 配置 | proxy10 | X3 | 加权(1:2) | |---|---|---|---|-| 机制开 hard steps=1（提交配置） | 51.89 | 45.76 | ≈47.8 |-| 机制开 soft steps=1 beta=0 | 51.93 | 未测 | - |-| 机制开 hard steps=3 | 51.95 | 未测 | - |-| beta=0.2 混合 | 46.50 | 39.84 | ≈42.1（混合平均化严重伤 covariation：variogram 28.5/24.3）|-| 机制关 --off | 52.36 | 47.54 | ≈49.1 |--**结论（如实报告）：机制未跑赢关闭对照。** 开启 vs 关闭在 proxy10 上 -0.5、X3 上 -1.8（X3 权重 2，节点分约 -1.4），超出 ~2 分噪声的边缘但不构成 PLAN 期望的 +2 分收益。--## 机制生效证据-- changed_fraction=1.00（proxy/X3，soft 与 hard steps=1）：全部输出细胞的后继≠自身。-- 转移矩阵熵：median top-1 概率 0.15（soft），frac(top1>0.8)=0 —— 图未退化为确定性映射，但转移高度弥散，抽样后继≈邻域内近随机重采样，因此输出分布≈输入分布（解释了 b0≈off）。-- hard 前向方案下 steps=3 时 changed_fraction=0.97（末端吸收态细胞停在原地），伪时间前向漂移确实发生，但幅度（1 天发育）远小于图局部混合噪声。-- 四组分相对 off：proxy10 cell_state 50.0 vs 52.0、covariation 48.8 vs 46.8、direction 56.8 vs 55.5（de_direction skill 相近）、de_recovery 51.7 vs 51.5——无一致方向性收益。-- 未做：leiden 簇间位移余弦矩阵（位移=后继−自身是两真实细胞之差，非平滑场，预期低相关，未量化）。--## 生物学知识来源-增殖基因集 (Mki67/Top2a/Pcna/Ccna2/Ccnb1) 为通用细胞周期标记知识（GO cell cycle / Reactome cell cycle 通路，非针对禁窗阶段的测量）。未使用任何保留阶段/基因型的测量信息、外部数据或文献比例。--## 已验证 / 未验证-- 已验证：proxy（单输入）与 X3（双输入、含 Unknown 标签、跨数据集）全流程跑通；--off 对照；beta 混合有害；soft vs hard；steps 1 vs 3。-- 未验证：graph_n/beta/steps 的系统调参（时间预算内仅抽查）；伪装视图重跑一致性（代码只用相对时间与视图数据，无路径/绝对时间分支，理论一致但未实测）；多 seed 稳定性。-- 确定性：np.random.default_rng(seed)+np.random.seed；palantir/cellrank 均传 seed 或 n_jobs=1。--## 给后续节点的建议-单阶段输入内图上做局部转移缺乏真实时间梯度，收益被邻域混合噪声淹没；graph_fate 若要有收益，需两个以上输入阶段提供真实位移监督（把 E8.5→E9.5 观测差作为转移的方向约束），或与组成趋势（节点 3）结合：先按趋势重加权再在型内做伪时间选择。+| 机制开（提交配置） | 52.11 | **55.27** | **54.2** |+| 机制关 --off | (52.36, node6 同行为) | 48.21 | ≈49.6 |+| 只阶段约束、不重加权 (--reweight 0) | — | 46.92 | — |+| 消失型也压到 0.25 | — | 53.63 | — |++**硬门槛（on ≥ off+2 加权）通过：54.2 vs ≈49.6，主要由 X3 +7.1 驱动。**++## 如实说明：哪个部件在起作用+- X3 的 +7.1 分**主要来自类型频率趋势重加权**（mmd_u skill 0.489→0.594、de_score 0.442→0.546）；单独的阶段约束转移（--reweight 0）反而略低于 off（46.92 vs 48.21）。graph_fate 的"时间约束后继"机制本身在两阶段视图上未证明增益，重加权（组成杠杆，与 node 3 一致）是驱动。+- proxy10 单阶段 top-2 伪时间选择分数中性（52.11 vs off ≈52.36，噪声内）；未叠加组成重加权，因为单输入阶段**没有任何可从数据观测的类型趋势**（不引入硬编码比例，规则禁止）。+- 机制生效证据：changed_fraction 0.24 (X3) < 1.0；后继 100% 来自更晚阶段（对移动的细胞，frac_succ_later_stage=moved 比例一致）；median top-1 概率 0.37 vs node 6 的 0.15；单阶段 mean 伪时间 gap +0.015 > 0。++## 验证过 / 未验证+- 已验证：X3、proxy10 上确定性（同 seed 重跑逐元素一致）、vec-check 通过、off 对照、reweight 消融。+- 未验证：final（E8.5+E9.5 两阶段，走 X3 同路径，预期重加权同样起作用但幅度未知）；gamma≠1、clip 界以外的调参（避免 A 半过拟合，未做）；伪装视图重跑（代码只用相对时间和数据内容，无路径/绝对时间分支）。++## 知识来源+增殖基因列表（通用细胞周期知识）；其余全部从输入数据现场计算（频率比、伪时间、图），无任何保留阶段测量或硬编码统计量。diff --git a/solution/run.py b/solution/run.pyindex ad4d345..15736c9 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,11 +1,11 @@-"""graph_fate: Palantir pseudotime + CellRank transition kernel, per-cell successor sampling.--Family: graph_fate (PLAN minimal version).-Mechanism: build kNN graph on (subsampled) input cells, orient it with Palantir-pseudotime rooted at a proliferation-based progenitor cell, compute a CellRank-PseudotimeKernel forward transition matrix, and predict each output cell as-beta * self + (1 - beta) * sampled successor (a real observed cell).---off replaces the transition matrix with identity => output equals copy of inputs.+"""graph_fate v2: stage-constrained successors (2+ stages) / concentrated pseudotime-forward+top-2 selection (single stage), optional type-frequency trend reweighting.++Family: graph_fate (PLAN fix of node 6).+Mechanism: successors are selected only from later-stage cells when 2+ input stages exist;+with a single stage, each cell's successor is the best-connected of its top-2 kNN neighbors+whose pseudotime exceeds the cell's by >= delta_min. Terminal cells stay unchanged.+--off replaces the transition with identity => output equals resampled inputs (copy_last). """  import argparse@@ -28,13 +28,15 @@ def parse_args():     p.add_argument("--seed", type=int, required=True)     p.add_argument("--off", action="store_true", help="mechanism-off control (identity transition)")     p.add_argument("--graph-n", type=int, default=4000)-    p.add_argument("--beta", type=float, default=0.0)     p.add_argument("--scheme", default="hard")-    p.add_argument("--steps", type=int, default=1)     p.add_argument("--n-hvg", type=int, default=2500)     p.add_argument("--n-pcs", type=int, default=25)-    p.add_argument("--n-neighbors", type=int, default=30)+    p.add_argument("--n-neighbors", type=int, default=15)     p.add_argument("--num-waypoints", type=int, default=400)+    p.add_argument("--delta-min", type=float, default=0.005,+                   help="min normalized-pseudotime gap for single-stage forward selection")+    p.add_argument("--reweight", type=float, default=1.0,+                   help="exponent gamma for two-stage type-frequency trend extrapolation (0=off)")     p.add_argument("--smoke", type=int, default=0, help="if >0, cap graph cells for a smoke test")     return p.parse_args() @@ -66,7 +68,9 @@ def main():     data.obs_names_make_unique()     days = np.asarray(days, dtype=float)     days = days - days[0]  # relative times only (view-invariant under time shifts)-    day_of_cell = days[data.obs["stage_k"].to_numpy()]+    n_stages = len(entries)+    stage_of_cell = data.obs["stage_k"].to_numpy()+    day_of_cell = days[stage_of_cell]      n_total = data.shape[0]     n_graph = min(n_total, args.graph_n)@@ -75,8 +79,8 @@ def main():      # stratified subsample by stage     idx_by_stage = {}-    for k in np.unique(data.obs["stage_k"].to_numpy()):-        idx_by_stage[int(k)] = np.flatnonzero(data.obs["stage_k"].to_numpy() == k)+    for k in np.unique(stage_of_cell):+        idx_by_stage[int(k)] = np.flatnonzero(stage_of_cell == k)     take = []     remaining = n_graph     ks = sorted(idx_by_stage)@@ -93,6 +97,7 @@ def main():      g = data[graph_idx].copy()     Xg = g.X.tocsr().astype(np.float32)+    gstage = stage_of_cell[graph_idx]      # ---- reduce: HVG + PCA ----     import scanpy as sc@@ -143,59 +148,113 @@ def main():             pt = pt.max() + pt.min() - pt             g.obs["palantir_pseudotime"] = pt -    # ---- CellRank transition matrix ----     n = g.shape[0]+    pt_n = (pt - pt.min()) / (pt.max() - pt.min() + 1e-12)+    C = g.obsp["connectivities"].tocsr()+     if args.off:-        T = sp.identity(n, format="csr", dtype=np.float64)-    else:+        succ_map = np.arange(n)+    elif n_stages >= 2:+        # ---- stage-constrained transition: successors only from strictly later stages ----         import cellrank as cr          pk = cr.kernels.PseudotimeKernel(g, time_key="palantir_pseudotime")         pk.compute_transition_matrix(threshold_scheme=args.scheme, frac_to_keep=0.3,                                      b=10.0, nu=0.5, show_progress_bar=False, n_jobs=1)-        T = pk.transition_matrix.tocsr().astype(np.float64)-        # diagnostic: how deterministic is the map-        Tp = np.asarray(T.max(axis=1).todense()).ravel()-        stats = dict(frac_top1_gt_0p8=float((Tp > 0.8).mean()), median_top1=float(np.median(Tp)))-        print("transition stats:", stats, flush=True)--    # ---- choose output cells ----+        T = pk.transition_matrix.tocsr()+        succ_map = np.arange(n)+        n_later = 0+        top1 = []+        for i in range(n):+            s, e = T.indptr[i], T.indptr[i + 1]+            if e <= s:+                continue+            js = T.indices[s:e]+            ws = T.data[s:e].astype(np.float64)+            keep = gstage[js] > gstage[i]+            if not keep.any():+                continue  # terminal (last-stage) cell: keep unchanged+            js, ws = js[keep], ws[keep]+            j = int(js[np.argmax(ws)])+            succ_map[i] = j+            n_later += 1+            top1.append(float(ws.max() / ws.sum()))+        top1 = np.asarray(top1) if top1 else np.zeros(0)+        stats = dict(moved_frac=float(n_later / n),+                     median_top1=float(np.median(top1)) if top1.size else None,+                     frac_top1_gt_0p3=float((top1 > 0.3).mean()) if top1.size else None)+        print("two-stage transition stats:", stats, flush=True)+    else:+        # ---- single stage: concentrated top-2 pseudotime-forward neighbor selection ----+        succ_map = np.arange(n)+        moved = 0+        gaps = []+        for i in range(n):+            s, e = C.indptr[i], C.indptr[i + 1]+            if e <= s:+                continue+            js = C.indices[s:e]+            ws = C.data[s:e].astype(np.float64)+            ok = pt_n[js] > pt_n[i] + args.delta_min+            if not ok.any():+                continue+            js, ws, pts = js[ok], ws[ok], pt_n[js[ok]]+            if len(js) > 2:+                top = np.argsort(-pts)[:2]+                js, ws = js[top], ws[top]+            j = int(js[np.argmax(ws)])+            succ_map[i] = j+            moved += 1+            gaps.append(float(pt_n[j] - pt_n[i]))+        stats = dict(moved_frac=float(moved / n),+                     mean_gap=float(np.mean(gaps)) if gaps else None)+        print("single-stage selection stats:", stats, flush=True)++    # ---- composition: two-stage type-frequency trend extrapolation ----+    cell_w = np.ones(n, dtype=np.float64)+    gamma = float(args.reweight)+    if (not args.off) and n_stages >= 2 and gamma > 0 and "celltype" in g.obs:+        ct = g.obs["celltype"].astype(str).to_numpy()+        last = gstage.max()+        f1 = {}+        f0 = {}+        n1 = int((gstage == last).sum())+        n0 = int((gstage == last - 1).sum())+        for t in np.unique(ct):+            f1[t] = float(((gstage == last) & (ct == t)).sum()) / max(n1, 1)+            f0[t] = float(((gstage == last - 1) & (ct == t)).sum()) / max(n0, 1)+        w_t = {}+        for t in f1:+            if f1[t] <= 0:+                w_t[t] = 1.0+                continue+            ratio = (f1[t] + 1e-4) / (f0[t] + 1e-4)+            w_t[t] = float(np.clip(ratio ** gamma, 0.25, 4.0))+        cell_w = np.array([w_t.get(t, 1.0) for t in ct], dtype=np.float64)+        cell_w *= (gstage == last).astype(np.float64) * 0.0 + 1.0  # keep all stages selectable+        print("reweight types:", {k: round(v, 2) for k, v in sorted(w_t.items(), key=lambda x: -x[1])[:8]}, flush=True)++    # ---- choose output cells (weighted by composition weights) ----     n_out = view_io.target_n_cells(man, n_total)     n_out = int(np.clip(min(n_out, n), man["min_cells"], min(man["max_cells"], n)))     n_out = max(n_out, int(man["min_cells"]))-    out_pos = np.sort(rng.choice(n, size=n_out, replace=False))+    pw = cell_w / cell_w.sum()+    out_pos = np.sort(rng.choice(n, size=n_out, replace=False if n_out <= n else True, p=pw)) -    # ---- sample successors -----    cum = np.asarray(T.indptr)--    def sample_succ(i):-        s, e = cum[i], cum[i + 1]-        if e <= s:-            return i-        if e - s == 1:-            return int(T.indices[s])-        pr = T.data[s:e].astype(np.float64)-        pr = pr / pr.sum()-        return int(T.indices[s + int(rng.choice(e - s, p=pr))])--    succ = out_pos.copy()-    for _ in range(max(1, args.steps)):-        succ = np.array([sample_succ(int(i)) for i in succ], dtype=np.int64)+    succ = succ_map[out_pos]      changed = float((succ != out_pos).mean())-    print(f"n_out={n_out} changed_fraction={changed:.3f} off={args.off}", flush=True)--    # ---- prediction: beta * self + (1 - beta) * successor -----    beta = float(args.beta)-    Xa = Xg[out_pos]-    Xb = Xg[succ]-    if beta >= 1.0:-        pred = Xa-    elif beta <= 0.0:-        pred = Xb+    gap_sel = float(np.mean(pt_n[succ] - pt_n[out_pos])) if succ.size else 0.0+    if n_stages >= 2 and not args.off and succ.size:+        frac_later = float((gstage[succ] > gstage[out_pos]).mean())     else:-        pred = (beta * Xa + (1.0 - beta) * Xb).tocsr()-    pred = pred.astype(np.float32)+        frac_later = None+    print(f"n_out={n_out} changed_fraction={changed:.3f} mean_pt_gap={gap_sel:.3f} "+          f"frac_succ_later_stage={frac_later} off={args.off}", flush=True)++    # ---- prediction: successor's raw observed expression (beta=0, no mixing) ----+    pred = Xg[succ].astype(np.float32)+    pred = pred.tocsr()     pred.eliminate_zeros()      view_io.write_prediction(pred, genes, args.out, seed=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）。

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

Done. Summary:

**做了什么**：修复 node 6 的 graph_fate——两阶段视图上后继只取更晚阶段细胞（确定性 top-1，median top-1 0.15→0.37，终末细胞不变），叠加数据驱动的类型频率趋势重加权（clip [0.25,4]）；单阶段视图改用 delta_min=0.005 的 top-2 伪时间前向选择（PLAN 的 0.05 只动 5% 细胞）。off 对照、消融、确定性、vec-check 全部通过。

**查分**（A 半）：X3 开 55.27 vs 关 48.21（+7.1）；proxy10 开 52.11 ≈ 关 52.36。加权节点分 ≈54.2 vs off ≈49.6，硬门槛通过。消融显示 X3 增益主要来自组成趋势重加权而非阶段约束本身（后者单独 46.92 < off），已在 METHOD.md 如实写明。

**下一步最值得试**：把趋势重加权嫁接到 node 7（56.23）的组成方案上比较两族重加权规则；以及在单阶段视图找不依赖保留知识的组成信号（如 external/ 窗外数据的类型动态先验）。

调研员

on. 4) If composition reweighting step fails (no type labels in some view), skip gracefully.",
  "family_id": "graph_fate",
  "mechanism": "Graph-based fate transition with temporal constraint: successors are selected only from later-stage cells (two-stage) or from top-2 pseudotime-forward neighbors with minimum gap (single-stage), ensuring the transition actually moves cells forward in developmental time rather than shuffling within a local neighborhood.",
  "vs_constant_shift": "Each cell's successor is determined by its position in the kNN graph and its pseudotime — cells in different graph regions transition to different successors. A constant shift would move all cells of a type by the same vector; here, two cells of the same type but different pseudotime values get different successors (one may be near a branch point and go to a different fate). The displacement is cell-specific, not type-constant.",
  "mechanism_evidence": "1) Median top-1 transition probability should rise from 0.15 (node 6) to >0.4 after stage constraint / top-2 selection. 2) changed_fraction should be <1.0 (some terminal cells unchanged) vs 1.0 in node 6. 3) The pseudotime of successors should be strictly greater than input cells' pseudotime (verify mean gap >0.05). 4) Four-group decomposition: cell_state (mmd_u) and direction (de_direction) should improve vs off-control; if only total score changes without group-level signal, mechanism is not operating as intended. 5) For X3 two-stage path: verify that >80% of selected successors come from the later stage.",
  "mechanism_off_control": "Same script with --off flag: transition matrix replaced by identity (T = I), so every cell is its own successor → output = resampled input (equivalent to copy_last). Compare on vs off on both proxy10 and X3. Expected difference: on should score ≥2 points higher than off on weighted node score if the temporal constraint provides real directional information. If on ≤ off, do NOT submit on.",
  "sources": []
}