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节点 n19 在终选来历上

growth_dynamics 最小版：两输入阶段做非平衡 Sinkhorn OT，取逐细胞生长权重 g_i（源边际失衡）驱动重采样 + 重心速度 α 外推（残差解码回真实细胞，稀疏门控）；单输入退路为 copy_last。

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 7f050577dd: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：这个提交的上一版（种子程序：相对空仓库）。改动的文件：solution/EXECUTION.json +1 −0、solution/METHOD.md +70 −0、solution/run.py +216 −0

diff --git a/solution/EXECUTION.json b/solution/EXECUTION.jsonnew file mode 100644index 0000000..9d5125c--- /dev/null+++ b/solution/EXECUTION.json@@ -0,0 +1 @@+{"gpu": false}diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..32b3a5a--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,70 @@+growth_dynamics 最小版：两输入阶段做非平衡 Sinkhorn OT，取逐细胞生长权重 g_i（源边际失衡）驱动重采样 + 重心速度 α 外推（残差解码回真实细胞，稀疏门控）；单输入退路为 copy_last。++## 方法族与机制++family_id = growth_dynamics（PLAN 指定的最小实现）。机制：非平衡最优传输（POT+`ot.sinkhorn_unbalanced`，reg=0.05）在 25 维 PCA 空间（2500 HVG，两阶段合并拟合，+randomized SVD，random_state=seed）耦合相邻两输入阶段；每个源细胞的耦合行和 /+(1/n) 给出局部生长权重 g_i（clip [0.4,2.5]），重心差给出速度 v_i（范数 clip 到+2×中位数）。(v,g) 经 5-NN 传到末阶段全部细胞；外推 z_pred = z_last + α·v，+α = 0.8·clip(Δt_out/Δt_train, 1, 1.5)（只用时间差，视图无关）；输出 N_out 个细胞+按 p ∝ g 有放回重采样（机制关闭时均匀采样）；解码为残差式：真实末阶段细胞的稀疏+表达 + PCA 逆投影位移，位移只加在该细胞已表达的基因上（稀疏门控，默认开），+clip ≥0；非 HVG 覆盖基因原样保留。单输入阶段（proxy10）生长不可辨识，退路为+copy_last 均匀子采样。++reg_m 自适应：依次尝试 2.0 → 1.0 → 0.5，取第一个使 std(g_src) ≥ 0.05 的（PLAN+风险 1 的处置）；X3 上实际选中 reg_m=1.0。++## 机制生效证据（X3_qiu_heart_early，seed 0，A 半）++- g_i 分布（NN 平滑后）：mean 0.845，std 0.047，min 0.705，max 0.928；+  15.4% 细胞 g∉[0.8,1.2]。std > 0.05 在源细胞层面成立（reg_m=1.0 时 g_src+  std=0.057），NN 平滑后略降 → 机制活性偏弱。+- 机制改变的内容：只改变被抽样细胞的组成（g 高的亚状态被过采样），不改变表达+  解码路径。生长开 vs 关（同一位移，均匀重采样）：+  - growth-on：47.16（cell_state 49.19，covariation 48.36，mmd_u 0.03475）+  - growth-off：47.41（cell_state 48.89，covariation 48.57）+  差值在 ±2 噪声内 → 生长重采样在 X3 上**惰性到轻微有害**，未产生可测收益。+- 位移外推（α）扫描：α=1.2 无门控 46.16（variogram 42.1，稠密位移破坏共变）；+  α=1.2 门控 47.16；α=0.6 门控 47.24；α=0（纯生长重采样）47.38；均 ≤ copy_last+  参考 47.92（节点 1）。OT 重心速度方向在 X3 上未带来 mmd/DE 收益。++## 查分记录（A 半）++| 配置 | X3 |+|---|---|+| resid 无门控 α=1.2 growth-on / off | 46.16 / 46.18 |+| resid 无门控 α=0.6 | 46.66 |+| 纯生长重采样 α=0 / sharpen^4 | 47.38 / 44.41 |+| 门控 α=1.2 / α=0.6 | 47.16 / 47.24 |+| 门控 α=1.2 growth-off（对照，提交配置） | 47.41 |++proxy10（copy_last 退路，提交配置）：52.97（种子 copy_last 节点 1 为 52.75，+噪声内一致）。++## 验证过 / 未验证++- 验证过：X3 与 proxy 两视图 vec-check 通过；seed 0 下跨 cwd 重跑输出逐位相同+  （确定性）；只用时间差与视图数据（视图无关）；纯 CPU（EXECUTION.json gpu=false），+  X3 全程 <40s、proxy <5s。+- 未验证：α、λ、reg 的系统扫描（时间预算内只做了上述点）；blend 解码（实现了+  GROWTH_DECODE=blend 但未查分）；生长机制在输入间隔更长/细胞更多的视图（如+  proxy2、final）上是否更强——X3 两阶段同为 Qiu 心脏细胞、间隔仅 0.25 天，+  生长信号弱。++## 结论与下一步++按 PLAN 要求提交时机制保持打开（growth-on、门控残差解码）。诚实结论：在 X3 上+该机制族的最小版未跑赢 copy_last（≈47.2 vs 47.9，噪声内），瓶颈是 0.25 天间隔+下非平衡 OT 的生长权重对比度太低（std≈0.05），以及重心速度对 mmd 无收益。+下一步最值得试：(1) 把生长权重按细胞类型聚合（去噪）再与组成趋势重加权（节点+7/13/16 已证明有效）组合；(2) 更低 reg_m / KL 权重扫描提高 g 对比度；(3) 在+proxy2（0.5 天间隔、跨数据集）上检验生长信号是否更可辨识。++## 知识来源++未使用任何保留阶段/保留基因型的测量信息；未用外部数据（X3 视图挂载的+external/ 未读取——输入本身即 Qiu E8.75/E9.0，输出目标 E9.5 在禁窗外但无对应+外部数据可用）。方法为通用算法知识（非平衡 OT：Séjourné et al. sinkhorn+divergences 框架；生长权重 = 边际失衡，Waddington-OT 生长项的标准做法）。diff --git a/solution/run.py b/solution/run.pynew file mode 100644index 0000000..58e5c44--- /dev/null+++ b/solution/run.py@@ -0,0 +1,216 @@+"""growth_dynamics draft: unbalanced OT growth weights + barycentric velocity extrapolation.++Two-input path: HVG -> PCA(25) -> unbalanced Sinkhorn OT between the two input+stages -> per-source-cell growth g_i (row marginal imbalance) and barycentric+velocity v_i -> transfer (v, g) to last-stage cells via 5-NN -> extrapolate+z_pred = z_last + alpha * v (alpha = damp * clip(dt_out/dt_train, 1, 1.5)) ->+resample N_out cells with probability proportional to g -> decode to gene space.++Single-input path: growth is not identifiable from one stage; fall back to+copy_last (uniform subsample of the last input stage).++Mechanism control: GROWTH_OFF=1 sets all g_i = 1 (uniform resampling), keeping+the velocity displacement identical. Kept ON for submission.++Only time differences (not absolute times) and view data are used; deterministic+under --seed.+"""+from __future__ import annotations++import argparse+import os+import sys++import numpy as np+from scipy import sparse++sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))++from src.task1_temporal import view_io  # noqa: E402+++# --- hyperparameters (PLAN.json) ---+N_HVG = 2500+N_PCA = 25+MAX_TRAIN = 3000+OT_REG = 0.05+OT_REG_M = 2.0+OT_REG_M_FALLBACK = 1.0+OT_REG_M_FALLBACK2 = 0.5+G_CLIP = (0.4, 2.5)+G_STD_MIN = 0.05+ALPHA_DAMP = float(os.environ.get("GROWTH_DAMP", "0.8"))+T_CLIP = (1.0, 1.5)+V_NORM_CLIP = 2.0  # clip displacement norm to 2x median+K_TRANSFER = 5+LAMBDA_RESID = 0.4+DECODE = os.environ.get("GROWTH_DECODE", "resid")  # "resid" | "blend"+GROWTH_OFF = os.environ.get("GROWTH_OFF", "0") == "1"+GROWTH_SHARP = float(os.environ.get("GROWTH_SHARP", "1.0"))+++def hvg_indices(X: sparse.csr_matrix, mask: np.ndarray, n_hvg: int) -> np.ndarray:+    """Top variable genes among covered genes (seurat-style on log data)."""+    sub = X[:, mask]+    mean = np.asarray(sub.mean(axis=0)).ravel()+    sq = np.asarray(sub.multiply(sub).mean(axis=0)).ravel()+    var = np.maximum(sq - mean**2, 0.0)+    with np.errstate(divide="ignore", invalid="ignore"):+        nzs = np.where(mean > 1e-8, var / mean, 0.0)+    nzs[mean <= 1e-8] = -1.0+    order = np.argsort(-nzs)+    keep = order[: min(n_hvg, order.size)]+    idx = np.flatnonzero(mask)+    return np.sort(idx[keep])+++def fit_pca(A: np.ndarray, k: int, seed: int):+    from sklearn.utils.extmath import randomized_svd++    mean = A.mean(axis=0)+    C = A - mean+    _, _, Vt = randomized_svd(C, k, random_state=seed, n_oversamples=30)+    comp = Vt.astype(np.float32)+    return mean.astype(np.float32), comp+++def main() -> None:+    ap = argparse.ArgumentParser()+    ap.add_argument("--data", required=True)+    ap.add_argument("--out", required=True)+    ap.add_argument("--seed", type=int, required=True)+    args = ap.parse_args()+    import time++    _t0 = time.time()++    def _tm(tag):+        print(f"[time] {tag} {time.time() - _t0:.1f}s", file=sys.stderr)+    view = args.data+    rng = np.random.default_rng(args.seed)++    manifest = view_io.load_manifest(view)+    genes = view_io.panel_genes(view, manifest)+    entries = view_io.inputs_by_time(manifest, include_external=True)+    last = entries[-1]+    adata_last = view_io.read_stage(view, last, genes, missing="fill")+    n_out = view_io.target_n_cells(manifest, adata_last.n_obs)++    if len(entries) < 2:+        # single-input fallback: copy_last (uniform subsample of real cells)+        idx = view_io.sample_rows(adata_last.n_obs, n_out, rng)+        view_io.write_prediction(adata_last.X[idx], genes, args.out, seed=args.seed)+        return++    first = entries[-2]+    adata_first = view_io.read_stage(view, first, genes, missing="fill")++    _tm("read")+    cov = np.asarray(adata_last.var["covered"], dtype=bool) & np.asarray(+        adata_first.var["covered"], dtype=bool+    )+    hvg = hvg_indices(adata_last.X, cov, N_HVG)++    _tm("hvg")+    # subsample for training+    def sub(a, n):+        if a.n_obs <= n:+            return np.arange(a.n_obs)+        return np.sort(rng.choice(a.n_obs, size=n, replace=False))++    i0 = sub(adata_first, MAX_TRAIN)+    i1 = sub(adata_last, MAX_TRAIN)+    X0 = np.asarray(adata_first.X[i0][:, hvg].todense(), dtype=np.float32)+    X1 = np.asarray(adata_last.X[i1][:, hvg].todense(), dtype=np.float32)++    mean, comp = fit_pca(np.vstack([X0, X1]), N_PCA, args.seed)+    Z0 = (X0 - mean) @ comp.T+    Z1 = (X1 - mean) @ comp.T+    Z1_full = (np.asarray(adata_last.X[:, hvg].todense(), dtype=np.float32) - mean) @ comp.T++    _tm("pca")+    # --- unbalanced OT: growth + barycentric velocity on source cells ---+    import ot as pot++    a = np.full(Z0.shape[0], 1.0 / Z0.shape[0])+    b = np.full(Z1.shape[0], 1.0 / Z1.shape[0])+    d = Z0[:, None, :] - Z1[None, :, :]+    M = (d * d).sum(-1).astype(np.float64)+    M /= max(np.median(M), 1e-8)++    P = rs = None+    for reg_m in (OT_REG_M, OT_REG_M_FALLBACK, OT_REG_M_FALLBACK2):+        P = pot.sinkhorn_unbalanced(a, b, M, reg=OT_REG, reg_m=reg_m)+        rs = P.sum(axis=1)+        if np.std(rs * Z0.shape[0]) >= G_STD_MIN:+            break+    g_src = np.clip(rs * Z0.shape[0], *G_CLIP)++    rs_safe = np.maximum(rs, 1e-12)+    v_src = (P @ Z1) / rs_safe[:, None] - Z0+    vn = np.linalg.norm(v_src, axis=1)+    med = np.median(vn[vn > 0]) if np.any(vn > 0) else 1.0+    scale = np.minimum(1.0, (V_NORM_CLIP * med) / np.maximum(vn, 1e-12))+    v_src = v_src * scale[:, None]++    _tm("ot")+    # --- transfer (v, g) to every last-stage cell via k-NN in PCA space ---+    from sklearn.neighbors import NearestNeighbors++    nn = NearestNeighbors(n_neighbors=K_TRANSFER).fit(Z0)+    _, ind = nn.kneighbors(Z1_full)+    v_full = v_src[ind].mean(axis=1)+    g_full = np.clip(g_src[ind].mean(axis=1), *G_CLIP)++    # --- extrapolation scale from time differences only ---+    dt_train = max(float(last["time"]) - float(first["time"]), 1e-6)+    dt_out = max(float(manifest["target"]["time"]) - float(last["time"]), 0.0)+    T = float(np.clip(dt_out / dt_train, *T_CLIP))+    alpha = ALPHA_DAMP * T++    z_pred = Z1_full + alpha * v_full++    # --- growth-weighted resampling ---+    if GROWTH_OFF:+        sel = view_io.sample_rows(Z1_full.shape[0], n_out, rng)+    else:+        p = g_full**GROWTH_SHARP+        p = p / p.sum()+        sel = rng.choice(Z1_full.shape[0], size=n_out, replace=True, p=p)+        sel = np.sort(sel)++    # --- decode to gene space ---+    full = np.zeros((n_out, len(genes)), dtype=np.float32)+    rows = adata_last.X[sel].toarray().astype(np.float64)+    non_hvg = np.setdiff1d(np.flatnonzero(cov), hvg)+    if non_hvg.size:+        full[:, non_hvg] = rows[:, non_hvg]+    if DECODE == "blend":+        decoded = z_pred[sel] @ comp + mean+        nn2 = NearestNeighbors(n_neighbors=K_TRANSFER).fit(Z1_full)+        _, ind2 = nn2.kneighbors(z_pred[sel])+        Xr = np.asarray(adata_last.X[:, hvg].todense(), dtype=np.float64)+        nn_mean = Xr[ind2].mean(axis=1)+        Xh = (1.0 - LAMBDA_RESID) * decoded + LAMBDA_RESID * nn_mean+    else:  # "resid": keep real sparse cell, add gene-space displacement+        disp = (alpha * v_full[sel]) @ comp  # n_out x len(hvg)+        if os.environ.get("GROWTH_GATE", "1") == "1":+            disp = disp * (rows[:, hvg] > 0)+        Xh = rows[:, hvg] + disp+    full[:, hvg] = np.maximum(Xh, 0.0).astype(np.float32)+    X_out = sparse.csr_matrix(full)++    _tm("decode")+    # diagnostics to stderr (not part of output)+    print(+        f"[growth_dynamics] n_out={n_out} T={T:.3f} alpha={alpha:.3f} "+        f"g: mean={g_full.mean():.3f} std={g_full.std():.3f} min={g_full.min():.3f} "+        f"max={g_full.max():.3f} frac_out_0.8_1.2={np.mean((g_full < 0.8) | (g_full > 1.2)):.3f} "+        f"decode={DECODE} growth_off={GROWTH_OFF}",+        file=sys.stderr,+    )+    view_io.write_prediction(X_out.tocsr(), genes, args.out, seed=args.seed)+++if __name__ == "__main__":+    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

**做了什么**：从零实现 PLAN 的 growth_dynamics 最小版（`solution/run.py`）：两输入阶段 HVG2500+PCA25 → 非平衡 Sinkhorn OT（reg_m 自适应 2.0→1.0→0.5，X3 实际选 1.0）→ 逐细胞生长权重 g_i 驱动重采样 + 重心速度 α 外推（残差解码回真实稀疏细胞、稀疏门控）；单输入退路 copy_last。含 GROWTH_OFF 机制对照开关（提交保持打开）、EXECUTION.json gpu=false。

**查分**（A 半，用了 9/20）：X3 提交配置 growth-on 47.16 vs growth-off 对照 47.41 vs copy_last 参考 47.92 —— 生长重采样在 X3 上惰性到轻微有害（g std≈0.05，0.25 天间隔下对比度太低）；无门控稠密位移更差（46.16，variogram 崩）。proxy10 退路 52.97（≈copy_last 52.75）。节点分估计 ≈49，与种子基线持平。两视图 vec-check 通过，seed 0 跨 cwd 逐位确定。

**下一步最值得试**：(1) 生长权重按细胞类型聚合去噪后与已验证有效的组成趋势重加权（节点 7/13/16）组合；(2) 在 proxy2（0.5 天间隔）上检验生长信号可辨识性；(3) reg_m/KL 更低扫描提高 g 对比度。

调研员

 check std(g_i) > 0.05, else lower reg_m to 1.0. 2) Resampling with replacement duplicates cells, harming variogram → check zero-inflation and pairwise gene correlation before full run. 3) Barycentric velocity in 25-dim PCA may be noisy for rare subpopulations → clip displacement norm to 2× median. 4) Runtime: POT sinkhorn_unbalanced on 3000×3000 is fast (<10s); full pipeline should be <5 min. If PCA inverse + NN residual is slow, reduce to 2000 HVGs.",
  "family_id": "growth_dynamics",
  "mechanism": "Unbalanced optimal transport jointly estimates per-cell growth (local mass creation/destruction) and velocity (barycentric displacement); growth weights drive non-uniform resampling of extrapolated cells, so sub-states within a lineage expand or contract at different rates.",
  "vs_constant_shift": "Constant shift applies one vector per type or one global composition ratio. Here each cell receives its own displacement vector from the coupling AND its own growth weight from the marginal imbalance. Two cells of the same type but different PCA positions can have g=1.8 vs g=0.6, leading to differential expansion—not achievable with per-type constants.",
  "mechanism_evidence": "Engineer should report: (1) distribution of growth weights g_i (mean, std, min, max; std>0.1 needed to claim mechanism active); (2) fraction of cells with g_i outside [0.8, 1.2]; (3) which cell types / PCA regions are expanded vs contracted; (4) four-group score breakdown for X3 comparing growth-on vs growth-off; (5) mmd_u raw value change attributable to resampling vs displacement separately.",
  "mechanism_off_control": "Same program with --growth-off flag: set all growth weights g_i = 1.0 (uniform resampling, no expansion/contraction), keep velocity displacement identical. Expected difference: growth-on should change cell_state (mmd_u) by resampling toward expanded sub-states; growth-off preserves original composition. If four-group scores are identical, growth term is inert.",
  "sources": []
}