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节点 n15

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 72102db813: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 +20 −0、solution/README.md +4 −0、solution/g37_common.py +101 −0、solution/run.py +223 −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..70def7a--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,20 @@+## 改了什么+相对父节点（ot_moscot）的全部改动：+1. 方法族替换为 generative_latent：小型高斯 VAE（MLP 256→128→15维latent→128→256→HVG）在所有输入阶段的 top-2000 HVG 上训练；+2. 状态依赖位移：在 latent 空间中用 k=20 近邻匹配估计每个细胞的位移（z_last - mean(z_prev_knn)），外推 λ=0.5 * (dt_out/dt_in)；+3. 解码后与原始表达保守混合：step = (decoded - original) * 0.25，只加在非零位点（addnz），非 HVG 基因不动；+4. 单输入阶段退化为生长加权复制；+5. ZERO_TRANSITION=1 环境变量可关闭位移（仅编码-解码对照）。++本轮（第2轮）修复：+- cell_state 33.61 说明解码器严重破坏细胞状态。将混合比例从 0.7 decoded / 0.3 original 改为 0.25 的步长系数（即 original + 0.25*(decoded-original)），大幅保守；+- LAMBDA 从 1.0 降到 0.5，减少外推幅度；+- latent dim 从 20 降到 15，减少过拟合；+- epochs 从 8 增到 25，改善重建质量；+- KL 权重从 0.1 降到 0.01，让 VAE 优先重建而非压缩。++## 用到的知识与出处+- Lopez R. et al. scVI, Nat Methods 2018 (10.1038/s41592-018-0229-2)：VAE 框架。+- 方法卡 k035：log1p(CP10k) 无 counts，用高斯 VAE；HVG 训练，非 HVG 保留。+- 方法卡 k030：latent 动力学弱可辨识，需保守外推。+- 方向规格：状态依赖转移、零转移对照、解码器不跌破照抄。diff --git a/solution/README.md b/solution/README.mdnew file mode 100644index 0000000..dc1cbaf--- /dev/null+++ b/solution/README.md@@ -0,0 +1,4 @@+# ot_moscot++Waddington-OT / moscot：两个最新输入阶段在联合 PCA 上做非平衡熵 OT 耦合（WOT 增殖/凋亡先验生长），最新阶段按 g^Δt 重抽样，每个细胞沿“kNN 平滑位置 − 耦合祖先均值”再走一个等比例步长，只加在非零基因上，夹到 ≥0。+来自 G37 候选 modeling/candidates/T1/ot_moscot；addnz 解码是在 X3 上选的（详见 METHOD.md）。纯 CPU，final 约 70 s、6.7 GB。diff --git a/solution/g37_common.py b/solution/g37_common.pynew file mode 100644index 0000000..8c991b3--- /dev/null+++ b/solution/g37_common.py@@ -0,0 +1,101 @@+"""Helpers of the ot_moscot seed (copy of modeling/candidates/T1/ot_moscot/g37_common.py, G37; stage_pair reads every+input stage of the view).++- ``stage_pair``: the two latest input stages (official, or external in proxy2 / test views) and the gene mask on+  which both are really measured (external stages lack part of the panel; filled columns must not drive dynamics);+- ``embed``: HVG -> z-score (clip 10) -> PCA, fitted on the input stages only (scanpy-style preprocessing);+- ``knn_mean``: per-cell kNN average in the embedding (smooths single-cell noise before taking displacements);+- ``growth_rates``: Waddington-OT / moscot prior growth from proliferation and apoptosis gene scores.+"""++from __future__ import annotations++import numpy as np+from scipy import sparse++from src.task1_temporal.view_io import covered_mask, inputs_by_time, read_stage+++def stage_pair(view, manifest: dict, genes: list[str]):+    """(prev, last, mask, dt_in) for the two latest inputs; prev None with a single input stage."""+    stages = inputs_by_time(manifest, include_external=True)  # every input stage, whatever its source+    last_e = stages[-1]+    last = read_stage(view, last_e, genes)+    mask = covered_mask(view, last_e, genes)+    if len(stages) < 2:+        return None, last, mask, None, last_e+    prev_e = stages[-2]+    prev = read_stage(view, prev_e, genes)+    mask &= covered_mask(view, prev_e, genes)+    return prev, last, mask, float(last_e["time"] - prev_e["time"]), last_e+++def _gene_moments(X: sparse.csr_matrix):+    n = X.shape[0]+    mean = np.asarray(X.mean(axis=0)).ravel()+    sq = np.asarray(X.multiply(X).sum(axis=0)).ravel() / n+    return mean, np.maximum(sq - mean ** 2, 0.0)+++def embed(mats: list, mask: np.ndarray, n_hvg: int, n_pcs: int, seed: int):+    """PCA coordinates of each matrix in ``mats`` (same order), fitted on all of them together.++    Returns (list of Z, info) with info = {hvg, mean, std, components} so displacements can be decoded."""+    from sklearn.decomposition import PCA++    allX = sparse.vstack(mats).tocsr()+    mean, var = _gene_moments(allX)+    var = np.where(mask, var, -1.0)+    hvg = np.sort(np.argsort(var)[::-1][:n_hvg])+    sub = allX[:, hvg].toarray().astype(np.float32)+    mu, sd = mean[hvg].astype(np.float32), np.sqrt(var[hvg]).astype(np.float32)+    sd[sd == 0] = 1.0+    sub -= mu+    sub /= sd+    np.clip(sub, -10, 10, out=sub)+    pca = PCA(n_components=n_pcs, svd_solver="randomized", random_state=seed)+    Z = pca.fit_transform(sub).astype(np.float32)+    out, start = [], 0+    for M in mats:+        out.append(Z[start:start + M.shape[0]])+        start += M.shape[0]+    return out, {"hvg": hvg, "mu": mu, "sd": sd, "components": pca.components_.astype(np.float32)}+++def knn_mean(Z: np.ndarray, X: sparse.csr_matrix, rows: np.ndarray, k: int) -> np.ndarray:+    """Dense (len(rows), n_genes): mean expression of the k nearest cells (in Z, the cell itself included)."""+    from sklearn.neighbors import NearestNeighbors++    nn = NearestNeighbors(n_neighbors=k).fit(Z)+    idx = nn.kneighbors(Z[rows], return_distance=False)+    W = sparse.csr_matrix((np.full(idx.size, 1.0 / k, dtype=np.float32), idx.ravel(),+                           np.arange(0, idx.size + 1, k)), shape=(len(rows), Z.shape[0]))+    return np.asarray((W @ X).todense(), dtype=np.float32)+++# Waddington-OT prior growth (Schiebinger et al. 2019, Cell; as implemented in moscot.base.problems.birth_death):+# birth = generalised logistic of the proliferation score, death = of the apoptosis score, g = exp(birth - death) / day.+def _gen_logistic(p, sup, inf, center, width):+    return inf + (sup - inf) / (1 + np.exp(-(p - center) / width))+++def growth_rates(adata_list: list, genes: list[str], mask: np.ndarray, seed: int) -> list[np.ndarray]:+    """Per-day growth rate of every cell of each AnnData (scores computed on all of them jointly)."""+    import anndata as ad+    import scanpy as sc+    from moscot.utils.data import apoptosis_markers, proliferation_markers++    ok = set(np.asarray(genes)[mask])+    joint = ad.concat(adata_list, join="inner")+    prolif = [g for g in proliferation_markers("mouse") if g in ok]+    apopt = [g for g in apoptosis_markers("mouse") if g in ok]+    sc.tl.score_genes(joint, prolif, score_name="proliferation", random_state=seed)+    sc.tl.score_genes(joint, apopt, score_name="apoptosis", random_state=seed)+    birth = _gen_logistic(joint.obs["proliferation"].to_numpy(float), 1.7, 0.3, 0.25, 0.5)+    death = _gen_logistic(joint.obs["apoptosis"].to_numpy(float), 1.7, 0.3, 0.1, 0.2)+    g = np.exp(birth - death)+    out, start = [], 0+    for a in adata_list:+        out.append(g[start:start + a.n_obs])+        start += a.n_obs+    return outdiff --git a/solution/run.py b/solution/run.pynew file mode 100644index 0000000..e2fd4c7--- /dev/null+++ b/solution/run.py@@ -0,0 +1,223 @@+#!/usr/bin/env python3+"""generative_latent draft: Gaussian VAE with state-dependent latent transition.++Trains a small Gaussian VAE on HVG from all input stages, computes per-cell latent+displacements between stages via kNN matching in latent space, extrapolates the+displacement for the target interval, and decodes back to gene space. The transition+is state-dependent: each cell's displacement depends on its position in latent space+(through its local neighborhood in the earlier stage), not a constant per-type shift.++Two input stages:+  1. Select top HVG by variance across all input cells;+  2. Train a 2-layer MLP Gaussian VAE (encoder: HVG->256->128->latent, decoder mirrors);+  3. Encode all cells to get latent representations;+  4. For each last-stage cell, find k nearest prev-stage neighbors in latent space;+     displacement = z_last - mean(z_prev_neighbors);+  5. Sample output cells from last stage (growth-weighted);+  6. Extrapolate: z_target = z_last + displacement * (dt_out / dt_in);+  7. Decode z_target through the trained decoder;+  8. Blend decoded HVG with original expression; copy non-HVG genes from originals;+  9. Apply addnz constraint (only modify non-zero entries), clip at 0.++One input stage: growth-weighted copy of the latest stage (same fallback as parent).++Mechanism control: set ZERO_TRANSITION=1 to disable the latent displacement+(encode-decode only, no extrapolation). Default is 0 (mechanism active).+"""++from __future__ import annotations++import argparse+import os+import sys+from pathlib import Path++import numpy as np+from scipy import sparse++sys.path.insert(0, str(Path(__file__).resolve().parent))+from g37_common import growth_rates, stage_pair  # noqa: E402++from src.task1_temporal.view_io import load_manifest, panel_genes, target_n_cells, write_prediction  # noqa: E402++N_HVG = 2000+LATENT_DIM = 15+HIDDEN1 = 256+HIDDEN2 = 128+N_EPOCHS = 25+BATCH_SIZE = 512+LR = 1e-3+K_NEIGHBORS = 20+LAMBDA = 0.5+BLEND_DECODED = 0.25+ZERO_TRANSITION = int(os.environ.get("ZERO_TRANSITION", "0"))+N_THREADS = 8+++def weighted_rows(w: np.ndarray, n: int, rng: np.random.Generator) -> np.ndarray:+    p = w / w.sum()+    return np.sort(rng.choice(len(w), size=n, replace=n > len(w), p=p))+++def select_hvg(X: sparse.csr_matrix, mask: np.ndarray, n_hvg: int) -> np.ndarray:+    n = X.shape[0]+    mean = np.asarray(X.mean(axis=0)).ravel()+    sq = np.asarray(X.multiply(X).sum(axis=0)).ravel() / n+    var = np.maximum(sq - mean ** 2, 0.0)+    var = np.where(mask, var, -1.0)+    return np.sort(np.argsort(var)[::-1][:n_hvg])+++def train_vae(X_train: np.ndarray, latent_dim: int, seed: int):+    import torch+    import torch.nn as nn+    from torch.utils.data import DataLoader, TensorDataset++    torch.manual_seed(seed)+    torch.set_num_threads(N_THREADS)++    n_genes = X_train.shape[1]+    dataset = TensorDataset(torch.from_numpy(X_train))+    loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True,+                        generator=torch.Generator().manual_seed(seed))++    class VAE(nn.Module):+        def __init__(self):+            super().__init__()+            self.enc = nn.Sequential(+                nn.Linear(n_genes, HIDDEN1), nn.ReLU(),+                nn.Linear(HIDDEN1, HIDDEN2), nn.ReLU(),+            )+            self.fc_mu = nn.Linear(HIDDEN2, latent_dim)+            self.fc_logvar = nn.Linear(HIDDEN2, latent_dim)+            self.dec = nn.Sequential(+                nn.Linear(latent_dim, HIDDEN2), nn.ReLU(),+                nn.Linear(HIDDEN2, HIDDEN1), nn.ReLU(),+                nn.Linear(HIDDEN1, n_genes),+            )++        def encode(self, x):+            h = self.enc(x)+            return self.fc_mu(h), self.fc_logvar(h)++        def reparameterize(self, mu, logvar):+            std = torch.exp(0.5 * logvar)+            eps = torch.randn_like(std)+            return mu + eps * std++        def decode(self, z):+            return self.dec(z)++        def forward(self, x):+            mu, logvar = self.encode(x)+            z = self.reparameterize(mu, logvar)+            return self.decode(z), mu, logvar++    model = VAE()+    optimizer = torch.optim.Adam(model.parameters(), lr=LR)++    model.train()+    for epoch in range(N_EPOCHS):+        total_loss = 0.0+        for (batch,) in loader:+            optimizer.zero_grad()+            recon, mu, logvar = model(batch)+            recon_loss = nn.functional.mse_loss(recon, batch, reduction="sum") / batch.shape[0]+            kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / batch.shape[0]+            loss = recon_loss + 0.01 * kl_loss+            loss.backward()+            optimizer.step()+            total_loss += loss.item()+        if epoch == 0 or epoch == N_EPOCHS - 1:+            print(f"vae epoch {epoch}: loss={total_loss / len(loader.dataset):.4f}", file=sys.stderr)++    model.eval()+    return model+++def encode_all(model, X: np.ndarray) -> np.ndarray:+    import torch+    with torch.no_grad():+        t = torch.from_numpy(X)+        mu, _ = model.encode(t)+    return mu.numpy()+++def decode_all(model, Z: np.ndarray) -> np.ndarray:+    import torch+    with torch.no_grad():+        t = torch.from_numpy(Z.astype(np.float32))+        out = model.decode(t)+    return out.numpy()+++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)+    args = parser.parse_args()++    manifest = load_manifest(args.data)+    genes = panel_genes(args.data, manifest)+    prev, last, mask, dt_in, last_e = stage_pair(args.data, manifest, genes)+    dt_out = float(manifest["target"]["time"] - last_e["time"])+    rng = np.random.default_rng(args.seed)+    n = target_n_cells(manifest, last.n_obs)++    if prev is None:+        (g_last,) = growth_rates([last], genes, mask, args.seed)+        rows = weighted_rows(g_last ** dt_out, n, rng)+        write_prediction(last.X[rows], genes, args.out, seed=args.seed)+        return++    import torch+    from sklearn.neighbors import NearestNeighbors++    torch.set_num_threads(N_THREADS)++    hvg = select_hvg(sparse.vstack([prev.X, last.X]).tocsr(), mask, N_HVG)+    n_hvg_actual = len(hvg)++    Xp_hvg = prev.X[:, hvg].toarray().astype(np.float32)+    Xl_hvg = last.X[:, hvg].toarray().astype(np.float32)++    mu_all = (Xp_hvg.mean(axis=0) + Xl_hvg.mean(axis=0)) / 2.0+    std_all = np.sqrt((Xp_hvg.var(axis=0) + Xl_hvg.var(axis=0)) / 2.0 + 1e-8)+    Xp_norm = (Xp_hvg - mu_all) / std_all+    Xl_norm = (Xl_hvg - mu_all) / std_all+    X_train = np.vstack([Xp_norm, Xl_norm])++    model = train_vae(X_train, LATENT_DIM, args.seed)++    Zp = encode_all(model, Xp_norm)+    Zl = encode_all(model, Xl_norm)++    (g_prev, g_last) = growth_rates([prev, last], genes, mask, args.seed)+    rows = weighted_rows(g_last ** dt_out, n, rng)++    if ZERO_TRANSITION:+        Z_target = Zl[rows]+    else:+        nn_model = NearestNeighbors(n_neighbors=K_NEIGHBORS).fit(Zp)+        _, idx = nn_model.kneighbors(Zl[rows], return_distance=True)+        anc_mean = Zp[idx].mean(axis=1)+        displacement = Zl[rows] - anc_mean+        Z_target = Zl[rows] + displacement * (LAMBDA * dt_out / dt_in)++    decoded_hvg = decode_all(model, Z_target)+    decoded_hvg = decoded_hvg * std_all + mu_all+    np.maximum(decoded_hvg, 0.0, out=decoded_hvg)++    X_out = last.X[rows].toarray()+    X_out_hvg = X_out[:, hvg].copy()+    step = (decoded_hvg - X_out_hvg) * BLEND_DECODED+    step *= X_out_hvg > 0+    X_out[:, hvg] = X_out_hvg + step+    np.maximum(X_out, 0.0, out=X_out)++    write_prediction(X_out, 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）。

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

审查员

REVIEW.json written

stream

The scores show cell_state (33.61) is catastrophically low - the VAE decoder is destroying cell identity. The blend is too aggressive (0.7 decoded) and training is insufficient. I'll make the blending much more conservative, reduce extrapolation, and improve VAE training.

<<<<<<< SEARCH
N_HVG = 2000
LATENT_DIM = 20
HIDDEN1 = 256
HIDDEN2 = 128
N_EPOCHS = 8
BATCH_SIZE = 512
LR = 1e-3
K_NEIGHBORS = 20
LAMBDA = 1.0
ZERO_TRANSITION = int(os.environ.get("ZERO_TRANSITION", "0"))
N_THREADS = 8
=======
N_HVG = 2000
LATENT_DIM = 15
HIDDEN1 = 256
HIDDEN2 = 128
N_EPOCHS = 25
BATCH_SIZE = 512
LR = 1e-3
K_NEIGHBORS = 20
LAMBDA = 0.5
BLEND_DECODED = 0.25
ZERO_TRANSITION = int(os.environ.get("ZERO_TRANSITION", "0"))
N_THREADS = 8
>>>>>>> REPLACE

<<<<<<< SEARCH
            recon_loss = nn.functional.mse_loss(recon, batch, reduction="sum") / batch.shape[0]
            kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / batch.shape[0]
            loss = recon_loss + 0.1 * kl_loss
=======
            recon_loss = nn.functional.mse_loss(recon, batch, reduction="sum") / batch.shape[0]
            kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / batch.shape[0]
            loss = recon_loss + 0.01 * kl_loss
>>>>>>> REPLACE

<<<<<<< SEARCH