总览 · ← 返回运行 20261003-151511-search-t1-integration-fake

节点 n7

heart_jcf_peri reweighting, heart x1.6, edge x0.1 (fake Engineer, node 7)

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 3c81f61362: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 cc8f8620e0。改动的文件：solution/METHOD.md +2 −71、solution/README.md +0 −4、solution/run.py +16 −184

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 3bc5139..cf9b13e 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,72 +1,3 @@-# composition_trend — type-level composition reweighting + within-type maturity selection+heart_jcf_peri reweighting, heart x1.6, edge x0.1 (fake Engineer, node 7) -**Provenance.** Derived from agent-produced node 33 of run `20261002-034201-search-t1-abc-r1-A-era`-(programs.git `refs/nodes/33`, commit `10400ee`; the program behind the official T1:val 50.1, submissions.tsv-2026-10-02). The mechanism and every constant are the node's own defaults; nothing was re-tuned. What was removed,-for the seed contract only (2026-10-03):--- the external-maturity axis `A_EXT` (read manifest `source == "external"`; a view-identity read; it never fired on-  final / proxy / patched X3);-- the multi-stage pooling `POOL`, the composition-trajectory axis `A_TR`, the kNN expression smoothing `A_S`, the-  apoptosis / cell-level proliferation terms (all 0 in the node's submitted defaults) and the CellRank fate mode;-- the `inputs_by_time(include_external=False)` base selection: the base is simply the latest input stage by time;-- the `VEC_*` environment overrides (constants are fixed in the code).--On every current neutral view (final, proxy, X1, X3–X6) the seed and node 33 select the same cells; see the admission-record for the digest check. The mechanism-split study (`notes/reports/dev/2026-10-02_mech_split_50.md`, candidate-`r1A:CUS` = `r1A:CU`) is the analysis of exactly this procedure.--**Naming.** "trend" is descriptive, not a two-stage model: the type weights come from a single snapshot (types whose-cells are, on average, less proliferative get a larger share). On the official final view this moves the predicted-pseudobulk along the previous step's direction (cosine with E9.5 − E8.5: 0.33 for the composition part, 0.34 for the-whole procedure; mech_split §4), and in the E8.5 → E9.5 rehearsal the composition part carries most of the gain-(+10.0 of +11.4 points; mech_split §2.1). Nothing in the code reads a second stage.--Contract: `python run.py --data <view> --out <pred.h5ad> --seed <int>`; CPU only (`EXECUTION.json {"gpu": false}`).--## Method--Base = the latest input stage of the view (final: E9.5; proxy: E8.5; rulers: their last input), all cells, its own-`celltype` labels (whatever vocabulary the view provides; `Unknown` is an ordinary type).--1. **Cell scores** (generic gene-set means, z-scored over the stage, clipped at ±3): proliferation (34 cell-cycle-   genes), metabolic maturity `z_met` = OXPHOS (13 genes) − glycolysis (10 genes).-2. **Within-type commitment** `z_fate`: HVG 2000 → PCA 30 → kNN 30 → diffusion map 15 → diffusion pseudotime rooted-   at the most progenitor-like cell (argmax 2·z_prolif − z_met, first index on ties); z-scored, the type mean-   removed, z-scored again (only the order inside a type matters).-3. **Weights.** Type layer `w_t = exp(−0.55 · z(type-mean z_prolif))` (types with < 5 cells: 1). Cell layer-   `w_i = exp(1.2 · z_met_i + 0.6 · z_fate_i)`. `w = clip(w_t · w_i, 1e-6, 1e6)`.-4. **Sampling.** n = the stage's cell count clipped to [min_cells, max_cells] (final: 5,118 of 17,057). Per-type-   quota ∝ the type's weight sum (largest remainder, capped by type size, overflow re-apportioned); inside a type 5-   equal-frequency `z_met` strata, stratum quotas ∝ stratum weight sums, Efraimidis–Spirakis weighted sampling-   without replacement inside a stratum. Expression values are copied unchanged.--Mechanism-off controls (for analysis; not code switches): `A_TP = 0` leaves the input composition (mech_split "U");-`A_CM = A_FATE = 0` with the same per-type quotas is uniform within type (mech_split "C").--## Data / knowledge used--Only the latest input stage of the view. Generic knowledge: textbook cell-cycle, OXPHOS and glycolysis gene sets-(pathway membership, not stage measurements). No held-out stage, no information from (E9.5, E13.5], no external-dataset, no `uns.celltype_palette`, no frozen probe, no pre-trained weights.--## Hyper-parameters--| Name | Value | Where it came from |-|---|---|---|-| `A_TP` | −0.55 | node 33's default (tuned by the agent on the old proxy / proxy2) |-| `A_CM` | 1.2 | node 33's default (same) |-| `A_FATE` | 0.6 | node 33's default (agent scan +0.2 … +1.0 on proxy seed 0, peak at 0.6) |-| `K_STRAT`, `MIN_TYPE_CELLS` | 5, 5 | node 33's defaults |-| HVG / PCA / kNN / diffmap | 2000 / 30 / 30 / 15 | node 33's defaults |--All were chosen on the old single-input proxy (E8.5 → E9.5), which is also where they look best; on the two-input-rulers the mechanism is near neutral (admission record).--## Known failure modes--- The type layer is a single-snapshot heuristic: it bets that low-proliferation types expand next. Where the next-  step's composition change does not go that way, every ranked metric gets worse (mech_split zero-development control).-- Within-type selection pushes the sample towards OXPHOS-high / late-pseudotime cells; on patched X3 this part was-  −2.4 points (MMD −1.8) on top of the composition (mech_split §3).-- Real cells only: no new expression states, no new cell types.+Test run only.diff --git a/solution/README.md b/solution/README.mddeleted file mode 100644index 7d4b398..0000000--- a/solution/README.md+++ /dev/null@@ -1,4 +0,0 @@-# composition_trend--r1-A 冠军（run 20261002-034201-search-t1-abc-r1-A-era 节点 33，官网 50.1）的组成部分：按类型平均增殖分数重加权类型配额（增殖低的类型占比升高），型内按代谢成熟度（OXPHOS − 糖酵解）分 5 层、再按扩散伪时间偏向更“承诺”的细胞，加权无放回抽取最新输入阶段的真实细胞。表达不改；去掉了 A_EXT（读 manifest `source` 的视图身份分支）和所有环境变量开关。-纯 CPU，final 约 75 s、峰值内存约 7.3 GB（2 线程）。准入记录：`agent/seeds/T1__val/ADMISSION_2026-10-03_composition_trend.md`。diff --git a/solution/run.py b/solution/run.pyindex dbddc68..982ee82 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,31 +1,13 @@ #!/usr/bin/env python3-"""composition_trend: type-level composition reweighting + within-type maturity selection of real cells (seed).+"""fake-7: heart_jcf_peri weights perturbed by the fake Engineer (test run).""" -Derived from agent-produced node 33 of run 20261002-034201-search-t1-abc-r1-A-era (commit 10400ee, official T1:val-50.1); METHOD.md has the provenance and what was removed. Expression is never changed: the output is a weighted,-stratified sample of the latest input stage's cells.--  type layer   w_t = exp(A_TP * z(mean z_prolif over the type's cells))        A_TP = -0.55-  cell layer   w_i = exp(A_CM * z_met_i + A_FATE * z_fate_i)                   A_CM = 1.2, A_FATE = 0.6-               z_met = z(mean OXPHOS - mean glycolysis), z_fate = within-type centred diffusion pseudotime-  sampling     per-type quota by weight sum (largest remainder, capped by type size, overflow re-apportioned);-               inside a type K = 5 equal-frequency z_met strata, stratum quota by weight sum, Efraimidis-Spirakis-               weighted sampling without replacement inside a stratum.-  n            the latest stage's cell count clipped to [min_cells, max_cells] (as copy_last).----ablate (G39.7): type_layer | cell_layer (maturity, fate) | anything else = both layers off (uniform weights).--Same code path on every view: the latest input stage by time; the program reads no manifest identity field-(mode / source / board / dataset), no file or directory name, no absolute stage time, no external dataset.-Deterministic for a given --seed. CPU only.-""" from __future__ import annotations  import argparse -import numpy as np-+from src.task1_temporal.reweight import heart_reweight from src.task1_temporal.view_io import (+    inputs_by_time,     labels_of,     load_manifest,     panel_genes,@@ -34,174 +16,24 @@ from src.task1_temporal.view_io import (     write_prediction, ) -A_TP = -0.55      # type layer: lower mean proliferation -> larger share-A_CM = 1.2        # cell layer: metabolic maturity (OXPHOS - glycolysis)-A_FATE = 0.6      # cell layer: within-type diffusion pseudotime (more committed cells)-K_STRAT = 5       # z_met strata per type-MIN_TYPE_CELLS = 5--CYCLE = [-    "Mki67", "Top2a", "Pcna", "Ccna2", "Ccnb1", "Ccnb2", "Ccnd1", "Ccneg", "Ccne1",-    "Cdk1", "Cdk2", "Cdk4", "Cdk6", "Mcm2", "Mcm3", "Mcm4", "Mcm5", "Mcm6", "Mcm7",-    "Orc1", "Cdc6", "Cdt1", "Rrm1", "Rrm2", "Tyms", "Dtl", "Cenpf", "Cenpe",-    "Birc5", "Aurkb", "Plk1", "Kif20a", "Kif11", "Nusap1",-]-OXPHOS = [-    "Ndufa4", "Ndufb8", "Uqcrb", "Uqcrc1", "Cox5a", "Cox6b1", "Cox7a2", "Atp5a1",-    "Atp5b", "Atp5f1b", "Atp5pb", "Sdha", "Sdhb",-]-GLYC = ["Slc2a1", "Slc2a3", "Hk1", "Hk2", "Pfkp", "Pgk1", "Pgam1", "Eno1", "Ldha", "Pkma"]---def group_score(X, genes, names):-    index = {g: i for i, g in enumerate(genes)}-    cols = [index[g] for g in names if g in index]-    if not cols:-        return np.zeros(X.shape[0], dtype=np.float64)-    return np.asarray(X[:, cols].mean(axis=1), dtype=np.float64).ravel()---def zscore(x):-    m, s = x.mean(), x.std()-    if not np.isfinite(s) or s < 1e-9:-        return np.zeros_like(x)-    return np.clip((x - m) / s, -3.0, 3.0)---def fate_pseudotime(adata, z_prolif, z_met, seed):-    """Diffusion pseudotime rooted at the most progenitor-like cell (argmax 2 z_prolif - z_met, first index wins)."""-    import anndata as ad-    import scanpy as sc--    tmp = ad.AnnData(X=np.asarray(adata.X.todense(), dtype=np.float32), obs=adata.obs.copy())-    sc.pp.highly_variable_genes(tmp, n_top_genes=2000)-    tmp = tmp[:, tmp.var["highly_variable"]].copy()-    sc.pp.pca(tmp, n_comps=30, random_state=seed)-    sc.pp.neighbors(tmp, n_neighbors=30, random_state=seed)-    root = int(np.argmax(2.0 * z_prolif - z_met))-    tmp.uns["iroot"] = root-    sc.tl.diffmap(tmp, n_comps=15, random_state=seed)-    tmp.uns["iroot"] = root-    sc.tl.dpt(tmp, n_branchings=0)-    return np.asarray(tmp.obs["dpt_pseudotime"], dtype=np.float64)---def apportion(wsum, caps, n):-    """Largest-remainder apportionment of n proportional to wsum, capped by caps, overflow redistributed."""-    alloc = np.zeros(len(wsum), dtype=np.int64)-    rem = int(n)-    for _ in range(len(wsum) + 5):-        if rem <= 0:-            break-        room = caps - alloc-        act = room > 0-        if not act.any():-            break-        tot = float(wsum[act].sum())-        target = np.zeros(len(wsum))-        if tot > 0:-            target[act] = rem * wsum[act] / tot-        else:-            target[act] = rem / float(act.sum())-        add = np.minimum(np.floor(target).astype(np.int64), room)-        if add.sum() == 0:-            cand = np.where(act)[0]-            cand = cand[np.argsort(-target[cand], kind="stable")][:rem]-            add = np.zeros(len(wsum), dtype=np.int64)-            add[cand] = 1-            add = np.minimum(add, room)-        alloc += add-        rem -= int(add.sum())-    return alloc---def es_sample(rows, w, q, rng):-    """Efraimidis-Spirakis weighted sample without replacement."""-    if q <= 0 or len(rows) == 0:-        return np.empty(0, dtype=np.int64)-    if q >= len(rows):-        return rows-    u = rng.random(len(rows))-    keys = np.log(np.maximum(u, 1e-300)) / np.maximum(w[rows], 1e-12)-    return rows[np.argpartition(-keys, q - 1)[:q]]---def stratified_sample(w, z_strat, inv, n_types, n_out, K, rng):-    total = len(w)-    if n_out >= total:-        return np.arange(total)-    counts = np.bincount(inv, minlength=n_types)-    n_t = apportion(np.bincount(inv, weights=w, minlength=n_types), counts, n_out)-    out = []-    for t in range(n_types):-        nt = int(n_t[t])-        if nt <= 0:-            continue-        rows = np.where(inv == t)[0]-        if nt >= len(rows) or K < 2 or len(rows) < 2 * K:-            out.append(es_sample(rows, w, nt, rng))-            continue-        srows = rows[np.argsort(z_strat[rows], kind="stable")]-        base, extra = divmod(len(srows), K)-        pos, bounds = 0, []-        for k in range(K):-            sz = base + (1 if k < extra else 0)-            bounds.append(srows[pos:pos + sz])-            pos += sz-        sizes = np.array([len(b) for b in bounds], dtype=np.int64)-        q = apportion(np.array([w[b].sum() for b in bounds]), sizes, nt)-        for k in range(K):-            if q[k] > 0:-                out.append(es_sample(bounds[k], w, int(q[k]), rng))-    return np.sort(np.concatenate(out)) if out else np.empty(0, dtype=np.int64)+HEART_WEIGHT = 1.6+EDGE_WEIGHT = 0.1+N_CELLS = 4000   def main() -> None:-    ap = argparse.ArgumentParser()-    ap.add_argument("--data", required=True)-    ap.add_argument("--out", required=True)-    ap.add_argument("--seed", type=int, default=0)-    # G39.7 mechanism-off control: type_layer -> A_TP = 0; cell_layer (or maturity / fate) -> A_CM = A_FATE = 0; any other-    # name -> both layers off (uniform weights: a stratified copy of the latest stage)-    ap.add_argument("--ablate", default=None)-    args = ap.parse_args()-+    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)-    last = sorted(manifest["inputs"], key=lambda e: float(e["time"]))[-1]   # every input stage treated alike-    adata = read_stage(args.data, last, genes, missing="fill")-    X = adata.X-    labels = labels_of(adata) if "celltype" in adata.obs.columns else np.full(adata.n_obs, "all")--    z_prolif = zscore(group_score(X, genes, CYCLE))-    z_met = zscore(group_score(X, genes, OXPHOS) - group_score(X, genes, GLYC))-    uniq, inv = np.unique(labels, return_inverse=True)-    counts = np.bincount(inv, minlength=len(uniq))--    def tmean(x):-        return np.bincount(inv, weights=x, minlength=len(uniq)) / np.maximum(counts, 1)--    z_prolif_t = zscore(tmean(z_prolif))-    raw = zscore(fate_pseudotime(adata, z_prolif, z_met, args.seed))-    raw = raw - tmean(raw)[inv]          # only the within-type order matters-    z_fate = zscore(raw)--    a_tp, a_cm, a_fate = A_TP, A_CM, A_FATE-    if args.ablate:-        if args.ablate in ("type_layer", "type"):-            a_tp = 0.0-        elif args.ablate in ("cell_layer", "cell", "maturity", "fate"):-            a_cm = a_fate = 0.0-        else:-            a_tp = a_cm = a_fate = 0.0-    w_type = np.exp(a_tp * z_prolif_t)-    w_type[counts < MIN_TYPE_CELLS] = 1.0-    w = np.clip(w_type[inv] * np.exp(a_cm * z_met + a_fate * z_fate), 1e-6, 1e6)--    n_out = target_n_cells(manifest, adata.n_obs)-    rng = np.random.default_rng(args.seed)-    idx = stratified_sample(w, z_met, inv, len(uniq), n_out, K_STRAT, rng)-    write_prediction(X[idx], genes, args.out, seed=args.seed)+    last = read_stage(args.data, inputs_by_time(manifest)[-1], genes)+    pass+    n = target_n_cells(manifest, N_CELLS)+    X = heart_reweight(last.X, labels_of(last), n_cells=n, heart_weight=HEART_WEIGHT, edge_weight=EDGE_WEIGHT, seed=args.seed)+    write_prediction(X, genes, args.out, seed=args.seed)   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

计划里引用的来源

• fake relay: tool protocol exercised

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

{"changed": "fake relay analysis", "effect_by_group": {}, "hypothesis_supported": "unclear", "failure_reason": null, "lessons": ["fake"], "next_suggestions": ["fake"], "mechanism_active": null}

工程师

wrote solution/run.py with heart x1.6, edge x0.1 (ok)

调研员

{"name": "relay-plan-7", "motivation": "test node 7 through the fake relay. E10.5 心脏中心肌占 28%（FAKE_MEASURED）。", "approach": "perturb the heart / edge weights of the parent's reweighting", "expected_groups": ["cell_state"], "risks": "none (fake)", "family_id": "other", "mechanism": "fake: weight perturbation", "vs_constant_shift": "fake: reweights cells, no shift", "mechanism_evidence": "fake: output differs", "mechanism_off_control": "fake: weights 1.0 reproduce the parent", "sources": ["fake relay: tool protocol exercised"]}