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节点 n23

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 a9bdae3c22: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 fbd55642fc。改动的文件：solution/METHOD.md +7 −8、solution/run.py +22 −4

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex c109b50..99890ba 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,11 +1,10 @@ ## 改了什么-相对父节点（seed pseudobulk_shift）的改动：-1. 用 `inputs_by_time(manifest, include_external=False)` 过滤外部输入阶段，修复 proxy2 上把 Qiu E9.0 当作最新官方阶段导致分数崩溃的问题（proxy2 从 27.43 恢复到 ~50）。-2. 移除无收缩的全量 delta（alpha=1），改用 SNR 自适应收缩：每个类型根据显著基因比例决定整体 alpha（0.1–0.75），每个基因根据信噪比决定收缩力度（(snr/(snr+K))^power），噪声大的基因几乎不动。-3. 单输入阶段退化为 copy_last，不再崩溃。-4. 输出前统一 clip 到 ≥0。-理由：方法卡 k018 明确指出 alpha=1 的常数位移在 T1 上低于 copy_last；实验表中所有高分节点都使用 include_external=False + SNR 收缩。+相对父节点（node 17，SNR 自适应收缩 power=2.0, K=2.5）的改动：+1. 恢复 Program 1 的最优参数：ALPHA_MAX=0.80, GENE_SHRINK_POWER=2.5, GENE_SHRINK_K=3.0, SNR_SIGNIFICANT_THRESHOLD=1.5（实验表证实该组合在 combined_score 49.18 为最优）。+2. 加回 sign disagreement 机制（SIGN_DISAGREE_FACTOR=0.82, SIGN_DISAGREE_THRESHOLD=0.25），对与主导方向相反的基因额外收缩，保护 covariation。+3. 新增 clip-awareness delta cap：对会导致 >5% 细胞被 clip 到 0 的基因，将 delta 减半，避免大量截断破坏分布形状和基因间共变结构。+理由：父节点 power=2.0/K=2.5 比 Program 1 的 power=2.5/K=3.0 弱（49.03 vs 49.18），且缺少 sign disagreement；clip 截断是 covariation 分低于 cell_state 的可能原因之一。 ## 用到的知识与出处 - 方法卡 k018（Damped per-type shift: shrinkage alpha on the observed delta）-- 方法卡 k004（OT recipe census，确认类型配对逻辑）-- 实验表中 Program 1（score 49.08）验证了 SNR 自适应收缩 + GENE_SHRINK_POWER=2.5, K=3.0 的效果；本节点微调为 power=2.0, K=2.5 以探索不同收缩强度+- 实验表 Program 1（score 49.1779）验证了 ALPHA_MAX=0.80, POWER=2.5, K=3.0, SIGN_DISAGREE 的效果+- 实验表 Program 2（score 49.0991）验证了 SIGN_DISAGREE_FACTOR=0.92/THRESHOLD=0.35 的替代参数diff --git a/solution/run.py b/solution/run.pyindex 966174d..863b41f 100644--- a/solution/run.py+++ b/solution/run.py@@ -26,11 +26,15 @@ from src.task1_temporal.view_io import (     write_prediction, ) -ALPHA_MAX = 0.75+ALPHA_MAX = 0.80 ALPHA_MIN = 0.1 SIGN_THRESHOLD = 0.05-GENE_SHRINK_POWER = 2.0-GENE_SHRINK_K = 2.5+GENE_SHRINK_POWER = 2.5+GENE_SHRINK_K = 3.0+SIGN_DISAGREE_FACTOR = 0.82+SIGN_DISAGREE_THRESHOLD = 0.25+SNR_SIGNIFICANT_THRESHOLD = 1.5+CLIP_AWARENESS_QUANTILE = 0.05   def _to_dense(X):@@ -88,7 +92,7 @@ def main() -> None:                 var_last / max(n_last, 1) + var_prev / max(n_prev, 1) + 1e-12             )             snr = np.abs(raw_delta) / (noise_se + 1e-12)-            significant = snr > 2.0+            significant = snr > SNR_SIGNIFICANT_THRESHOLD             frac_significant = significant.mean()              if frac_significant < SIGN_THRESHOLD:@@ -99,8 +103,22 @@ def main() -> None:                     frac_significant / 0.3, 1.0                 )                 gene_shrink = (snr / (snr + GENE_SHRINK_K)) ** GENE_SHRINK_POWER+                weighted_sign = np.sum(np.sign(raw_delta) * snr)+                total_snr = snr.sum() + 1e-12+                if abs(weighted_sign) > SIGN_DISAGREE_THRESHOLD * total_snr:+                    dominant_sign = np.sign(weighted_sign)+                    disagree = np.sign(raw_delta) != dominant_sign+                    gene_shrink = gene_shrink.copy()+                    gene_shrink[disagree] *= SIGN_DISAGREE_FACTOR              delta = (raw_delta * alpha * gene_shrink).astype(np.float32)++            cell_vals = X[mask_s]+            neg_frac = (cell_vals + delta[np.newaxis, :] < 0).mean(axis=0)+            clip_mask = neg_frac > CLIP_AWARENESS_QUANTILE+            if clip_mask.any():+                delta[clip_mask] *= 0.5+             X[mask_s] += delta[np.newaxis, :]          del prev, Xd_last, Xd_prev

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

Let me analyze the current situation:

**Round 0**: score 49.3493 (X3: 47.24, proxy: 50.40, proxy2: 50.40)
- cell_state: 50.12, covariation: 48.68, de_recovery: 48.83, direction: 49.47

**Round 1**: score 49.3456 (X3: 47.23, proxy: 50.40, proxy2: 50.40)
- cell_state: 50.12, covariation: 48.69, de_recovery: 48.83, direction: 49.46

The changes from round 0 to round 1 made virtually no difference (49.35 → 49.35). The clip-awareness and sign disagreement parameters had negligible effect. This suggests:

1. The clip-awareness mechanism barely triggers (threshold 0.10 is too high) or the effect is minimal
2. The sign disagreement parameters (0.88/0.30) are too mild to matter

Key observations:
- de_recovery (48.83) is the weakest group score — it was 49.15 in the parent node 17
- covariation improved dramatically (46.78 → 48.68) compared to parent
- The overall score 49.35 is already better than Program 1's 49.18 on A-half

The de_recovery drop from parent (49.15 → 48.83) is concerning. DE recovery measures how well differentially expressed genes between cell types are preserved. The uniform shift approach may be blurring DE boundaries between types.

Strategy for round 2:
1. **Remove the clip-awareness mechanism entirely** — it's not helping and may be hurting de_recovery by dampening real shifts
2. **Try a slightly higher ALPHA_MAX (0.85)** to push more signal through, which should help de_recovery
3. **Lower SNR_SIGNIFICANT_THRESHOLD to 1.2** to be more inclusive of moderate-signal genes in the alpha calculation, giving types with moderate but consistent changes more shift
4. **Keep sign disagreement but make it very mild** (factor 0.95, threshold 0.40) — essentially disabling it since it's not helping

Actually, let me reconsider. The parent node 17 had de_recovery 49.15 with POWER=2.0, K=2.5, ALPHA_MAX=0.75, no sign disagreement, no clip awareness. The current version has POWER=2.5, K=3.0, ALPHA_MAX=0.80 with sign disagreement and clip awareness, giving de_recovery 48.83.

The difference might be that higher POWER/K shrinks moderate-SNR genes more aggressively, losing DE signal. Let me try:
- Keep POWER=2.5, K=3.0 (good for covariation)
- Remove clip awareness (not helping)
- Make sign disagreement very mild or remove it
- Add a small "DE boost": for genes with very high SNR (>5), apply less shrinkage (closer to 1.0) to preserve strong DE signals

Actually, a simpler approach: the issue is that `(snr/(snr+K))^power` with K=3.0 is quite aggressive for moderate SNR genes. A gene with SNR=3 gets shrink = (3/6)^2.5 = 0.177. That's very aggressive. For DE recovery, we want high-SNR genes to move close to full delta.

Let me try a different shrinkage formula that's less aggressive for high-SNR genes while still protecting low-SNR genes:
- `gene_shrink = snr^2 / (snr^2 + K^2)` — this is a softer curve that gives more shrinkage to high-SNR genes

Or simpler: just reduce K to 2.0 and power to 2.0, which gives SNR=3 a shrink of (3/5)^2 = 0.36 vs (3/6)^2.5 = 0.177. But that was the parent's setting which had lower covariation...