提交其余项目实验变更

deep_learning/Q2/outputs/followups/D0_task_preference/expert_task_preference_summary.csv

@@ -0,0 +1,9 @@
+expert,n_seeds,conditions_averaged,corrupt_macro_f1_mean,corrupt_macro_f1_seed_sd,corrupt_mae_mean,corrupt_mae_seed_sd,corrupt_pearson_mean,corrupt_pearson_seed_sd,corrupt_available_position_fraction_mean,corrupt_available_position_fraction_seed_sd
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deep_learning/Q2/outputs/followups/D0_task_preference/forced_expert_metrics.csv

@@ -0,0 +1,385 @@
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deep_learning/Q2/outputs/followups/D0_task_preference/run_manifest.json

@@ -0,0 +1,41 @@
+{
+  "diagnostic": "forced-expert task preference for the retained single-router MoFE-7",
+  "checkpoint_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q2/outputs/mofe_7experts/models/B5_mofe_mlp",
+  "checkpoint_seeds": [
+    42,
+    3407,
+    2026
+  ],
+  "feature_file": "/home/gloamxun/modeling_zhaocui/E题数据/附件2-数据集特征文件/aligned_50.pkl",
+  "scaler_file": "/home/gloamxun/modeling_zhaocui/deep_learning/Q2/outputs/mofe_7experts/aligned_robust_stats.npz",
+  "scaler_sha256": "3b62802bef92f49a7846773741bc57fda1564aa9eed43f047a3e31a9c357c4e8",
+  "device": "cuda",
+  "conditions": [
+    "clean",
+    "text_10",
+    "audio_10",
+    "vision_10",
+    "audio_vision_10",
+    "all_modalities_10",
+    "text_20",
+    "audio_20",
+    "vision_20",
+    "audio_vision_20",
+    "all_modalities_20",
+    "text_30",
+    "audio_30",
+    "vision_30",
+    "audio_vision_30",
+    "all_modalities_30"
+  ],
+  "forced_expert_policy": "use the requested expert where its modality subset is available; fall back to the trained single router at positions where it is unavailable; all-missing positions use the learned missing token",
+  "scope_note": "50 official ordered wordpiece positions; no claim about physical-time reliability",
+  "interpretation_note": "Ranking agreement is descriptive on the supplied validation split; it is a motivation diagnostic, not an unbiased test-set estimate.",
+  "feature_sha256": "66e867aa74bc70a844e806e5571e371c9abb4a35f9e2887ce9b4d97ff2cb8fcd",
+  "cuda_device": "NVIDIA GeForce RTX 5070 Ti",
+  "python_version": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]",
+  "torch_version": "2.14.0+cu130",
+  "numpy_version": "2.5.3",
+  "valid_examples": 728,
+  "corruption_seed_protocol": "seed + 13 + pattern_index*101 + int(rate*1000)"
+}

deep_learning/Q2/outputs/followups/D0_task_preference/task_preference_diagnostic.md

@@ -0,0 +1,47 @@
+# Single-router MoFE 任务偏好诊断
+
+本诊断使用保留的 single-router 检查点和验证集，用于判断是否值得增加第二个 router；它不是测试集估计。
+
+## Forced-expert 规则
+
+所选模态子集可用的位置强制使用对应 expert；该子集不可用时，由已训练 router 在其他可用 expert 中选择；全模态缺失时沿用 learned missing token。可用率表示所选 expert 能被强制使用的位置比例。
+
+## 缺失条件平均指标
+
+下表先在每个 seed 内对 15 种连续块缺失条件求平均，再汇总三个 seed；seed 标准差见 CSV。
+
+| Expert | Macro-F1 ↑ | MAE ↓ | Pearson ↑ | 可强制使用比例 |
+| --- | ---: | ---: | ---: | ---: |
+| T | 0.590 | 0.643 | 0.606 | 0.471 |
+| A | 0.318 | 0.779 | 0.252 | 0.412 |
+| V | 0.429 | 0.747 | 0.340 | 0.390 |
+| TA | 0.567 | 0.659 | 0.577 | 0.393 |
+| TV | 0.561 | 0.667 | 0.567 | 0.372 |
+| AV | 0.393 | 0.799 | 0.349 | 0.372 |
+| TAV | 0.531 | 0.708 | 0.542 | 0.354 |
+| learned_router | 0.600 | 0.641 | 0.605 | 0.490 |
+
+## 两个任务的 expert 偏好
+
+在 48 个 seed—条件组合中，分类 Macro-F1 与回归 MAE 的平均 Spearman ρ 为 **-0.885**。MAE 越低越好，因此负相关表示两个指标倾向于选中相似的 expert。Macro-F1 与 Pearson 的平均 ρ 为 **0.931**。
+
+六个重点条件下的相关性先按三个 seed 求平均；最优 expert 一栏显示三个 seed 中的多数结果：
+
+| 条件 | ρ(Macro-F1, MAE) | ρ(Macro-F1, Pearson) | Macro-F1 最优 | MAE 最优 | Pearson 最优 |
+| --- | ---: | ---: | --- | --- | --- |
+| Clean | -0.881 | 0.940 | T (3/3) | T (3/3) | T (3/3) |
+| Text 30% | -0.905 | 0.929 | T (3/3) | T (3/3) | T (3/3) |
+| Audio 30% | -0.833 | 0.893 | T (1/3), TA (1/3), TAV (1/3) | T (2/3) | T (3/3) |
+| Vision 30% | -0.905 | 0.940 | T (3/3) | T (3/3) | T (3/3) |
+| Audio+Vision 30% | -0.869 | 0.905 | T (1/3), TA (1/3), TV (1/3) | TA (2/3) | T (3/3) |
+| All-modal 30% | -0.917 | 0.976 | T (3/3) | T (3/3) | T (3/3) |
+
+各指标的最优 expert 次数：Macro-F1（T: 40, A: 0, V: 0, TA: 4, TV: 2, AV: 0, TAV: 2）；MAE（T: 44, A: 0, V: 0, TA: 4, TV: 0, AV: 0, TAV: 0）；Pearson（T: 48, A: 0, V: 0, TA: 0, TV: 0, AV: 0, TAV: 0）。
+
+当前排名没有显示稳定的分类—回归 expert 分工：Macro-F1 较高通常同时对应较低 MAE 和较高 Pearson；文本 expert 在分类与回归指标上都是最常见的赢家。因此，这项诊断**没有提供增加第二个 router 所需的任务特异模态偏好证据**。目前保留 single-router 作为活动参照；这不代表两个任务在任何数据或设置下都不可能需要不同路由。
+
+## 结论范围
+
+输入是官方提供的 50 个有序 wordpiece 位置。结果只反映这些位置及本次缺失掩码下的任务与 expert 关系，不表示物理时间可靠性。
+
+逐条件结果见 `forced_expert_metrics.csv` 和 `rank_concordance.csv`；跨 seed 汇总见 `expert_task_preference_summary.csv`。