Add aligned missingness analyses and results
This commit is contained in:
@@ -1247,20 +1247,97 @@ Prediction
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四种模式下 EarlyConcat 的 AURC-MAE 点估计均较低，但区间均跨零。
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## 20.4 结论与边界
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## 20.4 缺失模态类型、缺失率与消融分析
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题目要求分析局部缺失的模态类型、位置和持续长度。本节在官方 aligned 验证集上对已选定的两个检查点做受控推理消融，不重新拟合模型，也不读取官方测试集。
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### 实验设计
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- 验证集为 728 条样本、239 个 source-video 组；只使用 R03 训练集拟合的同一个 median/MAD scaler。
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- 50 个位置是附件提供的有序 wordpiece positions，不是 50 个等长物理时间段。因此下文的 start/middle/end 与缺失比例都指这条 50-position 序列中的位置，不换算成视频秒数。
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- 构造 7 种被遮蔽模态集合：T、A、V、TA、TV、AV、TAV；每种分别设 10%、30%、50%、70% 缺失率，共 28 个条件。
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- 每个被选模态遮蔽一个居中的连续块，其他模态保持原观测状态。比例按该模态原本有效的位置数计算，至少保留 20% 原有观测。实际平均遮蔽率约为请求值：10% 条件实测 10.0%，30% 为 29.8%，50% 为 49.7%，70% 为 69.5%。两个模型使用完全相同的样本级掩码。
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- 下表先对同一缺失率下的 7 种模态集合不加权平均。干净验证集参考值为 EarlyConcat：Accuracy 0.6154、Macro-F1 0.5746、MAE 0.6343、Pearson 0.6095；MoFE：0.6058、0.5626、0.6368、0.6043。
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### 缺失率总体趋势
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| 缺失率 | Early Acc | Early Macro-F1 | Early MAE | Early Pearson | MoFE Acc | MoFE Macro-F1 | MoFE MAE | MoFE Pearson |
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|---:|---:|---:|---:|---:|---:|---:|---:|---:|
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| 10% | 0.6142 | **0.5736** | **0.6336** | **0.6084** | 0.6032 | 0.5641 | 0.6366 | 0.6042 |
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| 30% | 0.6126 | **0.5725** | **0.6337** | **0.6024** | 0.6054 | 0.5627 | 0.6384 | 0.6009 |
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| 50% | 0.6085 | **0.5665** | **0.6416** | 0.5865 | 0.6068 | 0.5390 | 0.6452 | **0.5901** |
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| 70% | 0.5899 | **0.5470** | **0.6605** | 0.5442 | **0.5907** | 0.5147 | 0.6679 | 0.5558 |
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随着遮蔽率从 10% 升到 70%，两模型的平均 Macro-F1 与 Pearson 都下降，MAE 上升。70% 时，EarlyConcat 的平均 Macro-F1 比 clean 低 0.0277、MAE 高 0.0257；MoFE 的 Macro-F1 低 0.0479、MAE 高 0.0310。总体退化在高缺失率更明显，且本轮点估计下 EarlyConcat 对缺失率增加更稳。
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### 哪种模态缺失更有影响？
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下表是相对同一模型 clean 验证结果的变化：
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- \(\Delta F1=F1_{missing}-F1_{clean}\)，负值表示 Macro-F1 下降；
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- \(\Delta MAE=MAE_{missing}-MAE_{clean}\)，正值表示回归误差上升。
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| 被遮蔽模态 | Early ΔF1（30% / 70%） | Early ΔMAE（30% / 70%） | MoFE ΔF1（30% / 70%） | MoFE ΔMAE（30% / 70%） |
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|---|---:|---:|---:|---:|
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| T | −0.021 / −0.080 | +0.003 / +0.060 | −0.037 / −0.128 | +0.019 / +0.103 |
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| A | +0.015 / +0.027 | −0.002 / −0.003 | −0.012 / −0.006 | +0.001 / +0.006 |
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| V | +0.001 / +0.002 | −0.003 / −0.002 | 0.000 / +0.011 | −0.005 / −0.010 |
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| TA | −0.007 / −0.062 | −0.003 / +0.053 | −0.041 / −0.120 | +0.012 / +0.040 |
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| TV | −0.014 / −0.089 | +0.001 / +0.073 | −0.034 / −0.121 | +0.008 / +0.075 |
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| AV | +0.009 / +0.010 | −0.003 / −0.003 | +0.009 / +0.023 | −0.006 / −0.006 |
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| TAV | +0.001 / −0.002 | 0.000 / +0.001 | +0.001 / +0.006 | +0.004 / +0.009 |
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文本局部缺失的影响最大，尤其对 MoFE：只遮蔽 70% 的 T 时，Macro-F1 下降 0.128、MAE 上升 0.103；EarlyConcat 分别下降 0.080、上升 0.060。遮蔽 T+A 或 T+V 后也出现明显下降，说明这一训练结果对文本位置包含的信息较敏感。单独遮蔽 A 或 V 的影响较小；EarlyConcat 在 A-only 条件中的 F1 点估计甚至上升。该现象只能说明当前验证集上的局部遮蔽结果，不能据此断言 A/V 对任务无用。TAV 的变化也不呈简单单调关系，反映不同模态组合与上下文仍会影响结果。
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### 位置、连续块长度与跨模态错位
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30% 单模态遮蔽的位置控制如下。这里 start/middle/end 是 wordpiece 序列位置：
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| 模型 | T：start / middle / end | A：start / middle / end | V：start / middle / end |
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|---|---:|---:|---:|
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| EarlyConcat + BiGRU | 0.529 / 0.554 / 0.556 | 0.581 / 0.590 / 0.591 | 0.578 / 0.576 / 0.571 |
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| MoFE-7 + MLP Router | 0.507 / 0.526 / 0.524 | 0.569 / 0.551 / 0.555 | 0.571 / 0.563 / 0.559 |
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T 的序列开头缺失是两个模型最差的位置；T 在中间或末尾缺失时 Macro-F1 较高。A/V 的位置效应较小且模型间方向不完全一致。
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将 30% 缺失做成一个长块或多个短块，没有出现跨模态一致的赢家：
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| 模型与模态 | 一个长块 Macro-F1 | 多个短块 Macro-F1 |
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|---|---:|---:|
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| Early T / A / V | 0.542 / 0.584 / 0.571 | 0.544 / 0.578 / 0.572 |
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| MoFE T / A / V | 0.518 / 0.557 / 0.573 | 0.522 / 0.564 / 0.566 |
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T/A/V 同时缺失 30% 时，EarlyConcat 对 sync、partial、async 的 Macro-F1 分别为 0.570、0.568、0.567，差异较小；MoFE 分别为 0.568、0.542、0.567，partial 条件低约 0.025。当前结果提示 MoFE 对多模态缺失区间错位更敏感；单种子验证结果尚不足以确认这种差异能否稳定复现。
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### 消融结论
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1. **输入模态局部遮蔽消融：**固定 R03 检查点，逐一遮蔽 T/A/V 及其组合。七种集合、四档缺失率均纳入逐条件 CSV；总体上文本缺失最伤，视觉或音频单独缺失影响较弱。
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2. **融合架构消融：**在相同特征、训练掩码和 28 个条件上比较 EarlyConcat 与 MoFE。28 条件 Macro-F1 平均值为 0.5648 vs 0.5384，MAE 为 0.6421 vs 0.6485；参数量分别是 253,124 和 306,523。EarlyConcat 的点估计更好且参数更少，但只有一个 seed，不能将此差异表述为稳定或显著优势。
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本节全部因素分析只在官方验证集和单个 seed 上进行，没有对 28 个单项条件逐一做显著性检验。结果适合描述趋势、定位敏感模态和选择后续实验，不应用来声称每个单项差异都具有统计显著性。
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## 20.5 结论与边界
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这次重训的点估计整体偏向 EarlyConcat + BiGRU：测试集 Macro-F1 和回归指标更高，验证集缺失情景的平均 Macro-F1、平均 MAE 与四种 AURC-MAE 也更好；MoFE-7 仅在测试 Accuracy 上略高。由于 Bootstrap 区间均跨零，且本次只使用一个 seed，不能声称 EarlyConcat 已被统计上确认优于 MoFE。当前应将 EarlyConcat 视为本轮较强候选，MoFE-7 继续保留比较，不宣称其具有总体优势。
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数学汇总文件中的 C5 测试结果为 Accuracy 0.6740、Macro-F1 0.5861、MAE 0.6980、RMSE 0.9674、Pearson 0.6300。C5 的概率输出头与损失函数不同，该结果只作背景参考，不是与本节两个模型的严格同结构消融比较。
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## 20.5 可复现产物
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## 20.6 可复现产物
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运行入口：
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```bash
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uv run python -m q2.train_math_protocol \
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--device auto \\
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--device auto \
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--output-dir outputs/followups/R04_math_protocol_retraining_replica
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uv run python -m q2.analyze_aligned_missingness --device auto
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```
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本次报告、检查点、scaler 和逐条件结果保存在独立目录：
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@@ -1271,6 +1348,11 @@ uv run python -m q2.train_math_protocol \
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- [controlled_metrics_by_scenario.csv](outputs/followups/R03_math_protocol_retraining/controlled_metrics_by_scenario.csv)：42 个验证情景的逐项结果；
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- [aurc_mae_by_mode_seed.csv](outputs/followups/R03_math_protocol_retraining/aurc_mae_by_mode_seed.csv) 与 [aurc_mae_paired_bootstrap.csv](outputs/followups/R03_math_protocol_retraining/aurc_mae_paired_bootstrap.csv)：AURC 与其区间；
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- [run_manifest.json](outputs/followups/R03_math_protocol_retraining/run_manifest.json)：输入特征 hash、数据划分、训练配置、设备和评估规程。
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- [aligned_missingness_analysis/modality_rate_metrics.csv](outputs/followups/R03_math_protocol_retraining/aligned_missingness_analysis/modality_rate_metrics.csv)：7 种缺失模态组合 × 4 档缺失率的全部指标；
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- [aligned_missingness_analysis/modality_rate_summary.csv](outputs/followups/R03_math_protocol_retraining/aligned_missingness_analysis/modality_rate_summary.csv)：按缺失率跨 7 种模态集合的平均结果；
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- [aligned_missingness_analysis/architecture_ablation_deltas.csv](outputs/followups/R03_math_protocol_retraining/aligned_missingness_analysis/architecture_ablation_deltas.csv)：逐条件的 MoFE − EarlyConcat 差值；
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- [aligned_missingness_analysis/location_span_synchrony_metrics.csv](outputs/followups/R03_math_protocol_retraining/aligned_missingness_analysis/location_span_synchrony_metrics.csv)：位置、长短块和同步方式消融明细；
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- [analyze_aligned_missingness.py](q2/analyze_aligned_missingness.py)：固定 R03 检查点，复现验证集因素分析的入口。
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---
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+29
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missing_modalities,requested_missing_rate,delta_macro_f1_mofe_minus_earlyconcat,delta_accuracy_mofe_minus_earlyconcat,delta_mae_mofe_minus_earlyconcat,delta_pearson_mofe_minus_earlyconcat
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T,0.1,-0.02095009705080475,-0.009615384615384692,0.00852203369140625,-0.005681651266488563
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T,0.3,-0.028133566640441643,0.004120879120879106,0.018436014652252197,-0.002954990120306089
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T,0.5,-0.04881210290283683,0.005494505494505475,0.017650365829467773,0.014302013497001664
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T,0.7,-0.05954842620562689,0.004120879120879106,0.04524129629135132,0.033624461771018854
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A,0.1,-0.018956056299722857,-0.013736263736263798,0.0020334720611572266,-0.0048390986225514965
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A,0.3,-0.03875077521640857,-0.02472527472527475,0.004995882511138916,-0.006971030876656625
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A,0.5,-0.03584453157618295,-0.019230769230769273,0.009491026401519775,-0.01138265791254478
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A,0.7,-0.045841740098851225,-0.027472527472527486,0.012121021747589111,-0.014525578199591993
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V,0.1,-0.011695298349532313,-0.006868131868131955,0.0014849305152893066,-0.003985309920647939
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V,0.3,-0.013327333479364767,-0.006868131868131955,-0.00032466650009155273,-0.003624933900228333
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V,0.5,-0.002668511995976064,0.0,-0.0026485323905944824,-0.0022958514057371815
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V,0.7,-0.002630957737063011,0.0013736263736263687,-0.005600035190582275,-0.0020966615400528354
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TA,0.1,-0.026265724265911672,-0.013736263736263798,0.008427262306213379,-0.005800806967002581
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TA,0.3,-0.04662354001073421,-0.006868131868131955,0.01767963171005249,-0.0060187289468149885
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TA,0.5,-0.05710396573590909,0.0,0.006686747074127197,0.01054346034623066
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TA,0.7,-0.07014314498368496,0.008241758241758212,-0.011094510555267334,0.04033479271711726
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TV,0.1,-0.023928513187945644,-0.010989010989011061,0.0070122480392456055,-0.005857719325507049
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TV,0.3,-0.031557457777281805,-0.008241758241758212,0.009562134742736816,-0.002108631807369088
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TV,0.5,-0.030348347592560665,0.0,0.0017394423484802246,0.0146511242496975
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TV,0.7,-0.04408148904110509,0.006868131868131844,0.0040721893310546875,0.026398519121162978
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AV,0.1,-0.014584844773992689,-0.009615384615384692,0.0004057884216308594,-0.0030162821741148704
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AV,0.3,-0.011436803022078279,-0.004120879120879106,-0.0004349946975708008,-0.003119705036767728
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AV,0.5,-0.004888362161646009,0.0013736263736263687,0.0004119873046875,-0.0037323543516377677
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AV,0.7,0.0007305745069405845,0.006868131868131844,-0.0005033612251281738,-0.003279997987449046
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TAV,0.1,-0.022677145584270697,-0.01236263736263743,0.0049256086349487305,-0.004991126746561547
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TAV,0.3,-0.01242502791792277,-0.004120879120879217,0.006239771842956543,-0.0018800190930987615
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TAV,0.5,-0.012817167512626293,0.0,0.001982390880584717,0.0035557032831754487
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TAV,0.7,-0.004505570262365088,0.005494505494505475,0.010849595069885254,0.0006202338937486562
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method,kind,variant,label,macro_f1,accuracy,mae,pearson,scenario
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B0_early_concat,span,multi,short,0.5724471470092743,0.6112637362637363,0.6319549083709717,0.609626822827826,0.3/span_multi_short_V
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B0_early_concat,synchrony,sync,TAV,0.5695955472926512,0.6071428571428571,0.6377636194229126,0.6014507911006194,0.3/synchrony_sync
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B0_early_concat,synchrony,async,TAV,0.5674224398503759,0.6126373626373627,0.6281272768974304,0.6032017513238488,0.3/synchrony_async
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B5_mofe_mlp,location,start,T,0.5074646922437304,0.5824175824175825,0.6651872396469116,0.5708209590446683,0.3/location_start_T
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B5_mofe_mlp,location,end,T,0.5241847210214233,0.6071428571428571,0.6566184759140015,0.5880970537326943,0.3/location_end_T
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B5_mofe_mlp,span,long,T,0.5179866041214112,0.592032967032967,0.6569079756736755,0.5832300823948291,0.3/span_long_T
|
||||
B5_mofe_mlp,span,multi,short,0.5217755256072807,0.5975274725274725,0.6542142033576965,0.5861358594281869,0.3/span_multi_short_T
|
||||
B5_mofe_mlp,location,start,A,0.5693105809612327,0.6153846153846154,0.6393567323684692,0.6030681972111498,0.3/location_start_A
|
||||
B5_mofe_mlp,location,middle,A,0.551121695858538,0.6002747252747253,0.6374183297157288,0.6040473734185878,0.3/location_middle_A
|
||||
B5_mofe_mlp,location,end,A,0.5553975345642012,0.6043956043956044,0.6361827850341797,0.6057378355308779,0.3/location_end_A
|
||||
B5_mofe_mlp,span,long,A,0.5568552904967875,0.6071428571428571,0.6383994817733765,0.603334225974666,0.3/span_long_A
|
||||
B5_mofe_mlp,span,multi,short,0.5639201931530282,0.6085164835164835,0.6405740976333618,0.6018797003793258,0.3/span_multi_short_A
|
||||
B5_mofe_mlp,location,start,V,0.5706553787814322,0.614010989010989,0.6286044120788574,0.607697827670399,0.3/location_start_V
|
||||
B5_mofe_mlp,location,middle,V,0.5625629140502925,0.6057692307692307,0.631475567817688,0.6066031598751728,0.3/location_middle_V
|
||||
B5_mofe_mlp,location,end,V,0.5591251566861323,0.6016483516483516,0.6326090097427368,0.6050984837070122,0.3/location_end_V
|
||||
B5_mofe_mlp,span,long,V,0.5732705025992542,0.6153846153846154,0.629095733165741,0.6077357791248426,0.3/span_long_V
|
||||
B5_mofe_mlp,span,multi,short,0.5658045462820314,0.6098901098901099,0.6311919093132019,0.6068331137272684,0.3/span_multi_short_V
|
||||
B5_mofe_mlp,synchrony,sync,TAV,0.5675933141063153,0.6085164835164835,0.6438319683074951,0.5957094024638004,0.3/synchrony_sync
|
||||
B5_mofe_mlp,synchrony,partial,TAV,0.542036764873725,0.6057692307692307,0.6417625546455383,0.5939879772923478,0.3/synchrony_partial
|
||||
B5_mofe_mlp,synchrony,async,TAV,0.5670250898329069,0.6153846153846154,0.6416157484054565,0.5924952604080301,0.3/synchrony_async
|
||||
|
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|
||||
method,missing_modalities,selected_modalities,requested_missing_rate,missing_layout,realized_selected_modality_rate_mean,realized_selected_modality_rate_min,realized_selected_modality_rate_max,n_valid,accuracy,macro_f1,mae,rmse,pearson,delta_accuracy_vs_clean,delta_macro_f1_vs_clean,delta_mae_vs_clean,delta_pearson_vs_clean
|
||||
B0_early_concat,T,T,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10076266369711513,0.0,0.16666666666666666,728,0.6098901098901099,0.5650784989965626,0.6360955834388733,0.8549598678294248,0.6046358533195865,-0.005494505494505475,-0.009568817766602566,0.0017796158790588379,-0.004824439656525237
|
||||
B5_mofe_mlp,T,T,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10076266369711513,0.0,0.16666666666666666,728,0.6002747252747253,0.5441284019457578,0.6446176171302795,0.8618670310910008,0.5989542020530979,-0.005494505494505475,-0.018516360699004863,0.0077977776527404785,-0.005383235137570441
|
||||
B0_early_concat,T,T,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2984805176440357,0.25,0.4,728,0.603021978021978,0.5536695658136993,0.6376606822013855,0.8514152889573482,0.5897726128244986,-0.01236263736263743,-0.020977750949465857,0.003344714641571045,-0.01968768015161315
|
||||
B5_mofe_mlp,T,T,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2984805176440357,0.25,0.4,728,0.6071428571428571,0.5255359991732577,0.6560966968536377,0.8699031271111219,0.5868176227041925,0.0013736263736263687,-0.037108763471505046,0.019276857376098633,-0.017519814486475882
|
||||
B0_early_concat,T,T,0.5,centered contiguous span per selected modality; other modalities unchanged,0.5008753198971786,0.4,0.6666666666666666,728,0.5947802197802198,0.537518703471417,0.6532965898513794,0.8696734563178178,0.5548372878355661,-0.020604395604395642,-0.037128613291748214,0.01898062229156494,-0.05462300514054563
|
||||
B5_mofe_mlp,T,T,0.5,centered contiguous span per selected modality; other modalities unchanged,0.5008753198971786,0.4,0.6666666666666666,728,0.6002747252747253,0.4887066005685801,0.6709469556808472,0.8831707050458881,0.5691393013325677,-0.005494505494505475,-0.07393816207618259,0.034127116203308105,-0.03519813585810061
|
||||
B0_early_concat,T,T,0.7,centered contiguous span per selected modality; other modalities unchanged,0.7002531296610403,0.6666666666666666,0.8,728,0.5590659340659341,0.4942462340015856,0.6943759322166443,0.9257402370719978,0.46778124322131553,-0.05631868131868134,-0.08040108276157953,0.060059964656829834,-0.14167904975479617
|
||||
B5_mofe_mlp,T,T,0.7,centered contiguous span per selected modality; other modalities unchanged,0.7002531296610403,0.6666666666666666,0.8,728,0.5631868131868132,0.43469780779595873,0.7396172285079956,0.9570429898534563,0.5014057049923344,-0.04258241758241754,-0.12794695484880397,0.10279738903045654,-0.10293173219833396
|
||||
B0_early_concat,A,A,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10093289445991736,0.0,0.16666666666666666,728,0.6181318681318682,0.5782422027002068,0.6336299180984497,0.8546216768515682,0.6098986851726621,0.0027472527472527375,0.003594885937041603,-0.0006860494613647461,0.00043839219655039674
|
||||
B5_mofe_mlp,A,A,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10093289445991736,0.0,0.16666666666666666,728,0.6043956043956044,0.5592861464004839,0.6356633901596069,0.8551002649444466,0.6050595865501106,-0.0013736263736263687,-0.0033586162442788003,-0.001156449317932129,0.000722149359442259
|
||||
B0_early_concat,A,A,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2993212718733642,0.0,0.5,728,0.625,0.5898724710749466,0.6324224472045898,0.8539363426497163,0.6110184042952445,0.009615384615384581,0.015225154311781397,-0.0018935203552246094,0.001558111319132749
|
||||
B5_mofe_mlp,A,A,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2993212718733642,0.0,0.5,728,0.6002747252747253,0.551121695858538,0.6374183297157288,0.8570439020438321,0.6040473734185878,-0.005494505494505475,-0.011523066786224723,0.0005984902381896973,-0.0002900637720805177
|
||||
B0_early_concat,A,A,0.5,centered contiguous span per selected modality; other modalities unchanged,0.4975768705069514,0.0,0.6666666666666666,728,0.625,0.5917541955954291,0.6310378909111023,0.8525212341751632,0.6126362378077966,0.009615384615384581,0.017106878832263916,-0.003278076648712158,0.0031759448316849292
|
||||
B5_mofe_mlp,A,A,0.5,centered contiguous span per selected modality; other modalities unchanged,0.4975768705069514,0.0,0.6666666666666666,728,0.6057692307692307,0.5559096640192461,0.6405289173126221,0.8608981073301772,0.6012535798952519,0.0,-0.006735098625516578,0.003709077835083008,-0.0030838572954164922
|
||||
B0_early_concat,A,A,0.7,centered contiguous span per selected modality; other modalities unchanged,0.694666169256934,0.0,0.8,728,0.6318681318681318,0.6019899077417187,0.6310633420944214,0.8517077960729005,0.6140614919880888,0.016483516483516425,0.027342590978553516,-0.0032526254653930664,0.004601199011977086
|
||||
B5_mofe_mlp,A,A,0.7,centered contiguous span per selected modality; other modalities unchanged,0.694666169256934,0.0,0.8,728,0.6043956043956044,0.5561481676428675,0.6431843638420105,0.8637607330339965,0.5995359137884968,-0.0013736263736263687,-0.006496595001895256,0.0063645243644714355,-0.004801523402171548
|
||||
B0_early_concat,V,V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.0981159123808491,0.0,0.16666666666666666,728,0.6126373626373627,0.5722385278601014,0.6324753165245056,0.852997227417173,0.6102436706088843,-0.0027472527472527375,-0.00240878890306373,-0.0018406510353088379,0.0007833776327725861
|
||||
B5_mofe_mlp,V,V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.0981159123808491,0.0,0.16666666666666666,728,0.6057692307692307,0.5605432295105691,0.6339602470397949,0.8504630524505615,0.6062583606882364,0.0,-0.0021015331341935894,-0.0028595924377441406,0.0019209234975680056
|
||||
B0_early_concat,V,V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2971709107860721,0.0,0.5,728,0.6126373626373627,0.5758902475296572,0.6318002343177795,0.8514377607625061,0.6102280937754011,-0.0027472527472527375,0.0012429307664920675,-0.002515733242034912,0.0007678007992893976
|
||||
B5_mofe_mlp,V,V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2971709107860721,0.0,0.5,728,0.6057692307692307,0.5625629140502925,0.631475567817688,0.8447082164734656,0.6066031598751728,0.0,-8.184859447024628e-05,-0.005344271659851074,0.002265722684504423
|
||||
B0_early_concat,V,V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49385700700886837,0.0,0.6666666666666666,728,0.6126373626373627,0.576255066455031,0.6313949227333069,0.8512688576714347,0.6093962042792834,-0.0027472527472527375,0.001607749691865834,-0.0029210448265075684,-6.408869682827945e-05
|
||||
B5_mofe_mlp,V,V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49385700700886837,0.0,0.6666666666666666,728,0.6126373626373627,0.5735865544590549,0.6287463903427124,0.8396628606408898,0.6071003528735462,0.006868131868131955,0.010941791814292223,-0.00807344913482666,0.002762915682877898
|
||||
B0_early_concat,V,V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6888359014759997,0.0,0.8,728,0.6112637362637363,0.5764169279126207,0.6323723793029785,0.8524589369832155,0.6072935574867208,-0.004120879120879106,0.0017696111494555078,-0.0019435882568359375,-0.0021667354893909474
|
||||
B5_mofe_mlp,V,V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6888359014759997,0.0,0.8,728,0.6126373626373627,0.5737859701755577,0.6267723441123962,0.8375960323446534,0.6051968959466679,0.006868131868131955,0.01114120753079495,-0.010047495365142822,0.0008594587559995759
|
||||
B0_early_concat,TA,T+A,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10084777907851622,0.0,0.14583333333333331,728,0.614010989010989,0.5717866205494326,0.6346916556358337,0.8532204193857846,0.6062922874472785,-0.0013736263736263687,-0.0028606962137325276,0.0003756880760192871,-0.0031680055288332287
|
||||
B5_mofe_mlp,TA,T+A,0.1,centered contiguous span per selected modality; other modalities unchanged,0.10084777907851622,0.0,0.14583333333333331,728,0.6002747252747253,0.545520896283521,0.6431189179420471,0.8585041835951804,0.6004914804802759,-0.005494505494505475,-0.017123866361241746,0.006299078464508057,-0.003845956710392451
|
||||
B0_early_concat,TA,T+A,0.3,centered contiguous span per selected modality; other modalities unchanged,0.29890089475869996,0.16666666666666666,0.4,728,0.614010989010989,0.5679903838930208,0.6313058137893677,0.8441793620883088,0.5974176946875417,-0.0013736263736263687,-0.00665693287014435,-0.0030101537704467773,-0.012042598288570017
|
||||
B5_mofe_mlp,TA,T+A,0.3,centered contiguous span per selected modality; other modalities unchanged,0.29890089475869996,0.16666666666666666,0.4,728,0.6071428571428571,0.5213668438822866,0.6489854454994202,0.857929852628108,0.5913989657407267,0.0013736263736263687,-0.04127791876247611,0.012165606021881104,-0.012938471449941646
|
||||
B0_early_concat,TA,T+A,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49922609520206496,0.3333333333333333,0.5333333333333333,728,0.5947802197802198,0.5439482478748752,0.6471090912818909,0.8599323546064853,0.5673420265716562,-0.020604395604395642,-0.030699068888289993,0.012793123722076416,-0.042118266404455484
|
||||
B5_mofe_mlp,TA,T+A,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49922609520206496,0.3333333333333333,0.5333333333333333,728,0.5947802197802198,0.4868442821389661,0.6537958383560181,0.858174960021945,0.5778854869178869,-0.01098901098901095,-0.07580048050579663,0.016975998878479004,-0.026451950272781466
|
||||
B0_early_concat,TA,T+A,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6974596494589871,0.3333333333333333,0.7571428571428571,728,0.5631868131868132,0.512956821905402,0.6876201033592224,0.916818393085796,0.4825813355899393,-0.052197802197802234,-0.06169049485776312,0.05330413579940796,-0.1268789573861724
|
||||
B5_mofe_mlp,TA,T+A,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6974596494589871,0.3333333333333333,0.7571428571428571,728,0.5714285714285714,0.4428136769217171,0.6765255928039551,0.8964248338262099,0.5229161283070566,-0.03434065934065933,-0.11983108572304563,0.039705753326416016,-0.08142130888361176
|
||||
B0_early_concat,TV,T+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09953039599122232,0.0,0.14583333333333331,728,0.6112637362637363,0.5692943420216147,0.6340571641921997,0.851291298333614,0.6058901685375406,-0.004120879120879106,-0.005352974741550498,-0.0002588033676147461,-0.003570124438571076
|
||||
B5_mofe_mlp,TV,T+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09953039599122232,0.0,0.14583333333333331,728,0.6002747252747253,0.545365828833669,0.6410694122314453,0.8558350566994001,0.6000324492120336,-0.005494505494505475,-0.01727893381109369,0.00424957275390625,-0.004304987978634767
|
||||
B0_early_concat,TV,T+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.297748643992044,0.1388888888888889,0.3970588235294118,728,0.6071428571428571,0.5603595289300786,0.6350727081298828,0.8462721956123026,0.5898738294264334,-0.008241758241758323,-0.014287787833086596,0.0007567405700683594,-0.0195864635496783
|
||||
B5_mofe_mlp,TV,T+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.297748643992044,0.1388888888888889,0.3970588235294118,728,0.5989010989010989,0.5288020711527968,0.6446348428726196,0.8541342721394217,0.5877651976190643,-0.006868131868131844,-0.03384269149196595,0.007815003395080566,-0.01657223957160403
|
||||
B0_early_concat,TV,T+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49748261556907564,0.25,0.5964912280701754,728,0.5989010989010989,0.5442779698638324,0.6596295237541199,0.8729321363864293,0.5472611098864947,-0.016483516483516536,-0.030369346899332794,0.02531355619430542,-0.06219918308961703
|
||||
B5_mofe_mlp,TV,T+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49748261556907564,0.25,0.5964912280701754,728,0.5989010989010989,0.5139296222712717,0.6613689661026001,0.8681901513761447,0.5619122341361922,-0.006868131868131844,-0.048715140373491006,0.024549126625061035,-0.04242520305447617
|
||||
B0_early_concat,TV,T+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6945825961963391,0.3333333333333333,0.7571428571428571,728,0.5508241758241759,0.4855914601333487,0.7077130079269409,0.9367642058350819,0.44116908072554145,-0.06456043956043955,-0.08905585662981647,0.07339704036712646,-0.16829121225057025
|
||||
B5_mofe_mlp,TV,T+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6945825961963391,0.3333333333333333,0.7571428571428571,728,0.5576923076923077,0.4415099710922436,0.7117851972579956,0.9348256749824289,0.46756759984670443,-0.04807692307692302,-0.1211347915525191,0.07496535778045654,-0.13676983734396392
|
||||
B0_early_concat,AV,A+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09947856691252001,0.0,0.16666666666666666,728,0.6153846153846154,0.5780535586223784,0.6322883367538452,0.8538497515220613,0.6099004767461359,0.0,0.0034062418592132326,-0.0020276308059692383,0.00044018377002419395
|
||||
B5_mofe_mlp,AV,A+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09947856691252001,0.0,0.16666666666666666,728,0.6057692307692307,0.5634687138483857,0.6326941251754761,0.8494563173128655,0.606884194572021,0.0,0.0008239512036229968,-0.004125714302062988,0.0025467573813526823
|
||||
B0_early_concat,AV,A+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.298476590661565,0.0,0.5,728,0.614010989010989,0.5835720646613662,0.63087397813797,0.8517257114056628,0.6112210355560509,-0.0013736263736263687,0.00892474789820108,-0.0034419894218444824,0.0017607425799391896
|
||||
B5_mofe_mlp,AV,A+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.298476590661565,0.0,0.5,728,0.6098901098901099,0.572135261639288,0.6304389834403992,0.8433678432673756,0.6081013305192832,0.004120879120879217,0.009490498994525254,-0.006380856037139893,0.00376389332861482
|
||||
B0_early_concat,AV,A+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49572274120123827,0.0,0.6666666666666666,728,0.6126373626373627,0.5833420760220777,0.6297139525413513,0.8514289401246513,0.6113098633411715,-0.0027472527472527375,0.008694759258912499,-0.004602015018463135,0.0018495703650598383
|
||||
B5_mofe_mlp,AV,A+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.49572274120123827,0.0,0.6666666666666666,728,0.614010989010989,0.5784537138604317,0.6301259398460388,0.8399236596703874,0.6075775089895338,0.008241758241758323,0.015808951215668943,-0.006693899631500244,0.0032400717988654293
|
||||
B0_early_concat,AV,A+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6916921691500518,0.0,0.8,728,0.6098901098901099,0.5849392724486678,0.6314220428466797,0.8550380162381634,0.6084118799755907,-0.005494505494505475,0.010291955685502674,-0.0028939247131347656,-0.0010484130005210535
|
||||
B5_mofe_mlp,AV,A+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6916921691500518,0.0,0.8,728,0.6167582417582418,0.5856698469556084,0.6309186816215515,0.8389920551414928,0.6051318819881416,0.010989010989011061,0.02302508431084571,-0.005901157855987549,0.0007944447974732594
|
||||
B0_early_concat,TAV,T+A+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09995224732741954,0.0,0.15277777777777776,728,0.6181318681318682,0.5794423437968373,0.6354190111160278,0.8559123939935158,0.6084679634558627,0.0027472527472527375,0.004795027033672183,0.001103043556213379,-0.000992329520248969
|
||||
B5_mofe_mlp,TAV,T+A+V,0.1,centered contiguous span per selected modality; other modalities unchanged,0.09995224732741954,0.0,0.15277777777777776,728,0.6057692307692307,0.5567651982125666,0.6403446197509766,0.8569256643596813,0.6034768367093012,0.0,-0.0058795644321960605,0.0035247802734375,-0.0008606004813671575
|
||||
B0_early_concat,TAV,T+A+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2983753764707696,0.1111111111111111,0.4166666666666667,728,0.6126373626373627,0.5759539997421909,0.6346108913421631,0.8520219245986547,0.6074596971344204,-0.0027472527472527375,0.0013066829790256973,0.0002949237823486328,-0.0020005958416913217
|
||||
B5_mofe_mlp,TAV,T+A+V,0.3,centered contiguous span per selected modality; other modalities unchanged,0.2983753764707696,0.1111111111111111,0.4166666666666667,728,0.6085164835164835,0.5635289718242681,0.6408506631851196,0.8547594099461256,0.6055796780413216,0.0027472527472527375,0.0008842091795053797,0.004030823707580566,0.0012422408506532756
|
||||
B0_early_concat,TAV,T+A+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.4974771506574596,0.2222222222222222,0.5777777777777778,728,0.6208791208791209,0.5881070025731289,0.6351321935653687,0.849459474866674,0.6024236422566064,0.005494505494505475,0.013459685809963706,0.0008162260055541992,-0.007036650719505322
|
||||
B5_mofe_mlp,TAV,T+A+V,0.5,centered contiguous span per selected modality; other modalities unchanged,0.4974771506574596,0.2222222222222222,0.5777777777777778,728,0.6208791208791209,0.5752898350605026,0.6371145844459534,0.8494591941956995,0.6059793455397818,0.015109890109890167,0.012645072415739866,0.00029474496841430664,0.0016419083491134856
|
||||
B0_early_concat,TAV,T+A+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6945781382684594,0.2222222222222222,0.7714285714285714,728,0.603021978021978,0.5727510362427203,0.6353143453598022,0.8534884241164965,0.5880249849999647,-0.01236263736263743,-0.0018962805204448818,0.000998377799987793,-0.02143530797614701
|
||||
B5_mofe_mlp,TAV,T+A+V,0.7,centered contiguous span per selected modality; other modalities unchanged,0.6945781382684594,0.2222222222222222,0.7714285714285714,728,0.6085164835164835,0.5682454659803552,0.6461639404296875,0.8649528510413363,0.5886452188937134,0.0027472527472527375,0.005600703335592483,0.009344100952148438,-0.015692218296954996
|
||||
|
+9
@@ -0,0 +1,9 @@
|
||||
method,requested_missing_rate,n_modality_sets,accuracy,macro_f1,mae,pearson,mean_macro_f1_drop_vs_clean,mean_mae_increase_vs_clean
|
||||
B0_early_concat,0.1,7,0.6142072213500784,0.5734480135067334,0.6340938551085336,0.6079041578982787,0.0011993032564317576,-0.00022211245128086636
|
||||
B0_early_concat,0.3,7,0.6126373626373626,0.5724726088064227,0.6333923935890198,0.6024273382427986,0.002174707956742366,-0.0009235739707946777
|
||||
B0_early_concat,0.5,7,0.6085164835164836,0.5664576088365416,0.6410448806626456,0.5864580531397964,0.008189707926623577,0.006728913102831159
|
||||
B0_early_concat,0.7,7,0.5898744113029827,0.5469845229122948,0.6599830218723842,0.5441890819981657,0.02766279385087033,0.025667054312569753
|
||||
B5_mofe_mlp,0.1,7,0.6032182103610675,0.553582630719279,0.6387811899185181,0.6030224443235824,0.009062131925483679,0.001961350440979004
|
||||
B5_mofe_mlp,0.3,7,0.6053767660910517,0.5464362510829611,0.641414361340659,0.5986161897026213,0.016208511561801635,0.0045945218631199426
|
||||
B5_mofe_mlp,0.5,7,0.6067503924646782,0.5389600389111505,0.6460896560123989,0.5901211156692516,0.023684723733612252,0.009269816534859794
|
||||
B5_mofe_mlp,0.7,7,0.5906593406593406,0.5146958437949012,0.6678524783679417,0.5557713348233022,0.04794891884986154,0.03103263889040266
|
||||
|
+54
@@ -0,0 +1,54 @@
|
||||
{
|
||||
"experiment": "Factorial local missingness type x rate on supplied aligned_50 validation data",
|
||||
"feature_file": "/home/gloamxun/modeling_zhaocui/E\u9898\u6570\u636e/\u9644\u4ef62-\u6570\u636e\u96c6\u7279\u5f81\u6587\u4ef6/aligned_50.pkl",
|
||||
"feature_sha256": "66e867aa74bc70a844e806e5571e371c9abb4a35f9e2887ce9b4d97ff2cb8fcd",
|
||||
"representation": "aligned_50 ordered wordpiece positions, not physical-time bins",
|
||||
"split": "official validation only",
|
||||
"n_valid": 728,
|
||||
"source_video_groups": 239,
|
||||
"checkpoint_source": "/home/gloamxun/modeling_zhaocui/deep_learning/Q2/outputs/followups/R03_math_protocol_retraining/models",
|
||||
"seed": 20260924,
|
||||
"device": "cuda",
|
||||
"cuda_device": "NVIDIA GeForce RTX 5070 Ti",
|
||||
"missing_rate_grid": [
|
||||
0.1,
|
||||
0.3,
|
||||
0.5,
|
||||
0.7
|
||||
],
|
||||
"missing_modality_sets": {
|
||||
"T": [
|
||||
0
|
||||
],
|
||||
"A": [
|
||||
1
|
||||
],
|
||||
"V": [
|
||||
2
|
||||
],
|
||||
"TA": [
|
||||
0,
|
||||
1
|
||||
],
|
||||
"TV": [
|
||||
0,
|
||||
2
|
||||
],
|
||||
"AV": [
|
||||
1,
|
||||
2
|
||||
],
|
||||
"TAV": [
|
||||
0,
|
||||
1,
|
||||
2
|
||||
]
|
||||
},
|
||||
"factorial_design": "28 local-missingness conditions plus clean reference; each selected modality receives a centered contiguous span; other modalities remain unchanged",
|
||||
"missing_rate_definition": "newly hidden observed positions divided by originally observed positions, averaged over selected modalities; realized rate reported per condition",
|
||||
"preserve_at_least_fraction": 0.2,
|
||||
"scenario_seed": 20261833,
|
||||
"additional_existing_controls": "30% start/middle/end location, one-long/multiple-short span, and sync/partial/async controls imported from the R03 42-scenario audit",
|
||||
"test_split_read_or_evaluated": false,
|
||||
"label_usage": "validation labels used only for metric computation; no model fitting or checkpoint selection in this analysis"
|
||||
}
|
||||
@@ -0,0 +1,361 @@
|
||||
"""Factorial local-missingness analysis for the aligned Q2 checkpoints."""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from .data import ATTACHMENT2, RobustStats, apply_robust_stats, load_aligned
|
||||
from .evaluate_math_protocol import (
|
||||
SCENARIO_SEED,
|
||||
continuous_mask,
|
||||
metrics,
|
||||
scenario_seed,
|
||||
sha256,
|
||||
write_csv,
|
||||
)
|
||||
from .models import AlignedFusionModel
|
||||
from .mofe import MixtureOfFusionExperts
|
||||
from .train_mofe import EARLYCONCAT, MODEL_CONFIG, MOFE7_MLP, _predict
|
||||
|
||||
|
||||
Q2_ROOT = Path(__file__).resolve().parents[1]
|
||||
RUN_DIR = Q2_ROOT / "outputs" / "followups" / "R03_math_protocol_retraining"
|
||||
OUTPUT_DIR = RUN_DIR / "aligned_missingness_analysis"
|
||||
SEED = 20260924
|
||||
BOOTSTRAP_SEED = 20260927
|
||||
MISSING_RATES = (0.1, 0.3, 0.5, 0.7)
|
||||
MODALITY_SETS: dict[str, tuple[int, ...]] = {
|
||||
"T": (0,),
|
||||
"A": (1,),
|
||||
"V": (2,),
|
||||
"TA": (0, 1),
|
||||
"TV": (0, 2),
|
||||
"AV": (1, 2),
|
||||
"TAV": (0, 1, 2),
|
||||
}
|
||||
METHODS = (EARLYCONCAT, MOFE7_MLP)
|
||||
METRICS = ("accuracy", "macro_f1", "mae", "pearson")
|
||||
|
||||
|
||||
def _device(name: str) -> torch.device:
|
||||
if name == "auto":
|
||||
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
return torch.device(name)
|
||||
|
||||
|
||||
def _load_model(method: str, dims: tuple[int, int, int], device: torch.device) -> torch.nn.Module:
|
||||
checkpoint_path = RUN_DIR / "models" / method / f"seed_{SEED}" / "model_best.pt"
|
||||
state = torch.load(checkpoint_path, map_location=device, weights_only=False)
|
||||
if method == EARLYCONCAT:
|
||||
if state.get("kind") != "concat":
|
||||
raise ValueError(f"unexpected EarlyConcat checkpoint format: {checkpoint_path}")
|
||||
model: torch.nn.Module = AlignedFusionModel("concat", dims=dims).to(device)
|
||||
elif method == MOFE7_MLP:
|
||||
if state.get("config") != MODEL_CONFIG:
|
||||
raise ValueError(f"unexpected MoFE checkpoint configuration: {checkpoint_path}")
|
||||
model = MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device)
|
||||
else:
|
||||
raise ValueError(f"unknown model: {method}")
|
||||
if int(state.get("seed", -1)) != SEED or tuple(state.get("dims", ())) != dims:
|
||||
raise ValueError(f"checkpoint metadata mismatch: {checkpoint_path}")
|
||||
model.load_state_dict(state["state_dict"])
|
||||
return model.eval()
|
||||
|
||||
|
||||
def _scenario_key(label: str, rate: float) -> str:
|
||||
return f"{label}/{rate:.1f}/middle_sync"
|
||||
|
||||
|
||||
def _factorial_masks(valid) -> tuple[dict[str, np.ndarray], dict[str, dict[str, float]]]:
|
||||
scenarios = {"clean": valid.mask.copy()}
|
||||
realized: dict[str, dict[str, float]] = {}
|
||||
for label, selected in MODALITY_SETS.items():
|
||||
for rate in MISSING_RATES:
|
||||
key = _scenario_key(label, rate)
|
||||
mask_rows = []
|
||||
for sample_id, original in zip(valid.ids, valid.mask):
|
||||
rng = np.random.default_rng(scenario_seed(SCENARIO_SEED, sample_id, key))
|
||||
corrupted = continuous_mask(
|
||||
original,
|
||||
rate,
|
||||
"single",
|
||||
rng,
|
||||
modalities=selected,
|
||||
location="middle",
|
||||
)
|
||||
mask_rows.append(corrupted)
|
||||
current = np.stack(mask_rows)
|
||||
scenarios[key] = current
|
||||
# Mean realized missing fraction among selected modalities. The
|
||||
# unselected modalities are deliberately excluded from this rate.
|
||||
per_sample = []
|
||||
for original, corrupted in zip(valid.mask, current):
|
||||
before = original[:, selected].sum(axis=0)
|
||||
hidden = (original[:, selected] & ~corrupted[:, selected]).sum(axis=0)
|
||||
rates = np.divide(hidden, before, out=np.full(len(selected), np.nan), where=before > 0)
|
||||
if np.isfinite(rates).any():
|
||||
per_sample.append(float(np.nanmean(rates)))
|
||||
realized[key] = {
|
||||
"requested_rate": float(rate),
|
||||
"selected_modality_rate_mean": float(np.mean(per_sample)) if per_sample else float("nan"),
|
||||
"selected_modality_rate_min": float(np.min(per_sample)) if per_sample else float("nan"),
|
||||
"selected_modality_rate_max": float(np.max(per_sample)) if per_sample else float("nan"),
|
||||
}
|
||||
return scenarios, realized
|
||||
|
||||
|
||||
def _condition_summary_rows(
|
||||
valid,
|
||||
predictions: dict[tuple[str, str], dict[str, np.ndarray]],
|
||||
realized: dict[str, dict[str, float]],
|
||||
) -> list[dict[str, Any]]:
|
||||
rows: list[dict[str, Any]] = []
|
||||
clean_key = "clean"
|
||||
for label in MODALITY_SETS:
|
||||
for rate in MISSING_RATES:
|
||||
scenario = _scenario_key(label, rate)
|
||||
for method in METHODS:
|
||||
pred = predictions[(method, scenario)]
|
||||
clean = predictions[(method, clean_key)]
|
||||
current_metrics = metrics(valid, pred["logits"], pred["intensity"])
|
||||
clean_metrics = metrics(valid, clean["logits"], clean["intensity"])
|
||||
rows.append({
|
||||
"method": method,
|
||||
"missing_modalities": label,
|
||||
"selected_modalities": "+".join(label),
|
||||
"requested_missing_rate": rate,
|
||||
"missing_layout": "centered contiguous span per selected modality; other modalities unchanged",
|
||||
"realized_selected_modality_rate_mean": realized[scenario]["selected_modality_rate_mean"],
|
||||
"realized_selected_modality_rate_min": realized[scenario]["selected_modality_rate_min"],
|
||||
"realized_selected_modality_rate_max": realized[scenario]["selected_modality_rate_max"],
|
||||
"n_valid": valid.n,
|
||||
**current_metrics,
|
||||
"delta_accuracy_vs_clean": current_metrics["accuracy"] - clean_metrics["accuracy"],
|
||||
"delta_macro_f1_vs_clean": current_metrics["macro_f1"] - clean_metrics["macro_f1"],
|
||||
"delta_mae_vs_clean": current_metrics["mae"] - clean_metrics["mae"],
|
||||
"delta_pearson_vs_clean": current_metrics["pearson"] - clean_metrics["pearson"],
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _aggregate_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
result: list[dict[str, Any]] = []
|
||||
methods = list(METHODS)
|
||||
for method in methods:
|
||||
for rate in MISSING_RATES:
|
||||
subset = [r for r in rows if r["method"] == method and r["requested_missing_rate"] == rate]
|
||||
result.append({
|
||||
"method": method,
|
||||
"requested_missing_rate": rate,
|
||||
"n_modality_sets": len(subset),
|
||||
**{metric: float(np.mean([float(r[metric]) for r in subset])) for metric in METRICS},
|
||||
"mean_macro_f1_drop_vs_clean": float(-np.mean([float(r["delta_macro_f1_vs_clean"]) for r in subset])),
|
||||
"mean_mae_increase_vs_clean": float(np.mean([float(r["delta_mae_vs_clean"]) for r in subset])),
|
||||
})
|
||||
return result
|
||||
|
||||
|
||||
def _ablation_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
result: list[dict[str, Any]] = []
|
||||
for row in rows:
|
||||
other_method = MOFE7_MLP if row["method"] == EARLYCONCAT else EARLYCONCAT
|
||||
other = next(r for r in rows if r["method"] == other_method
|
||||
and r["missing_modalities"] == row["missing_modalities"]
|
||||
and r["requested_missing_rate"] == row["requested_missing_rate"])
|
||||
if row["method"] != EARLYCONCAT:
|
||||
continue
|
||||
result.append({
|
||||
"missing_modalities": row["missing_modalities"],
|
||||
"requested_missing_rate": row["requested_missing_rate"],
|
||||
"delta_macro_f1_mofe_minus_earlyconcat": float(other["macro_f1"] - row["macro_f1"]),
|
||||
"delta_accuracy_mofe_minus_earlyconcat": float(other["accuracy"] - row["accuracy"]),
|
||||
"delta_mae_mofe_minus_earlyconcat": float(other["mae"] - row["mae"]),
|
||||
"delta_pearson_mofe_minus_earlyconcat": float(other["pearson"] - row["pearson"]),
|
||||
})
|
||||
return result
|
||||
|
||||
|
||||
def _plot_rate_curves(rows: list[dict[str, Any]], clean_metrics: dict[str, dict[str, float]], output_dir: Path) -> None:
|
||||
colors = {"T": "#3366cc", "A": "#dc3912", "V": "#ff9900", "TA": "#109618", "TV": "#990099", "AV": "#0099c6", "TAV": "#dd4477"}
|
||||
fig, axes = plt.subplots(1, 2, figsize=(13, 5), sharey=True)
|
||||
for ax, method in zip(axes, METHODS):
|
||||
for label in MODALITY_SETS:
|
||||
subset = sorted(
|
||||
(r for r in rows if r["method"] == method and r["missing_modalities"] == label),
|
||||
key=lambda r: r["requested_missing_rate"],
|
||||
)
|
||||
xs = [float(r["requested_missing_rate"]) for r in subset]
|
||||
ys = [float(r["macro_f1"]) for r in subset]
|
||||
ax.plot(xs, ys, marker="o", linewidth=1.8, label=label, color=colors[label])
|
||||
base = clean_metrics[method]["macro_f1"]
|
||||
ax.axhline(base, color="#333333", linestyle="--", linewidth=1.2, label="clean")
|
||||
ax.set_title(method)
|
||||
ax.set_xlabel("Requested missing rate of selected modalities")
|
||||
ax.set_xticks(MISSING_RATES)
|
||||
ax.grid(alpha=0.25)
|
||||
axes[0].set_ylabel("Validation Macro-F1")
|
||||
axes[1].legend(title="Missing set", bbox_to_anchor=(1.02, 1), loc="upper left")
|
||||
fig.suptitle("Aligned Q2: local missingness rate and modality type")
|
||||
fig.tight_layout()
|
||||
fig.savefig(output_dir / "macro_f1_by_modality_and_rate.png", dpi=180, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)
|
||||
for ax, method in zip(axes, METHODS):
|
||||
matrix = np.asarray([
|
||||
[next(float(r["delta_macro_f1_vs_clean"]) for r in rows
|
||||
if r["method"] == method and r["missing_modalities"] == label
|
||||
and r["requested_missing_rate"] == rate)
|
||||
for rate in MISSING_RATES]
|
||||
for label in MODALITY_SETS
|
||||
])
|
||||
image = ax.imshow(matrix, aspect="auto", cmap="RdYlGn", vmin=-0.12, vmax=0.04)
|
||||
ax.set_title(method)
|
||||
ax.set_xticks(range(len(MISSING_RATES)), [f"{int(r*100)}%" for r in MISSING_RATES])
|
||||
ax.set_yticks(range(len(MODALITY_SETS)), list(MODALITY_SETS))
|
||||
ax.set_xlabel("Requested missing rate")
|
||||
for i in range(matrix.shape[0]):
|
||||
for j in range(matrix.shape[1]):
|
||||
ax.text(j, i, f"{matrix[i, j]:+.3f}", ha="center", va="center", fontsize=8)
|
||||
axes[0].set_ylabel("Selected modality set")
|
||||
fig.colorbar(image, ax=axes.ravel().tolist(), label="Macro-F1 change vs clean")
|
||||
fig.suptitle("Aligned Q2: Macro-F1 change under modality ablation")
|
||||
fig.savefig(output_dir / "macro_f1_drop_heatmap.png", dpi=180, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def _plot_location_span(location_rows: list[dict[str, Any]], output_dir: Path) -> None:
|
||||
locations = ("start", "middle", "end")
|
||||
fig, axes = plt.subplots(1, 2, figsize=(12, 4.5), sharey=True)
|
||||
for ax, method in zip(axes, METHODS):
|
||||
for modality in ("T", "A", "V"):
|
||||
values = []
|
||||
for location in locations:
|
||||
row = next(r for r in location_rows if r["method"] == method
|
||||
and r["kind"] == "location" and r["label"] == modality
|
||||
and r["variant"] == location)
|
||||
values.append(float(row["macro_f1"]))
|
||||
ax.plot(locations, values, marker="o", label=modality)
|
||||
ax.set_title(method)
|
||||
ax.set_ylabel("Validation Macro-F1")
|
||||
ax.set_xlabel("30% missing-block location")
|
||||
ax.grid(alpha=0.25)
|
||||
axes[1].legend(title="Modality")
|
||||
fig.suptitle("Aligned Q2: sensitivity to missing-block location")
|
||||
fig.tight_layout()
|
||||
fig.savefig(output_dir / "location_sensitivity.png", dpi=180, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR) -> None:
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
device = _device(device_name)
|
||||
if device.type == "cuda" and not torch.cuda.is_available():
|
||||
raise RuntimeError("CUDA requested but not available")
|
||||
feature_path = ATTACHMENT2 / "aligned_50.pkl"
|
||||
splits = load_aligned(feature_path)
|
||||
valid = apply_robust_stats(splits["valid"], RobustStats.load(RUN_DIR / "aligned_robust_stats.npz"))
|
||||
dims = tuple(int(x.shape[-1]) for x in valid.x)
|
||||
scenarios, realized = _factorial_masks(valid)
|
||||
predictions: dict[tuple[str, str], dict[str, np.ndarray]] = {}
|
||||
clean_metrics: dict[str, dict[str, float]] = {}
|
||||
for method in METHODS:
|
||||
model = _load_model(method, dims, device)
|
||||
for scenario, mask in scenarios.items():
|
||||
predictions[(method, scenario)] = _predict(model, valid, mask, device, batch_size=64)
|
||||
clean_metrics[method] = metrics(
|
||||
valid,
|
||||
predictions[(method, "clean")]["logits"],
|
||||
predictions[(method, "clean")]["intensity"],
|
||||
)
|
||||
del model
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
rows = _condition_summary_rows(valid, predictions, realized)
|
||||
aggregate_rows = _aggregate_rows(rows)
|
||||
ablation_rows = _ablation_rows(rows)
|
||||
write_csv(output_dir / "modality_rate_metrics.csv", rows)
|
||||
write_csv(output_dir / "modality_rate_summary.csv", aggregate_rows)
|
||||
write_csv(output_dir / "architecture_ablation_deltas.csv", ablation_rows)
|
||||
|
||||
# Analyze the existing fixed 30% location, span, and synchrony controls.
|
||||
old_conditions_path = RUN_DIR / "controlled_metrics_by_scenario.csv"
|
||||
with old_conditions_path.open("r", newline="", encoding="utf-8-sig") as stream:
|
||||
old_rows = list(csv.DictReader(stream))
|
||||
location_rows: list[dict[str, Any]] = []
|
||||
for row in old_rows:
|
||||
scenario = row["scenario"]
|
||||
pieces = scenario.split("/")
|
||||
if len(pieces) != 2 or pieces[0] != "0.3":
|
||||
continue
|
||||
subparts = pieces[1].split("_")
|
||||
if subparts[0] == "location":
|
||||
kind, variant, label = "location", subparts[1], subparts[2]
|
||||
elif subparts[0] == "span":
|
||||
kind, variant, label = "span", subparts[1], subparts[2]
|
||||
elif subparts[0] == "synchrony":
|
||||
kind, variant, label = "synchrony", subparts[1], "TAV"
|
||||
else:
|
||||
continue
|
||||
location_rows.append({
|
||||
"method": row["method"],
|
||||
"kind": kind,
|
||||
"variant": variant,
|
||||
"label": label,
|
||||
"macro_f1": float(row["macro_f1"]),
|
||||
"accuracy": float(row["accuracy"]),
|
||||
"mae": float(row["mae"]),
|
||||
"pearson": float(row["pearson"]),
|
||||
"scenario": scenario,
|
||||
})
|
||||
write_csv(output_dir / "location_span_synchrony_metrics.csv", location_rows)
|
||||
_plot_rate_curves(rows, clean_metrics, output_dir)
|
||||
_plot_location_span(location_rows, output_dir)
|
||||
|
||||
manifest = {
|
||||
"experiment": "Factorial local missingness type x rate on supplied aligned_50 validation data",
|
||||
"feature_file": str(feature_path),
|
||||
"feature_sha256": sha256(feature_path),
|
||||
"representation": "aligned_50 ordered wordpiece positions, not physical-time bins",
|
||||
"split": "official validation only",
|
||||
"n_valid": valid.n,
|
||||
"source_video_groups": len({sid.split("$_$", 1)[0] for sid in valid.ids}),
|
||||
"checkpoint_source": str(RUN_DIR / "models"),
|
||||
"seed": SEED,
|
||||
"device": str(device),
|
||||
"cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None,
|
||||
"missing_rate_grid": list(MISSING_RATES),
|
||||
"missing_modality_sets": {k: list(v) for k, v in MODALITY_SETS.items()},
|
||||
"factorial_design": "28 local-missingness conditions plus clean reference; each selected modality receives a centered contiguous span; other modalities remain unchanged",
|
||||
"missing_rate_definition": "newly hidden observed positions divided by originally observed positions, averaged over selected modalities; realized rate reported per condition",
|
||||
"preserve_at_least_fraction": 0.2,
|
||||
"scenario_seed": SCENARIO_SEED,
|
||||
"additional_existing_controls": "30% start/middle/end location, one-long/multiple-short span, and sync/partial/async controls imported from the R03 42-scenario audit",
|
||||
"test_split_read_or_evaluated": False,
|
||||
"label_usage": "validation labels used only for metric computation; no model fitting or checkpoint selection in this analysis",
|
||||
}
|
||||
(output_dir / "run_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8")
|
||||
print(f"saved aligned missingness analysis to {output_dir}", flush=True)
|
||||
print(f"conditions={len(rows)}; methods={len(METHODS)}; device={device}", flush=True)
|
||||
for row in aggregate_rows:
|
||||
print(
|
||||
f"{row['method']} rate={row['requested_missing_rate']:.1f} "
|
||||
f"F1={row['macro_f1']:.4f} MAE={row['mae']:.4f}",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--device", default="auto", choices=("auto", "cuda", "cpu"))
|
||||
parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR)
|
||||
args = parser.parse_args()
|
||||
run(device_name=args.device, output_dir=args.output_dir)
|
||||
Generated
+1
-390
@@ -2,46 +2,6 @@ version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.14"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
version = "0.0.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.15.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.15'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a9/d2/f4d173e22df740bc37b1db102b386ba719b66e95b0f0d751f556b387e6d2/anyio-4.15.1.tar.gz", hash = "sha256:9f28306018cbd6d329e64a36d58256edff76dd996fe423bc957326e578b82a94", size = 276966, upload-time = "2026-09-05T10:42:39.44Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/12/b8/4bd346e22b28902df4d651910f5242c28d84e4a5c2435ca5c3f797ed7e2e/anyio-4.15.1-py3-none-any.whl", hash = "sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101", size = 132079, upload-time = "2026-09-05T10:42:37.923Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2026.7.22"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a3/c2/24167ea9858356b47a87a50d39908bfdb72ceeefe0041586e704e5376b3a/certifi-2026.7.22.tar.gz", hash = "sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55", size = 138112, upload-time = "2026-07-22T03:35:12.644Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983, upload-time = "2026-07-22T03:35:11.276Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.5.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c7/0e/7fa0ef50764b67090eca4114772a2abf8b6148198475e54c660b97caeee6/click-8.5.0.tar.gz", hash = "sha256:ba0d2089de75ea0310e2dde03160e6ca10009947fb95a182f9b54021bb272e34", size = 382235, upload-time = "2026-08-26T13:33:14.56Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/58/50/6c0d534c5f134586a8e1ba4e330569e32f057e33372ae556463212fb4cd3/click-8.5.0-py3-none-any.whl", hash = "sha256:255bc9599cf7748b4b1a446ccc735421bd08a2ae529a8b88597d3de5664ee360", size = 125251, upload-time = "2026-08-26T13:33:12.928Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cloudpickle"
|
||||
version = "3.1.2"
|
||||
@@ -51,15 +11,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.4.0"
|
||||
@@ -198,7 +149,7 @@ wheels = [
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "deep-learning-q2-q3-selection"
|
||||
name = "deep-learning-q2"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
@@ -206,7 +157,6 @@ dependencies = [
|
||||
{ name = "numpy" },
|
||||
{ name = "scikit-learn" },
|
||||
{ name = "torch" },
|
||||
{ name = "transformers" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
@@ -215,7 +165,6 @@ requires-dist = [
|
||||
{ name = "numpy", specifier = ">=2.5.3" },
|
||||
{ name = "scikit-learn", specifier = ">=1.9.1" },
|
||||
{ name = "torch", specifier = ">=2.14.0", index = "https://download.pytorch.org/whl/cu130" },
|
||||
{ name = "transformers", specifier = ">=5.17.0" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -277,96 +226,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/6c/c0/a98505f18594f1bce828bb159cec0fcf9860562f1a2c85913409fc8f3d9e/fsspec-2026.9.0-py3-none-any.whl", hash = "sha256:8dd6e646e99ea382bd85f97a45e6b526a442d79423a7dc673f1e2756d05fcb5f", size = 221738, upload-time = "2026-09-18T17:50:41.341Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "hf-xet"
|
||||
version = "1.6.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1b/ab/522a2ab67f27971a9d48ca666d4fca85ef7d5282d142e31fd087e27b1bbe/hf_xet-1.6.0.tar.gz", hash = "sha256:2e58454a340b3556dfa4972d5451aff4fba8dd42a236600ba1a1d2b1514f0fef", size = 920527, upload-time = "2026-08-03T22:33:13.243Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/41/62/3c062f593bd92ef4e77a0ef39541e3d82a0a1d3947c8a777a02a13a27828/hf_xet-1.6.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:70cbb9c896901600128cb9b6f06e132954fbede1db30f31f7c6c63f84cb7c31d", size = 4074584, upload-time = "2026-08-03T22:32:47.364Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/1e/c0ad437dd267a8e435bef594acf781bbc3874ff0b6435b4962d03ecf7cc4/hf_xet-1.6.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:23379c2f9ec8696d952b16414a2bae72cad86a52df869b050698ba60f538c675", size = 3867381, upload-time = "2026-08-03T22:32:49.049Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d5/ee/7c0d7b6ab336167531b1c30af2af003f054af4c749becbd7209ae33a77c3/hf_xet-1.6.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f2f7278c05c22fd60cb436cda1269649b3e81db65ecdc8496e5e164aa4143e7b", size = 4453982, upload-time = "2026-08-03T22:32:50.568Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/63/06/ad8eab1c9525246650cbaa821caa3cdbaca734ab1a5b8c91bea09cbd8d69/hf_xet-1.6.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:948f15d3a9545cfe5932f6bd8b440f6ae630aee108f14b7bd6c561f7c2dcc522", size = 4249445, upload-time = "2026-08-03T22:32:52.391Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d8/26/1eee8aedb0dafc1ab9717dc9ac602cde33361b232dc06803f1f6ed18b58c/hf_xet-1.6.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5153e6bb103ad49d6ea9f1b2e230db5a2ea32551ad09a706d2f61d7c7c80d80e", size = 4451099, upload-time = "2026-08-03T22:32:54.114Z" },
|
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Reference in New Issue
Block a user