diff --git a/.gitignore b/.gitignore index 2f01eaf..798b8b5 100644 --- a/.gitignore +++ b/.gitignore @@ -3,15 +3,23 @@ /.uv/ /.python-version /uv.lock -/math/.venv/ -/math/.uv/ -/math/.python-version -/math/uv.lock -/math/.cache/ -/math/cache/ -/math/**/__pycache__/ -/math/**/*.py[cod] +/math/Q1/.venv/ +/math/Q1/.uv/ +/math/Q1/.python-version +/math/Q1/uv.lock +/math/Q1/.cache/ +/math/Q1/cache/ +/math/Q1/**/__pycache__/ +/math/Q1/**/*.py[cod] /Q1/.venv/ /Q1/models/ /Q1/__pycache__/ /Q1/outputs/ +/final/Q1/.venv/ +/final/Q1/.uv/ +/final/Q1/.python-version +/final/Q1/uv.lock +/final/Q1/.cache/ +/final/Q1/cache/ +/final/Q1/**/__pycache__/ +/final/Q1/**/*.py[cod] diff --git a/deep_learning/Q1/README.md b/deep_learning/Q1/README.md index 55b90b8..99b2864 100644 --- a/deep_learning/Q1/README.md +++ b/deep_learning/Q1/README.md @@ -270,6 +270,7 @@ stay fixed. Emotion labels are not used. cd Q1 uv run python -m q1.tsfa_experiment --device cuda --seed 42 uv run python -m q1.tsfa_alignment_only_eval --device cuda --seed 42 +uv run python -m q1.tsfa_emotion_probe --device cuda --seed 42 ``` The first command fits one semantic branch and equal-seed correspondence probes @@ -282,5 +283,101 @@ paired video-group confidence intervals, monotonicity/span/entropy diagnostics, plots, manifests, and checkpoints are written to `outputs/tsfa/`; the small `report_bundle/` contains shareable summaries and figures. See [RESULTS.md](RESULTS.md) for the measured tradeoff -and interpretation limits. +and interpretation limits. The third command evaluates the same frozen +representations with the grouped, five-segment logistic-regression and Ridge +probes; emotion labels are used only by these downstream probes. + +With the math OOF predictions and fold assignment CSV available at their +workspace paths, compare TSFA against B0–B4 on the identical held-out samples +and bootstrap by source video: + +```bash +uv run python -m q1.compare_emotion_probes --bootstrap-repeats 2000 --seed 42 +``` + +The local Audio–Vision edge and modality-private residual experiment runs a +fixed 2×2 ablation with the same five grouped folds, temporal windows, +self-supervised objective, and emotion probes: + +```bash +uv run python -m q1.tsfa_av_private_ablation --device cuda --seed 42 +uv run python -m q1.compare_emotion_probes --tsfa-predictions outputs/tsfa_av_private_ablation/predictions.csv --candidate-method TSFA-T+Private --output-dir outputs/tsfa_av_private_ablation --output-stem private_vs_math --bootstrap-repeats 2000 --seed 42 +``` + +The ablation files and figure are in `outputs/tsfa_av_private_ablation/`. + +## TSFA-SPR shared/private factorization + +Run the first grouped five-fold experiment with M4 temporal checkpoints frozen: + +```bash +cd Q1 +uv run python -m q1.tsfa_shared_private --device cuda --seed 42 +``` + +The experiment pools each fold-standardized original text, audio, and vision +source stream with the matching frozen `M4_sourceTime` attention weights. A +shared MLP and three independent private MLPs learn from same-slot cross-modal +InfoNCE, within-modality orthogonality, and per-modality reconstruction. No +emotion labels enter representation training. The run also fits a train-fold +PCA control on raw private slots to match the 256-dimensional-per-slot SPR +control, then evaluates all representations with the existing grouped +LogisticRegression/Ridge probes. Outputs include the fold checkpoints, +training history, OOF predictions, grouped bootstrap contrasts, source and +reconstruction diagnostics, and figures under +`outputs/tsfa_shared_private/`. + +If you only need to regenerate the derived source contrasts, assessment, and +plots from an existing run without retraining, use: + +```bash +uv run python -m q1.tsfa_shared_private --finalize-existing +``` + +For the first seed's interpretation, diagnostic caveats, and next-step +decision, see [RESULTS.md](RESULTS.md#tsfa-spr时序共享私有分解) and +[`outputs/tsfa_shared_private/README.md`](outputs/tsfa_shared_private/README.md). + +## Shared information definition comparison + +Compare three concrete definitions of shared information using the same +fold-standardized original source features pooled by the frozen M4 temporal +checkpoints: Similarity (reuse the existing SPR weights), Correlation +(regularized MAXVAR/GCCA), and Predictability (cross-modal Ridge prediction +and its private residual). The Similarity network is loaded for inference and +is not retrained. Emotion labels are used only by the identical outer-fold +emotion probes. + +```bash +cd Q1 +uv run python -m q1.shared_definition_comparison --device cuda --seed 42 +``` + +The run reuses the fixed five-fold `video_id` splits, compares same-slot, +shifted, and within-video-shuffled predictive controls, and reports shared +variance, residual predictability, dimension-matched 1,280D emotion probes, +private-source ablations, and paired video-group bootstrap contrasts against +RawPrivate-PCA, Similarity-SPR, TSFA+RawPrivate, and math B0/B4. The math +artifacts are read-only inputs. Tables, figures, fold assignments, and the run +manifest are written to `outputs/shared_definition_comparison/`; measured +results and limitations are in [RESULTS.md](RESULTS.md#shared-information-definition-similarity-vs-correlation-vs-predictability). + +## Shared time coordinate with modality-private content + +This follow-up uses the corrected definition: M4 supplies a frozen 50-slot +temporal coordinate, while BERT text, eGeMAPS audio, and DeiT vision retain +modality-specific content. Optional same-slot product/difference features are +evaluated only as task-specific interactions; they do not assert shared +semantics. Feature standardization, PCA, and emotion probes are fitted inside +each training fold. The math outputs are read-only historical controls. + +```bash +cd Q1 +uv run python -m q1.shared_time_private_content --device cuda --seed 42 --bootstrap-draws 2000 +``` + +The run writes 19 new representation OOF probes, prior-result controls, +video-group bootstrap contrasts, temporal monitoring, fold/PCA audits, and nine +figures to `outputs/shared_time_private_content/`. See the dedicated +[results and seven-question interpretation](RESULTS.md#shared-time-private-content修正版验证). diff --git a/deep_learning/Q1/RESULTS.md b/deep_learning/Q1/RESULTS.md index c299b76..cd48cb5 100644 --- a/deep_learning/Q1/RESULTS.md +++ b/deep_learning/Q1/RESULTS.md @@ -309,7 +309,7 @@ uv run python -m q1.m4_shared_latent_eval --device cuda --seed 42 --probe-epochs ### TSFA：冻结时间候选范围与局部内容选择 -在上述 D5/M4 结果之上，本轮验证“先用时间确定候选范围，再在范围内按内容选择”能否同时改善时间定位和跨模态内容对应。五折 `M4_sourceTime` 检查点保持冻结；每个共享槽的 M4 注意力期望时间作为候选中心，主方案在归一化时间 `±0.10` 的范围内，用文本内容查询音频和视觉内容。局部语义分支只用训练折的同槽正例和相隔 `±2、±3、±5` 槽的难负例训练 40 个 epoch。它的 Q/K 不显式接收源时间码、位置编码或槽编号；M4 选中的内容仍可能间接携带时间。情绪标签没有进入训练。原有 BERT 文本、eGeMAPS 音频和 DeiT 视觉特征均保持不变。 +在上述 D5/M4 结果之上，本轮验证“先用时间确定候选范围，再在范围内按内容选择”能否同时改善时间定位和跨模态内容对应。五折 `M4_sourceTime` 检查点保持冻结；每个共享槽的 M4 注意力期望时间作为候选中心，主方案在归一化时间 `±0.10` 的范围内，用文本内容查询音频和视觉内容。局部语义分支只用训练折的同槽正例和相隔 `±2、±3、±5` 槽的难负例训练 40 个 epoch。它的 Q/K 不显式接收源时间码、位置编码或槽编号；M4 选中的内容仍可能间接携带时间。情绪标签没有进入训练。原有 BERT 文本、eGeMAPS 音频和 DeiT 视觉特征均保持不变。当前语义分支只有 `T→A` 和 `T→V` 两条边，准确地说是“M4 时间定位 + 文本条件化的音视频内容细化”，尚不包含直接的 A–V 语义交互。 比较包括 M3 无/有时间码、冻结 M4、有时间候选的 TSFA 主方案、乘以 M4 时间注意力的变体、20 次同宽度随机候选窗口，以及允许搜索全部源位置的全局变体。全部方法复用相同的 5 个 `video_id` 分组折、训练折归一化、每折相同的评估探针随机种子。100 条样本各作一次留出预测，涉及 37 个原视频。表中 AUC 是 Text–Audio、Text–Vision、Audio–Vision 三组“同槽对错槽”AUC 的逐样本平均，再按 `video_id` 宏平均；`0.5` 为机会水平。时间 MAE 是三个正向跨模态映射在归一化视频时间上的平均，越低越好。 @@ -354,3 +354,311 @@ Fedora WSL 复现（在 `deep_learning/Q1` 中执行，Python 环境由 `uv` 管 uv run python -m q1.tsfa_experiment --device cuda --seed 42 uv run python -m q1.tsfa_alignment_only_eval --device cuda --seed 42 ``` + +### TSFA 的情感标签探针 + +为了和已有的情感探针口径对照，额外用连续 `label` 的符号生成三类标签：小于 0 为 Negative，等于 0 为 Neutral，大于 0 为 Positive。100 条样本的类别数是 18/25/57；因此恒预测 Positive 的 Accuracy 为 `0.57`，固定三类计算的 Macro-F1 为 `0.242`。分类用 `StandardScaler` + 逻辑回归 (`C=0.05`)；连续分数回归用 `StandardScaler` + Ridge (`alpha=25`)。二者的 scaler 和预测器都只在每折 80 条训练样本上拟合，按 `video_id` 分组的 20 条留出样本只用于预测。对齐后的 50 个槽按顺序切成 5 段，每段平均，再拼接作为探针输入。Ridge 的折外预测按相同报告口径限幅到 `[-3, 3]` 后计算 MAE/Pearson；未限幅预测另存以供诊断。情感标签没有用于训练 TSFA 对齐分支。 + +同时报告 M3/M4 冻结表示作为参照。每种方法分别测试 Text、Audio、Vision 单模态以及三模态拼接；指标汇总全部 100 条折外预测。Macro-F1 固定包含三类，未预测到的类别按 0 计。折均值和标准差按每折 20 条留出预测计算，标准差使用样本标准差。 + +| 表示 | 视图 | Accuracy ↑ | OOF Macro-F1 ↑ | 折 Macro-F1 均值 ± SD | 限幅 MAE ↓ | 限幅 Pearson ↑ | +| --- | --- | ---: | ---: | ---: | ---: | ---: | +| M3 无时间码 | 三模态 | 0.56 | 0.385 | 0.347 ± 0.098 | 0.698 | 0.225 | +| M3 有时间码 | 三模态 | **0.60** | 0.411 | **0.367 ± 0.094** | 0.761 | 0.117 | +| M4 有时间码 | 三模态 | 0.52 | 0.351 | 0.327 ± 0.035 | **0.736** | **0.239** | +| TSFA 主方案 | 三模态 | 0.54 | **0.431** | 0.363 ± 0.091 | 0.820 | 0.104 | +| 恒预测 Positive | 基线 | 0.57 | 0.242 | — | — | — | + +TSFA 的 Accuracy 低于 `0.57` 多数类基线，但 Macro-F1 高于该基线，说明它确实预测了一部分 Negative 和 Neutral；混淆矩阵中分别正确识别 5/18 和 7/25。相对 M4，TSFA 的 OOF Macro-F1 从 `0.351` 升至 `0.431`，但五折 Macro-F1 均值只从 `0.327` 升至 `0.363`，二者折间波动范围较大；Accuracy 从 `0.52` 升至 `0.54`，仍低于多数类基线。TSFA 的限幅 MAE 为 `0.820`，Pearson 为 `0.104`，均未超过 M4。M3 有时间码的三模态 Accuracy 最高，为 `0.60`。Text 单模态中 TSFA 和 M4 完全相同，因为两者共用冻结 M4 文本表示；TSFA 的 Vision 单模态表现较弱（Accuracy `0.34`、Pearson `-0.232`）。 + +#### 与 math 报告中 B0–B4 的参照 + +下表的 B0–B4 数字来自 math 的 OOF 预测文件；TSFA 行来自本目录这次按相同分类器/回归器参数、五段池化、训练折标准化、五折 `GroupKFold` 重新运行的结果。已逐一核对 `sample_id`：100 条样本全部相同，且 math 的 `split_assignments.csv` 与 TSFA 的 fold 完全一致。因此下面进一步按相同样本做配对比较。 + +| 方法 | OOF Accuracy | OOF Macro-F1 | Macro-F1 折均值 ± SD | MAE（限幅） | Pearson（限幅） | +| --- | ---: | ---: | ---: | ---: | ---: | +| B0（math） | 0.600 | 0.361 | 0.337 ± 0.098 | **0.564** | **0.346** | +| B1（math） | **0.610** | 0.387 | 0.349 ± 0.119 | 0.599 | 0.238 | +| B2（math） | 0.580 | 0.331 | 0.313 ± 0.087 | 0.588 | 0.279 | +| B3（math） | 0.590 | 0.359 | 0.333 ± 0.096 | 0.600 | 0.230 | +| B4（math） | 0.600 | 0.422 | **0.398 ± 0.089** | 0.593 | 0.199 | +| TSFA 主方案（三模态） | 0.540 | **0.431** | 0.363 ± 0.091 | 0.820 | 0.104 | + +对相同 `sample_id` 的预测按 `video_id` 成组进行 2,000 次配对 bootstrap，报告 TSFA 减去各基线的 95% 百分位区间。表中区间未作多重比较校正；正的 MAE 差表示 TSFA 误差更大。 + +| 对照 | Δ Accuracy [95% CI] | Δ Macro-F1 [95% CI] | Δ MAE [95% CI] | Δ Pearson [95% CI] | +| --- | ---: | ---: | ---: | ---: | +| B0 | -0.060 [-0.181, 0.063] | +0.070 [-0.099, 0.211] | +0.256 [0.154, 0.369] | -0.241 [-0.447, -0.048] | +| B1 | -0.070 [-0.188, 0.053] | +0.044 [-0.129, 0.191] | +0.221 [0.112, 0.337] | -0.133 [-0.356, 0.097] | +| B2 | -0.040 [-0.165, 0.081] | +0.100 [-0.048, 0.220] | +0.232 [0.123, 0.346] | -0.175 [-0.382, 0.035] | +| B3 | -0.050 [-0.172, 0.076] | +0.072 [-0.087, 0.205] | +0.220 [0.111, 0.334] | -0.126 [-0.349, 0.103] | +| B4 | -0.060 [-0.186, 0.065] | +0.009 [-0.155, 0.165] | +0.228 [0.108, 0.357] | -0.094 [-0.338, 0.144] | + +TSFA 的 OOF Macro-F1 点估计略高于 B4（`+0.009`），但所有五个分类 Macro-F1 差值区间都跨过 0；因此没有证据说明 TSFA 的分类表现稳定优于任一 B0–B4。TSFA 的折均值 Macro-F1（`0.363 ± 0.091`）也低于 B4（`0.398 ± 0.089`）。五个回归 MAE 差值点估计均为正，Pearson 点估计均低于对应基线。最清楚的差异是 TSFA 相对 B0 的 Pearson 较低（未校正区间 `[-0.447, -0.048]`）；该结果仍需考虑多重比较和仅 37 个视频组的限制。整体而言，TSFA 没有显示比 B0–B4 更好的情感探针表现。 + +折外逐样本预测、指标、混淆矩阵和复现配置保存在 [emotion_probe_predictions.csv](outputs/tsfa_emotion_probe/emotion_probe_predictions.csv)、[emotion_probe_metrics.csv](outputs/tsfa_emotion_probe/emotion_probe_metrics.csv)、[emotion_probe_confusion_matrix.csv](outputs/tsfa_emotion_probe/emotion_probe_confusion_matrix.csv) 和 [emotion_probe_manifest.json](outputs/tsfa_emotion_probe/emotion_probe_manifest.json)。配对比较的区间表和折分核验记录见 [paired_math_comparison.csv](outputs/tsfa_emotion_probe/paired_math_comparison.csv) 与 [paired_math_comparison_manifest.json](outputs/tsfa_emotion_probe/paired_math_comparison_manifest.json)；比较程序在 [compare_emotion_probes.py](q1/compare_emotion_probes.py)。该程序只读取 math 的预测和分折 CSV，所有输出写在本目录。 + +复现命令： + +```bash +uv run python -m q1.tsfa_emotion_probe --device cuda --seed 42 +uv run python -m q1.compare_emotion_probes --bootstrap-repeats 2000 --seed 42 +``` + +### TSFA 的 A–V 局部边 × 原模态残差消融 + +针对 TSFA 的情感强度回归短板，在相同的 M4 冻结时间定位、`δ=0.10` 候选范围、BERT/eGeMAPS/DeiT 原始提取结果、五折 `video_id` 切分、40 epoch 局部 T–A/T–V 对比目标和轻量情感探针下，只改变两个因素：是否增加双向 Audio–Vision 局部语义边，以及是否把未经语义选择的三个模态特征送入探针。A–V 边的两个方向都使用原有 M4 对应源模态的候选掩码；原模态残差由训练折标准化后的原始模态特征经冻结 M4 时间权重池化到 50 槽，再作相同的五段池化。A–V 分支训练不使用情感标签。原版 `TSFA-T` 的 100 条分类与回归预测已逐样本核对，和上节 `TSFA-main` 完全一致。 + +| 结构 | A–V 边 | 原模态残差 | OOF Accuracy | OOF Macro-F1 | 折 Macro-F1 均值 ± SD | 限幅 MAE | 限幅 Pearson | +| --- | :---: | :---: | ---: | ---: | ---: | ---: | ---: | +| TSFA-T | 无 | 无 | 0.540 | **0.431** | 0.363 ± 0.091 | 0.820 | 0.104 | +| TSFA-AV | 有 | 无 | 0.520 | 0.413 | 0.359 ± 0.105 | 0.803 | 0.101 | +| TSFA-T+Private | 无 | 有 | **0.590** | 0.390 | 0.357 ± 0.118 | **0.572** | **0.368** | +| TSFA-AV+Private | 有 | 有 | 0.550 | 0.317 | 0.306 ± 0.113 | 0.579 | 0.366 | + +![A–V 局部边与原模态残差对情感探针的影响](outputs/tsfa_av_private_ablation/factorial_effects.png) + +按相同留出样本配对，并以 37 个 `video_id` 为单位做 2,000 次 bootstrap。下表差值为左侧结构减右侧结构；负的 MAE 差表示回归改善，区间均为未作多重比较校正的 95% 百分位区间。 + +| 结构差异 | Δ Macro-F1 [95% CI] | Δ MAE [95% CI] | Δ Pearson [95% CI] | +| --- | ---: | ---: | ---: | +| 加 A–V，无残差 | -0.018 [-0.085, 0.044] | -0.017 [-0.102, 0.065] | -0.003 [-0.165, 0.205] | +| 加残差，无 A–V | -0.041 [-0.146, 0.080] | **-0.249 [-0.356, -0.155]** | **+0.263 [0.111, 0.434]** | +| 加 A–V，已有残差 | -0.073 [-0.129, -0.013] | +0.008 [-0.015, 0.028] | -0.002 [-0.059, 0.065] | +| 加残差，已有 A–V | -0.096 [-0.219, 0.054] | **-0.224 [-0.342, -0.124]** | **+0.264 [0.038, 0.464]** | +| A–V × 残差交互效应 | -0.055 [-0.128, 0.031] | +0.025 [-0.048, 0.096] | +0.001 [-0.145, 0.120] | + +这次最明确的结果是原模态残差：在没有 A–V 边时，MAE 降低 `0.249`，Pearson 提高 `0.263`，两个配对区间均不跨 0；加入 A–V 后同样出现明显回归改善。单独增加 A–V 边只使 MAE 从 `0.820` 到 `0.803`，区间跨 0；在已有残差时，A–V 边的 Macro-F1 点估计反而下降 `0.073`（未校正区间 `[-0.129, -0.013]`）。当前证据支持“保留未经语义筛选的模态信息”比增加这条 A–V 边更能修复回归问题，尚不支持 A–V 边能稳定改善情感强度。A–V 注意力在候选范围内归一化，时间中心诊断见下方文件；这本身不能证明它学到了正确的语义对应。 + +`TSFA-T+Private` 的 MAE 为 `0.572`，接近 math B0 的 `0.564` 和 B4 的 `0.593`。同样本的配对比较中，它相对 B0 的 MAE 差为 `+0.007`，区间 `[-0.027, 0.039]`；相对 B4 为 `-0.021`，区间 `[-0.075, 0.027]`。这些区间说明本轮未观察到稳定差异，不能把它们解释成统计等价。它相对 B4 的 Pearson 差为 `+0.169`，未校正区间 `[0.012, 0.318]`；Macro-F1 差为 `-0.032`，区间 `[-0.208, 0.112]`。 + +原模态残差同时包含 Text、Audio、Vision，当前四格结果不能把回归增益单独归因于音频或视觉；加入残差也使探针输入从 1,920 维增至 6,845 维。A–V 边增加了可训练参数，单种子实验也不能证明模型已经学到真实的 Audio–Vision 语义关系。实验仅有 100 条样本和 37 个来源视频，所有配对区间未作多重比较校正。 + +四格的 [OOF 指标](outputs/tsfa_av_private_ablation/metrics.csv)、[逐样本预测](outputs/tsfa_av_private_ablation/predictions.csv)、[配对区间](outputs/tsfa_av_private_ablation/paired_contrasts.csv)、[A–V 局部注意力诊断](outputs/tsfa_av_private_ablation/av_attention_diagnostics.csv)、[残差版本对 math 的配对比较](outputs/tsfa_av_private_ablation/private_vs_math.csv) 和 [运行清单](outputs/tsfa_av_private_ablation/run_manifest.json) 均位于 `deep_learning/Q1`。复现命令： + +```bash +uv run python -m q1.tsfa_av_private_ablation --device cuda --seed 42 +uv run python -m q1.compare_emotion_probes --tsfa-predictions outputs/tsfa_av_private_ablation/predictions.csv --candidate-method TSFA-T+Private --output-dir outputs/tsfa_av_private_ablation --output-stem private_vs_math --bootstrap-repeats 2000 --seed 42 +``` + +### TSFA-SPR：时序—共享—私有分解 + +这轮检验一个具体假设：TSFA 的强度回归短板是否主要来自缺少模态私有信息，以及学习式 shared/private 分解能否优于简单保留原始源特征。实验使用既有五折 `M4_sourceTime` checkpoint，全部冻结；先用各训练折统计量标准化原始 BERT、eGeMAPS、DeiT 特征，再用 M4 attention 权重池化到 50 个 latent slots。每种模态经过独立 adapter，再进入参数共享的 shared MLP 和各自的 private MLP，最后用每模态 decoder 重构输入。表示训练没有使用情感标签、时间编码或显式 slot index。 + +训练对照为 `SP-noOrth-noRec`、`SP+Orth`、`SPR`、`SPR-noSharedContrastive`；主损失为共享同槽 InfoNCE、同模态 shared/private 归一化正交项、逐模态均衡的重构项。固定 5-fold `GroupKFold(video_id)`、seed 42、40 epochs。情感 probe 沿用 `StandardScaler + LogisticRegression(C=0.05)`、`StandardScaler + Ridge(alpha=25)`、50 槽到五个连续时间段的均值池化，回归限幅 `[-3,3]`。所有拟合只用训练折，100 条样本均作一次 OOF 预测。 + +| 表示 / probe 视图 | 特征维数 | Accuracy | OOF Macro-F1 | Macro-F1 折均值 ± SD | MAE | Pearson | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| TSFA-old / 三模态 | 1920 | 0.540 | **0.431** | 0.363 ± 0.091 | 0.820 | 0.104 | +| TSFA+RawPrivate / 三模态 | 6845 | **0.590** | 0.390 | 0.357 ± 0.118 | 0.572 | **0.368** | +| SPR / shared-unfused + 全部 private | 1920 | 0.560 | 0.389 | 0.341 ± 0.094 | 0.697 | 0.071 | +| SPR-dim-matched / fused shared + 全部 private | 1280 | 0.550 | 0.318 | 0.315 ± 0.084 | 0.701 | 0.059 | +| RawPrivate-PCA / 训练折 PCA | 1280 | 0.560 | 0.297 | 0.281 ± 0.068 | **0.559** | 0.251 | + +四种因子器损失配置都使用 `shared-unfused + private-all` 作为同一 probe 视图： + +| 因子器 | Accuracy | OOF Macro-F1 | Macro-F1 折均值 ± SD | MAE | Pearson | +| --- | ---: | ---: | ---: | ---: | ---: | +| SP-noOrth-noRec | 0.530 | 0.392 | 0.355 ± 0.090 | 0.724 | 0.085 | +| SP+Orth | 0.470 | 0.298 | 0.290 ± 0.074 | 0.719 | 0.031 | +| SPR | 0.560 | 0.389 | 0.341 ± 0.094 | 0.697 | 0.071 | +| SPR-noSharedContrastive | 0.540 | 0.347 | 0.326 ± 0.100 | **0.670** | **0.248** | + +这四行的点估计没有配对区间；不能由其排序认定某个 loss 有统计优势。去掉 shared contrastive 后回归点估计反而更好，与 shared 同槽对应指标接近机会水平相符，但仍需独立数据验证。 + +`RawPrivate-PCA` 把每个 slot 的 985 维原始私有源特征用训练折 slots 拟合 PCA 压到 256 维；与 `SPR-dim-matched` 同为 256 维/slot、1280 维/clip。它的 MAE `0.559` 低于维度匹配 SPR 的 `0.701`，而 Macro-F1 和 Pearson 较低。SPR 主视图相对 RawPrivate-PCA 的 MAE 差为 `+0.138`，按 37 个 `video_id` 做 2,000 次 paired bootstrap 的 95% CI 为 `[+0.062, +0.216]`，区间未作多重比较校正。维度压缩后的原始特征保留了强度预测信息，但 learned decomposition 没有带来更好的回归效果。 + +直接比较两个同维视图时，`SPR-dim-matched − RawPrivate-PCA` 的 MAE 差为 `+0.142`，95% CI `[+0.076, +0.214]`；Macro-F1 差 `+0.020`，CI `[-0.094, +0.119]`；Pearson 差 `-0.192`，CI `[-0.412, +0.026]`。因此维度匹配以后，SPR 没有在强度回归上胜过 raw-source PCA；分类差异区间跨 0。 + +SPR 主视图相对 `TSFA+RawPrivate` 的 Macro-F1 差为 `-0.002`，95% CI `[-0.130, +0.114]`；MAE 差为 `+0.125`，CI `[+0.055, +0.197]`；Pearson 差为 `-0.297`，CI `[-0.555, -0.066]`。相对 math B0，Macro-F1 差 `+0.027` 的区间跨 0，MAE 差 `+0.132` `[+0.060, +0.204]`；相对 B4，Macro-F1 差 `-0.034` 的区间跨 0，MAE 差 `+0.104` `[+0.024, +0.188]`。因此情感分类没有显示出稳定优势，强度回归明显弱于 TSFA+RawPrivate 和 B0/B4 点估计。Pearson 也没有随 MAE 改善而同步提高。 + +#### shared/private 是否分开 + +| 诊断 | SPR 结果 | 解释 | +| --- | ---: | --- | +| modality probe accuracy：shared / private | 0.729 / 0.999 | private 可识别来源；shared 仍带有来源信息 | +| SP-noOrth-noRec（没有 private 训练损失）private source accuracy | 1.000 | 私有来源分类器即使在随机、未受训练的 private 支路上也饱和，因此不能将 private probe 高分解释为学到了私有语义 | +| T–A / T–V / A–V matched-vs-shifted AUC | 0.512 / 0.499 / 0.498 | 跨模态同槽匹配近似机会水平 | +| slot shuffle 后的对应 AUC | 0.499 / 0.498 / 0.496 | 打乱 slots 几乎没有破坏 shared correspondence | +| shared-only / private-only / both 重构 MSE | 0.563 / 0.442 / 0.422 | 合并略优于单支，但改善幅度有限 | + +private 分支能够从输出识别来源 modality，但无 private 训练损失的 `SP-noOrth-noRec` 控制也达到 `1.000`；因此这个 probe 很可能部分读出了模态专属 adapter/encoder 或输入分布特征，不足以证明学到了有用的 private 语义。合并重构误差最低，但只比 private-only 略低；shared 的对应 AUC 接近 `0.5`，shuffle 控制几乎不变，shared/private 归一化 cross-covariance 仍在 `0.582–0.732`。综合判定为**没有证据支持成功实现 shared/private 语义解耦或跨模态共享对齐**。这里的 `0.60 AUC` 和 `0.05` shuffle drop 只用于首轮描述性判读，并非显著性阈值或人工对齐真值。不能仅凭私有来源 probe 高分宣称完成解耦。 + +#### private 来源 probe + +单模态 private probe 中，Audio 的 Macro-F1 点估计最高（`0.445`），Vision 的 MAE 点估计最低（`0.657`）；Audio 的 MAE 为 `0.664`。Audio 对极性分类和 Vision 对强度回归分别是当前点估计领先者，但两两 video-group bootstrap 区间均跨 0，且不同 view 的维数不同，因此不足以断言某个模态贡献最大。Audio+Vision 与三模态 private 拼接也没有稳定超过最佳单模态 probe。 + +#### 结论与后续 + +本轮没有理由把现有 TSFA 正式升级为 TSFA-SPR。现有结果更支持“原始源特征残差保留了强度信息”这一较窄结论，而不是“shared/private 已被正确分解”。情感分类和强度回归也不是由同一私有模态驱动：Audio 的分类 Macro-F1 点估计最高，RawPrivate-PCA / Vision 的回归 MAE 点估计更低，但都仍需更多数据与种子确认。 + +下一步优先检查同槽对比目标为什么未能通过 slot shuffle 验证，并用独立于 M4 slot convention 的人工/弱时间事件参照验证 shared correspondence；仅当结构诊断改善后，再运行 seed `3407`、`2026` 检查稳定性。当前为 100 条 clip、37 个来源视频、单 seed；bootstrap 区间不含 seed 不确定性，所有多重比较未校正。 + +模型权重、逐 clip 诊断、OOF 预测、维度与来源对照、配对区间和图表均保存在 [outputs/tsfa_shared_private](outputs/tsfa_shared_private/README.md)。运行方式： + +```bash +uv run python -m q1.tsfa_shared_private --device cuda --seed 42 +``` + +## Shared Information Definition：Similarity vs Correlation vs Predictability + +### 研究问题与可比设置 + +这轮不再调整 SPR 网络，而把“shared information”拆成三种可检验的定义： + +1. **Similarity / 相似性**：复用已训练的 SPR 权重，把各模态映射到 shared branch，再检查同一时间槽的表示能否支持跨模态对应。SPR 只载入推理，没有重训。 +2. **Correlation / 相关性**：在每个外层训练折内先对三模态做 PCA（Text 64、Audio 24、Vision 64 维），再拟合 ridge-regularized MAXVAR/GCCA；共享维度固定为 32。留出片段通过训练折映射投影到共同空间。 +3. **Predictability / 可预测性**：以另外两种模态预测目标模态，预测值定义为线性可预测成分，残差定义为当前模型下的私有成分。比较只用同槽输入、相邻 `±1` 槽上下文、源模态偏移 `-5…+5` 槽以及片段内独立时间打乱。所有偏移控制用槽位 6–43 计分，确保偏移与局部上下文不会碰到序列边界；每个控制都使用同一个训练好的预测器。 + +全部方法使用各外层训练折拟合的标准化统计量和既有冻结 `M4_sourceTime` 权重，对相同的 BERT、eGeMAPS、DeiT 原始序列池化到 50 个槽。五折按 `video_id` 分组，每折约 80 条训练、20 条留出；100 条样本各产生一次 OOF 预测。情绪标签只用于冻结表示后的探针，不进入 GCCA、预测器或 SPR 表征提取。 + +主要表示先按槽拼接各分支，再仅用训练折槽拟合 PCA，将每槽压到 256 维；池化为五个连续时间段后得到统一的 1,280 维 clip 向量。`RawPrivate-PCA` 复用既有训练折 PCA。情绪分类和回归探针分别是 `StandardScaler + LogisticRegression(C=0.05)` 与 `StandardScaler + Ridge(alpha=25)`，连续预测限幅到 `[-3, 3]`。`TSFA+RawPrivate` 为既有 6,845 维 OOF 结果；math B0/B4 使用 math 目录中已有 OOF 预测，未改动 math 文件。它们是外部性能参照，不属于 1,280 维匹配比较。 + +### 主要结果 + +| 表示 / OOF 探针视图 | 维数 | Accuracy | Macro-F1 | 折 Macro-F1 均值 ± SD | MAE | Pearson | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| RawPrivate-PCA | 1,280 | 0.560 | 0.297 | 0.281 ± 0.068 | **0.559** | **0.251** | +| Similarity-SPR（维度匹配） | 1,280 | 0.490 | **0.298** | 0.292 ± 0.126 | 0.672 | 0.106 | +| GCCA | 1,280 | 0.550 | 0.292 | 0.276 ± 0.072 | 0.581 | 0.176 | +| Predictive-Same | 1,280 | 0.530 | 0.255 | 0.255 ± 0.080 | 0.617 | 0.127 | +| Predictive-Local1（相邻 ±1 槽） | 1,280 | 0.530 | 0.270 | 0.261 ± 0.042 | 0.616 | 0.137 | +| TSFA+RawPrivate（外部参照） | 6,845 | 0.590 | 0.390 | 0.357 ± 0.118 | 0.572 | 0.368 | +| math B0（外部参照） | 未导出 | 0.600 | 0.361 | 0.337 ± 0.098 | 0.564 | 0.346 | +| math B4（外部参照） | 未导出 | 0.600 | **0.422** | 0.398 ± 0.089 | 0.593 | 0.199 | + +标签分布为 Negative 18、Neutral 25、Positive 57；恒预测 Positive 的 Accuracy 为 0.57。因此不能把 0.49–0.56 的 1,280 维方法 Accuracy 解读为超过多数类基线。维度匹配组内，Similarity-SPR 的 Macro-F1 点估计仅比 RawPrivate-PCA 高 0.001，RawPrivate-PCA 的 MAE 和 Pearson 较好；没有一个 shared-information 定义同时改善分类和强度预测。作为额外对照，Similarity-SPR 原生 `shared-unfused + private-all` 1,920 维视图为 Macro-F1 0.389、MAE 0.697、Pearson 0.071；它不能与 1,280 维行直接排名。 + +### 对八个问题的回答 + +#### 1. 三种 shared-information 定义分别检验什么？ + +Similarity 检验表示能否被映射到可匹配的共同空间；GCCA 检验不同视图的投影在留出数据上是否共同变化；Predictability 检验同槽跨模态特征能否互相预测，以及预测之后剩下多少不能被当前线性预测器解释的残差。它们不是同一个数学目标，也不能只凭情绪分数相互替代。 + +#### 2. Predictability 是否找到同槽跨模态关系？ + +没有清晰的时间特异性。按视频组宏平均的留出 MSE 在偏移 `-5…+5` 间大多近乎平坦，没有稳定的零偏移最低点。以 Same-slot 为基准，Audio 在同槽模型下偏移 `-5` 的 MSE 只高约 4.3%，偏移 `+5` 反而低约 0.9%；Text 和 Vision 的变化也很小。更宽的 Local1 模型中，片段内打乱后 MSE 反而低于同槽：Text 约低 1.4%、Audio 低 4.6%、Vision 低 6.8%。这表示当前线性预测结果没有显示稳定的槽位对应证据。 + +预测原始目标的外层折平均 R² 对 Same-slot 的 Text/Audio/Vision 分别约为 `-2.74/-2.30/-1.27`；Local1 分别约为 `-5.10/-2.72/-1.37`。负 R² 表示留出预测没有优于用训练均值作参照。该线性预测器对原始目标本身都较弱，因此不能从这里推出数据不存在非线性或局部跨模态关系。 + +#### 3. 预测残差是否表现为私有信息？ + +按这里的操作定义，残差比原始目标更难由另外两模态的线性预测器解释。Same-slot 下，原始目标到残差的外层折平均 R² 从 Text `-2.74`、Audio `-2.30`、Vision `-1.27` 降到约 `-2.95`、`-2.78`、`-2.37`；残差预测 MSE 也更高。它只能说明残差对当前线性预测器不可预测，不能说明残差包含有用的、语义独立的私有信息。 + +#### 4. GCCA 是否在留出视频上建立了相关的 shared 表示？ + +没有。三组共享投影分量的留出平均相关为 T–A `-0.021`、T–V `0.013`、A–V `0.031`，均接近零。用共同 GCCA 表示重构留出原始池化特征的折平均 R² 为 Text `-0.258`、Audio `-0.181`、Vision `-0.525`，没有解释正向留出方差。训练折 GCCA 奇异谱在五折间的有效秩约 `102.2–103.9`，解释 90% 谱能量约需 `83–85` 个分量；这个谱形在折间相对稳定，但不等于留出跨模态相关稳定。 + +#### 5. shared、private 与合并表示对情感探针有什么影响？ + +各分支的输入维数不同，这些是诊断性探针，不是维度受控的分支因果比较。点估计也没有一致模式：Similarity-SPR 的 shared-only Macro-F1 为 `0.411`，private-only 为 `0.342`，合并为 `0.389`；GCCA 为 `0.385/0.311/0.316`；Predictive-Same 为 `0.297/0.288/0.284`；Predictive-Local1 则是 `0.296/0.349/0.297`。没有证据显示“shared + private”必然优于单支。Similarity-SPR 的 private Audio 单模态 Macro-F1 点估计为 `0.445`，但单模态维数不同，且未对这些多重探针比较作校正，不能据此断言 Audio 是稳定的私有信息来源。 + +#### 6. 维度匹配后哪个方法占优？ + +没有稳定胜者。五个 1,280 维表示的 Macro-F1 在 `0.255–0.298`，MAE 在 `0.559–0.672`；RawPrivate-PCA 的回归点估计最好，Similarity-SPR 的 Macro-F1 只领先 0.001。更高维 TSFA+RawPrivate、math B0/B4 只作为 OOF 外部参照，不与 1,280 维表示声称公平的表示维数胜负。 + +#### 7. 按 video_id 配对 Bootstrap 支持哪些差异？ + +每项差值均为左侧候选减右侧参照，2,000 次按 37 个来源视频重采样；Macro-F1、Pearson 越高越好，MAE 越低越好。区间为未校正的 95% 百分位区间。 + +| 配对差异 | Δ Macro-F1 [95% CI] | Δ MAE [95% CI] | Δ Pearson [95% CI] | +| --- | ---: | ---: | ---: | +| Predictive-Same − RawPrivate-PCA | -0.042 [-0.144, 0.036] | +0.058 [+0.012, +0.110] | -0.124 [-0.287, 0.032] | +| GCCA − RawPrivate-PCA | -0.005 [-0.072, 0.045] | +0.022 [+0.001, +0.043] | -0.076 [-0.146, -0.002] | +| Predictive-Same − Similarity-SPR | -0.043 [-0.112, 0.031] | -0.055 [-0.131, 0.026] | +0.021 [-0.199, 0.245] | +| Predictive-Same − TSFA+RawPrivate | -0.135 [-0.232, -0.028] | +0.046 [-0.009, +0.107] | -0.240 [-0.425, -0.079] | +| Predictive-Same − math B0 | -0.106 [-0.220, 0.005] | +0.053 [-0.000, +0.109] | -0.218 [-0.384, -0.059] | +| Predictive-Same − math B4 | -0.167 [-0.290, -0.045] | +0.024 [-0.035, +0.088] | -0.071 [-0.258, 0.116] | + +Predictive-Same 的 MAE 高于 RawPrivate-PCA，区间不跨零；GCCA 对 RawPrivate-PCA 的 MAE 和 Pearson 也更差。Predictive-Same 与 Similarity-SPR 的三个区间都跨零。它在 TSFA+RawPrivate 的 Macro-F1 和 Pearson 上较低，但维数不同。B0/B4 的折分已核对与 Q1 固定五折一致；跨方法比较仍是探索性结果，未作多重比较校正。 + +#### 8. 本轮结论与下一步是什么？ + +这轮不支持宣布 Similarity、Correlation 或 Predictability 中任何一个建立了可靠的共享时序对应。Predictive 的 shift/shuffle 控制没有显示稳定的同槽优势；GCCA 的留出相关近零；已有 SPR 表示可以复用，但情绪 Probe 不足以证明它的 shared branch 真正对齐。下一步应先加入少量人工标注的跨模态事件时间，直接核验对应关系；在该参照能区分正确、错位和打乱之前，不继续围绕 SPR 结构做大规模调参。 + +### 质量检查、产物与限制 + +本次通过 Fedora WSL 中的 `uv` 环境在 NVIDIA GeForce RTX 5070 Ti 上运行，seed 为 42，总运行约 809.5 秒。原始特征、固定 M4 权重与 SPR 权重哈希、折分和环境写入运行清单。共生成 6,300 条 Probe 预测，覆盖 63 个方法/视图组合；每个组合都覆盖相同的 100 个 OOF 样本，没有重复、缺失或非有限预测值。math 目录只读；本次没有写入 math 文件。 + +Ridge 计算期间 scikit-learn 发出少量病态矩阵警告；运行完成且保存的预测均为有限数值，但小幅指标差异应谨慎看待。逐片段 R² 对短片可能因目标方差接近零而非常负，因此 shifted/shuffle 的主汇总和图使用按 `video_id` 宏平均 MSE；逐片段 R² 只保留作诊断。研究仍只有 100 条样本、37 个来源视频和一个随机种子；Bootstrap 没有包含随机种子不确定性，也没有多重比较校正。情感探针是下游效用诊断，不是对齐真值；本轮没有人工事件标注。 + +- [维度匹配结果与外部参照](outputs/shared_definition_comparison/dimension_matched_summary.csv) +- [全部 OOF 探针指标和逐样本预测](outputs/shared_definition_comparison/emotion_probe_metrics.csv)、[emotion_probe_predictions.csv](outputs/shared_definition_comparison/emotion_probe_predictions.csv) +- [GCCA 相关与谱诊断](outputs/shared_definition_comparison/gcca_summary.csv)、[shared_explained_variance.csv](outputs/shared_definition_comparison/shared_explained_variance.csv) +- [预测性同槽/错位/打乱控制](outputs/shared_definition_comparison/predictive_shift_summary.csv)、[逐片段控制](outputs/shared_definition_comparison/predictive_shift_controls.csv)、[私有残差可预测性](outputs/shared_definition_comparison/private_residual_predictability.csv) +- [视频组配对 Bootstrap](outputs/shared_definition_comparison/paired_contrasts.csv)、[私有来源探针](outputs/shared_definition_comparison/private_source_ablation.csv)、[折分](outputs/shared_definition_comparison/fold_assignments.csv)、[运行清单](outputs/shared_definition_comparison/run_manifest.json) + +![三种共享信息定义](outputs/shared_definition_comparison/figures/01_shared_definitions.png) + +![同槽、错位与局部上下文的预测误差](outputs/shared_definition_comparison/figures/02_predictive_shift_curves.png) + +![GCCA 留出稳定性与解释方差](outputs/shared_definition_comparison/figures/10_gcca_stability_and_variance.png) + +```bash +cd Q1 +uv run python -m q1.shared_definition_comparison --device cuda --seed 42 +``` + +如果只调整结果表或诊断图，可在已有完整产物上重新汇总，不会重跑五折： + +```bash +uv run python -m q1.shared_definition_comparison --finalize-existing +``` + +## Shared Time, Private Content：修正版验证 + +### 概念与实验口径 + +本轮把“共享”限定为**公共时间坐标**：每条视频划成 50 个归一化时间位置；Text、Audio、Vision 在各位置保留各自的内容表示。跨模态同槽乘积和差值只是情感任务可选的交互特征，不表示三种模态有相同语义，也不构造 shared semantic encoder。 + +使用每个固定分组折对应的冻结 `M4_sourceTime` 权重池化原始 BERT、eGeMAPS、DeiT 特征。特征标准化、PCA 和情感探针只在训练折拟合；标签只进入下游探针。五折每折 80 条训练、20 条留组预测，完整覆盖 100 条样本和 37 个来源视频。运行使用 Fedora WSL 中的 `uv` 与 NVIDIA GeForce RTX 5070 Ti。 + +分类标签按严格符号映射为 Negative/Neutral/Positive，样本数分别为 18/25/57，恒预测 Positive 的 Accuracy 为 0.57。分类器为 LogisticRegression(C=0.05)，直接强度回归为 Ridge(α=25)，连续预测限幅到 [-3,3]。双头版本仍用同一个极性分类器，另以 Ridge(α=25) 预测 |label|，限幅至 [0,3] 后乘以预测极性；Neutral 输出严格为 0。 + +主维度控制先在训练折的 50 个时间槽上拟合每槽至多 256 维 PCA，再按五段顺序池化为 1,280 维片段向量。输入有效秩小于 256 的子集以零填充至相同宽度。私有分支单模态 PCA 使用 Text 64、Audio 24、Vision 64 维。Audio 只有 25 个原始维度，因此交互维度设为 d=24，交互写作 `[p_m ⊙ p_n ; |p_m-p_n|]`。交互候选 I1=TA、I2=TV、I3=TA+TV、I4=AV、I5=TA+AV、I6=TV+AV、I7=TA+TV+AV；I0 不含交互。 + +### OOF 主结果 + +| 新表示 | 片段维度 | Accuracy | Macro-F1 | 直接 Ridge MAE | Pearson | 双头 MAE | +|---|---:|---:|---:|---:|---:|---:| +| M4-TimeOnly-full-raw | 4,925 | 0.590 | 0.407 | 0.574 | 0.375 | 0.578 | +| Private-PCA T64/A24/V64 | 760 | 0.530 | 0.397 | 0.854 | -0.035 | 0.667 | +| Private-DimMatched-PCA256 | 1,280 | 0.560 | 0.297 | 0.559 | 0.251 | 0.618 | +| Subset-A-PCA256 | 1,280 | 0.540 | **0.414** | 0.858 | 0.059 | 0.746 | +| Subset-TV-PCA256 | 1,280 | 0.570 | 0.301 | **0.555** | 0.271 | 0.610 | +| Subset-TAV-PCA256 | 1,280 | 0.570 | 0.301 | 0.556 | 0.269 | 0.610 | +| TAV-I0 | 1,280 | 0.530 | 0.397 | 0.854 | -0.035 | 0.667 | +| TAV-I4（AV） | 1,280 | 0.490 | 0.400 | 0.734 | 0.116 | 0.784 | +| TAV-I7（TA+TV+AV） | 1,280 | 0.450 | 0.329 | 0.736 | 0.106 | 0.713 | + +作为外部历史参照，旧 OOF 结果为：TSFA-old `0.540/0.431/0.820/0.104`，TSFA+RawPrivate `0.590/0.390/0.572/0.368`，RawPrivate-PCA `0.560/0.297/0.559/0.251`，SPR private-all `0.560/0.342/0.740/0.107`，GCCA `0.550/0.292/0.581/0.176`，math B0 `0.600/0.361/0.564/0.346`，math B4 `0.600/0.422/0.593/0.199`（顺序均为 Accuracy/Macro-F1/MAE/Pearson）。这些数字从历史预测文件读取，没有重训；特征维度和个别原探针细节不同，因此作为上下文对照，不宣称完全匹配的表示维度实验。新 `Private-DimMatched-PCA256` 与旧 `RawPrivate-PCA` 的 OOF 预测和指标逐项一致，构成实现复核。 + +### 对本轮问题的回答 + +1. **时间坐标是否合理？** 冻结 M4 的平均 MVR(ε=0.02) 为 0.00027，期望时间与 50 槽坐标的相关为 0.993，模态间平均时间 MAE 为视频时长的 0.060，时间相关为 0.968。这说明估计的坐标顺序平滑、模态时间大体接近；由于 M4 使用时间先验，这些数字不能证明语义事件已跨模态对齐。 +2. **情感指标是否整体优于 B0/B4？** 没有。新方法最高 Macro-F1 是 Audio-only 的 0.414，低于 B4 的 0.422；最好直接回归 MAE 是 TV 的 0.555，略低于 B0 的 0.564。两项按 37 个视频组配对的 2,000 次 Bootstrap 差值区间都跨过 0：Audio Macro-F1−B4 为 -0.008 [-0.186, 0.133]；TV MAE−B0 为 -0.009 [-0.053, 0.033]。不能据此声称有稳定提升。 +3. **哪种模态更有用？** Audio-only 的 Macro-F1 点估计最高，但 Accuracy 为 0.540，低于 0.570 的多数类基线，而且强度 MAE 为 0.858。Vision 单模态分类 Accuracy 0.440，强度 MAE 1.157。TV 联合视图的直接 MAE 最低为 0.555；加上 Text 后基本不变为 0.556。当前小样本结果提示分类与强度可能依赖不同特征，但组间区间和多重比较限制了结论强度。 +4. **TA/TV/AV 交互是否有稳定价值？** 没有观察到稳定的极性收益。I0 Macro-F1 为 0.397，I1–I7 为 0.329–0.400；I7 相对 I0 的 Macro-F1 差为 -0.068 [-0.175, 0.041]。I7 的直接 Ridge MAE 从 I0 的 0.854 降至 0.736，但区间为 -0.119 [-0.248, 0.016]，仍远高于 TV 的 0.555。交互目前只能视作待验证的任务特征候选。 +5. **双头是否比单头直接 Ridge 好？** 不普遍。全量原始池化的直接 MAE 0.574，双头 0.578；TV 直接 0.555，双头 0.610；维度匹配 PCA 直接 0.559，双头 0.618。双头确实改善了 PCA 私有表示的 MAE（0.854→0.667）和 Audio-only（0.858→0.746），但未成为全局最佳的强度预测方式。 +6. **降维与维度匹配影响多大？** 按模态降至 T64/A24/V64 后，Macro-F1 为 0.397，但 MAE 从全量原始表示的 0.574 升至 0.854，表明这组压缩明显损伤了当前强度探针。联合 PCA 256/槽后为 1,280 维，指标为 `0.560/0.297/0.559/0.251`，与历史 RawPrivate-PCA 完全相同。固定输出宽度控制了向量长度，但低维模态子集的有效 PCA 秩仍较低；相同列数不代表相同信息量。 +7. **修正后的理论定义是什么？** 当前证据支持把 M4 描述为一个冻结的**公共时间坐标提供器**，把 BERT/eGeMAPS/DeiT 特征描述为各自的**模态私有内容**；任何乘积/差值都明确归属于具体下游任务。此实验没有证明跨模态语义等价，也没有发现交互或情感性能可反向证明语义对齐的证据。 + +Audio Macro-F1−B4 的 95% 视频组 Bootstrap 区间跨零；维度匹配表示相对 B0 的 Macro-F1 差为 -0.064 [-0.204, 0.076]、MAE 差为 -0.005 [-0.050, 0.036]，相对 B4 的 Macro-F1 差为 -0.125 [-0.283, 0.032]、MAE 差为 -0.034 [-0.089, 0.020]，均不支持稳定胜负。交互和历史对照区间同样未作多重比较校正。 + +### 产物与复现 + +所有本轮文件在 [`outputs/shared_time_private_content/`](outputs/shared_time_private_content/)： + +- [运行结果与限制](outputs/shared_time_private_content/RESULTS.md) +- [OOF 指标](outputs/shared_time_private_content/metrics_oof.csv)、[逐样本预测](outputs/shared_time_private_content/predictions_oof.csv) +- [按视频组 Bootstrap 差异](outputs/shared_time_private_content/group_bootstrap_contrasts.csv) +- [冻结 M4 时间诊断](outputs/shared_time_private_content/temporal_monitoring.csv) +- [PCA 折内诊断](outputs/shared_time_private_content/pca_fold_diagnostics.csv)、[折分审计](outputs/shared_time_private_content/fold_manifest.csv) +- 九张图：结构、模态子集、交互消融、极性与强度模态差异、双头与 Ridge、维度控制、时间质量与任务误差、代表样本私有内容和 M4 权重。 + +```bash +cd Q1 +uv run python -m q1.shared_time_private_content --device cuda --seed 42 --bootstrap-draws 2000 +``` + +本轮每折训练/验证为 80/20，五折无 `video_id` 重叠，全部 100 条 OOF 预测完整且无重复。`math` 目录只读。研究仍只有 100 条片段、37 个来源视频和一个随机种子；Bootstrap 只表示当前来源视频上的抽样不确定性，不含随机种子不确定性，也未做多重比较校正。 diff --git a/deep_learning/Q1/outputs/shared_definition_comparison/config.json b/deep_learning/Q1/outputs/shared_definition_comparison/config.json new file mode 100644 index 0000000..d39844a --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/config.json @@ -0,0 +1,98 @@ +{ + "experiment": "Similarity vs Correlation vs Predictability shared information definitions", + "seed": 42, + "device": "cuda", + "sample_count": 100, + "video_id_count": 37, + "fold_count": 5, + "grouped_by": "video_id/group_id", + "grid_size": 50, + "frozen_temporal_model": "M4_sourceTime fold checkpoints", + "source_features": { + "text": 768, + "audio": 25, + "vision": 192 + }, + "gcca_pca_components": { + "text": 64, + "audio": 24, + "vision": 64 + }, + "gcca_shared_dimension": 32, + "predictive_contexts": { + "same_slot": 0, + "local_pm1": 1 + }, + "predictive_ridge_alphas": [ + 1.0, + 10.0, + 100.0 + ], + "predictive_ridge_solver": "cholesky", + "predictive_shift_slots": [ + -5, + -4, + -3, + -2, + -1, + 0, + 1, + 2, + 3, + 4, + 5 + ], + "within_video_shuffle_repeats": 20, + "dimension_matched_per_slot": 256, + "dimension_matched_clip": 1280, + "emotion_probe": { + "classification": "StandardScaler + LogisticRegression(C=0.05, max_iter=5000)", + "regression": "StandardScaler + Ridge(alpha=25), clipped to [-3, 3]", + "temporal_pooling": "50 slots to five consecutive 10-slot mean bins", + "emotion_labels_used_for_representation_learning": false + }, + "paired_video_bootstrap_repeats": 2000, + "multiple_comparison_correction": false, + "predictive_eval_core_slots": [ + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43 + ], + "predictive_shift_metric": "video_id-macro mean MSE; core slots 6-43", + "private_residual_alpha_selection": "reuse the selected original-target alpha; no separate residual alpha search" +} diff --git a/deep_learning/Q1/outputs/shared_definition_comparison/dimension_matched_summary.csv b/deep_learning/Q1/outputs/shared_definition_comparison/dimension_matched_summary.csv new file mode 100644 index 0000000..34c5967 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/dimension_matched_summary.csv @@ -0,0 +1,9 @@ +method,view,sample_count,feature_dimension,accuracy,macro_f1,macro_f1_fold_mean,macro_f1_fold_sd,mae,pearson,dimension_matched,comparison_note +GCCA,main_1280d,100,1280,0.55,0.29199014682885654,0.27575396825396825,0.07194702715577127,0.5809971535205841,0.17584072860758496,True,"1280D, five-bin pooled" +Math-B0,math_all_modalities,100,-1,0.6,0.3612633181126332,0.33701813174429807,0.09810807621270928,0.5642678308486938,0.34585652345724877,False,math OOF comparator; feature dimension not exported +Math-B4,math_all_modalities,100,-1,0.6,0.4222603610475464,0.3976841268630668,0.08882960164587973,0.59276591360569,0.19864510121331094,False,math OOF comparator; feature dimension not exported +Predictive-Local1,main_1280d,100,1280,0.53,0.2701337701337701,0.2610748002904866,0.04242392094165223,0.6158707904815673,0.13745442565394256,True,"1280D, five-bin pooled" +Predictive-Same,main_1280d,100,1280,0.53,0.2550675675675676,0.25492362044086186,0.07977838373245828,0.6171366663277149,0.12742805173707503,True,"1280D, five-bin pooled" +RawPrivate-PCA,main_1280d,100,1280,0.56,0.2974099099099099,0.28073240535039046,0.06810513535887727,0.5592040342092514,0.25147186244044006,True,"1280D, five-bin pooled" +Similarity-SPR,main_1280d,100,1280,0.49,0.29803793124961003,0.29245951044245444,0.12601058362856532,0.6723750464618206,0.10648974261028547,True,"1280D, five-bin pooled" +TSFA+RawPrivate,main_external_nonmatched,100,6845,0.59,0.3900927013970492,0.35717374324270873,0.11754258899949505,0.5715452416241169,0.3678028818592164,False,external 6845D comparator diff --git a/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_metrics.csv b/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_metrics.csv new file mode 100644 index 0000000..8886ed8 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_metrics.csv @@ -0,0 +1,64 @@ +method,view,sample_count,feature_dimension,accuracy,macro_f1,macro_f1_fold_mean,macro_f1_fold_sd,mae,pearson,dimension_matched,comparison_note +GCCA,main,100,5085,0.5,0.3164368748310354,0.3114430284857571,0.1781494490873969,0.6453792677819729,0.20639013340740217,, +GCCA,main_1280d,100,1280,0.55,0.29199014682885654,0.27575396825396825,0.07194702715577127,0.5809971535205841,0.17584072860758496,True,"1280D, five-bin pooled" +GCCA,private_all,100,4925,0.49,0.3105353252412076,0.29777815393378576,0.16268555312030744,0.6381345400214196,0.24411285503696467,, +GCCA,private_audio,100,125,0.4,0.3422222222222222,0.3243867243867244,0.062061372482630495,1.0638805805146694,0.01185673770636373,, +GCCA,private_audio_vision,100,1085,0.45,0.3878008114440514,0.3324835153479655,0.14212796856928422,1.0489839884638785,-0.05851178324820417,, +GCCA,private_only,100,4925,0.49,0.3105353252412076,0.29777815393378576,0.16268555312030744,0.6381345400214196,0.24411285503696467,, +GCCA,private_text,100,3840,0.53,0.36372549019607847,0.3865123456790124,0.2056323472787936,0.6116107559204101,0.32904757294289094,, +GCCA,private_vision,100,960,0.36,0.3006061398918542,0.2484057959658983,0.07331939456910933,1.116003330796957,-0.14272604015620127,, +GCCA,shared+private,100,5085,0.5,0.3164368748310354,0.3114430284857571,0.1781494490873969,0.6453792677819729,0.20639013340740217,, +GCCA,shared+private_all,100,5085,0.5,0.3164368748310354,0.3114430284857571,0.1781494490873969,0.6453792677819729,0.20639013340740217,, +GCCA,shared+private_audio,100,605,0.53,0.45328874024526195,0.40256141989354327,0.10591820796717709,0.9081077292561531,0.010628148843966791,, +GCCA,shared+private_audio_vision,100,1565,0.53,0.4382583500230559,0.39530752745855724,0.16651195754535963,0.8730936111509799,-0.0057790255408232466,, +GCCA,shared+private_text,100,4320,0.54,0.3657290470723306,0.38811185727852393,0.18301133447828877,0.6315095682442188,0.23151906028221614,, +GCCA,shared+private_vision,100,1440,0.55,0.4609875518966428,0.43896221009264486,0.20575777685060637,0.8764690694212913,-0.025205066640862053,, +GCCA,shared_fused,100,160,0.52,0.3845878136200717,0.4074298700105151,0.19299178776930628,0.7261679700016975,0.0182106742665506,, +GCCA,shared_only,100,160,0.52,0.3845878136200717,0.4074298700105151,0.19299178776930628,0.7261679700016975,0.0182106742665506,, +Math-B0,math_all_modalities,100,-1,0.6,0.3612633181126332,0.33701813174429807,0.09810807621270928,0.5642678308486938,0.34585652345724877,False,math OOF comparator; feature dimension not exported +Math-B4,math_all_modalities,100,-1,0.6,0.4222603610475464,0.3976841268630668,0.08882960164587973,0.59276591360569,0.19864510121331094,False,math OOF comparator; feature dimension not exported +Predictive-Local1,main_1280d,100,1280,0.53,0.2701337701337701,0.2610748002904866,0.04242392094165223,0.6158707904815673,0.13745442565394256,True,"1280D, five-bin pooled" +Predictive-Local1,private_all,100,4925,0.47,0.3491612933370713,0.32063368730035396,0.12359555650000849,0.7061580650508403,0.048079151499744316,, +Predictive-Local1,private_audio,100,125,0.52,0.33956337289670624,0.30542005306521436,0.11746105311524951,0.7893927852809429,-0.036399706475176596,, +Predictive-Local1,private_audio_vision,100,1085,0.35,0.24447212993724624,0.2286305619120264,0.02494891846606891,0.9728319205343723,-0.2698621201296275,, +Predictive-Local1,private_only,100,4925,0.47,0.3491612933370713,0.32063368730035396,0.12359555650000849,0.7061580650508403,0.048079151499744316,, +Predictive-Local1,private_text,100,3840,0.49,0.31398809523809523,0.3094690001356668,0.08095449407981482,0.7170058532059193,0.135441658531918,, +Predictive-Local1,private_vision,100,960,0.32,0.23278816199376942,0.21660346151574222,0.03369436269778459,0.9817608284950257,-0.2579173006549722,, +Predictive-Local1,shared+private,100,9850,0.46,0.2974922839506173,0.27180355846042115,0.08667841454999592,0.6897253362834453,0.11606322147144543,, +Predictive-Local1,shared+private_all,100,9850,0.46,0.2974922839506173,0.27180355846042115,0.08667841454999592,0.6897253362834453,0.11606322147144543,, +Predictive-Local1,shared+private_audio,100,5050,0.42,0.3112666830426594,0.27057424057424057,0.12900049213013415,0.7584960924088955,0.09924663244346835,, +Predictive-Local1,shared+private_audio_vision,100,6010,0.44,0.30963718060492257,0.2839025117005482,0.12070618832957708,0.7632865631580352,0.09723031559364234,, +Predictive-Local1,shared+private_text,100,8765,0.45,0.268623893562043,0.25351290684624017,0.08825026516116621,0.6991888205707073,0.12418641729717865,, +Predictive-Local1,shared+private_vision,100,5885,0.45,0.3146856330014225,0.2910453688434053,0.12587698063858388,0.7702110797166825,0.09646653556225342,, +Predictive-Local1,shared_only,100,4925,0.44,0.296023088023088,0.2706805612687966,0.12176549256180318,0.7636446696519852,0.09511877007270733,, +Predictive-Same,main_1280d,100,1280,0.53,0.2550675675675676,0.25492362044086186,0.07977838373245828,0.6171366663277149,0.12742805173707503,True,"1280D, five-bin pooled" +Predictive-Same,private_all,100,4925,0.44,0.2877730496453901,0.2761752524547443,0.10585601362451127,0.6653340481221676,0.0748206652296143,, +Predictive-Same,private_audio,100,125,0.55,0.3535636476812947,0.32380910282382935,0.12198350487550638,0.761080129891634,0.0031009280128652486,, +Predictive-Same,private_audio_vision,100,1085,0.37,0.2646538827689378,0.24763841825321614,0.04073226755373256,1.0110525147616862,-0.2551553195088091,, +Predictive-Same,private_only,100,4925,0.44,0.2877730496453901,0.2761752524547443,0.10585601362451127,0.6653340481221676,0.0748206652296143,, +Predictive-Same,private_text,100,3840,0.49,0.31427648578811374,0.3079753367109689,0.11717734423552462,0.7032011260092258,0.15607561180012414,, +Predictive-Same,private_vision,100,960,0.31,0.22717847051317108,0.21131751938769477,0.04067211322495786,1.0077504561841488,-0.27344926561837696,, +Predictive-Same,shared+private,100,9850,0.43,0.28388648388648385,0.25441531154545616,0.09876079389532164,0.6828779272735119,0.10700525866838041,, +Predictive-Same,shared+private_all,100,9850,0.43,0.28388648388648385,0.25441531154545616,0.09876079389532164,0.6828779272735119,0.10700525866838041,, +Predictive-Same,shared+private_audio,100,5050,0.39,0.2869191468656232,0.26831649831649834,0.1399272682485645,0.8010145276784897,0.03798281192039285,, +Predictive-Same,shared+private_audio_vision,100,6010,0.41,0.29445241511238646,0.274178374016455,0.11185921437250612,0.8014056612551212,0.03736171947727452,, +Predictive-Same,shared+private_text,100,8765,0.46,0.2844636524822695,0.26925140300663064,0.11878211156477378,0.692372799962759,0.11863797563685076,, +Predictive-Same,shared+private_vision,100,5885,0.4,0.2777777777777778,0.26090276074084173,0.1275439985743432,0.8116071523725986,0.031905871174764025,, +Predictive-Same,shared_only,100,4925,0.41,0.29726162190702476,0.27908417508417516,0.14590114597115605,0.8118315364420414,0.029270737781183585,, +RawPrivate-PCA,main_1280d,100,1280,0.56,0.2974099099099099,0.28073240535039046,0.06810513535887727,0.5592040342092514,0.25147186244044006,True,"1280D, five-bin pooled" +Similarity-SPR,main_1280d,100,1280,0.49,0.29803793124961003,0.29245951044245444,0.12601058362856532,0.6723750464618206,0.10648974261028547,True,"1280D, five-bin pooled" +Similarity-SPR,private_all,100,960,0.56,0.3422461170848268,0.37624965583586273,0.15736280871885258,0.7403087028861046,0.10689943077519994,, +Similarity-SPR,private_audio,100,320,0.58,0.44527282143811814,0.44808402325643704,0.1756353399244467,0.664284773916006,0.16018710957308058,, +Similarity-SPR,private_audio_vision,100,640,0.52,0.3762946162007981,0.35838425128280205,0.07524748645413382,0.7737086206674576,0.06584870766947938,, +Similarity-SPR,private_only,100,960,0.56,0.3422461170848268,0.37624965583586273,0.15736280871885258,0.7403087028861046,0.10689943077519994,, +Similarity-SPR,private_text,100,320,0.53,0.32908496732026143,0.3360490934186587,0.10497969320977503,0.680117053836584,0.22364254532859437,, +Similarity-SPR,private_vision,100,320,0.55,0.3761385380788366,0.3597317926303433,0.08409860508359142,0.6574729996919632,0.0358601314770418,, +Similarity-SPR,shared+private,100,1920,0.56,0.38857214680744095,0.3409979462853026,0.09369875802146937,0.6967655114829541,0.07067944370969742,, +Similarity-SPR,shared+private_all,100,1920,0.56,0.38857214680744095,0.3409979462853026,0.09369875802146937,0.6967655114829541,0.07067944370969742,, +Similarity-SPR,shared+private_audio,100,1280,0.52,0.38684274243163963,0.3291406677613574,0.10633083326814055,0.7466380800306797,0.15595479528174308,, +Similarity-SPR,shared+private_audio_vision,100,1600,0.52,0.3970556131096266,0.34774860303595934,0.05188926537805234,0.7238257251679897,0.05238536484252928,, +Similarity-SPR,shared+private_text,100,1280,0.55,0.39236111111111116,0.33999664823194237,0.07899237801541087,0.6752975443005562,0.24203161589253674,, +Similarity-SPR,shared+private_vision,100,1280,0.47,0.3501074822240937,0.2963646356093662,0.07656504965618638,0.7524771577119828,0.05942080933032528,, +Similarity-SPR,shared_fused,100,320,0.52,0.3706666666666667,0.32213212075281045,0.09277771178723276,0.7274691557884216,0.18597000505750821,, +Similarity-SPR,shared_only,100,960,0.52,0.41123506874013976,0.33181720302370504,0.06777133926752461,0.7628524225950241,0.17064299398621885,, +TSFA+RawPrivate,main_external_nonmatched,100,6845,0.59,0.3900927013970492,0.35717374324270873,0.11754258899949505,0.5715452416241169,0.3678028818592164,False,external 6845D comparator diff --git a/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_predictions.csv b/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_predictions.csv new file mode 100644 index 0000000..146b31f --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/emotion_probe_predictions.csv @@ -0,0 +1,6301 @@ +method,view,fold,sample_id,video_id,true_class_id,true_class,predicted_class_id,predicted_class,true_label,predicted_label,predicted_label_unclipped,feature_dimension +Similarity-SPR,private_all,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.6416890621185303,-0.6416890621185303,960 +Similarity-SPR,private_all,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,2,Positive,0.0,-0.6532131433486938,-0.6532131433486938,960 +Similarity-SPR,private_all,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,-0.5472473502159119,-0.5472473502159119,960 +Similarity-SPR,private_all,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,2,Positive,0.6666666865348816,0.020969659090042114,0.020969659090042114,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,2,Positive,1.6666666269302368,0.4007582664489746,0.4007582664489746,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,-0.13613799214363098,-0.13613799214363098,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,-0.7555891871452332,-0.7555891871452332,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.2389732301235199,0.2389732301235199,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,-0.3045348525047302,-0.3045348525047302,960 +Similarity-SPR,private_all,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,-0.4260205626487732,-0.4260205626487732,960 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+Predictive-Same,private_audio_vision,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,0,Negative,-0.6666666865348816,-0.8404974937438965,-0.8404974937438965,1085 +Predictive-Same,private_audio_vision,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,1,Neutral,-0.3333333432674408,-0.4142340123653412,-0.4142340123653412,1085 +Predictive-Same,private_audio_vision,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,2,Positive,0.0,-0.1745465099811554,-0.1745465099811554,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,0,Negative,0.3333333432674408,-0.01440417766571045,-0.01440417766571045,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,0.5802798271179199,0.5802798271179199,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,2,Positive,-1.3333333730697632,0.7809659242630005,0.7809659242630005,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,2,Positive,-0.3333333432674408,0.5851505994796753,0.5851505994796753,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.8241682052612305,0.8241682052612305,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,0,Negative,0.0,0.6097011566162109,0.6097011566162109,1085 +Predictive-Same,private_audio_vision,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,0,Negative,0.6666666865348816,-0.1271459460258484,-0.1271459460258484,1085 +Predictive-Same,private_audio_vision,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,2,Positive,0.0,-0.2596593201160431,-0.2596593201160431,1085 +Predictive-Same,private_audio_vision,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,1,Neutral,0.3333333432674408,-0.6334103345870972,-0.6334103345870972,1085 +Predictive-Same,private_audio_vision,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.35415753722190857,0.35415753722190857,1085 +Predictive-Same,private_only,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,1,Neutral,2.0,-0.17377585172653198,-0.17377585172653198,4925 +Predictive-Same,private_only,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,2,Positive,1.3333333730697632,-0.2727384865283966,-0.2727384865283966,4925 +Predictive-Same,private_only,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,2,Positive,2.6666667461395264,-0.14064940810203552,-0.14064940810203552,4925 +Predictive-Same,private_only,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,0,Negative,1.6666666269302368,-0.5309290885925293,-0.5309290885925293,4925 +Predictive-Same,private_only,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,-0.09805735945701599,-0.09805735945701599,4925 +Predictive-Same,private_only,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,1,Neutral,0.3333333432674408,-0.1315385103225708,-0.1315385103225708,4925 +Predictive-Same,private_only,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,1,Neutral,-0.3333333432674408,-0.9587868452072144,-0.9587868452072144,4925 +Predictive-Same,private_only,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,2,Positive,-0.6666666865348816,0.1469440907239914,0.1469440907239914,4925 +Predictive-Same,private_only,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,2,Positive,-0.3333333432674408,0.003028005361557007,0.003028005361557007,4925 +Predictive-Same,private_only,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,1,Neutral,0.0,-0.25442931056022644,-0.25442931056022644,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,-0.03939440846443176,-0.03939440846443176,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,-0.2846929728984833,-0.2846929728984833,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,1,Neutral,-1.3333333730697632,-0.14978843927383423,-0.14978843927383423,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,1,Neutral,-0.3333333432674408,-0.22073540091514587,-0.22073540091514587,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.2436082661151886,0.2436082661151886,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,1,Neutral,0.0,-0.08215099573135376,-0.08215099573135376,4925 +Predictive-Same,private_only,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,1,Neutral,0.6666666865348816,-0.40143176913261414,-0.40143176913261414,4925 +Predictive-Same,private_only,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,1,Neutral,0.0,-0.2848106920719147,-0.2848106920719147,4925 +Predictive-Same,private_only,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,1,Neutral,0.3333333432674408,0.5872368812561035,0.5872368812561035,4925 +Predictive-Same,private_only,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.42880451679229736,0.42880451679229736,4925 +Predictive-Same,private_text,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,2,Positive,2.0,0.5790481567382812,0.5790481567382812,3840 +Predictive-Same,private_text,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,2,Positive,1.3333333730697632,0.3344176411628723,0.3344176411628723,3840 +Predictive-Same,private_text,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,2,Positive,2.6666667461395264,0.7343183755874634,0.7343183755874634,3840 +Predictive-Same,private_text,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,0,Negative,1.6666666269302368,-0.9691510200500488,-0.9691510200500488,3840 +Predictive-Same,private_text,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,0.23960146307945251,0.23960146307945251,3840 +Predictive-Same,private_text,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,2,Positive,0.3333333432674408,0.3026598393917084,0.3026598393917084,3840 +Predictive-Same,private_text,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,1,Neutral,-0.3333333432674408,-0.4646664559841156,-0.4646664559841156,3840 +Predictive-Same,private_text,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,2,Positive,-0.6666666865348816,0.4311792850494385,0.4311792850494385,3840 +Predictive-Same,private_text,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,2,Positive,-0.3333333432674408,0.6161887645721436,0.6161887645721436,3840 +Predictive-Same,private_text,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,1,Neutral,0.0,-0.16003632545471191,-0.16003632545471191,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,-0.35388240218162537,-0.35388240218162537,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,-1.0480672121047974,-1.0480672121047974,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,1,Neutral,-1.3333333730697632,-0.8127883672714233,-0.8127883672714233,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,1,Neutral,-0.3333333432674408,-0.6900582313537598,-0.6900582313537598,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,1,Neutral,1.0,-0.20452287793159485,-0.20452287793159485,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,1,Neutral,0.0,-0.27206018567085266,-0.27206018567085266,3840 +Predictive-Same,private_text,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,1,Neutral,0.6666666865348816,-0.49369922280311584,-0.49369922280311584,3840 +Predictive-Same,private_text,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,1,Neutral,0.0,0.11207430064678192,0.11207430064678192,3840 +Predictive-Same,private_text,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,2,Positive,0.3333333432674408,1.1419563293457031,1.1419563293457031,3840 +Predictive-Same,private_text,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.2948399782180786,0.2948399782180786,3840 +Predictive-Same,private_vision,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,0,Negative,2.0,-1.1459044218063354,-1.1459044218063354,960 +Predictive-Same,private_vision,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,0,Negative,1.3333333730697632,-0.8111447095870972,-0.8111447095870972,960 +Predictive-Same,private_vision,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,0,Negative,2.6666667461395264,-1.1438484191894531,-1.1438484191894531,960 +Predictive-Same,private_vision,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,2,Positive,1.6666666269302368,0.08840528130531311,0.08840528130531311,960 +Predictive-Same,private_vision,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,0,Negative,2.6666667461395264,-0.722759485244751,-0.722759485244751,960 +Predictive-Same,private_vision,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,2,Positive,0.3333333432674408,0.11874336004257202,0.11874336004257202,960 +Predictive-Same,private_vision,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,2,Positive,-0.3333333432674408,0.04850229620933533,0.04850229620933533,960 +Predictive-Same,private_vision,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,0,Negative,-0.6666666865348816,-0.49699482321739197,-0.49699482321739197,960 +Predictive-Same,private_vision,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,1,Neutral,-0.3333333432674408,-0.5146856307983398,-0.5146856307983398,960 +Predictive-Same,private_vision,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,0,Negative,0.0,-0.2374763786792755,-0.2374763786792755,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,0,Negative,0.3333333432674408,-0.14428213238716125,-0.14428213238716125,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,0.33265528082847595,0.33265528082847595,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,2,Positive,-1.3333333730697632,0.5014912486076355,0.5014912486076355,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,2,Positive,-0.3333333432674408,0.43352869153022766,0.43352869153022766,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,1,Neutral,1.0,0.4440041184425354,0.4440041184425354,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,0,Negative,0.0,0.32832401990890503,0.32832401990890503,960 +Predictive-Same,private_vision,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,0,Negative,0.6666666865348816,-0.2218104898929596,-0.2218104898929596,960 +Predictive-Same,private_vision,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,0,Negative,0.0,-0.8078306913375854,-0.8078306913375854,960 +Predictive-Same,private_vision,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,1,Neutral,0.3333333432674408,-0.9777973890304565,-0.9777973890304565,960 +Predictive-Same,private_vision,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.48824402689933777,0.48824402689933777,960 +Predictive-Same,shared+private,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,1,Neutral,2.0,-0.35364672541618347,-0.35364672541618347,9850 +Predictive-Same,shared+private,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,1,Neutral,1.3333333730697632,-0.4153362214565277,-0.4153362214565277,9850 +Predictive-Same,shared+private,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,1,Neutral,2.6666667461395264,-0.29093560576438904,-0.29093560576438904,9850 +Predictive-Same,shared+private,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,1,Neutral,1.6666666269302368,-0.2083832025527954,-0.2083832025527954,9850 +Predictive-Same,shared+private,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,-0.14350613951683044,-0.14350613951683044,9850 +Predictive-Same,shared+private,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,1,Neutral,0.3333333432674408,-0.522144079208374,-0.522144079208374,9850 +Predictive-Same,shared+private,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,0,Negative,-0.3333333432674408,-1.3200243711471558,-1.3200243711471558,9850 +Predictive-Same,shared+private,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,2,Positive,-0.6666666865348816,0.30655601620674133,0.30655601620674133,9850 +Predictive-Same,shared+private,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,2,Positive,-0.3333333432674408,0.023531317710876465,0.023531317710876465,9850 +Predictive-Same,shared+private,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,2,Positive,0.0,0.0754745602607727,0.0754745602607727,9850 +Predictive-Same,shared+private,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,0,Negative,0.3333333432674408,-0.153153657913208,-0.153153657913208,9850 +Predictive-Same,shared+private,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,0.05335664749145508,0.05335664749145508,9850 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+Predictive-Same,private_all,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.5990330576896667,0.5990330576896667,4925 +Predictive-Same,private_all,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,1,Neutral,1.3333333730697632,-0.08157628774642944,-0.08157628774642944,4925 +Predictive-Same,private_audio,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,0,Negative,0.0,0.15250009298324585,0.15250009298324585,125 +Predictive-Same,private_audio,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,0.693276047706604,0.693276047706604,125 +Predictive-Same,private_audio,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.040043652057647705,0.040043652057647705,125 +Predictive-Same,private_audio,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,0,Negative,-0.3333333432674408,0.18829835951328278,0.18829835951328278,125 +Predictive-Same,private_audio,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.40824732184410095,0.40824732184410095,125 +Predictive-Same,private_audio,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.9777714610099792,0.9777714610099792,125 +Predictive-Same,private_audio,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.4438203275203705,0.4438203275203705,125 +Predictive-Same,private_audio,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.9061097502708435,0.9061097502708435,125 +Predictive-Same,private_audio,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.6659178733825684,0.6659178733825684,125 +Predictive-Same,private_audio,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,0.3156716823577881,0.3156716823577881,125 +Predictive-Same,private_audio,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.5479328036308289,0.5479328036308289,125 +Predictive-Same,private_audio,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.5134644508361816,0.5134644508361816,125 +Predictive-Same,private_audio,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.5744218230247498,0.5744218230247498,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,-0.011966973543167114,-0.011966973543167114,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.3823283910751343,0.3823283910751343,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,1,Neutral,-2.0,0.23640935122966766,0.23640935122966766,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,1,Neutral,0.0,0.2570936381816864,0.2570936381816864,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,1,Neutral,1.0,-0.18497627973556519,-0.18497627973556519,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,0,Negative,0.0,-0.13631367683410645,-0.13631367683410645,125 +Predictive-Same,private_audio,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.4571891725063324,0.4571891725063324,125 +Predictive-Same,private_audio_vision,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.8512790203094482,0.8512790203094482,1085 +Predictive-Same,private_audio_vision,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,2,Positive,-1.0,0.6076927185058594,0.6076927185058594,1085 +Predictive-Same,private_audio_vision,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.4372292757034302,0.4372292757034302,1085 +Predictive-Same,private_audio_vision,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.6472031474113464,0.6472031474113464,1085 +Predictive-Same,private_audio_vision,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.9069623351097107,0.9069623351097107,1085 +Predictive-Same,private_audio_vision,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.35566920042037964,0.35566920042037964,1085 +Predictive-Same,private_audio_vision,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,1,Neutral,0.3333333432674408,-0.206218421459198,-0.206218421459198,1085 +Predictive-Same,private_audio_vision,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,0,Negative,0.6666666865348816,-0.022232472896575928,-0.022232472896575928,1085 +Predictive-Same,private_audio_vision,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,-0.24848222732543945,-0.24848222732543945,1085 +Predictive-Same,private_audio_vision,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,0.017294496297836304,0.017294496297836304,1085 +Predictive-Same,private_audio_vision,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,2.132166624069214,2.132166624069214,1085 +Predictive-Same,private_audio_vision,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.819976806640625,1.819976806640625,1085 +Predictive-Same,private_audio_vision,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.6517865657806396,1.6517865657806396,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.02904704213142395,0.02904704213142395,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.12134307622909546,0.12134307622909546,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,-0.19870227575302124,-0.19870227575302124,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,0,Negative,0.0,-0.6279217600822449,-0.6279217600822449,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,-0.6154946684837341,-0.6154946684837341,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.24368879199028015,0.24368879199028015,1085 +Predictive-Same,private_audio_vision,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,-0.09079161286354065,-0.09079161286354065,1085 +Predictive-Same,private_only,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,1,Neutral,0.0,0.0016224086284637451,0.0016224086284637451,4925 +Predictive-Same,private_only,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.5705755352973938,-0.5705755352973938,4925 +Predictive-Same,private_only,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.11785286664962769,0.11785286664962769,4925 +Predictive-Same,private_only,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.47490018606185913,0.47490018606185913,4925 +Predictive-Same,private_only,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.018748193979263306,0.018748193979263306,4925 +Predictive-Same,private_only,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.6967903971672058,0.6967903971672058,4925 +Predictive-Same,private_only,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.6087349057197571,0.6087349057197571,4925 +Predictive-Same,private_only,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.15304940938949585,0.15304940938949585,4925 +Predictive-Same,private_only,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,0,Negative,1.0,-0.07819938659667969,-0.07819938659667969,4925 +Predictive-Same,private_only,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.8752310872077942,0.8752310872077942,4925 +Predictive-Same,private_only,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,1.3354408740997314,1.3354408740997314,4925 +Predictive-Same,private_only,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.0231037139892578,1.0231037139892578,4925 +Predictive-Same,private_only,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.6803833842277527,0.6803833842277527,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.5108877420425415,0.5108877420425415,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.10673889517784119,0.10673889517784119,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.44044968485832214,0.44044968485832214,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,1,Neutral,0.0,0.33230865001678467,0.33230865001678467,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.2742822766304016,0.2742822766304016,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.5990330576896667,0.5990330576896667,4925 +Predictive-Same,private_only,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,1,Neutral,1.3333333730697632,-0.08157628774642944,-0.08157628774642944,4925 +Predictive-Same,private_text,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,1,Neutral,0.0,-0.02702304720878601,-0.02702304720878601,3840 +Predictive-Same,private_text,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.27566832304000854,-0.27566832304000854,3840 +Predictive-Same,private_text,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,-0.2952369451522827,-0.2952369451522827,3840 +Predictive-Same,private_text,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.2774456739425659,0.2774456739425659,3840 +Predictive-Same,private_text,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.05158713459968567,0.05158713459968567,3840 +Predictive-Same,private_text,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.8207378387451172,0.8207378387451172,3840 +Predictive-Same,private_text,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.6028478145599365,0.6028478145599365,3840 +Predictive-Same,private_text,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,-0.13408058881759644,-0.13408058881759644,3840 +Predictive-Same,private_text,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.017106473445892334,0.017106473445892334,3840 +Predictive-Same,private_text,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.9786686301231384,0.9786686301231384,3840 +Predictive-Same,private_text,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.8451353311538696,0.8451353311538696,3840 +Predictive-Same,private_text,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.48647165298461914,0.48647165298461914,3840 +Predictive-Same,private_text,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.1020662784576416,0.1020662784576416,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.6251487731933594,0.6251487731933594,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,-0.13352417945861816,-0.13352417945861816,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.2902006506919861,0.2902006506919861,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,1,Neutral,0.0,0.13049882650375366,0.13049882650375366,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,-0.02682894468307495,-0.02682894468307495,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.06004178524017334,0.06004178524017334,3840 +Predictive-Same,private_text,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,1,Neutral,1.3333333730697632,-0.207119882106781,-0.207119882106781,3840 +Predictive-Same,private_vision,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.6526727676391602,0.6526727676391602,960 +Predictive-Same,private_vision,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,2,Positive,-1.0,0.5224114060401917,0.5224114060401917,960 +Predictive-Same,private_vision,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.7073180079460144,0.7073180079460144,960 +Predictive-Same,private_vision,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.5809183716773987,0.5809183716773987,960 +Predictive-Same,private_vision,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,1.0037248134613037,1.0037248134613037,960 +Predictive-Same,private_vision,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,-0.12478101253509521,-0.12478101253509521,960 +Predictive-Same,private_vision,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,1,Neutral,0.3333333432674408,-0.4280511736869812,-0.4280511736869812,960 +Predictive-Same,private_vision,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,0,Negative,0.6666666865348816,-0.5136066675186157,-0.5136066675186157,960 +Predictive-Same,private_vision,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,-0.46541261672973633,-0.46541261672973633,960 +Predictive-Same,private_vision,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,-0.0910966694355011,-0.0910966694355011,960 +Predictive-Same,private_vision,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,2.1505212783813477,2.1505212783813477,960 +Predictive-Same,private_vision,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.7240068912506104,1.7240068912506104,960 +Predictive-Same,private_vision,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.2548539638519287,1.2548539638519287,960 +Predictive-Same,private_vision,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,-0.23593443632125854,-0.23593443632125854,960 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+Predictive-Local1,shared+private_vision,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.4879304766654968,-0.4879304766654968,5885 +Predictive-Local1,shared+private_vision,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.11709314584732056,0.11709314584732056,5885 +Predictive-Local1,shared+private_vision,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.4319397211074829,0.4319397211074829,5885 +Predictive-Local1,shared+private_vision,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,0,Negative,-2.0,-0.6894755959510803,-0.6894755959510803,5885 +Predictive-Local1,shared+private_vision,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.3523391783237457,0.3523391783237457,5885 +Predictive-Local1,shared+private_vision,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.46266549825668335,0.46266549825668335,5885 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+Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.1350730061531067,0.1350730061531067,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.07855647802352905,0.07855647802352905,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,1,Neutral,-2.0,0.48968827724456787,0.48968827724456787,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,1,Neutral,0.0,0.47442740201950073,0.47442740201950073,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,0,Negative,1.0,-0.4524931311607361,-0.4524931311607361,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,-0.14013218879699707,-0.14013218879699707,5885 +Predictive-Local1,shared+private_vision,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.28227338194847107,0.28227338194847107,5885 +Predictive-Local1,shared_only,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,1,Neutral,0.0,-0.4950034022331238,-0.4950034022331238,4925 +Predictive-Local1,shared_only,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.45481735467910767,-0.45481735467910767,4925 +Predictive-Local1,shared_only,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.1271766722202301,0.1271766722202301,4925 +Predictive-Local1,shared_only,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.19634808599948883,0.19634808599948883,4925 +Predictive-Local1,shared_only,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,0,Negative,-2.0,-0.8255310654640198,-0.8255310654640198,4925 +Predictive-Local1,shared_only,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.4617748260498047,0.4617748260498047,4925 +Predictive-Local1,shared_only,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,0,Negative,0.3333333432674408,0.2301519364118576,0.2301519364118576,4925 +Predictive-Local1,shared_only,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,-0.05354568362236023,-0.05354568362236023,4925 +Predictive-Local1,shared_only,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,0,Negative,1.0,-0.007006913423538208,-0.007006913423538208,4925 +Predictive-Local1,shared_only,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,0.5432224869728088,0.5432224869728088,4925 +Predictive-Local1,shared_only,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,2.2504732608795166,2.2504732608795166,4925 +Predictive-Local1,shared_only,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,2.1977272033691406,2.1977272033691406,4925 +Predictive-Local1,shared_only,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.95750093460083,1.95750093460083,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.054713934659957886,0.054713934659957886,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,1,Neutral,-0.3333333432674408,-0.01384928822517395,-0.01384928822517395,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,1,Neutral,-2.0,0.47253596782684326,0.47253596782684326,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,1,Neutral,0.0,0.4707077145576477,0.4707077145576477,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,1,Neutral,1.0,-0.6630309224128723,-0.6630309224128723,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,-0.2737364172935486,-0.2737364172935486,4925 +Predictive-Local1,shared_only,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.2048637866973877,0.2048637866973877,4925 +Predictive-Local1,main_1280d,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,-0.12960726022720337,-0.12960726022720337,1280 +Predictive-Local1,main_1280d,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.3228502869606018,-0.3228502869606018,1280 +Predictive-Local1,main_1280d,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,1.1278588771820068,1.1278588771820068,1280 +Predictive-Local1,main_1280d,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.15227164328098297,0.15227164328098297,1280 +Predictive-Local1,main_1280d,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.06644991040229797,0.06644991040229797,1280 +Predictive-Local1,main_1280d,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.8884359002113342,0.8884359002113342,1280 +Predictive-Local1,main_1280d,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.32967379689216614,0.32967379689216614,1280 +Predictive-Local1,main_1280d,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.4194074869155884,0.4194074869155884,1280 +Predictive-Local1,main_1280d,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.887661874294281,0.887661874294281,1280 +Predictive-Local1,main_1280d,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,0.13675624132156372,0.13675624132156372,1280 +Predictive-Local1,main_1280d,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,1.308184266090393,1.308184266090393,1280 +Predictive-Local1,main_1280d,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.0144660472869873,1.0144660472869873,1280 +Predictive-Local1,main_1280d,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.7445317506790161,0.7445317506790161,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.7621322870254517,0.7621322870254517,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.6643788814544678,0.6643788814544678,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.7302680611610413,0.7302680611610413,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.8086034059524536,0.8086034059524536,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.44687920808792114,0.44687920808792114,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.5120640397071838,0.5120640397071838,1280 +Predictive-Local1,main_1280d,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,1.243272066116333,1.243272066116333,1280 +RawPrivate-PCA,main_1280d,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.24939189851284027,0.24939189851284027,1280 +RawPrivate-PCA,main_1280d,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.2563818097114563,-0.2563818097114563,1280 +RawPrivate-PCA,main_1280d,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.2901126444339752,0.2901126444339752,1280 +RawPrivate-PCA,main_1280d,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,-0.07723253965377808,-0.07723253965377808,1280 +RawPrivate-PCA,main_1280d,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.34644433856010437,0.34644433856010437,1280 +RawPrivate-PCA,main_1280d,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.6875933408737183,0.6875933408737183,1280 +RawPrivate-PCA,main_1280d,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,-0.012514472007751465,-0.012514472007751465,1280 +RawPrivate-PCA,main_1280d,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.14919760823249817,0.14919760823249817,1280 +RawPrivate-PCA,main_1280d,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.5156265497207642,0.5156265497207642,1280 +RawPrivate-PCA,main_1280d,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.46425890922546387,0.46425890922546387,1280 +RawPrivate-PCA,main_1280d,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.8978202939033508,0.8978202939033508,1280 +RawPrivate-PCA,main_1280d,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.0468685626983643,1.0468685626983643,1280 +RawPrivate-PCA,main_1280d,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.24844887852668762,0.24844887852668762,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.5410467386245728,0.5410467386245728,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.2969447374343872,0.2969447374343872,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.5088579058647156,0.5088579058647156,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.606143593788147,0.606143593788147,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.37357640266418457,0.37357640266418457,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.05711880326271057,0.05711880326271057,1280 +RawPrivate-PCA,main_1280d,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.3474348783493042,0.3474348783493042,1280 +Similarity-SPR,private_all,4,-aNfi7CP8vM/7,-aNfi7CP8vM,1,Neutral,2,Positive,0.0,1.306077241897583,1.306077241897583,960 +Similarity-SPR,private_all,4,-iRBcNs9oI8/3,-iRBcNs9oI8,2,Positive,2,Positive,0.6666666865348816,0.6861422061920166,0.6861422061920166,960 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+Predictive-Same,private_audio,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,3.0,7.818612098693848,125 +Predictive-Same,private_audio,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,3.0,7.883086204528809,125 +Predictive-Same,private_audio,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,2,Positive,0.3333333432674408,0.6195147037506104,0.6195147037506104,125 +Predictive-Same,private_audio,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.7413913011550903,0.7413913011550903,125 +Predictive-Same,private_audio,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.22639921307563782,0.22639921307563782,125 +Predictive-Same,private_audio,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,0.8115605115890503,0.8115605115890503,125 +Predictive-Same,private_audio,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.6490051746368408,0.6490051746368408,125 +Predictive-Same,private_audio,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.7588026523590088,0.7588026523590088,125 +Predictive-Same,private_audio,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,2,Positive,1.3333333730697632,0.6203691363334656,0.6203691363334656,125 +Predictive-Same,private_audio,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,0.0781693160533905,0.0781693160533905,125 +Predictive-Same,private_audio,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,2,Positive,-0.6666666865348816,0.7826213240623474,0.7826213240623474,125 +Predictive-Same,private_audio,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,0.5448044538497925,0.5448044538497925,125 +Predictive-Same,private_audio_vision,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,Neutral,2,Positive,0.0,1.3968629837036133,1.3968629837036133,1085 +Predictive-Same,private_audio_vision,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,-0.22713661193847656,-0.22713661193847656,1085 +Predictive-Same,private_audio_vision,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,2,Positive,0.0,0.14704233407974243,0.14704233407974243,1085 +Predictive-Same,private_audio_vision,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,2,Positive,0.6666666865348816,0.09649966657161713,0.09649966657161713,1085 +Predictive-Same,private_audio_vision,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,1,Neutral,1.6666666269302368,-0.8298386931419373,-0.8298386931419373,1085 +Predictive-Same,private_audio_vision,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,0,Negative,1.0,-0.6656563878059387,-0.6656563878059387,1085 +Predictive-Same,private_audio_vision,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,1,Neutral,0.6666666865348816,-0.3009209632873535,-0.3009209632873535,1085 +Predictive-Same,private_audio_vision,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,1,Neutral,0.6666666865348816,0.3456045389175415,0.3456045389175415,1085 +Predictive-Same,private_audio_vision,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,2.172821044921875,2.172821044921875,1085 +Predictive-Same,private_audio_vision,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,2.109762668609619,2.109762668609619,1085 +Predictive-Same,private_audio_vision,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,2,Positive,0.3333333432674408,0.983049750328064,0.983049750328064,1085 +Predictive-Same,private_audio_vision,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.30618348717689514,0.30618348717689514,1085 +Predictive-Same,private_audio_vision,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.35100919008255005,0.35100919008255005,1085 +Predictive-Same,private_audio_vision,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,0.2244647741317749,0.2244647741317749,1085 +Predictive-Same,private_audio_vision,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.010934263467788696,0.010934263467788696,1085 +Predictive-Same,private_audio_vision,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.0587940514087677,0.0587940514087677,1085 +Predictive-Same,private_audio_vision,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,2,Positive,1.3333333730697632,0.5642712712287903,0.5642712712287903,1085 +Predictive-Same,private_audio_vision,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.2722211480140686,-0.2722211480140686,1085 +Predictive-Same,private_audio_vision,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,1,Neutral,-0.6666666865348816,0.5808654427528381,0.5808654427528381,1085 +Predictive-Same,private_audio_vision,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,1,Neutral,0.0,0.05379703640937805,0.05379703640937805,1085 +Predictive-Same,private_only,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,Neutral,0,Negative,0.0,0.03236067295074463,0.03236067295074463,4925 +Predictive-Same,private_only,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,-0.11097538471221924,-0.11097538471221924,4925 +Predictive-Same,private_only,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,1,Neutral,0.0,0.23965588212013245,0.23965588212013245,4925 +Predictive-Same,private_only,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,2,Positive,0.6666666865348816,0.5295090675354004,0.5295090675354004,4925 +Predictive-Same,private_only,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,2,Positive,1.6666666269302368,0.943278431892395,0.943278431892395,4925 +Predictive-Same,private_only,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,2,Positive,1.0,1.0275225639343262,1.0275225639343262,4925 +Predictive-Same,private_only,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.3281276822090149,0.3281276822090149,4925 +Predictive-Same,private_only,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.6921967267990112,0.6921967267990112,4925 +Predictive-Same,private_only,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,-0.18812286853790283,-0.18812286853790283,4925 +Predictive-Same,private_only,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,-0.10230705142021179,-0.10230705142021179,4925 +Predictive-Same,private_only,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,2,Positive,0.3333333432674408,1.9433469772338867,1.9433469772338867,4925 +Predictive-Same,private_only,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,1.4522242546081543,1.4522242546081543,4925 +Predictive-Same,private_only,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.8919404745101929,0.8919404745101929,4925 +Predictive-Same,private_only,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,0.5861947536468506,0.5861947536468506,4925 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+Predictive-Same,private_text,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,0.0070455074310302734,0.0070455074310302734,3840 +Predictive-Same,private_text,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,1,Neutral,0.0,0.5015344023704529,0.5015344023704529,3840 +Predictive-Same,private_text,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,1,Neutral,0.6666666865348816,0.8383798599243164,0.8383798599243164,3840 +Predictive-Same,private_text,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,2,Positive,1.6666666269302368,1.5828945636749268,1.5828945636749268,3840 +Predictive-Same,private_text,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,2,Positive,1.0,1.3120572566986084,1.3120572566986084,3840 +Predictive-Same,private_text,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.8618869185447693,0.8618869185447693,3840 +Predictive-Same,private_text,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,1.0433087348937988,1.0433087348937988,3840 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+Predictive-Local1,shared+private_text,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.8075690269470215,0.8075690269470215,8765 +Predictive-Local1,shared+private_text,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,2,Positive,1.3333333730697632,0.588486909866333,0.588486909866333,8765 +Predictive-Local1,shared+private_text,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.16728049516677856,-0.16728049516677856,8765 +Predictive-Local1,shared+private_text,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,1,Neutral,-0.6666666865348816,-0.24378639459609985,-0.24378639459609985,8765 +Predictive-Local1,shared+private_text,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.35989320278167725,-0.35989320278167725,8765 +Predictive-Local1,shared+private_vision,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,Neutral,2,Positive,0.0,0.47967684268951416,0.47967684268951416,5885 +Predictive-Local1,shared+private_vision,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,-0.024283260107040405,-0.024283260107040405,5885 +Predictive-Local1,shared+private_vision,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,2,Positive,0.0,0.29819270968437195,0.29819270968437195,5885 +Predictive-Local1,shared+private_vision,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,0,Negative,0.6666666865348816,-0.4377938508987427,-0.4377938508987427,5885 +Predictive-Local1,shared+private_vision,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,2,Positive,1.6666666269302368,-0.16706490516662598,-0.16706490516662598,5885 +Predictive-Local1,shared+private_vision,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,2,Positive,1.0,-0.24715429544448853,-0.24715429544448853,5885 +Predictive-Local1,shared+private_vision,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,-0.19109225273132324,-0.19109225273132324,5885 +Predictive-Local1,shared+private_vision,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,-0.5631387829780579,-0.5631387829780579,5885 +Predictive-Local1,shared+private_vision,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,-0.17002719640731812,-0.17002719640731812,5885 +Predictive-Local1,shared+private_vision,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.06936207413673401,0.06936207413673401,5885 +Predictive-Local1,shared+private_vision,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,1,Neutral,0.3333333432674408,0.3574075400829315,0.3574075400829315,5885 +Predictive-Local1,shared+private_vision,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,0,Negative,0.0,-0.5421709418296814,-0.5421709418296814,5885 +Predictive-Local1,shared+private_vision,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.09701339900493622,0.09701339900493622,5885 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+Predictive-Local1,shared_only,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,1.0312442779541016,1.0312442779541016,4925 +Predictive-Local1,shared_only,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,Positive,1,Neutral,0.3333333432674408,0.16027064621448517,0.16027064621448517,4925 +Predictive-Local1,shared_only,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.8921411037445068,0.8921411037445068,4925 +Predictive-Local1,shared_only,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,0,Negative,1.3333333730697632,0.5844798684120178,0.5844798684120178,4925 +Predictive-Local1,shared_only,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.2498517632484436,-0.2498517632484436,4925 +Predictive-Local1,shared_only,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,2,Positive,-0.6666666865348816,-0.3251926302909851,-0.3251926302909851,4925 +Predictive-Local1,shared_only,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.7247216105461121,-0.7247216105461121,4925 +Predictive-Local1,main_1280d,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,Neutral,2,Positive,0.0,0.06295496225357056,0.06295496225357056,1280 +Predictive-Local1,main_1280d,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,-0.33588188886642456,-0.33588188886642456,1280 +Predictive-Local1,main_1280d,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,1,Neutral,0.0,-0.3888482451438904,-0.3888482451438904,1280 +Predictive-Local1,main_1280d,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,2,Positive,0.6666666865348816,0.934627115726471,0.934627115726471,1280 +Predictive-Local1,main_1280d,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,2,Positive,1.6666666269302368,0.29167819023132324,0.29167819023132324,1280 +Predictive-Local1,main_1280d,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,0,Negative,1.0,0.07152065634727478,0.07152065634727478,1280 +Predictive-Local1,main_1280d,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.4360455572605133,0.4360455572605133,1280 +Predictive-Local1,main_1280d,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.48566529154777527,0.48566529154777527,1280 +Predictive-Local1,main_1280d,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,-0.36441415548324585,-0.36441415548324585,1280 +Predictive-Local1,main_1280d,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,-0.24602001905441284,-0.24602001905441284,1280 +Predictive-Local1,main_1280d,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,2,Positive,0.3333333432674408,0.5158414840698242,0.5158414840698242,1280 +Predictive-Local1,main_1280d,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.7396478652954102,0.7396478652954102,1280 +Predictive-Local1,main_1280d,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.6406883001327515,0.6406883001327515,1280 +Predictive-Local1,main_1280d,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,0.8433528542518616,0.8433528542518616,1280 +Predictive-Local1,main_1280d,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.6478390693664551,0.6478390693664551,1280 +Predictive-Local1,main_1280d,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.267782062292099,0.267782062292099,1280 +Predictive-Local1,main_1280d,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,2,Positive,1.3333333730697632,0.5060253143310547,0.5060253143310547,1280 +Predictive-Local1,main_1280d,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,0.36403799057006836,0.36403799057006836,1280 +Predictive-Local1,main_1280d,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,2,Positive,-0.6666666865348816,0.21434269845485687,0.21434269845485687,1280 +Predictive-Local1,main_1280d,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,-0.08508336544036865,-0.08508336544036865,1280 +RawPrivate-PCA,main_1280d,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,Neutral,1,Neutral,0.0,-0.17385566234588623,-0.17385566234588623,1280 +RawPrivate-PCA,main_1280d,5,-NFrJFQijFE/1,-NFrJFQijFE,2,Positive,2,Positive,0.3333333432674408,0.5138274431228638,0.5138274431228638,1280 +RawPrivate-PCA,main_1280d,5,-NFrJFQijFE/2,-NFrJFQijFE,1,Neutral,2,Positive,0.0,0.45659929513931274,0.45659929513931274,1280 +RawPrivate-PCA,main_1280d,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,Positive,2,Positive,0.6666666865348816,0.22984905540943146,0.22984905540943146,1280 +RawPrivate-PCA,main_1280d,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,Positive,2,Positive,1.6666666269302368,0.3566318154335022,0.3566318154335022,1280 +RawPrivate-PCA,main_1280d,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,Positive,2,Positive,1.0,0.6010764241218567,0.6010764241218567,1280 +RawPrivate-PCA,main_1280d,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.6869093775749207,0.6869093775749207,1280 +RawPrivate-PCA,main_1280d,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,Positive,2,Positive,0.6666666865348816,0.2796421945095062,0.2796421945095062,1280 +RawPrivate-PCA,main_1280d,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.2392444610595703,0.2392444610595703,1280 +RawPrivate-PCA,main_1280d,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.49064797163009644,0.49064797163009644,1280 +RawPrivate-PCA,main_1280d,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,Positive,2,Positive,0.3333333432674408,0.25315290689468384,0.25315290689468384,1280 +RawPrivate-PCA,main_1280d,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,Neutral,2,Positive,0.0,0.4184722304344177,0.4184722304344177,1280 +RawPrivate-PCA,main_1280d,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,Negative,2,Positive,-0.3333333432674408,0.08137384057044983,0.08137384057044983,1280 +RawPrivate-PCA,main_1280d,5,-tANM6ETl_M/3,-tANM6ETl_M,2,Positive,2,Positive,0.3333333432674408,0.36814093589782715,0.36814093589782715,1280 +RawPrivate-PCA,main_1280d,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.7233710885047913,0.7233710885047913,1280 +RawPrivate-PCA,main_1280d,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,Positive,2,Positive,0.3333333432674408,0.17928390204906464,0.17928390204906464,1280 +RawPrivate-PCA,main_1280d,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,Positive,2,Positive,1.3333333730697632,0.5143433809280396,0.5143433809280396,1280 +RawPrivate-PCA,main_1280d,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,Neutral,0,Negative,0.0,-0.2131059765815735,-0.2131059765815735,1280 +RawPrivate-PCA,main_1280d,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,2,Positive,-0.6666666865348816,0.15582183003425598,0.15582183003425598,1280 +RawPrivate-PCA,main_1280d,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,2,Positive,0.0,0.08862429857254028,0.08862429857254028,1280 +TSFA+RawPrivate,main_external_nonmatched,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.31241708993911743,-0.31241708993911743,6845 +TSFA+RawPrivate,main_external_nonmatched,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.09650483727455139,0.09650483727455139,6845 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a/deep_learning/Q1/outputs/shared_definition_comparison/paired_contrasts.csv b/deep_learning/Q1/outputs/shared_definition_comparison/paired_contrasts.csv new file mode 100644 index 0000000..c31415d --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/paired_contrasts.csv @@ -0,0 +1,22 @@ +candidate,reference,metric,delta_candidate_minus_reference,ci95_low,ci95_high,bootstrap_repeats,video_group_count,paired_sample_count +Predictive-Same/main_1280d,RawPrivate-PCA/main_1280d,macro_f1,-0.0423423423423423,-0.14391275521891694,0.03594107328515464,2000,37,100 +Predictive-Same/main_1280d,RawPrivate-PCA/main_1280d,mae,0.057932632118463556,0.011887524749618022,0.10996719407986377,2000,37,100 +Predictive-Same/main_1280d,RawPrivate-PCA/main_1280d,pearson,-0.12404381070336498,-0.2871420461079301,0.03243093726033124,2000,37,100 +GCCA/main_1280d,RawPrivate-PCA/main_1280d,macro_f1,-0.005419763081053353,-0.07177698199530066,0.04546358250719302,2000,37,100 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+42,Predictive-Same,private_vision,100,960,0.31,0.22717847051317108,0.21131751938769477,0.04067211322495786,1.0077504561841488,-0.27344926561837696,, +42,Predictive-Same,shared+private,100,9850,0.43,0.28388648388648385,0.25441531154545616,0.09876079389532164,0.6828779272735119,0.10700525866838041,, +42,Predictive-Same,shared+private_all,100,9850,0.43,0.28388648388648385,0.25441531154545616,0.09876079389532164,0.6828779272735119,0.10700525866838041,, +42,Predictive-Same,shared+private_audio,100,5050,0.39,0.2869191468656232,0.26831649831649834,0.1399272682485645,0.8010145276784897,0.03798281192039285,, +42,Predictive-Same,shared+private_audio_vision,100,6010,0.41,0.29445241511238646,0.274178374016455,0.11185921437250612,0.8014056612551212,0.03736171947727452,, +42,Predictive-Same,shared+private_text,100,8765,0.46,0.2844636524822695,0.26925140300663064,0.11878211156477378,0.692372799962759,0.11863797563685076,, 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+42,Similarity-SPR,private_audio_vision,100,640,0.52,0.3762946162007981,0.35838425128280205,0.07524748645413382,0.7737086206674576,0.06584870766947938,, +42,Similarity-SPR,private_only,100,960,0.56,0.3422461170848268,0.37624965583586273,0.15736280871885258,0.7403087028861046,0.10689943077519994,, +42,Similarity-SPR,private_text,100,320,0.53,0.32908496732026143,0.3360490934186587,0.10497969320977503,0.680117053836584,0.22364254532859437,, +42,Similarity-SPR,private_vision,100,320,0.55,0.3761385380788366,0.3597317926303433,0.08409860508359142,0.6574729996919632,0.0358601314770418,, +42,Similarity-SPR,shared+private,100,1920,0.56,0.38857214680744095,0.3409979462853026,0.09369875802146937,0.6967655114829541,0.07067944370969742,, +42,Similarity-SPR,shared+private_all,100,1920,0.56,0.38857214680744095,0.3409979462853026,0.09369875802146937,0.6967655114829541,0.07067944370969742,, +42,Similarity-SPR,shared+private_audio,100,1280,0.52,0.38684274243163963,0.3291406677613574,0.10633083326814055,0.7466380800306797,0.15595479528174308,, +42,Similarity-SPR,shared+private_audio_vision,100,1600,0.52,0.3970556131096266,0.34774860303595934,0.05188926537805234,0.7238257251679897,0.05238536484252928,, +42,Similarity-SPR,shared+private_text,100,1280,0.55,0.39236111111111116,0.33999664823194237,0.07899237801541087,0.6752975443005562,0.24203161589253674,, +42,Similarity-SPR,shared+private_vision,100,1280,0.47,0.3501074822240937,0.2963646356093662,0.07656504965618638,0.7524771577119828,0.05942080933032528,, +42,Similarity-SPR,shared_fused,100,320,0.52,0.3706666666666667,0.32213212075281045,0.09277771178723276,0.7274691557884216,0.18597000505750821,, +42,Similarity-SPR,shared_only,100,960,0.52,0.41123506874013976,0.33181720302370504,0.06777133926752461,0.7628524225950241,0.17064299398621885,, +42,TSFA+RawPrivate,main_external_nonmatched,100,6845,0.59,0.3900927013970492,0.35717374324270873,0.11754258899949505,0.5715452416241169,0.3678028818592164,False,external 6845D comparator diff --git a/deep_learning/Q1/outputs/shared_definition_comparison/shared_explained_variance.csv b/deep_learning/Q1/outputs/shared_definition_comparison/shared_explained_variance.csv new file mode 100644 index 0000000..9d36f51 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_definition_comparison/shared_explained_variance.csv @@ -0,0 +1,16 @@ +fold,modality,heldout_shared_reconstruction_r2,heldout_shared_reconstruction_mse,heldout_private_residual_variance +1,text,-0.0847855806350708,0.44095146656036377,0.4409497082233429 +1,audio,-0.5232381224632263,0.14654655754566193,0.1408616453409195 +1,vision,-0.59672611951828,0.9208552241325378,0.9208263158798218 +2,text,-0.1331416219472885,0.5271061062812805,0.5271058082580566 +2,audio,-0.5854305624961853,0.18038667738437653,0.16723152995109558 +2,vision,-0.3093641400337219,0.9315150380134583,0.9315005540847778 +3,text,-0.11728420108556747,0.5850533843040466,0.5850533246994019 +3,audio,-0.14537958800792694,0.13369984924793243,0.13082465529441833 +3,vision,-0.2316446751356125,0.9783102869987488,0.9782962203025818 +4,text,-0.15422017872333527,0.5754497051239014,0.5754497647285461 +4,audio,0.1343856006860733,0.19353337585926056,0.18452468514442444 +4,vision,-0.3868156373500824,1.104612946510315,1.1045827865600586 +5,text,-0.802847683429718,0.8055017590522766,0.8054860234260559 +5,audio,0.21544073522090912,0.6320556402206421,0.6111369132995605 +5,vision,-1.0986065864562988,1.7937532663345337,1.793732762336731 diff --git a/deep_learning/Q1/outputs/shared_time_private_content/01_shared_time_private_content_structure.png b/deep_learning/Q1/outputs/shared_time_private_content/01_shared_time_private_content_structure.png new file mode 100644 index 0000000..5d7f6b3 Binary files /dev/null and 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b/deep_learning/Q1/outputs/shared_time_private_content/09_representative_m4_weights.png differ diff --git a/deep_learning/Q1/outputs/shared_time_private_content/RESULTS.md b/deep_learning/Q1/outputs/shared_time_private_content/RESULTS.md new file mode 100644 index 0000000..175ef26 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/RESULTS.md @@ -0,0 +1,76 @@ +# Shared Time, Private Content: Q1 experiment + +## Core definition + +The shared object is the **temporal coordinate**: all modalities are indexed by 50 normalized video-time slots. The content at a slot remains private to its modality (BERT text, eGeMAPS audio, or DeiT vision). Same-slot products and absolute differences are optional inputs to an emotion task probe; they are not treated as evidence that modalities share semantics. + +M4_sourceTime weights are frozen per grouped fold. They provide a common temporal basis, after which the original standardized modality features are pooled. All scalers and PCA bases are fit on training folds only. Emotion labels are used only by the downstream LogisticRegression/Ridge probes. + +## Protocol + +- Samples/groups: 100 clips from 37 source videos; fixed five-fold GroupKFold predictions cover each clip once. +- Runtime device: `cuda` (NVIDIA GeForce RTX 5070 Ti). +- Feature dimensions: BERT 768, eGeMAPS 25, DeiT 192. Each fold standardizes from training clips, then pools with its frozen M4_sourceTime checkpoint. +- Private PCA: Text 64, Audio 24, Vision 64 components. The joint dimension-matched comparison uses up to 256 slot components, then pools five consecutive slot bins to 1280 clip features. +- Emotion probes: strict sign classes {-1,0,+1}; LogisticRegression C=0.05; direct Ridge alpha=25; dual-head intensity Ridge alpha=25 on |label|, clipped to [0,3], then multiplied by predicted polarity. Direct Ridge output is clipped to [-3,3]. +- Cross-modal interactions use `[p_m ⊙ p_n ; |p_m-p_n|]` on the same slot, with d=24. This is a task-specific interaction. No shared encoder, contrastive loss, orthogonality loss, or semantic reconstruction objective is used. +- Confidence intervals use 2,000 paired bootstrap draws over source-video groups. No multiple-comparison correction is applied; treat small metric differences as exploratory. + +## Main results + +| Representation | Dim | Accuracy | Macro-F1 | Direct MAE | Direct Pearson | Dual-head MAE | Dual Pearson | +|---|---:|---:|---:|---:|---:|---:|---:| +| Private-DimMatched-PCA256 | 1280 | 0.560 | 0.297 | 0.559 | 0.251 | 0.618 | 0.173 | +| Subset-T-PCA256 | 1280 | 0.560 | 0.284 | 0.585 | 0.241 | 0.652 | 0.040 | +| Subset-A-PCA256 | 1280 | 0.540 | 0.414 | 0.858 | 0.059 | 0.746 | 0.166 | +| Subset-V-PCA256 | 1280 | 0.440 | 0.393 | 1.157 | 0.039 | 1.296 | -0.116 | +| Subset-TAV-PCA256 | 1280 | 0.570 | 0.301 | 0.556 | 0.269 | 0.610 | 0.198 | +| TAV-I0 | 1280 | 0.530 | 0.397 | 0.854 | -0.035 | 0.667 | 0.127 | +| TAV-I1 | 1280 | 0.480 | 0.375 | 0.702 | 0.109 | 0.683 | 0.122 | +| TAV-I2 | 1280 | 0.510 | 0.344 | 0.702 | 0.114 | 0.709 | 0.181 | +| TAV-I3 | 1280 | 0.500 | 0.334 | 0.657 | 0.151 | 0.707 | 0.090 | +| TAV-I4 | 1280 | 0.490 | 0.400 | 0.734 | 0.116 | 0.784 | 0.114 | +| TAV-I5 | 1280 | 0.480 | 0.364 | 0.699 | 0.095 | 0.776 | 0.150 | +| TAV-I6 | 1280 | 0.460 | 0.326 | 0.731 | 0.068 | 0.816 | 0.065 | +| TAV-I7 | 1280 | 0.450 | 0.329 | 0.736 | 0.106 | 0.713 | 0.087 | + +Highest exploratory OOF Macro-F1: **Subset-A-PCA256** (0.414). +Lowest direct Ridge OOF MAE: **Subset-TV-PCA256** (0.555). +Lowest dual-head OOF MAE: **M4-TimeOnly-full-raw** (0.578). + +The strict sign class distribution is 18 Negative, 25 Neutral, and 57 Positive; an always-Positive classifier has 0.57 accuracy. Macro-F1 reports the three fixed classes with zero F1 for a class that is never predicted. + +## Temporal monitoring (frozen M4; not an emotion score) + +- Mean MVR at ε=0.02: 0.000. +- Mean absolute start-to-end span: 0.782 of normalized clip time. +- Mean correlation of expected source time with the 50-slot coordinate: 0.993. +- Mean pairwise source-time MAE: 0.060; mean pairwise time correlation: 0.968. +These values describe the frozen coordinate map. They do not establish that the three feature streams encode the same meaning. + +## Files + +- `predictions_oof.csv`: one row per clip and new representation, with polarity, direct-regression, and dual-head predictions. +- `metrics_oof.csv`: OOF metrics for new representations plus fixed historical controls. +- `group_bootstrap_contrasts.csv`: paired group-bootstrap differences, where matching prior OOF rows exist. +- `temporal_monitoring.csv`: clip-level MVR, span, expected-time correlation, and pairwise temporal error. +- `pca_fold_diagnostics.csv`: component counts and explained variance by fold. +- `fold_manifest.csv`: split and group-leakage audit. + +## Figures + +- `01_shared_time_private_content_structure.png` +- `02_modality_subset_contribution.png` +- `03_interaction_ablation.png` +- `04_polarity_vs_intensity_modality.png` +- `05_dual_head_vs_direct_ridge.png` +- `06_dimension_match_comparison.png` +- `07_temporal_quality_vs_task.png` +- `08_representative_private_content.png` +- `09_representative_m4_weights.png` + +## Interpretation limits + +This is a 100-clip exploratory evaluation from 37 source videos. The OOF probes reuse an existing fixed split, and the 50 slots within a clip are dependent observations for PCA. Slot-level PCA is fitted only to training clips, but confidence intervals resample videos rather than slots. We do not use the emotion labels to train the temporal model or PCA. Historical comparisons retain their original feature and probe definitions; they are controls, not perfectly matched re-runs. + +M4-TimeOnly-full-raw and Private-Identity have identical numerical content because Private-Identity means independently retained modality feature streams after the same frozen temporal pooling. Their duplicate rows are an explicit conceptual alias, not an independent ablation. diff --git a/deep_learning/Q1/outputs/shared_time_private_content/config.json b/deep_learning/Q1/outputs/shared_time_private_content/config.json new file mode 100644 index 0000000..fda51b8 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/config.json @@ -0,0 +1,67 @@ +{ + "experiment": "shared_time_private_content", + "generated_utc": "2026-09-24T02:30:25.923476+00:00", + "sample_count": 100, + "video_group_count": 37, + "class_counts": { + "Negative": 18, + "Neutral": 25, + "Positive": 57 + }, + "folds": 5, + "fold_source": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/method_comparison/splits.json", + "checkpoint_root": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/alignment_debug/heldout", + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "seed": 42, + "bootstrap_draws": 2000, + "features": "frozen BERT / eGeMAPS / DeiT from Q1 feature extraction; fold-standardized before M4 weighting", + "frozen_time_model": "M4_sourceTime checkpoint trained for each fixed grouped fold", + "public_coordinate": "50 normalized video-time slots; shared coordinate only", + "private_content": "modality-specific pooled BERT/eGeMAPS/DeiT vectors; no cross-modal semantic equality or shared contrastive objective", + "pca_protocol": "PCA fitted on the 50 pooled slots of training clips only; at most 256 components per slot; zero-pad to a common width when input rank is lower; downstream five-bin vector is 1280D for PCA256 views", + "private_modality_pca_dimensions": { + "text": 64, + "audio": 24, + "vision": 64 + }, + "interaction_definition": "per same slot [p_m * p_n ; abs(p_m - p_n)] with d=24 (limited by audio feature width), only supplied as task-specific features; bit mask: I1=TA, I2=TV, I3=TA+TV, I4=AV, I5=TA+AV, I6=TV+AV, I7=TA+TV+AV; no shared semantic encoder", + "dual_head": "LogisticRegression(C=0.05) predicts strict sign class; Ridge(alpha=25) predicts abs(label), clipped to [0,3]; signed output=class sign times magnitude; neutral gives exactly zero", + "direct_regression": "Ridge(alpha=25) predicts signed label directly and is clipped to [-3,3]", + "feature_and_probe_scalers": "fit on each training fold only", + "labels_used_in_alignment_or_pca": false, + "time_only_equals_private_identity": true, + "temporal_summary": { + "mvr_eps_0_02_mean": 0.00027210884353741496, + "span_abs_mean": 0.781566062635293, + "expected_time_correlation_mean": 0.9933735238185997, + "pairwise_time_mae_mean": 0.060424662191121525, + "pairwise_time_correlation_mean": 0.9683857161626767, + "sample_count": 100 + }, + "plots": [ + "01_shared_time_private_content_structure.png", + "02_modality_subset_contribution.png", + "03_interaction_ablation.png", + "04_polarity_vs_intensity_modality.png", + "05_dual_head_vs_direct_ridge.png", + "06_dimension_match_comparison.png", + "07_temporal_quality_vs_task.png", + "08_representative_private_content.png", + "09_representative_m4_weights.png" + ], + "runtime_seconds": 89.23, + "versions": { + "python": "3.14.7", + "torch": "2.14.0+cu130", + "sklearn": "1.9.1", + "numpy": "2.5.2" + }, + "notes": [ + "M4 weights provide temporal coordinate estimates; this experiment treats them as a frozen temporal basis and does not claim semantic equivalence across modalities.", + "TimeOnly-full-raw and Private-Identity have identical numerical features by definition; duplicate rows make the conceptual alias explicit.", + "Interaction dimension d=24 is capped by the 25D eGeMAPS source; the same 24D PCA coordinates are used in TA, TV, and AV interactions.", + "Bootstrap intervals resample the 37 source video groups and do not apply multiple-comparison correction.", + "Historical methods are read from their previous fixed-OOF prediction files; math files are read-only." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/shared_time_private_content/fold_manifest.csv b/deep_learning/Q1/outputs/shared_time_private_content/fold_manifest.csv new file mode 100644 index 0000000..1930909 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/fold_manifest.csv @@ -0,0 +1,6 @@ +fold,train_count,validation_count,train_video_count,validation_video_count,video_group_overlap,feature_standardization_fit,PCA_fit +1,80,20,31,6,0,training fold only,training fold 50-slot observations only +2,80,20,30,7,0,training fold only,training fold 50-slot observations only +3,80,20,29,8,0,training fold only,training fold 50-slot observations only +4,80,20,29,8,0,training fold only,training fold 50-slot observations only +5,80,20,29,8,0,training fold only,training fold 50-slot observations only diff --git a/deep_learning/Q1/outputs/shared_time_private_content/group_bootstrap_contrasts.csv b/deep_learning/Q1/outputs/shared_time_private_content/group_bootstrap_contrasts.csv new file mode 100644 index 0000000..04c4c7b --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/group_bootstrap_contrasts.csv @@ -0,0 +1,35 @@ +model_a,model_b,metric,difference_a_minus_b,bootstrap_95_low,bootstrap_95_high,video_group_count,bootstrap_draws,note +TAV-I7,TAV-I0,accuracy,-0.08000000000000002,-0.22582166768107118,0.0682014106583071,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +TAV-I7,TAV-I0,macro_f1,-0.06841323070857697,-0.17472345114025753,0.0410527044513058,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +TAV-I7,TAV-I0,direct_ridge_mae,-0.11868902847170826,-0.24761985546113394,0.016025095949880778,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +TAV-I7,TAV-I0,direct_ridge_pearson,0.1414418421449515,-0.06061988054570168,0.35213599031169507,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +TAV-I7,TAV-I0,dual_head_mae,0.04595443129539489,-0.09520655752096042,0.17227534752043866,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +TAV-I7,TAV-I0,dual_head_pearson,-0.03993578703306737,-0.3852879268660384,0.3541123154841853,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-A-PCA256,Math-B4,accuracy,-0.05999999999999994,-0.18087524177949707,0.05195369847543759,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-A-PCA256,Math-B4,macro_f1,-0.008106514893700234,-0.1861482244263489,0.13273513661234948,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-A-PCA256,Math-B4,direct_ridge_mae,0.26490522399544714,0.09610248693271449,0.47994668288455317,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-A-PCA256,Math-B4,direct_ridge_pearson,-0.13939796574665037,-0.3725446859559365,0.0941971186011628,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-TV-PCA256,Math-B0,accuracy,-0.030000000000000027,-0.07258904569892473,0.01098901098901095,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-TV-PCA256,Math-B0,macro_f1,-0.06026416227476766,-0.172633300437718,0.04688010135897051,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-TV-PCA256,Math-B0,direct_ridge_mae,-0.009040305614471444,-0.05340241262592203,0.033168035063147555,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Subset-TV-PCA256,Math-B0,direct_ridge_pearson,-0.07444875356538061,-0.2345193826115544,0.08097869910352536,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +M4-TimeOnly-full-raw,TSFA+RawPrivate,accuracy,0.0,-0.04763049450549445,0.0458741959295582,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +M4-TimeOnly-full-raw,TSFA+RawPrivate,macro_f1,0.017023317313172415,-0.07338264972996257,0.10829845537281034,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +M4-TimeOnly-full-raw,TSFA+RawPrivate,direct_ridge_mae,0.0023961503803729967,-0.03183401471645938,0.03684745112750479,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +M4-TimeOnly-full-raw,TSFA+RawPrivate,direct_ridge_pearson,0.0074231300699314495,-0.0643629490103999,0.0744345316841044,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,TSFA+RawPrivate,accuracy,-0.029999999999999916,-0.09375,0.03191489361702127,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,TSFA+RawPrivate,macro_f1,-0.09268279148713932,-0.18349774910663605,0.014662775614001474,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,TSFA+RawPrivate,direct_ridge_mae,-0.012341202497482273,-0.05703915573786493,0.03385730922128022,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,TSFA+RawPrivate,direct_ridge_pearson,-0.11633098177811252,-0.23895111370209401,0.023781091846298088,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,RawPrivate-PCA-old,accuracy,0.0,0.0,0.0,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,RawPrivate-PCA-old,macro_f1,0.0,0.0,0.0,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,RawPrivate-PCA-old,direct_ridge_mae,0.0,0.0,0.0,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,RawPrivate-PCA-old,direct_ridge_pearson,0.0,0.0,0.0,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B0,accuracy,-0.039999999999999925,-0.09616935483870973,0.02020717377860223,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B0,macro_f1,-0.06385340820272328,-0.2097097434526039,0.07699294469141622,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B0,direct_ridge_mae,-0.005063791722059174,-0.049152315803743754,0.03744660644375769,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B0,direct_ridge_pearson,-0.09438462337614484,-0.25385797945164634,0.07788145170814763,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B4,accuracy,-0.039999999999999925,-0.12659232469359052,0.04257631822386667,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B4,macro_f1,-0.12485045113763649,-0.278671478148539,0.03847865517873422,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B4,direct_ridge_mae,-0.03356187447905534,-0.08799651361037883,0.01969149319002485,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." +Private-DimMatched-PCA256,Math-B4,direct_ridge_pearson,0.05282679886779296,-0.13181892342230528,0.2511032746240181,37,2000,"Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction." diff --git a/deep_learning/Q1/outputs/shared_time_private_content/metrics_oof.csv b/deep_learning/Q1/outputs/shared_time_private_content/metrics_oof.csv new file mode 100644 index 0000000..ced0009 --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/metrics_oof.csv @@ -0,0 +1,31 @@ +method_family,representation,feature_dimension,accuracy,macro_f1,direct_ridge_mae,direct_ridge_pearson,dual_head_mae,dual_head_pearson,accuracy_fold_mean,accuracy_fold_sd,macro_f1_fold_mean,macro_f1_fold_sd +SharedTime-PrivateContent,M4-TimeOnly-full-raw,4925,0.59,0.4071160187102216,0.5739413920044899,0.3752260119291483,0.5776897639036178,0.3991175755801305,0.5900000000000001,0.17102631376487074,0.4385813322479989,0.20972404272411235 +SharedTime-PrivateContent,Private-Identity,4925,0.59,0.4071160187102216,0.5739413920044899,0.3752260119291483,0.5776897639036178,0.3991175755801305,0.5900000000000001,0.17102631376487074,0.4385813322479989,0.20972404272411235 +SharedTime-PrivateContent,Private-PCA-T64-A24-V64,760,0.53,0.397379397342774,0.8544335103034973,-0.035090922538975194,0.6667693001031876,0.1269090246541039,0.53,0.12041594578792292,0.38219928219928223,0.16206912954291441 +SharedTime-PrivateContent,Private-DimMatched-PCA256,1280,0.56,0.2974099099099099,0.5592040391266346,0.25147190008110387,0.6176537299156188,0.1728542188507843,0.56,0.10839741694339397,0.28073240535039046,0.06810513535887727 +SharedTime-PrivateContent,Subset-T-PCA256,1280,0.56,0.28364728364728364,0.5848394468426704,0.2405692574260619,0.6517676076292992,0.040453140866811226,0.5599999999999999,0.15165750888103102,0.278694885361552,0.1051699900427055 +SharedTime-PrivateContent,Subset-A-PCA256,1280,0.54,0.41415384615384615,0.8576711376011371,0.0592471354666606,0.7464621433615685,0.16621646999317566,0.54,0.12942179105544785,0.4175824175824176,0.10959181464965649 +SharedTime-PrivateContent,Subset-V-PCA256,1280,0.44,0.3928758169934641,1.1574889883399009,0.03879618775945609,1.2959232833981513,-0.11646566167874028,0.44000000000000006,0.15165750888103102,0.3805572497677761,0.1028400537195545 +SharedTime-PrivateContent,Subset-TA-PCA256,1280,0.56,0.26594982078853047,0.590192968994379,0.22700013175756376,0.6653251749277115,0.02109603916613595,0.5599999999999999,0.14317821063276354,0.2639712947758925,0.0694350310870926 +SharedTime-PrivateContent,Subset-TV-PCA256,1280,0.57,0.3009991558378655,0.5552275252342224,0.2714077698918681,0.6097314590215683,0.20347208860122343,0.57,0.1095445115010332,0.2909720209923049,0.07727216547528806 +SharedTime-PrivateContent,Subset-AV-PCA256,1280,0.46,0.3990668695211186,1.041767364293337,0.03353904024552095,1.1726952528953551,-0.012311365534357837,0.45999999999999996,0.12942179105544788,0.3777611277611278,0.09402282731908926 +SharedTime-PrivateContent,Subset-TAV-PCA256,1280,0.57,0.301037540086776,0.5558185236155987,0.269101883396212,0.6095418477058411,0.19764421948186228,0.5700000000000001,0.1095445115010332,0.28204481792717084,0.07110311972060454 +SharedTime-PrivateContent,TAV-I0,1280,0.53,0.397379397342774,0.8544335031509399,-0.03509092552555159,0.6667693039774895,0.12690897737846074,0.53,0.12041594578792292,0.38219928219928223,0.16206912954291441 +SharedTime-PrivateContent,TAV-I1,1280,0.48,0.37455748552522755,0.702035290747881,0.10927288301542826,0.6828949844837189,0.12163926306615037,0.4800000000000001,0.10368220676663861,0.40343074209740876,0.1479516221842248 +SharedTime-PrivateContent,TAV-I2,1280,0.51,0.34422296050203033,0.7024269095063209,0.11434564739952886,0.7094765597581864,0.18079496964213534,0.51,0.0821583836257749,0.3448289241622575,0.11419069007722668 +SharedTime-PrivateContent,TAV-I3,1280,0.5,0.33423892982119385,0.6569717380404473,0.15114076877419508,0.7073900982737541,0.09016655326318078,0.5,0.07071067811865474,0.33061020461020457,0.1008595207383396 +SharedTime-PrivateContent,TAV-I4,1280,0.49,0.4002835379231972,0.7342324909567833,0.11558881415267117,0.7840914106369019,0.11408784435445586,0.49000000000000005,0.08215838362577493,0.40327805527805527,0.14351182354938802 +SharedTime-PrivateContent,TAV-I5,1280,0.48,0.3644433383109342,0.699248592555523,0.095364362780094,0.7764464724063873,0.15006701778558076,0.48,0.11510864433221339,0.37901621727708684,0.13131748631953244 +SharedTime-PrivateContent,TAV-I6,1280,0.46,0.3264175311419406,0.7314607065916061,0.06769069714362111,0.8161805200576783,0.06523533503188975,0.45999999999999996,0.08944271909999157,0.36651504051504047,0.12353508677123004 +SharedTime-PrivateContent,TAV-I7,1280,0.45,0.32896616663419703,0.7357444746792317,0.10635091661940012,0.7127237352728844,0.08697319034539328,0.45,0.061237243569579464,0.34182037715371044,0.06165137519495801 +Historical-Control,TSFA-old,1920,0.54,0.4310819293870141,0.8202796086668969,0.10443752941446603,nan,nan,,,, +Historical-Control,TSFA+RawPrivate,6845,0.59,0.3900927013970492,0.5715452416241169,0.3678028818592164,nan,nan,,,, +Historical-Control,RawPrivate-PCA-old,1280,0.56,0.2974099099099099,0.5592040391266346,0.25147190008110387,nan,nan,,,, +Historical-Control,SPR-private-all,960,0.56,0.3422461170848268,0.7403087028861046,0.10689943077519994,nan,nan,,,, +Historical-Control,SPR-dim-matched,1280,0.55,0.31766680893441457,0.7007618999481201,0.059041191015689186,nan,nan,,,, +Historical-Control,GCCA-old,1280,0.55,0.29199014682885654,0.5809971535205841,0.17584072860758496,nan,nan,,,, +Historical-Control,Predictive-Same-old,1280,0.53,0.2550675675675676,0.6171366663277149,0.12742805173707503,nan,nan,,,, +Historical-Control,Predictive-Local1-old,1280,0.53,0.2701337701337701,0.6158707904815673,0.13745442565394256,nan,nan,,,, +Historical-Control,Similarity-SPR-old,1280,0.49,0.29803793124961003,0.6723750464618206,0.10648974261028547,nan,nan,,,, +Historical-Control,Math-B0,-1,0.6,0.3612633181126332,0.5642678308486938,0.34585652345724877,nan,nan,,,, +Historical-Control,Math-B4,-1,0.6,0.4222603610475464,0.59276591360569,0.19864510121331094,nan,nan,,,, diff --git a/deep_learning/Q1/outputs/shared_time_private_content/pca_fold_diagnostics.csv b/deep_learning/Q1/outputs/shared_time_private_content/pca_fold_diagnostics.csv new file mode 100644 index 0000000..1f31ebd --- /dev/null +++ b/deep_learning/Q1/outputs/shared_time_private_content/pca_fold_diagnostics.csv @@ -0,0 +1,96 @@ +fold,projection,input_dim,components,explained_variance_ratio_sum,train_slot_count +1,modality_text,768,64,0.7558237314224243,4000 +1,modality_audio,25,24,0.9999939203262329,4000 +1,modality_vision,192,64,0.9825369119644165,4000 +1,all_modalities_joint_256,985,256,0.986522912979126,4000 +1,Subset-T-PCA256,768,256,0.9846392869949341,4000 +1,Subset-A-PCA256,25,25,0.9999999403953552,4000 +1,Subset-V-PCA256,192,192,1.0,4000 +1,Subset-TA-PCA256,793,256,0.9844452142715454,4000 +1,Subset-TV-PCA256,960,256,0.986709713935852,4000 +1,Subset-AV-PCA256,217,217,1.0,4000 +1,Subset-TAV-PCA256,985,256,0.986540675163269,4000 +1,TAV-I0,152,152,nan,4000 +1,TAV-I1,200,200,1.0,4000 +1,TAV-I2,200,200,1.0,4000 +1,TAV-I3,248,248,1.0,4000 +1,TAV-I4,200,200,1.0,4000 +1,TAV-I5,248,248,1.0,4000 +1,TAV-I6,248,248,0.9999999403953552,4000 +1,TAV-I7,296,256,0.9999604225158691,4000 +2,modality_text,768,64,0.756224513053894,4000 +2,modality_audio,25,24,0.9999940395355225,4000 +2,modality_vision,192,64,0.9852473139762878,4000 +2,all_modalities_joint_256,985,256,0.9870184063911438,4000 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+Historical-Control,SPR-dim-matched,1,-UUCSKoHeMA/0,-UUCSKoHeMA,1,,2,,0.0,-0.26140040159225464,,-0.26140040159225464,,historical +Historical-Control,SPR-dim-matched,2,-THoVjtIkeU/12,-THoVjtIkeU,2,,0,,2.0,-0.2511492073535919,,-0.2511492073535919,,historical +Historical-Control,SPR-dim-matched,2,-THoVjtIkeU/2,-THoVjtIkeU,2,,2,,1.3333333730697632,-0.21307983994483948,,-0.21307983994483948,,historical +Historical-Control,SPR-dim-matched,2,-THoVjtIkeU/6,-THoVjtIkeU,2,,2,,2.6666667461395264,-0.24064955115318298,,-0.24064955115318298,,historical +Historical-Control,SPR-dim-matched,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,,1,,1.6666666269302368,-0.25039926171302795,,-0.25039926171302795,,historical +Historical-Control,SPR-dim-matched,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,,2,,2.6666667461395264,0.1542503386735916,,0.1542503386735916,,historical +Historical-Control,SPR-dim-matched,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,,2,,0.3333333432674408,-0.019935518503189087,,-0.019935518503189087,,historical +Historical-Control,SPR-dim-matched,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,,0,,-0.3333333432674408,-0.7216401100158691,,-0.7216401100158691,,historical +Historical-Control,SPR-dim-matched,2,-lzEya4AM_4/5,-lzEya4AM_4,0,,1,,-0.6666666865348816,-0.0186326801776886,,-0.0186326801776886,,historical +Historical-Control,SPR-dim-matched,2,-lzEya4AM_4/6,-lzEya4AM_4,0,,1,,-0.3333333432674408,-0.03633221983909607,,-0.03633221983909607,,historical +Historical-Control,SPR-dim-matched,2,-mqbVkbCndg/0,-mqbVkbCndg,1,,0,,0.0,-0.7150843143463135,,-0.7150843143463135,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/1,-wny0OAz3g8,2,,2,,0.3333333432674408,-0.23757728934288025,,-0.23757728934288025,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/0,-wny0OAz3g8,2,,2,,0.3333333432674408,-0.13236036896705627,,-0.13236036896705627,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/3,-wny0OAz3g8,0,,2,,-1.3333333730697632,-0.12827202677726746,,-0.12827202677726746,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/2,-wny0OAz3g8,0,,2,,-0.3333333432674408,0.11237671971321106,,0.11237671971321106,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/5,-wny0OAz3g8,2,,2,,1.0,0.6392519474029541,,0.6392519474029541,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/7,-wny0OAz3g8,1,,2,,0.0,0.11136041581630707,,0.11136041581630707,,historical +Historical-Control,SPR-dim-matched,2,-wny0OAz3g8/9,-wny0OAz3g8,2,,2,,0.6666666865348816,0.41464942693710327,,0.41464942693710327,,historical +Historical-Control,SPR-dim-matched,2,-571d8cVauQ/0,-571d8cVauQ,1,,2,,0.0,0.24300521612167358,,0.24300521612167358,,historical +Historical-Control,SPR-dim-matched,2,-571d8cVauQ/5,-571d8cVauQ,2,,2,,0.3333333432674408,0.8129171133041382,,0.8129171133041382,,historical +Historical-Control,SPR-dim-matched,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,,2,,0.6666666865348816,0.35466304421424866,,0.35466304421424866,,historical +Historical-Control,SPR-dim-matched,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,,1,,0.0,0.5961953997612,,0.5961953997612,,historical +Historical-Control,SPR-dim-matched,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,,2,,-1.0,-0.33114248514175415,,-0.33114248514175415,,historical +Historical-Control,SPR-dim-matched,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,,0,,0.0,-0.48341280221939087,,-0.48341280221939087,,historical +Historical-Control,SPR-dim-matched,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,,2,,-0.3333333432674408,0.14140306413173676,,0.14140306413173676,,historical +Historical-Control,SPR-dim-matched,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,,2,,-2.0,0.5820786952972412,,0.5820786952972412,,historical +Historical-Control,SPR-dim-matched,3,-vxjVxOeScU/4,-vxjVxOeScU,2,,2,,1.0,1.1288630962371826,,1.1288630962371826,,historical +Historical-Control,SPR-dim-matched,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,,2,,0.3333333432674408,0.5222343802452087,,0.5222343802452087,,historical +Historical-Control,SPR-dim-matched,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,,2,,0.6666666865348816,0.7529511451721191,,0.7529511451721191,,historical +Historical-Control,SPR-dim-matched,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,,2,,1.0,1.2067720890045166,,1.2067720890045166,,historical +Historical-Control,SPR-dim-matched,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,,1,,0.6666666865348816,0.6332323551177979,,0.6332323551177979,,historical +Historical-Control,SPR-dim-matched,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,,2,,2.0,0.33134186267852783,,0.33134186267852783,,historical +Historical-Control,SPR-dim-matched,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,,2,,0.3333333432674408,0.29921287298202515,,0.29921287298202515,,historical +Historical-Control,SPR-dim-matched,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,,2,,0.3333333432674408,0.22955021262168884,,0.22955021262168884,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,,2,,0.6666666865348816,0.5599125623703003,,0.5599125623703003,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,,2,,-0.3333333432674408,0.5154906511306763,,0.5154906511306763,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,,2,,-2.0,0.4799790382385254,,0.4799790382385254,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,,2,,0.0,0.6305359601974487,,0.6305359601974487,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,,2,,1.0,0.7401822805404663,,0.7401822805404663,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,,2,,0.0,1.0246288776397705,,1.0246288776397705,,historical +Historical-Control,SPR-dim-matched,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,,2,,1.3333333730697632,1.0950536727905273,,1.0950536727905273,,historical +Historical-Control,SPR-dim-matched,4,-aNfi7CP8vM/7,-aNfi7CP8vM,1,,2,,0.0,1.1280910968780518,,1.1280910968780518,,historical +Historical-Control,SPR-dim-matched,4,-iRBcNs9oI8/3,-iRBcNs9oI8,2,,2,,0.6666666865348816,0.16877798736095428,,0.16877798736095428,,historical +Historical-Control,SPR-dim-matched,4,-iRBcNs9oI8/7,-iRBcNs9oI8,2,,2,,0.6666666865348816,0.27240222692489624,,0.27240222692489624,,historical +Historical-Control,SPR-dim-matched,4,-iRBcNs9oI8/6,-iRBcNs9oI8,2,,2,,0.3333333432674408,0.8030750155448914,,0.8030750155448914,,historical +Historical-Control,SPR-dim-matched,4,-iRBcNs9oI8/9,-iRBcNs9oI8,2,,2,,1.3333333730697632,0.4041907787322998,,0.4041907787322998,,historical +Historical-Control,SPR-dim-matched,4,-iRBcNs9oI8/8,-iRBcNs9oI8,2,,2,,1.0,0.7078574895858765,,0.7078574895858765,,historical +Historical-Control,SPR-dim-matched,4,-t217m2on-s/2,-t217m2on-s,2,,2,,0.6666666865348816,0.7247806787490845,,0.7247806787490845,,historical +Historical-Control,SPR-dim-matched,4,-t217m2on-s/7,-t217m2on-s,0,,2,,-0.6666666865348816,0.14494004845619202,,0.14494004845619202,,historical +Historical-Control,SPR-dim-matched,4,-tPCytz4rww/11,-tPCytz4rww,1,,2,,0.0,0.501767098903656,,0.501767098903656,,historical +Historical-Control,SPR-dim-matched,4,-tPCytz4rww/10,-tPCytz4rww,1,,2,,0.0,1.1330032348632812,,1.1330032348632812,,historical +Historical-Control,SPR-dim-matched,4,-tPCytz4rww/12,-tPCytz4rww,1,,2,,0.0,0.4680120348930359,,0.4680120348930359,,historical +Historical-Control,SPR-dim-matched,4,-tPCytz4rww/16,-tPCytz4rww,1,,2,,0.0,0.34142082929611206,,0.34142082929611206,,historical +Historical-Control,SPR-dim-matched,4,-tPCytz4rww/18,-tPCytz4rww,1,,2,,0.0,0.5514241456985474,,0.5514241456985474,,historical +Historical-Control,SPR-dim-matched,4,-UacrmKiTn4/10,-UacrmKiTn4,0,,2,,-0.6666666865348816,0.9290489554405212,,0.9290489554405212,,historical +Historical-Control,SPR-dim-matched,4,-UacrmKiTn4/4,-UacrmKiTn4,0,,2,,-1.3333333730697632,0.8620119690895081,,0.8620119690895081,,historical +Historical-Control,SPR-dim-matched,4,-uywlfIYOS8/4,-uywlfIYOS8,2,,2,,0.3333333432674408,0.7535353899002075,,0.7535353899002075,,historical +Historical-Control,SPR-dim-matched,4,-6rXp3zJ3kc/8,-6rXp3zJ3kc,0,,2,,-1.0,0.36947867274284363,,0.36947867274284363,,historical +Historical-Control,SPR-dim-matched,4,-ri04Z7vwnc/0,-ri04Z7vwnc,1,,0,,0.0,0.32896918058395386,,0.32896918058395386,,historical +Historical-Control,SPR-dim-matched,4,-ri04Z7vwnc/2,-ri04Z7vwnc,0,,2,,-0.3333333432674408,0.46470561623573303,,0.46470561623573303,,historical +Historical-Control,SPR-dim-matched,4,-ri04Z7vwnc/5,-ri04Z7vwnc,2,,2,,0.3333333432674408,0.9589472413063049,,0.9589472413063049,,historical +Historical-Control,SPR-dim-matched,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,,2,,0.0,0.24043503403663635,,0.24043503403663635,,historical +Historical-Control,SPR-dim-matched,5,-NFrJFQijFE/1,-NFrJFQijFE,2,,2,,0.3333333432674408,-0.037209272384643555,,-0.037209272384643555,,historical +Historical-Control,SPR-dim-matched,5,-NFrJFQijFE/2,-NFrJFQijFE,1,,2,,0.0,0.46988046169281006,,0.46988046169281006,,historical +Historical-Control,SPR-dim-matched,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,,2,,0.6666666865348816,-0.13384422659873962,,-0.13384422659873962,,historical +Historical-Control,SPR-dim-matched,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,,2,,1.6666666269302368,0.8890440464019775,,0.8890440464019775,,historical +Historical-Control,SPR-dim-matched,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,,2,,1.0,0.45025864243507385,,0.45025864243507385,,historical +Historical-Control,SPR-dim-matched,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,,2,,0.6666666865348816,0.8880241513252258,,0.8880241513252258,,historical +Historical-Control,SPR-dim-matched,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,,2,,0.6666666865348816,1.0543546676635742,,1.0543546676635742,,historical +Historical-Control,SPR-dim-matched,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,,2,,0.0,1.2000724077224731,,1.2000724077224731,,historical +Historical-Control,SPR-dim-matched,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,,2,,0.0,1.2087104320526123,,1.2087104320526123,,historical +Historical-Control,SPR-dim-matched,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,,2,,0.3333333432674408,-0.34746724367141724,,-0.34746724367141724,,historical +Historical-Control,SPR-dim-matched,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,,2,,0.0,-0.10531899333000183,,-0.10531899333000183,,historical +Historical-Control,SPR-dim-matched,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,,2,,-0.3333333432674408,0.09558582305908203,,0.09558582305908203,,historical +Historical-Control,SPR-dim-matched,5,-tANM6ETl_M/3,-tANM6ETl_M,2,,2,,0.3333333432674408,0.08492675423622131,,0.08492675423622131,,historical +Historical-Control,SPR-dim-matched,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,,2,,0.3333333432674408,0.5081594586372375,,0.5081594586372375,,historical +Historical-Control,SPR-dim-matched,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,,2,,0.3333333432674408,0.9170579314231873,,0.9170579314231873,,historical +Historical-Control,SPR-dim-matched,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,,2,,1.3333333730697632,0.5636589527130127,,0.5636589527130127,,historical +Historical-Control,SPR-dim-matched,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,,0,,0.0,-0.3981946110725403,,-0.3981946110725403,,historical +Historical-Control,SPR-dim-matched,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,,0,,-0.6666666865348816,-0.07507112622261047,,-0.07507112622261047,,historical +Historical-Control,SPR-dim-matched,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,,2,,0.0,-0.5413902997970581,,-0.5413902997970581,,historical +Historical-Control,GCCA-old,1,-3g5yACwYnA/13,-3g5yACwYnA,2,,1,,0.6666666865348816,-0.5078049302101135,,-0.5078049302101135,,historical +Historical-Control,GCCA-old,1,-3g5yACwYnA/3,-3g5yACwYnA,1,,1,,0.0,0.17674624919891357,,0.17674624919891357,,historical +Historical-Control,GCCA-old,1,-3g5yACwYnA/2,-3g5yACwYnA,1,,2,,0.0,0.1670924425125122,,0.1670924425125122,,historical +Historical-Control,GCCA-old,1,-3g5yACwYnA/9,-3g5yACwYnA,2,,1,,0.6666666865348816,-0.008438527584075928,,-0.008438527584075928,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/26,-egA8-b7-3M,2,,2,,1.6666666269302368,0.5575234889984131,,0.5575234889984131,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/17,-egA8-b7-3M,2,,2,,0.3333333432674408,0.052891314029693604,,0.052891314029693604,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/18,-egA8-b7-3M,2,,2,,1.0,0.22910374402999878,,0.22910374402999878,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/16,-egA8-b7-3M,2,,2,,0.6666666865348816,0.5562823414802551,,0.5562823414802551,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/13,-egA8-b7-3M,2,,2,,0.3333333432674408,0.12831827998161316,,0.12831827998161316,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/1,-egA8-b7-3M,2,,2,,0.6666666865348816,0.19850534200668335,,0.19850534200668335,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/6,-egA8-b7-3M,2,,2,,0.6666666865348816,0.386540949344635,,0.386540949344635,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/9,-egA8-b7-3M,2,,2,,0.3333333432674408,0.4393078088760376,,0.4393078088760376,,historical +Historical-Control,GCCA-old,1,-egA8-b7-3M/20,-egA8-b7-3M,2,,2,,0.3333333432674408,0.27374404668807983,,0.27374404668807983,,historical +Historical-Control,GCCA-old,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,,2,,-0.6666666865348816,0.13547950983047485,,0.13547950983047485,,historical +Historical-Control,GCCA-old,1,-I_e4mIh0yE/3,-I_e4mIh0yE,2,,2,,0.6666666865348816,0.2306697964668274,,0.2306697964668274,,historical +Historical-Control,GCCA-old,1,-qDkUB0GgYY/6,-qDkUB0GgYY,2,,2,,1.3333333730697632,0.9293526411056519,,0.9293526411056519,,historical +Historical-Control,GCCA-old,1,-9y-fZ3swSY/0,-9y-fZ3swSY,2,,2,,1.0,0.7926177382469177,,0.7926177382469177,,historical +Historical-Control,GCCA-old,1,-9y-fZ3swSY/4,-9y-fZ3swSY,1,,2,,0.0,0.555331826210022,,0.555331826210022,,historical +Historical-Control,GCCA-old,1,-9y-fZ3swSY/8,-9y-fZ3swSY,2,,2,,1.6666666269302368,0.22610650956630707,,0.22610650956630707,,historical +Historical-Control,GCCA-old,1,-UUCSKoHeMA/0,-UUCSKoHeMA,1,,2,,0.0,0.32553377747535706,,0.32553377747535706,,historical +Historical-Control,GCCA-old,2,-THoVjtIkeU/12,-THoVjtIkeU,2,,2,,2.0,-0.3995042145252228,,-0.3995042145252228,,historical +Historical-Control,GCCA-old,2,-THoVjtIkeU/2,-THoVjtIkeU,2,,2,,1.3333333730697632,0.1853637844324112,,0.1853637844324112,,historical +Historical-Control,GCCA-old,2,-THoVjtIkeU/6,-THoVjtIkeU,2,,2,,2.6666667461395264,0.10317812860012054,,0.10317812860012054,,historical +Historical-Control,GCCA-old,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,,2,,1.6666666269302368,0.13481996953487396,,0.13481996953487396,,historical +Historical-Control,GCCA-old,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,,2,,2.6666667461395264,0.6337321996688843,,0.6337321996688843,,historical +Historical-Control,GCCA-old,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,,2,,0.3333333432674408,0.024993568658828735,,0.024993568658828735,,historical +Historical-Control,GCCA-old,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,,1,,-0.3333333432674408,-0.24614855647087097,,-0.24614855647087097,,historical +Historical-Control,GCCA-old,2,-lzEya4AM_4/5,-lzEya4AM_4,0,,2,,-0.6666666865348816,0.23524527251720428,,0.23524527251720428,,historical +Historical-Control,GCCA-old,2,-lzEya4AM_4/6,-lzEya4AM_4,0,,0,,-0.3333333432674408,-0.22469303011894226,,-0.22469303011894226,,historical +Historical-Control,GCCA-old,2,-mqbVkbCndg/0,-mqbVkbCndg,1,,2,,0.0,-0.03964301943778992,,-0.03964301943778992,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/1,-wny0OAz3g8,2,,0,,0.3333333432674408,-0.08394578099250793,,-0.08394578099250793,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/0,-wny0OAz3g8,2,,1,,0.3333333432674408,0.034212976694107056,,0.034212976694107056,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/3,-wny0OAz3g8,0,,2,,-1.3333333730697632,-0.08162879943847656,,-0.08162879943847656,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/2,-wny0OAz3g8,0,,2,,-0.3333333432674408,0.46016302704811096,,0.46016302704811096,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/5,-wny0OAz3g8,2,,2,,1.0,0.18515312671661377,,0.18515312671661377,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/7,-wny0OAz3g8,1,,2,,0.0,-0.04976511001586914,,-0.04976511001586914,,historical +Historical-Control,GCCA-old,2,-wny0OAz3g8/9,-wny0OAz3g8,2,,2,,0.6666666865348816,0.1964123249053955,,0.1964123249053955,,historical +Historical-Control,GCCA-old,2,-571d8cVauQ/0,-571d8cVauQ,1,,2,,0.0,0.05706360936164856,,0.05706360936164856,,historical +Historical-Control,GCCA-old,2,-571d8cVauQ/5,-571d8cVauQ,2,,2,,0.3333333432674408,0.11030840873718262,,0.11030840873718262,,historical +Historical-Control,GCCA-old,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,,2,,0.6666666865348816,0.49480289220809937,,0.49480289220809937,,historical +Historical-Control,GCCA-old,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,,2,,0.0,0.35919809341430664,,0.35919809341430664,,historical +Historical-Control,GCCA-old,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,,1,,-1.0,-0.21764177083969116,,-0.21764177083969116,,historical +Historical-Control,GCCA-old,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,,2,,0.0,0.14081023633480072,,0.14081023633480072,,historical +Historical-Control,GCCA-old,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,,2,,-0.3333333432674408,-0.011710286140441895,,-0.011710286140441895,,historical +Historical-Control,GCCA-old,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,,2,,-2.0,0.382511168718338,,0.382511168718338,,historical +Historical-Control,GCCA-old,3,-vxjVxOeScU/4,-vxjVxOeScU,2,,2,,1.0,0.6644254326820374,,0.6644254326820374,,historical +Historical-Control,GCCA-old,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,,2,,0.3333333432674408,0.22029095888137817,,0.22029095888137817,,historical +Historical-Control,GCCA-old,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,,2,,0.6666666865348816,0.05114877223968506,,0.05114877223968506,,historical +Historical-Control,GCCA-old,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,,2,,1.0,0.5701652765274048,,0.5701652765274048,,historical +Historical-Control,GCCA-old,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,,2,,0.6666666865348816,0.45411786437034607,,0.45411786437034607,,historical +Historical-Control,GCCA-old,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,,2,,2.0,1.040008783340454,,1.040008783340454,,historical +Historical-Control,GCCA-old,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,,2,,0.3333333432674408,1.049943447113037,,1.049943447113037,,historical +Historical-Control,GCCA-old,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,,2,,0.3333333432674408,0.08544847369194031,,0.08544847369194031,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,,2,,0.6666666865348816,0.6051899194717407,,0.6051899194717407,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,,2,,-0.3333333432674408,0.30292341113090515,,0.30292341113090515,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,,2,,-2.0,0.6882851719856262,,0.6882851719856262,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,,2,,0.0,0.6951227784156799,,0.6951227784156799,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,,2,,1.0,0.2999699115753174,,0.2999699115753174,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,,2,,0.0,0.09513705968856812,,0.09513705968856812,,historical +Historical-Control,GCCA-old,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,,2,,1.3333333730697632,0.4697009325027466,,0.4697009325027466,,historical +Historical-Control,GCCA-old,4,-aNfi7CP8vM/7,-aNfi7CP8vM,1,,2,,0.0,0.1742255687713623,,0.1742255687713623,,historical +Historical-Control,GCCA-old,4,-iRBcNs9oI8/3,-iRBcNs9oI8,2,,2,,0.6666666865348816,0.07781189680099487,,0.07781189680099487,,historical +Historical-Control,GCCA-old,4,-iRBcNs9oI8/7,-iRBcNs9oI8,2,,2,,0.6666666865348816,0.6219112873077393,,0.6219112873077393,,historical +Historical-Control,GCCA-old,4,-iRBcNs9oI8/6,-iRBcNs9oI8,2,,2,,0.3333333432674408,0.5128915905952454,,0.5128915905952454,,historical +Historical-Control,GCCA-old,4,-iRBcNs9oI8/9,-iRBcNs9oI8,2,,2,,1.3333333730697632,0.608780562877655,,0.608780562877655,,historical +Historical-Control,GCCA-old,4,-iRBcNs9oI8/8,-iRBcNs9oI8,2,,2,,1.0,0.6128740310668945,,0.6128740310668945,,historical +Historical-Control,GCCA-old,4,-t217m2on-s/2,-t217m2on-s,2,,2,,0.6666666865348816,0.3170757293701172,,0.3170757293701172,,historical +Historical-Control,GCCA-old,4,-t217m2on-s/7,-t217m2on-s,0,,2,,-0.6666666865348816,0.48610448837280273,,0.48610448837280273,,historical +Historical-Control,GCCA-old,4,-tPCytz4rww/11,-tPCytz4rww,1,,2,,0.0,0.43436551094055176,,0.43436551094055176,,historical +Historical-Control,GCCA-old,4,-tPCytz4rww/10,-tPCytz4rww,1,,2,,0.0,0.5654422044754028,,0.5654422044754028,,historical +Historical-Control,GCCA-old,4,-tPCytz4rww/12,-tPCytz4rww,1,,2,,0.0,0.6271084547042847,,0.6271084547042847,,historical +Historical-Control,GCCA-old,4,-tPCytz4rww/16,-tPCytz4rww,1,,2,,0.0,0.21599557995796204,,0.21599557995796204,,historical +Historical-Control,GCCA-old,4,-tPCytz4rww/18,-tPCytz4rww,1,,2,,0.0,0.1790892481803894,,0.1790892481803894,,historical +Historical-Control,GCCA-old,4,-UacrmKiTn4/10,-UacrmKiTn4,0,,2,,-0.6666666865348816,-0.000804901123046875,,-0.000804901123046875,,historical +Historical-Control,GCCA-old,4,-UacrmKiTn4/4,-UacrmKiTn4,0,,2,,-1.3333333730697632,0.1952269822359085,,0.1952269822359085,,historical +Historical-Control,GCCA-old,4,-uywlfIYOS8/4,-uywlfIYOS8,2,,2,,0.3333333432674408,0.08195000886917114,,0.08195000886917114,,historical +Historical-Control,GCCA-old,4,-6rXp3zJ3kc/8,-6rXp3zJ3kc,0,,2,,-1.0,0.21226295828819275,,0.21226295828819275,,historical +Historical-Control,GCCA-old,4,-ri04Z7vwnc/0,-ri04Z7vwnc,1,,2,,0.0,0.7474653720855713,,0.7474653720855713,,historical +Historical-Control,GCCA-old,4,-ri04Z7vwnc/2,-ri04Z7vwnc,0,,2,,-0.3333333432674408,0.542270302772522,,0.542270302772522,,historical +Historical-Control,GCCA-old,4,-ri04Z7vwnc/5,-ri04Z7vwnc,2,,2,,0.3333333432674408,0.43339893221855164,,0.43339893221855164,,historical +Historical-Control,GCCA-old,5,-3nNcZdcdvU/5,-3nNcZdcdvU,1,,2,,0.0,-0.006881117820739746,,-0.006881117820739746,,historical +Historical-Control,GCCA-old,5,-NFrJFQijFE/1,-NFrJFQijFE,2,,2,,0.3333333432674408,0.4975571036338806,,0.4975571036338806,,historical +Historical-Control,GCCA-old,5,-NFrJFQijFE/2,-NFrJFQijFE,1,,2,,0.0,0.5221647024154663,,0.5221647024154663,,historical +Historical-Control,GCCA-old,5,-a55Q6RWvTA/3,-a55Q6RWvTA,2,,2,,0.6666666865348816,0.3029855787754059,,0.3029855787754059,,historical +Historical-Control,GCCA-old,5,-dxfTGcXJoc/1,-dxfTGcXJoc,2,,2,,1.6666666269302368,0.19649943709373474,,0.19649943709373474,,historical +Historical-Control,GCCA-old,5,-dxfTGcXJoc/0,-dxfTGcXJoc,2,,2,,1.0,0.4478803873062134,,0.4478803873062134,,historical +Historical-Control,GCCA-old,5,-dxfTGcXJoc/2,-dxfTGcXJoc,2,,2,,0.6666666865348816,0.5589083433151245,,0.5589083433151245,,historical +Historical-Control,GCCA-old,5,-dxfTGcXJoc/6,-dxfTGcXJoc,2,,2,,0.6666666865348816,0.3303954005241394,,0.3303954005241394,,historical +Historical-Control,GCCA-old,5,-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,,2,,0.0,0.5948190689086914,,0.5948190689086914,,historical +Historical-Control,GCCA-old,5,-mJ2ud6oKI8/2,-mJ2ud6oKI8,1,,2,,0.0,0.7992900013923645,,0.7992900013923645,,historical +Historical-Control,GCCA-old,5,-mJ2ud6oKI8/6,-mJ2ud6oKI8,2,,2,,0.3333333432674408,0.32836708426475525,,0.32836708426475525,,historical +Historical-Control,GCCA-old,5,-mJ2ud6oKI8/9,-mJ2ud6oKI8,1,,2,,0.0,0.3094785213470459,,0.3094785213470459,,historical +Historical-Control,GCCA-old,5,-mJ2ud6oKI8/8,-mJ2ud6oKI8,0,,2,,-0.3333333432674408,0.24070852994918823,,0.24070852994918823,,historical +Historical-Control,GCCA-old,5,-tANM6ETl_M/3,-tANM6ETl_M,2,,2,,0.3333333432674408,0.4282436966896057,,0.4282436966896057,,historical +Historical-Control,GCCA-old,5,-MeTTeMJBNc/0,-MeTTeMJBNc,2,,2,,0.3333333432674408,0.753618597984314,,0.753618597984314,,historical +Historical-Control,GCCA-old,5,-MeTTeMJBNc/13,-MeTTeMJBNc,2,,2,,0.3333333432674408,0.332926869392395,,0.332926869392395,,historical +Historical-Control,GCCA-old,5,-MeTTeMJBNc/7,-MeTTeMJBNc,2,,2,,1.3333333730697632,0.596319317817688,,0.596319317817688,,historical +Historical-Control,GCCA-old,5,-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,,0,,0.0,-0.35594940185546875,,-0.35594940185546875,,historical +Historical-Control,GCCA-old,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,,2,,-0.6666666865348816,0.15543992817401886,,0.15543992817401886,,historical +Historical-Control,GCCA-old,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,,2,,0.0,-0.07713335752487183,,-0.07713335752487183,,historical +Historical-Control,Predictive-Same-old,1,-3g5yACwYnA/13,-3g5yACwYnA,2,,1,,0.6666666865348816,-0.3627808690071106,,-0.3627808690071106,,historical +Historical-Control,Predictive-Same-old,1,-3g5yACwYnA/3,-3g5yACwYnA,1,,1,,0.0,0.04289020597934723,,0.04289020597934723,,historical +Historical-Control,Predictive-Same-old,1,-3g5yACwYnA/2,-3g5yACwYnA,1,,2,,0.0,-0.14982551336288452,,-0.14982551336288452,,historical +Historical-Control,Predictive-Same-old,1,-3g5yACwYnA/9,-3g5yACwYnA,2,,2,,0.6666666865348816,-0.13201218843460083,,-0.13201218843460083,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/26,-egA8-b7-3M,2,,2,,1.6666666269302368,0.5860574841499329,,0.5860574841499329,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/17,-egA8-b7-3M,2,,2,,0.3333333432674408,0.13808760046958923,,0.13808760046958923,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/18,-egA8-b7-3M,2,,2,,1.0,-0.16645032167434692,,-0.16645032167434692,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/16,-egA8-b7-3M,2,,2,,0.6666666865348816,0.6854637265205383,,0.6854637265205383,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/13,-egA8-b7-3M,2,,2,,0.3333333432674408,0.2560122311115265,,0.2560122311115265,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/1,-egA8-b7-3M,2,,2,,0.6666666865348816,0.4438669979572296,,0.4438669979572296,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/6,-egA8-b7-3M,2,,2,,0.6666666865348816,0.4909226894378662,,0.4909226894378662,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/9,-egA8-b7-3M,2,,2,,0.3333333432674408,0.5360617637634277,,0.5360617637634277,,historical +Historical-Control,Predictive-Same-old,1,-egA8-b7-3M/20,-egA8-b7-3M,2,,2,,0.3333333432674408,0.2841499447822571,,0.2841499447822571,,historical +Historical-Control,Predictive-Same-old,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,,2,,-0.6666666865348816,0.3791179955005646,,0.3791179955005646,,historical 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All Python runs use the project uv environment. Read `run_manifest.json` for the complete protocol and limitations. `paired_contrasts.csv` reports left minus right with video-ID-cluster bootstrap confidence intervals. The full `outputs/tsfa/` directory retains per-clip CSVs and probe checkpoints. The `alignment_only_*` summaries pool identical train-fold-standardized raw source features through each method's alignment matrix. They isolate source selection from native model value/output projections. `tsfa_temporal_diagnostics_summary.csv` reports MVR, span, entropy, and source coverage. + +`emotion_probe_metrics.csv` adds five-fold, video-group-held-out LogisticRegression/Ridge probes on five-segment pooled representations. The classification metrics use fixed three-class Macro-F1; Ridge predictions are clipped to `[-3, 3]` before reported MAE/Pearson. The CSV also includes fold Macro-F1 mean/sample SD and unclipped regression metrics for diagnosis. These are small-sample downstream probes, not end-to-end emotion model scores. + +`paired_math_comparison.csv` compares the TSFA all-modalities OOF predictions with math B0–B4 using the identical 100 sample IDs and fold assignments. Confidence intervals use paired bootstrap resampling of the 37 source-video groups; the comparison script only reads the math prediction and split CSVs. diff --git a/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_confusion_matrix.csv b/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_confusion_matrix.csv new file mode 100644 index 0000000..73fcf48 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_confusion_matrix.csv @@ -0,0 +1,145 @@ +method,view,true_class,predicted_class,count +M3_noSourceTime,text,Negative,Negative,4 +M3_noSourceTime,text,Negative,Neutral,6 +M3_noSourceTime,text,Negative,Positive,8 +M3_noSourceTime,text,Neutral,Negative,2 +M3_noSourceTime,text,Neutral,Neutral,5 +M3_noSourceTime,text,Neutral,Positive,18 +M3_noSourceTime,text,Positive,Negative,2 +M3_noSourceTime,text,Positive,Neutral,8 +M3_noSourceTime,text,Positive,Positive,47 +M3_noSourceTime,audio,Negative,Negative,6 +M3_noSourceTime,audio,Negative,Neutral,4 +M3_noSourceTime,audio,Negative,Positive,8 +M3_noSourceTime,audio,Neutral,Negative,4 +M3_noSourceTime,audio,Neutral,Neutral,2 +M3_noSourceTime,audio,Neutral,Positive,19 +M3_noSourceTime,audio,Positive,Negative,5 +M3_noSourceTime,audio,Positive,Neutral,8 +M3_noSourceTime,audio,Positive,Positive,44 +M3_noSourceTime,vision,Negative,Negative,2 +M3_noSourceTime,vision,Negative,Neutral,5 +M3_noSourceTime,vision,Negative,Positive,11 +M3_noSourceTime,vision,Neutral,Negative,3 +M3_noSourceTime,vision,Neutral,Neutral,6 +M3_noSourceTime,vision,Neutral,Positive,16 +M3_noSourceTime,vision,Positive,Negative,6 +M3_noSourceTime,vision,Positive,Neutral,13 +M3_noSourceTime,vision,Positive,Positive,38 +M3_noSourceTime,all_modalities,Negative,Negative,3 +M3_noSourceTime,all_modalities,Negative,Neutral,3 +M3_noSourceTime,all_modalities,Negative,Positive,12 +M3_noSourceTime,all_modalities,Neutral,Negative,4 +M3_noSourceTime,all_modalities,Neutral,Neutral,4 +M3_noSourceTime,all_modalities,Neutral,Positive,17 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+TSFA-main,vision,Negative,Negative,5 +TSFA-main,vision,Negative,Neutral,3 +TSFA-main,vision,Negative,Positive,10 +TSFA-main,vision,Neutral,Negative,9 +TSFA-main,vision,Neutral,Neutral,7 +TSFA-main,vision,Neutral,Positive,9 +TSFA-main,vision,Positive,Negative,24 +TSFA-main,vision,Positive,Neutral,11 +TSFA-main,vision,Positive,Positive,22 +TSFA-main,all_modalities,Negative,Negative,5 +TSFA-main,all_modalities,Negative,Neutral,5 +TSFA-main,all_modalities,Negative,Positive,8 +TSFA-main,all_modalities,Neutral,Negative,7 +TSFA-main,all_modalities,Neutral,Neutral,7 +TSFA-main,all_modalities,Neutral,Positive,11 +TSFA-main,all_modalities,Positive,Negative,7 +TSFA-main,all_modalities,Positive,Neutral,8 +TSFA-main,all_modalities,Positive,Positive,42 diff --git a/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_manifest.json b/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_manifest.json new file mode 100644 index 0000000..ed21838 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa/report_bundle/emotion_probe_manifest.json @@ -0,0 +1,119 @@ +{ + "created_utc": "2026-09-23T15:57:28.473166+00:00", + "experiment": "Grouped five-fold emotion probes on frozen TSFA and M3/M4 representations", + "sample_count": 100, + "video_id_count": 37, + "fold_count": 5, + "heldout_prediction_count_per_method_view": 100, + "split_rule": "Fixed five-fold GroupKFold by group_id/video_id, loaded from the Q1 method-comparison split file.", + "seed": 42, + "batch_size_for_feature_inference": 8, + "input_paths": { + "feature_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/q1_features/features", + "feature_manifest": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/audit/manifest.csv", + "grouped_splits": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/method_comparison/splits.json", + "alignment_checkpoint_root": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/alignment_debug/heldout", + "tsfa_output_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa", + "tsfa_probe_checkpoint": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa/probe_checkpoints.pt" + }, + "class_mapping": { + "label_lt_0": "Negative", + "label_eq_0": "Neutral", + "label_gt_0": "Positive" + }, + "class_counts": { + "Negative": 18, + "Neutral": 25, + "Positive": 57 + }, + "classification": { + "estimator": "LogisticRegression", + "C": 0.05, + "max_iter": 5000, + "features": "StandardScaler fitted on the training fold, then 5-segment pooled aligned representations", + "macro_f1": "fixed labels [Negative, Neutral, Positive]; zero_division=0" + }, + "temporal_pooling": "Five contiguous equal-width bins over the 50 shared slots; mean each bin and concatenate.", + "regression": { + "estimator": "Ridge", + "alpha": 25.0, + "features": "StandardScaler fitted on the training fold, then the same 5-segment pooled representations", + "prediction_clipping": [ + -3.0, + 3.0 + ], + "reported_mae_pearson": 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b/deep_learning/Q1/outputs/tsfa_av_private_ablation/factorial_effects.png new file mode 100644 index 0000000..d37e550 Binary files /dev/null and b/deep_learning/Q1/outputs/tsfa_av_private_ablation/factorial_effects.png differ diff --git a/deep_learning/Q1/outputs/tsfa_av_private_ablation/metrics.csv b/deep_learning/Q1/outputs/tsfa_av_private_ablation/metrics.csv new file mode 100644 index 0000000..34b4943 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/metrics.csv @@ -0,0 +1,5 @@ +method,av_edge,private_residual,sample_count,feature_dimension,accuracy,macro_f1,mae,pearson,macro_f1_fold_mean,macro_f1_fold_sd +TSFA-T,0,0,100,1920,0.54,0.4310819293870141,0.8202796086668969,0.10443752941446602,0.36259462759462757,0.09089510359034791 +TSFA-AV,1,0,100,1920,0.52,0.4127735152031827,0.8033503513038158,0.10127907590031264,0.35865411367899513,0.10547659395165765 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diff --git a/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv b/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv new file mode 100644 index 0000000..81e15dc --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv @@ -0,0 +1,21 @@ +comparison,metric,tsfa_oof,baseline_oof,delta_tsfa_minus_baseline,video_cluster_bootstrap_ci95_low,video_cluster_bootstrap_ci95_high,bootstrap_repeats,video_group_count +TSFA-T+Private-B0,accuracy,0.59,0.6,-0.010000000000000009,-0.06666666666666665,0.04673999294657021,2000,37 +TSFA-T+Private-B0,macro_f1,0.3900927013970492,0.3612633181126332,0.028829383284416032,-0.09162392227104062,0.1393739818917356,2000,37 +TSFA-T+Private-B0,mae,0.5715452416241169,0.5642678308486938,0.007277410775423099,-0.027015220896741916,0.03924678899886661,2000,37 +TSFA-T+Private-B0,pearson,0.36780288185921645,0.34585652345724877,0.02194635840196768,-0.09956490260679603,0.13686716969344476,2000,37 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for every sample_id.", + "metrics": [ + "accuracy", + "macro_f1", + "mae", + "pearson" + ], + "macro_f1_labels": [ + 0, + 1, + 2 + ], + "regression_prediction_clipping": [ + -3.0, + 3.0 + ], + "bootstrap": { + "unit": "video_id cluster", + "repeats": 2000, + "seed": 42, + "interval": "percentile 95% confidence interval for paired TSFA-minus-baseline metric differences" + }, + "math_inputs_read_only": [ + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/oof_predictions.csv", + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/split_assignments.csv" + ], + "tsfa_input": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/predictions.csv", + "output": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv" +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/README.md b/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/README.md new file mode 100644 index 0000000..c6929fd --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/README.md @@ -0,0 +1,3 @@ +# TSFA A–V edge × private residual ablation + +Four OOF cells on the same 100 clips and five video-group folds. `metrics.csv` gives Accuracy, fixed-three-class Macro-F1, clipped MAE, and Pearson. `paired_contrasts.csv` reports video-cluster bootstrap intervals for the two factors and their interaction. `private_vs_math.csv` compares the strongest regression cell with math B0–B4 when available. The original math files were read only. See `run_manifest.json` for the protocol and limitations. 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0000000..81e15dc --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/private_vs_math.csv @@ -0,0 +1,21 @@ +comparison,metric,tsfa_oof,baseline_oof,delta_tsfa_minus_baseline,video_cluster_bootstrap_ci95_low,video_cluster_bootstrap_ci95_high,bootstrap_repeats,video_group_count +TSFA-T+Private-B0,accuracy,0.59,0.6,-0.010000000000000009,-0.06666666666666665,0.04673999294657021,2000,37 +TSFA-T+Private-B0,macro_f1,0.3900927013970492,0.3612633181126332,0.028829383284416032,-0.09162392227104062,0.1393739818917356,2000,37 +TSFA-T+Private-B0,mae,0.5715452416241169,0.5642678308486938,0.007277410775423099,-0.027015220896741916,0.03924678899886661,2000,37 +TSFA-T+Private-B0,pearson,0.36780288185921645,0.34585652345724877,0.02194635840196768,-0.09956490260679603,0.13686716969344476,2000,37 +TSFA-T+Private-B1,accuracy,0.59,0.61,-0.020000000000000018,-0.07766990291262144,0.039613861386138594,2000,37 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], + "macro_f1_labels": [ + 0, + 1, + 2 + ], + "regression_prediction_clipping": [ + -3.0, + 3.0 + ], + "bootstrap": { + "unit": "video_id cluster", + "repeats": 2000, + "seed": 42, + "interval": "percentile 95% confidence interval for paired TSFA-minus-baseline metric differences" + }, + "math_inputs_read_only": [ + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/oof_predictions.csv", + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/split_assignments.csv" + ], + "tsfa_input": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/predictions.csv", + "output": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv" +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/run_manifest.json b/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/run_manifest.json new file mode 100644 index 0000000..0afde4a --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/report_bundle/run_manifest.json @@ -0,0 +1,83 @@ +{ + "created_utc": "2026-09-23T16:24:23.243950+00:00", + "experiment": "2x2 TSFA ablation: local reciprocal Audio-Vision edge x private modality residual", + "sample_count": 100, + "video_id_count": 37, + "fold_count": 5, + "folds": [ + { + "fold": 1, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 31, + "heldout_video_id_count": 6, + "video_id_overlap": [] + }, + { + "fold": 2, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 30, + "heldout_video_id_count": 7, + "video_id_overlap": [] + }, + { + "fold": 3, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + }, + { + "fold": 4, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + }, + { + "fold": 5, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + } + ], + "original_tsfa_control_predictions_identical": true, + "av_edge": "M4 aligned Audio queries raw projected Vision, and M4 aligned Vision queries raw projected Audio; both directions use the existing M4 source-center +/-delta masks. Messages add to the original Text-to-Audio/Vision content.", + "private_residual": "For each modality, frozen M4 temporal attention pools its training-fold-standardized original BERT/eGeMAPS/DeiT source features onto 50 slots; the same five-segment probe concatenates all three private streams after shared streams.", + "semantic_training": "same 40-epoch Text-Audio/Text-Vision local contrastive loss, optimizer, temperature, batch size and seed as original TSFA; no emotion labels", + "probe": "StandardScaler + LogisticRegression(C=0.05) and StandardScaler + Ridge(alpha=25); trained per fold; regression predictions clipped to [-3,3]", + "bootstrap": "2,000 paired video_id-cluster samples; percentile 95% intervals; no multiple-comparison correction", + "parameters": { + "seed": 42, + "delta": 0.1, + "semantic_epochs": 40, + "semantic_learning_rate": 0.001, + "local_temperature": 0.1, + "batch_size": 8, + "bootstrap_repeats": 2000 + }, + "input_paths": { + "feature_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/q1_features/features", + "feature_manifest": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/audit/manifest.csv", + "splits": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/method_comparison/splits.json", + "frozen_alignment_checkpoint_root": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/alignment_debug/heldout", + "original_tsfa_checkpoints": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa/probe_checkpoints.pt", + "original_tsfa_predictions": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_predictions.csv" + }, + "device": "cuda", + "python": "3.14.7", + "pytorch": "2.14.0+cu130", + "scikit_learn": "1.9.1", + "elapsed_seconds": 83.80609822273254, + "interpretation_limits": [ + "An added edge also adds trainable parameters; an edge effect alone does not prove correct semantic alignment.", + "Private residuals increase probe input dimension; any gain cannot be assigned to a particular raw feature without further controlled study.", + "Only one semantic-branch seed was trained on 100 clips and 37 source videos.", + "Clip-level emotion labels cannot directly validate event-level correspondence." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_av_private_ablation/run_manifest.json b/deep_learning/Q1/outputs/tsfa_av_private_ablation/run_manifest.json new file mode 100644 index 0000000..0afde4a --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_av_private_ablation/run_manifest.json @@ -0,0 +1,83 @@ +{ + "created_utc": "2026-09-23T16:24:23.243950+00:00", + "experiment": "2x2 TSFA ablation: local reciprocal Audio-Vision edge x private modality residual", + "sample_count": 100, + "video_id_count": 37, + "fold_count": 5, + "folds": [ + { + "fold": 1, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 31, + "heldout_video_id_count": 6, + "video_id_overlap": [] + }, + { + "fold": 2, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 30, + "heldout_video_id_count": 7, + "video_id_overlap": [] + }, + { + "fold": 3, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + }, + { + "fold": 4, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + }, + { + "fold": 5, + "train_count": 80, + "heldout_count": 20, + "train_video_id_count": 29, + "heldout_video_id_count": 8, + "video_id_overlap": [] + } + ], + "original_tsfa_control_predictions_identical": true, + "av_edge": "M4 aligned Audio queries raw projected Vision, and M4 aligned Vision queries raw projected Audio; both directions use the existing M4 source-center +/-delta masks. Messages add to the original Text-to-Audio/Vision content.", + "private_residual": "For each modality, frozen M4 temporal attention pools its training-fold-standardized original BERT/eGeMAPS/DeiT source features onto 50 slots; the same five-segment probe concatenates all three private streams after shared streams.", + "semantic_training": "same 40-epoch Text-Audio/Text-Vision local contrastive loss, optimizer, temperature, batch size and seed as original TSFA; no emotion labels", + "probe": "StandardScaler + LogisticRegression(C=0.05) and StandardScaler + Ridge(alpha=25); trained per fold; regression predictions clipped to [-3,3]", + "bootstrap": "2,000 paired video_id-cluster samples; percentile 95% intervals; no multiple-comparison correction", + "parameters": { + "seed": 42, + "delta": 0.1, + "semantic_epochs": 40, + "semantic_learning_rate": 0.001, + "local_temperature": 0.1, + "batch_size": 8, + "bootstrap_repeats": 2000 + }, + "input_paths": { + "feature_dir": 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dimension; any gain cannot be assigned to a particular raw feature without further controlled study.", + "Only one semantic-branch seed was trained on 100 clips and 37 source videos.", + "Clip-level emotion labels cannot directly validate event-level correspondence." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_confusion_matrix.csv b/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_confusion_matrix.csv new file mode 100644 index 0000000..73fcf48 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_confusion_matrix.csv @@ -0,0 +1,145 @@ +method,view,true_class,predicted_class,count +M3_noSourceTime,text,Negative,Negative,4 +M3_noSourceTime,text,Negative,Neutral,6 +M3_noSourceTime,text,Negative,Positive,8 +M3_noSourceTime,text,Neutral,Negative,2 +M3_noSourceTime,text,Neutral,Neutral,5 +M3_noSourceTime,text,Neutral,Positive,18 +M3_noSourceTime,text,Positive,Negative,2 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a/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_manifest.json b/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_manifest.json new file mode 100644 index 0000000..ed21838 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_manifest.json @@ -0,0 +1,119 @@ +{ + "created_utc": "2026-09-23T15:57:28.473166+00:00", + "experiment": "Grouped five-fold emotion probes on frozen TSFA and M3/M4 representations", + "sample_count": 100, + "video_id_count": 37, + "fold_count": 5, + "heldout_prediction_count_per_method_view": 100, + "split_rule": "Fixed five-fold GroupKFold by group_id/video_id, loaded from the Q1 method-comparison split file.", + "seed": 42, + "batch_size_for_feature_inference": 8, + "input_paths": { + "feature_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/q1_features/features", + "feature_manifest": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/audit/manifest.csv", + "grouped_splits": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/method_comparison/splits.json", + "alignment_checkpoint_root": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/alignment_debug/heldout", + "tsfa_output_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa", + "tsfa_probe_checkpoint": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa/probe_checkpoints.pt" + }, + "class_mapping": { + "label_lt_0": "Negative", + "label_eq_0": "Neutral", + "label_gt_0": "Positive" + }, + "class_counts": { + "Negative": 18, + "Neutral": 25, + "Positive": 57 + }, + "classification": { + "estimator": "LogisticRegression", + "C": 0.05, + "max_iter": 5000, + "features": "StandardScaler fitted on the training fold, then 5-segment pooled aligned representations", + "macro_f1": "fixed labels [Negative, Neutral, Positive]; zero_division=0" + }, + "temporal_pooling": "Five contiguous equal-width bins over the 50 shared slots; mean each bin and concatenate.", + 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true, + "identical_video_ids": true, + "identical_fold_assignments": true, + "fold_assignment_validation": "TSFA prediction fold matched math split_assignments.csv for every sample_id.", + "metrics": [ + "accuracy", + "macro_f1", + "mae", + "pearson" + ], + "macro_f1_labels": [ + 0, + 1, + 2 + ], + "regression_prediction_clipping": [ + -3.0, + 3.0 + ], + "bootstrap": { + "unit": "video_id cluster", + "repeats": 2000, + "seed": 42, + "interval": "percentile 95% confidence interval for paired TSFA-minus-baseline metric differences" + }, + "math_inputs_read_only": [ + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/oof_predictions.csv", + "/home/gloamxun/modeling_zhaocui/math/results/model_comparison/split_assignments.csv" + ], + "tsfa_input": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_emotion_probe/emotion_probe_predictions.csv", + "output": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_emotion_probe/paired_math_comparison.csv" +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/README.md b/deep_learning/Q1/outputs/tsfa_shared_private/README.md new file mode 100644 index 0000000..d401f7d --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/README.md @@ -0,0 +1,46 @@ +# TSFA-SPR：时序—共享—私有分解 + +## 首轮结果（seed 42） + +M4_sourceTime 的五折 temporal checkpoints 已冻结；共享/私有因子器只用折内标准化的原始 BERT、eGeMAPS、DeiT 特征及 M4 权重训练，没有使用情感标签。Probe 口径沿用现有五折 GroupKFold、五段顺序池化、LogisticRegression(C=0.05)、Ridge(alpha=25) 与 [-3,3] 限幅。 + +SPR 主表示（unfused shared + all private）：Accuracy 0.560，Macro-F1 0.389，MAE 0.697，Pearson 0.071。 +来源模态 probe：shared accuracy 0.729，SPR private accuracy 0.999；但不含 private 训练损失的 SP-noOrth-noRec 控制也达到 1.000，说明该识别率可能来自模态专属分支/特征分布，不能当成已学会私有语义。shared 三组匹配 AUC 为 audio-vision=0.498, text-audio=0.512, text-vision=0.499，slot shuffle 后几乎不下降。 +合并重构 MSE 0.422，shared-only 0.563，private-only 0.442。 + +诊断结论：**不支持已成功实现 shared/private 语义解耦**。合并分支能改善源特征重构，但 shared 跨模态同槽检索约为机会水平，且 shuffle 控制没有明显下降；private 来源识别在随机未训练控制中也已饱和。因此不应把本轮写成成功的 shared/private 语义对齐。 + +## 私有来源探针 + +- private-only Macro-F1 最高：private_audio（0.445）。 +- private-only MAE 最低：private_vision（0.657）。 +- Audio 的单独分类探针 Macro-F1 点估计领先，Vision 的单独回归 probe MAE 点估计最低；video-group paired 区间跨 0，不能据此确定模态贡献差异。 + +## 维度控制和基线 + +- TSFA-old: d=1920 pooled，Macro-F1 0.431，MAE 0.820，Pearson 0.104。 +- TSFA+RawPrivate: d=6845 pooled，Macro-F1 0.390，MAE 0.572，Pearson 0.368。 +- SPR: d=1920 pooled，Macro-F1 0.389，MAE 0.697，Pearson 0.071。 +- SPR-dim-matched: d=1280 pooled，Macro-F1 0.318，MAE 0.701，Pearson 0.059。 +- RawPrivate-PCA: d=1280 pooled，Macro-F1 0.297，MAE 0.559，Pearson 0.251。 +- SPR 主表示相对 RawPrivate-PCA 的 MAE 差为 +0.138，视频组 bootstrap 95% CI [+0.062, +0.216]。 RawPrivate-PCA 有较低 MAE，维度匹配后没有证据支持 learned shared/private 分解带来强度回归增益。 +- 直接比较同为 1280 维 pooled 的 SPR-dim-matched 与 RawPrivate-PCA，MAE 差为 +0.142，95% CI [+0.076, +0.214]。 这是控制输出维数后的成对对照。 + +RawPrivate-PCA 将每个 slot 的 985 维原始私有特征仅用训练折做 PCA 到 256 维；与 fused-shared + all-private 的 SPR-dim-matched 都是 256 维/slot、1280 维/clip。RawPrivate-PCA 的低 MAE 说明可压缩 raw residual 已足以保留强度信息，但分类和 Pearson 并未同步领先。 + +## 文件 + +- `emotion_probe_metrics.csv` / `emotion_probe_predictions.csv`：全部五折 OOF probe。 +- `paired_contrasts.csv`：SPR 主表示对 TSFA-old、TSFA+RawPrivate、math B0/B4 和维度对照的 2,000 次 video_id paired bootstrap；未做多重比较校正。 +- `private_source_ablation.csv` 与 `private_source_paired_contrasts.csv`：模态来源 probe 及来源间配对区间。 +- `decomposition_assessment.json`：shared/private 诊断判定和逐项依据。 +- `representation_diagnostics.csv`、`shared_correspondence_summary.csv`、`modality_probe_summary.csv`、`reconstruction_summary.csv`：结构诊断明细。 +- `architecture.png` 及其余七张 `.png`：实验结构、对应、重构、probe 与维度控制图。 + +## 解释范围 + +当前只有一个表示学习 seed，样本为 100 条 clips / 37 个来源视频；bootstrap 区间反映来源视频抽样不确定性，不含 seed 不确定性。same-slot 检索依赖 M4 槽位约定，并非人工事件级对齐真值。分类与回归来自独立轻量 probe。 + +## 下一步 + +暂不升级现有 TSFA 为正式 TSFA-SPR。最值得先做的是： (1) 检查同槽对比目标是否能在独立视频内建立可打乱验证的 shared correspondence；(2) 若共享诊断改善，再运行 seed 3407 和 2026，确认模态来源与情感 probe 差异是否稳定。 diff --git 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0000000..80ba6fb Binary files /dev/null and b/deep_learning/Q1/outputs/tsfa_shared_private/checkpoints/fold_05/raw_private_pca.npz differ diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/config.json b/deep_learning/Q1/outputs/tsfa_shared_private/config.json new file mode 100644 index 0000000..61a7409 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/config.json @@ -0,0 +1,80 @@ +{ + "experiment": "TSFA-SPR: Temporal-Shared-Private Representation", + "seed": 42, + "folds": 5, + "grouped_by": "video_id/group_id", + "grid_size": 50, + "feature_dimensions": { + "text": 768, + "audio": 25, + "vision": 192 + }, + "common_dim": 128, + "shared_dim": 64, + "private_dim": 64, + "batch_size": 8, + "epochs": 40, + "learning_rate": 0.001, + "temperature": 0.1, + "orthogonality_weight": 0.1, + "reconstruction_weight": 1.0, + "contrastive_pairs": [ + [ + "text", + "audio" + ], + [ + "text", + "vision" + ], + [ + "audio", + "vision" + ] + ], + "hard_negative_offsets": [ + -5, + -3, + -2, + 2, + 3, + 5 + ], + "loss_variants": { + "SP-noOrth-noRec": [ + true, + false, + false + ], + "SP+Orth": [ + true, + true, + false + ], + "SPR": [ + true, + true, + true + ], + "SPR-noSharedContrastive": [ + false, + true, + true + ] + }, + "emotion_probe": { + "classification": "StandardScaler + LogisticRegression(C=0.05, max_iter=5000)", + "regression": "StandardScaler + Ridge(alpha=25), clipped to [-3, 3]", + "temporal_pooling": "50 slots to five consecutive 10-slot mean bins", + "all_scalers_fit_on_training_fold_only": true + }, + "dimension_controls": { + "SPR_main": "unfused shared 3x64 plus private 3x64 = 384 per slot; 1920 after five-bin pooling", + "SPR_dim_matched": "mean fused shared 64 plus all private 3x64 = 256 per slot; 1280 after pooling", + "RawPrivate_PCA": "train-fold PCA from raw private 985-d slot vectors to 256 per slot; 1280 after pooling if 256 components" + }, + "emotion_labels_used_in_factorizer_training": false, + "frozen_m4_temporal_branch": true, + "explicit_time_code_or_slot_index_in_shared_encoder": false, + "bootstrap_repeats": 2000 +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/decomposition_assessment.json b/deep_learning/Q1/outputs/tsfa_shared_private/decomposition_assessment.json new file mode 100644 index 0000000..39ac1fb --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/decomposition_assessment.json @@ -0,0 +1,53 @@ +{ + "shared_private_factorization_supported": false, + "criteria": { + "private_source_probe_exceeds_shared_and_untrained_private_control_by_at_least_0_05": false, + "each_shared_pair_auc_at_least_0_60_and_shuffle_drop_at_least_0_05": false, + "both_stream_reconstruction_beats_each_single_stream": true + }, + "spr_shared_source_probe_accuracy": 0.7289333333333333, + "spr_private_source_probe_accuracy": 0.9994666666666667, + "untrained_private_control_source_probe_accuracy": 1.0, + "spr_private_probe_gain_over_untrained_control": -0.0005333333333332746, + "private_minus_shared_source_probe_accuracy": 0.2705333333333334, + "shared_pair_checks": [ + { + "pair": "audio-vision", + "matched_vs_shifted_auc": 0.49831304927376363, + "slot_shuffle_auc": 0.4955911010755653, + "auc_drop_after_shuffle": 0.0027219481981983207, + "shared_correspondence_supported": false + }, + { + "pair": "text-audio", + "matched_vs_shifted_auc": 0.5115870503156217, + "slot_shuffle_auc": 0.49898146626218043, + "auc_drop_after_shuffle": 0.012605584053441254, + "shared_correspondence_supported": false + }, + { + "pair": "text-vision", + "matched_vs_shifted_auc": 0.49935402494331077, + "slot_shuffle_auc": 0.4976403269596126, + "auc_drop_after_shuffle": 0.0017136979836981925, + "shared_correspondence_supported": false + } + ], + "reconstruction_mse_by_branch": { + "shared_only": 0.563333765342251, + "private_only": 0.4421985727274131, + "both": 0.4220905622318305 + }, + "normalized_cross_covariance_by_modality": { + "text": 0.5997944387688591, + "audio": 0.581767881730183, + "vision": 0.7318022905545436 + }, + "shared_norm_ratio_by_modality": { + "text": 0.2463289037977494, + "audio": 0.28401892939803025, + "vision": 0.21813397738274684 + }, + "interpretation": "The shared/private branches are distinguishable by source classification, but the no-private-loss control already identifies modality perfectly, so this is not evidence that training learned private semantic content. The shared stream also does not establish robust same-slot cross-modal correspondence; do not claim successful shared/private semantic disentanglement.", + "criteria_note": "These are operational descriptive checks for this small first pass, not inferential significance thresholds." +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control.png b/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control.png new file mode 100644 index 0000000..c904235 Binary files /dev/null and b/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control.png differ diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control_summary.csv b/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control_summary.csv new file mode 100644 index 0000000..4e39866 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/dimension_control_summary.csv @@ -0,0 +1,6 @@ +sample_count,feature_dimension,accuracy,macro_f1,macro_f1_fold_mean,macro_f1_fold_sd,mae,pearson,method,view,per_slot_dimension,dimension_control_note +100,1920,0.54,0.4310819293870141,0.36259462759462757,0.09089510359034791,0.8202796086668969,0.10443752941446603,TSFA-old,all_modalities,384,existing baseline; native dimension +100,6845,0.59,0.3900927013970492,0.35717374324270873,0.11754258899949505,0.5715452416241169,0.3678028818592164,TSFA+RawPrivate,all_modalities,1369,existing baseline; native dimension +100,1920,0.56,0.38857214680744095,0.3409979462853026,0.09369875802146937,0.6967655114829541,0.07067944370969742,SPR,shared_unfused+private_all,384,existing baseline; native dimension +100,1280,0.55,0.31766680893441457,0.3153708732731721,0.08435043538646683,0.7007618999481201,0.059041191015689186,SPR-dim-matched,SPR-dim-matched,256,matched learned fused-shared + all-private +100,1280,0.56,0.2974099099099099,0.28073240535039046,0.06810513535887727,0.5592040391266346,0.25147190008110387,RawPrivate-PCA,RawPrivate-PCA,256,training-fold PCA on raw private slots diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_metrics.csv b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_metrics.csv new file mode 100644 index 0000000..7d38146 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_metrics.csv @@ -0,0 +1,59 @@ +sample_count,feature_dimension,accuracy,macro_f1,macro_f1_fold_mean,macro_f1_fold_sd,mae,pearson,method,view +100,-1,0.6,0.3612633181126332,0.33701813174429807,0.09810807621270928,0.5642678308486938,0.34585652345724877,B0,all_modalities +100,-1,0.6,0.4222603610475464,0.3976841268630668,0.08882960164587973,0.59276591360569,0.19864510121331094,B4,all_modalities +100,1280,0.56,0.2974099099099099,0.28073240535039046,0.06810513535887727,0.5592040391266346,0.25147190008110387,RawPrivate-PCA,RawPrivate-PCA 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+100,1280,0.53,0.3552318295739348,0.3196463817153472,0.11403494890873508,0.7039517660439014,0.19253711055547493,SPR-noSharedContrastive,shared_fused+private_all +100,640,0.58,0.46056910569105697,0.4478451982799809,0.07516714091153132,0.6719031555950642,0.20549881784826418,SPR-noSharedContrastive,shared_fused+private_audio +100,960,0.56,0.44472774236553764,0.4003972729779182,0.13813679316428398,0.6796640332043171,0.14957047435603935,SPR-noSharedContrastive,shared_fused+private_audio_vision +100,640,0.5,0.3492059313854523,0.3103976235945546,0.08553012162856155,0.7241699741780758,0.1346325925641368,SPR-noSharedContrastive,shared_fused+private_text +100,640,0.5,0.35200000000000004,0.3478187403993856,0.14980477007424306,0.6062408247590065,0.2557587727509002,SPR-noSharedContrastive,shared_fused+private_vision +100,320,0.46,0.2743145743145743,0.24473777538738611,0.05569451185470404,0.613256411999464,0.2126650075125743,SPR-noSharedContrastive,shared_only_fused +100,960,0.56,0.44745854190659723,0.4045862381346252,0.1650850484873889,0.6682626706361771,0.26189520163985924,SPR-noSharedContrastive,shared_only_unfused +100,1920,0.54,0.34673721340388014,0.32638729568480823,0.0995576357790951,0.6695841646194458,0.24840306190687497,SPR-noSharedContrastive,shared_unfused+private_all +100,6845,0.59,0.3900927013970492,0.35717374324270873,0.11754258899949505,0.5715452416241169,0.3678028818592164,TSFA+RawPrivate,all_modalities +100,1920,0.54,0.4310819293870141,0.36259462759462757,0.09089510359034791,0.8202796086668969,0.10443752941446603,TSFA-old,all_modalities diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_predictions.csv b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_predictions.csv new file mode 100644 index 0000000..25b2040 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_probe_predictions.csv @@ -0,0 +1,5801 @@ +method,view,fold,sample_id,video_id,true_class_id,true_class,predicted_class_id,predicted_class,true_label,predicted_label,predicted_label_unclipped,feature_dimension +TSFA-old,all_modalities,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.6251015067100525,-0.6251015067100525,1920 +TSFA-old,all_modalities,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.08468341827392578,0.08468341827392578,1920 +TSFA-old,all_modalities,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.25343722105026245,0.25343722105026245,1920 +TSFA-old,all_modalities,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,2,Positive,0.6666666865348816,0.1095770001411438,0.1095770001411438,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,0,Negative,1.6666666269302368,-0.19180142879486084,-0.19180142879486084,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,-0.1482873558998108,-0.1482873558998108,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,0.41235995292663574,0.41235995292663574,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.22104409337043762,0.22104409337043762,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,0,Negative,0.3333333432674408,-0.4590145945549011,-0.4590145945549011,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.03902813792228699,0.03902813792228699,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/6,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.9927929043769836,0.9927929043769836,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/9,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.18242229521274567,0.18242229521274567,1920 +TSFA-old,all_modalities,1,-egA8-b7-3M/20,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.2958115041255951,0.2958115041255951,1920 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+TSFA-old,all_modalities,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,1,Neutral,2.0,-0.17535021901130676,-0.17535021901130676,1920 +TSFA-old,all_modalities,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,2,Positive,1.3333333730697632,-0.21261069178581238,-0.21261069178581238,1920 +TSFA-old,all_modalities,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,0,Negative,2.6666667461395264,-0.4265860617160797,-0.4265860617160797,1920 +TSFA-old,all_modalities,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,1,Neutral,1.6666666269302368,0.3585001230239868,0.3585001230239868,1920 +TSFA-old,all_modalities,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,1,Neutral,2.6666667461395264,0.8665460348129272,0.8665460348129272,1920 +TSFA-old,all_modalities,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,0,Negative,0.3333333432674408,-0.2707330286502838,-0.2707330286502838,1920 +TSFA-old,all_modalities,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,0,Negative,-0.3333333432674408,-0.906612753868103,-0.906612753868103,1920 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+SPR-dim-matched,SPR-dim-matched,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.4990502595901489,-0.4990502595901489,1280 +SPR-dim-matched,SPR-dim-matched,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,2,Positive,0.0,-0.6186553835868835,-0.6186553835868835,1280 +SPR-dim-matched,SPR-dim-matched,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,-0.549686849117279,-0.549686849117279,1280 +SPR-dim-matched,SPR-dim-matched,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,2,Positive,0.6666666865348816,0.10369500517845154,0.10369500517845154,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,2,Positive,1.6666666269302368,0.3142598271369934,0.3142598271369934,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.49849992990493774,0.49849992990493774,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,-0.41323167085647583,-0.41323167085647583,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.6981284618377686,0.6981284618377686,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.0917583703994751,0.0917583703994751,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,-0.23190706968307495,-0.23190706968307495,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/6,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.29654258489608765,0.29654258489608765,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/9,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.9441797137260437,0.9441797137260437,1280 +SPR-dim-matched,SPR-dim-matched,1,-egA8-b7-3M/20,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.8557280898094177,0.8557280898094177,1280 +SPR-dim-matched,SPR-dim-matched,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,Negative,1,Neutral,-0.6666666865348816,0.2380986213684082,0.2380986213684082,1280 +SPR-dim-matched,SPR-dim-matched,1,-I_e4mIh0yE/3,-I_e4mIh0yE,2,Positive,1,Neutral,0.6666666865348816,-0.022772520780563354,-0.022772520780563354,1280 +SPR-dim-matched,SPR-dim-matched,1,-qDkUB0GgYY/6,-qDkUB0GgYY,2,Positive,2,Positive,1.3333333730697632,1.866504192352295,1.866504192352295,1280 +SPR-dim-matched,SPR-dim-matched,1,-9y-fZ3swSY/0,-9y-fZ3swSY,2,Positive,2,Positive,1.0,0.029383301734924316,0.029383301734924316,1280 +SPR-dim-matched,SPR-dim-matched,1,-9y-fZ3swSY/4,-9y-fZ3swSY,1,Neutral,2,Positive,0.0,0.30346354842185974,0.30346354842185974,1280 +SPR-dim-matched,SPR-dim-matched,1,-9y-fZ3swSY/8,-9y-fZ3swSY,2,Positive,2,Positive,1.6666666269302368,0.24260646104812622,0.24260646104812622,1280 +SPR-dim-matched,SPR-dim-matched,1,-UUCSKoHeMA/0,-UUCSKoHeMA,1,Neutral,2,Positive,0.0,-0.26140040159225464,-0.26140040159225464,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.39496511220932007,-0.39496511220932007,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.18791574239730835,0.18791574239730835,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.36073580384254456,0.36073580384254456,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,2,Positive,0.6666666865348816,0.09231799840927124,0.09231799840927124,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,2,Positive,1.6666666269302368,0.6105647683143616,0.6105647683143616,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.21931451559066772,0.21931451559066772,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,0.2837773561477661,0.2837773561477661,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.5871979594230652,0.5871979594230652,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.151081383228302,0.151081383228302,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.08635206520557404,0.08635206520557404,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/6,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.32667097449302673,0.32667097449302673,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/9,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.6113671064376831,0.6113671064376831,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-egA8-b7-3M/20,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.34250298142433167,0.34250298142433167,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,Negative,2,Positive,-0.6666666865348816,0.38043296337127686,0.38043296337127686,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-I_e4mIh0yE/3,-I_e4mIh0yE,2,Positive,2,Positive,0.6666666865348816,0.38613441586494446,0.38613441586494446,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-qDkUB0GgYY/6,-qDkUB0GgYY,2,Positive,2,Positive,1.3333333730697632,0.851084291934967,0.851084291934967,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-9y-fZ3swSY/0,-9y-fZ3swSY,2,Positive,2,Positive,1.0,0.7685738205909729,0.7685738205909729,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-9y-fZ3swSY/4,-9y-fZ3swSY,1,Neutral,2,Positive,0.0,0.44231536984443665,0.44231536984443665,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-9y-fZ3swSY/8,-9y-fZ3swSY,2,Positive,2,Positive,1.6666666269302368,0.27837884426116943,0.27837884426116943,1280 +RawPrivate-PCA,RawPrivate-PCA,1,-UUCSKoHeMA/0,-UUCSKoHeMA,1,Neutral,2,Positive,0.0,0.4604625105857849,0.4604625105857849,1280 +SPR-noSharedContrastive,shared_only_fused,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,0,Negative,0.6666666865348816,-0.3038814961910248,-0.3038814961910248,320 +SPR-noSharedContrastive,shared_only_fused,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,0,Negative,0.0,0.06224510073661804,0.06224510073661804,320 +SPR-noSharedContrastive,shared_only_fused,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.5303127765655518,0.5303127765655518,320 +SPR-noSharedContrastive,shared_only_fused,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,0.3297780454158783,0.3297780454158783,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,2,Positive,1.6666666269302368,0.35065758228302,0.35065758228302,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.7599910497665405,0.7599910497665405,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,0.23729757964611053,0.23729757964611053,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.3909575641155243,0.3909575641155243,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.32177162170410156,0.32177162170410156,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.24883189797401428,0.24883189797401428,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/6,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.21094948053359985,0.21094948053359985,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/9,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.29422080516815186,0.29422080516815186,320 +SPR-noSharedContrastive,shared_only_fused,1,-egA8-b7-3M/20,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.6609466671943665,0.6609466671943665,320 +SPR-noSharedContrastive,shared_only_fused,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,Negative,1,Neutral,-0.6666666865348816,0.3362843692302704,0.3362843692302704,320 +SPR-noSharedContrastive,shared_only_fused,1,-I_e4mIh0yE/3,-I_e4mIh0yE,2,Positive,2,Positive,0.6666666865348816,0.5136008858680725,0.5136008858680725,320 +SPR-noSharedContrastive,shared_only_fused,1,-qDkUB0GgYY/6,-qDkUB0GgYY,2,Positive,2,Positive,1.3333333730697632,0.994290828704834,0.994290828704834,320 +SPR-noSharedContrastive,shared_only_fused,1,-9y-fZ3swSY/0,-9y-fZ3swSY,2,Positive,2,Positive,1.0,1.2557041645050049,1.2557041645050049,320 +SPR-noSharedContrastive,shared_only_fused,1,-9y-fZ3swSY/4,-9y-fZ3swSY,1,Neutral,2,Positive,0.0,0.3351508677005768,0.3351508677005768,320 +SPR-noSharedContrastive,shared_only_fused,1,-9y-fZ3swSY/8,-9y-fZ3swSY,2,Positive,2,Positive,1.6666666269302368,0.7259576320648193,0.7259576320648193,320 +SPR-noSharedContrastive,shared_only_fused,1,-UUCSKoHeMA/0,-UUCSKoHeMA,1,Neutral,2,Positive,0.0,0.6755824089050293,0.6755824089050293,320 +SPR-noSharedContrastive,shared_only_unfused,1,-3g5yACwYnA/13,-3g5yACwYnA,2,Positive,1,Neutral,0.6666666865348816,-0.35477304458618164,-0.35477304458618164,960 +SPR-noSharedContrastive,shared_only_unfused,1,-3g5yACwYnA/3,-3g5yACwYnA,1,Neutral,1,Neutral,0.0,-0.2791874408721924,-0.2791874408721924,960 +SPR-noSharedContrastive,shared_only_unfused,1,-3g5yACwYnA/2,-3g5yACwYnA,1,Neutral,2,Positive,0.0,0.491781622171402,0.491781622171402,960 +SPR-noSharedContrastive,shared_only_unfused,1,-3g5yACwYnA/9,-3g5yACwYnA,2,Positive,2,Positive,0.6666666865348816,0.7947636246681213,0.7947636246681213,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/26,-egA8-b7-3M,2,Positive,2,Positive,1.6666666269302368,0.23372283577919006,0.23372283577919006,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/17,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.6452361941337585,0.6452361941337585,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/18,-egA8-b7-3M,2,Positive,2,Positive,1.0,-0.29681509733200073,-0.29681509733200073,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/16,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.6149170398712158,0.6149170398712158,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/13,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,-0.08112004399299622,-0.08112004399299622,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/1,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.7745252847671509,0.7745252847671509,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/6,-egA8-b7-3M,2,Positive,2,Positive,0.6666666865348816,0.30782777070999146,0.30782777070999146,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/9,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.026425540447235107,0.026425540447235107,960 +SPR-noSharedContrastive,shared_only_unfused,1,-egA8-b7-3M/20,-egA8-b7-3M,2,Positive,2,Positive,0.3333333432674408,0.9355459809303284,0.9355459809303284,960 +SPR-noSharedContrastive,shared_only_unfused,1,-I_e4mIh0yE/1,-I_e4mIh0yE,0,Negative,2,Positive,-0.6666666865348816,0.9418631196022034,0.9418631196022034,960 +SPR-noSharedContrastive,shared_only_unfused,1,-I_e4mIh0yE/3,-I_e4mIh0yE,2,Positive,2,Positive,0.6666666865348816,1.4698185920715332,1.4698185920715332,960 +SPR-noSharedContrastive,shared_only_unfused,1,-qDkUB0GgYY/6,-qDkUB0GgYY,2,Positive,2,Positive,1.3333333730697632,1.4821016788482666,1.4821016788482666,960 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+SPR-noSharedContrastive,shared_only_fused,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,2,Positive,2.0,0.12729871273040771,0.12729871273040771,320 +SPR-noSharedContrastive,shared_only_fused,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,2,Positive,1.3333333730697632,0.42475032806396484,0.42475032806396484,320 +SPR-noSharedContrastive,shared_only_fused,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,2,Positive,2.6666667461395264,0.32093048095703125,0.32093048095703125,320 +SPR-noSharedContrastive,shared_only_fused,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,2,Positive,1.6666666269302368,0.0058441162109375,0.0058441162109375,320 +SPR-noSharedContrastive,shared_only_fused,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,0.5703631639480591,0.5703631639480591,320 +SPR-noSharedContrastive,shared_only_fused,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,2,Positive,0.3333333432674408,0.4010712504386902,0.4010712504386902,320 +SPR-noSharedContrastive,shared_only_fused,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,2,Positive,-0.3333333432674408,0.17928612232208252,0.17928612232208252,320 +SPR-noSharedContrastive,shared_only_fused,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,1,Neutral,-0.6666666865348816,-0.8113563656806946,-0.8113563656806946,320 +SPR-noSharedContrastive,shared_only_fused,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,1,Neutral,-0.3333333432674408,-0.41554832458496094,-0.41554832458496094,320 +SPR-noSharedContrastive,shared_only_fused,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,2,Positive,0.0,0.020240068435668945,0.020240068435668945,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,0.37983036041259766,0.37983036041259766,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,0.5156155824661255,0.5156155824661255,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,2,Positive,-1.3333333730697632,0.5450612902641296,0.5450612902641296,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,2,Positive,-0.3333333432674408,0.3582581877708435,0.3582581877708435,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.6847739219665527,0.6847739219665527,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,2,Positive,0.0,0.5884393453598022,0.5884393453598022,320 +SPR-noSharedContrastive,shared_only_fused,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,2,Positive,0.6666666865348816,0.14496789872646332,0.14496789872646332,320 +SPR-noSharedContrastive,shared_only_fused,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,2,Positive,0.0,0.02904266119003296,0.02904266119003296,320 +SPR-noSharedContrastive,shared_only_fused,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,1,Neutral,0.3333333432674408,-0.1420692503452301,-0.1420692503452301,320 +SPR-noSharedContrastive,shared_only_fused,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.21039168536663055,0.21039168536663055,320 +SPR-noSharedContrastive,shared_only_unfused,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,2,Positive,2.0,0.03133934736251831,0.03133934736251831,960 +SPR-noSharedContrastive,shared_only_unfused,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,2,Positive,1.3333333730697632,0.4328785836696625,0.4328785836696625,960 +SPR-noSharedContrastive,shared_only_unfused,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,2,Positive,2.6666667461395264,0.45056721568107605,0.45056721568107605,960 +SPR-noSharedContrastive,shared_only_unfused,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,2,Positive,1.6666666269302368,0.12366986274719238,0.12366986274719238,960 +SPR-noSharedContrastive,shared_only_unfused,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,1.1565308570861816,1.1565308570861816,960 +SPR-noSharedContrastive,shared_only_unfused,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,2,Positive,0.3333333432674408,0.26695647835731506,0.26695647835731506,960 +SPR-noSharedContrastive,shared_only_unfused,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,2,Positive,-0.3333333432674408,-0.18945303559303284,-0.18945303559303284,960 +SPR-noSharedContrastive,shared_only_unfused,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,0,Negative,-0.6666666865348816,-0.49930551648139954,-0.49930551648139954,960 +SPR-noSharedContrastive,shared_only_unfused,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,0,Negative,-0.3333333432674408,-0.10940960049629211,-0.10940960049629211,960 +SPR-noSharedContrastive,shared_only_unfused,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,1,Neutral,0.0,0.09995517134666443,0.09995517134666443,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,-0.36439839005470276,-0.36439839005470276,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,-0.5732790231704712,-0.5732790231704712,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,2,Positive,-1.3333333730697632,-0.5151796340942383,-0.5151796340942383,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,2,Positive,-0.3333333432674408,-0.18760398030281067,-0.18760398030281067,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.46686652302742004,0.46686652302742004,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,2,Positive,0.0,-0.08265718817710876,-0.08265718817710876,960 +SPR-noSharedContrastive,shared_only_unfused,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,2,Positive,0.6666666865348816,-0.6562627553939819,-0.6562627553939819,960 +SPR-noSharedContrastive,shared_only_unfused,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,1,Neutral,0.0,-0.7107521295547485,-0.7107521295547485,960 +SPR-noSharedContrastive,shared_only_unfused,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,1,Neutral,0.3333333432674408,-0.46713486313819885,-0.46713486313819885,960 +SPR-noSharedContrastive,shared_only_unfused,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.3945501446723938,0.3945501446723938,960 +SPR-noSharedContrastive,private_text,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,2,Positive,2.0,0.18230374157428741,0.18230374157428741,320 +SPR-noSharedContrastive,private_text,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,1,Neutral,1.3333333730697632,0.3850666284561157,0.3850666284561157,320 +SPR-noSharedContrastive,private_text,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,2,Positive,2.6666667461395264,0.8443142175674438,0.8443142175674438,320 +SPR-noSharedContrastive,private_text,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,2,Positive,1.6666666269302368,-1.2907238006591797,-1.2907238006591797,320 +SPR-noSharedContrastive,private_text,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,1,Neutral,2.6666667461395264,0.2350085973739624,0.2350085973739624,320 +SPR-noSharedContrastive,private_text,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,2,Positive,0.3333333432674408,0.5335230827331543,0.5335230827331543,320 +SPR-noSharedContrastive,private_text,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,1,Neutral,-0.3333333432674408,-0.16178202629089355,-0.16178202629089355,320 +SPR-noSharedContrastive,private_text,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,2,Positive,-0.6666666865348816,0.13981617987155914,0.13981617987155914,320 +SPR-noSharedContrastive,private_text,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,1,Neutral,-0.3333333432674408,-0.16864871978759766,-0.16864871978759766,320 +SPR-noSharedContrastive,private_text,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,2,Positive,0.0,0.05010196566581726,0.05010196566581726,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,-0.2863784730434418,-0.2863784730434418,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,0,Negative,0.3333333432674408,-0.47485843300819397,-0.47485843300819397,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,0,Negative,-1.3333333730697632,-0.08586594462394714,-0.08586594462394714,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,0,Negative,-0.3333333432674408,-0.4586038887500763,-0.4586038887500763,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.8366267681121826,0.8366267681121826,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,2,Positive,0.0,1.2169378995895386,1.2169378995895386,320 +SPR-noSharedContrastive,private_text,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,2,Positive,0.6666666865348816,-0.21257516741752625,-0.21257516741752625,320 +SPR-noSharedContrastive,private_text,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,1,Neutral,0.0,-1.3135462999343872,-1.3135462999343872,320 +SPR-noSharedContrastive,private_text,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,2,Positive,0.3333333432674408,0.9615280628204346,0.9615280628204346,320 +SPR-noSharedContrastive,private_text,2,-HeZS2-Prhc/2,-HeZS2-Prhc,2,Positive,2,Positive,0.6666666865348816,0.16150301694869995,0.16150301694869995,320 +SPR-noSharedContrastive,private_audio,2,-THoVjtIkeU/12,-THoVjtIkeU,2,Positive,2,Positive,2.0,0.29661667346954346,0.29661667346954346,320 +SPR-noSharedContrastive,private_audio,2,-THoVjtIkeU/2,-THoVjtIkeU,2,Positive,1,Neutral,1.3333333730697632,-0.18930968642234802,-0.18930968642234802,320 +SPR-noSharedContrastive,private_audio,2,-THoVjtIkeU/6,-THoVjtIkeU,2,Positive,1,Neutral,2.6666667461395264,0.2453993260860443,0.2453993260860443,320 +SPR-noSharedContrastive,private_audio,2,-UuX1xuaiiE/1,-UuX1xuaiiE,2,Positive,1,Neutral,1.6666666269302368,-0.0855010449886322,-0.0855010449886322,320 +SPR-noSharedContrastive,private_audio,2,-UuX1xuaiiE/0,-UuX1xuaiiE,2,Positive,2,Positive,2.6666667461395264,0.27674609422683716,0.27674609422683716,320 +SPR-noSharedContrastive,private_audio,2,-UuX1xuaiiE/3,-UuX1xuaiiE,2,Positive,1,Neutral,0.3333333432674408,0.04664209485054016,0.04664209485054016,320 +SPR-noSharedContrastive,private_audio,2,-UuX1xuaiiE/6,-UuX1xuaiiE,0,Negative,0,Negative,-0.3333333432674408,-0.12264499068260193,-0.12264499068260193,320 +SPR-noSharedContrastive,private_audio,2,-lzEya4AM_4/5,-lzEya4AM_4,0,Negative,2,Positive,-0.6666666865348816,-0.04846161603927612,-0.04846161603927612,320 +SPR-noSharedContrastive,private_audio,2,-lzEya4AM_4/6,-lzEya4AM_4,0,Negative,2,Positive,-0.3333333432674408,0.5371660590171814,0.5371660590171814,320 +SPR-noSharedContrastive,private_audio,2,-mqbVkbCndg/0,-mqbVkbCndg,1,Neutral,1,Neutral,0.0,-0.4201168119907379,-0.4201168119907379,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/1,-wny0OAz3g8,2,Positive,1,Neutral,0.3333333432674408,0.5874484777450562,0.5874484777450562,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/0,-wny0OAz3g8,2,Positive,2,Positive,0.3333333432674408,0.6086592078208923,0.6086592078208923,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/3,-wny0OAz3g8,0,Negative,2,Positive,-1.3333333730697632,0.44442516565322876,0.44442516565322876,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/2,-wny0OAz3g8,0,Negative,1,Neutral,-0.3333333432674408,0.3923242688179016,0.3923242688179016,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/5,-wny0OAz3g8,2,Positive,2,Positive,1.0,0.040277183055877686,0.040277183055877686,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/7,-wny0OAz3g8,1,Neutral,1,Neutral,0.0,0.14544835686683655,0.14544835686683655,320 +SPR-noSharedContrastive,private_audio,2,-wny0OAz3g8/9,-wny0OAz3g8,2,Positive,2,Positive,0.6666666865348816,-0.023561716079711914,-0.023561716079711914,320 +SPR-noSharedContrastive,private_audio,2,-571d8cVauQ/0,-571d8cVauQ,1,Neutral,2,Positive,0.0,0.1252623200416565,0.1252623200416565,320 +SPR-noSharedContrastive,private_audio,2,-571d8cVauQ/5,-571d8cVauQ,2,Positive,2,Positive,0.3333333432674408,0.27137869596481323,0.27137869596481323,320 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+SP-noOrth-noRec,shared_only_unfused,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,0,Negative,1.0,0.1110960841178894,0.1110960841178894,960 +SP-noOrth-noRec,shared_only_unfused,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.2044837772846222,0.2044837772846222,960 +SP-noOrth-noRec,shared_only_unfused,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,1,Neutral,1.3333333730697632,-0.4501652121543884,-0.4501652121543884,960 +SP-noOrth-noRec,private_text,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,0,Negative,0.0,-0.6351781487464905,-0.6351781487464905,320 +SP-noOrth-noRec,private_text,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,0,Negative,-1.0,-0.19943255186080933,-0.19943255186080933,320 +SP-noOrth-noRec,private_text,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.9891096949577332,0.9891096949577332,320 +SP-noOrth-noRec,private_text,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,1,Neutral,-0.3333333432674408,0.21216237545013428,0.21216237545013428,320 +SP-noOrth-noRec,private_text,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.6412523984909058,0.6412523984909058,320 +SP-noOrth-noRec,private_text,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.7548856735229492,0.7548856735229492,320 +SP-noOrth-noRec,private_text,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,0,Negative,0.3333333432674408,-0.2718258500099182,-0.2718258500099182,320 +SP-noOrth-noRec,private_text,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,1.0485246181488037,1.0485246181488037,320 +SP-noOrth-noRec,private_text,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.7312972545623779,0.7312972545623779,320 +SP-noOrth-noRec,private_text,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.6869311332702637,0.6869311332702637,320 +SP-noOrth-noRec,private_text,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,-0.2036581039428711,-0.2036581039428711,320 +SP-noOrth-noRec,private_text,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.7456804513931274,0.7456804513931274,320 +SP-noOrth-noRec,private_text,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.1129364967346191,1.1129364967346191,320 +SP-noOrth-noRec,private_text,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.7532820701599121,0.7532820701599121,320 +SP-noOrth-noRec,private_text,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.4264209270477295,0.4264209270477295,320 +SP-noOrth-noRec,private_text,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.35016965866088867,0.35016965866088867,320 +SP-noOrth-noRec,private_text,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.9186205267906189,0.9186205267906189,320 +SP-noOrth-noRec,private_text,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.8887730836868286,0.8887730836868286,320 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+SPR-dim-matched,SPR-dim-matched,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,0.5222343802452087,0.5222343802452087,1280 +SPR-dim-matched,SPR-dim-matched,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.7529511451721191,0.7529511451721191,1280 +SPR-dim-matched,SPR-dim-matched,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,1.2067720890045166,1.2067720890045166,1280 +SPR-dim-matched,SPR-dim-matched,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,1,Neutral,0.6666666865348816,0.6332323551177979,0.6332323551177979,1280 +SPR-dim-matched,SPR-dim-matched,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.33134186267852783,0.33134186267852783,1280 +SPR-dim-matched,SPR-dim-matched,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.29921287298202515,0.29921287298202515,1280 +SPR-dim-matched,SPR-dim-matched,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.22955021262168884,0.22955021262168884,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.5599125623703003,0.5599125623703003,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.5154906511306763,0.5154906511306763,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.4799790382385254,0.4799790382385254,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.6305359601974487,0.6305359601974487,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.7401822805404663,0.7401822805404663,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,1.0246288776397705,1.0246288776397705,1280 +SPR-dim-matched,SPR-dim-matched,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,1.0950536727905273,1.0950536727905273,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.2493920475244522,0.2493920475244522,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,1,Neutral,-1.0,-0.25638169050216675,-0.25638169050216675,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.29011255502700806,0.29011255502700806,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,-0.07723262906074524,-0.07723262906074524,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.3464440703392029,0.3464440703392029,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.6875934600830078,0.6875934600830078,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,2,Positive,0.3333333432674408,-0.012514352798461914,-0.012514352798461914,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.14919736981391907,0.14919736981391907,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.5156264901161194,0.5156264901161194,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.46425896883010864,0.46425896883010864,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.8978203535079956,0.8978203535079956,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.0468683242797852,1.0468683242797852,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.24844884872436523,0.24844884872436523,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.5410467982292175,0.5410467982292175,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.29694467782974243,0.29694467782974243,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.5088579654693604,0.5088579654693604,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.6061435341835022,0.6061435341835022,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.37357640266418457,0.37357640266418457,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.057118743658065796,0.057118743658065796,1280 +RawPrivate-PCA,RawPrivate-PCA,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.34743475914001465,0.34743475914001465,1280 +SPR-noSharedContrastive,shared_only_fused,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,-0.24523550271987915,-0.24523550271987915,320 +SPR-noSharedContrastive,shared_only_fused,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,2,Positive,-1.0,-0.4971429705619812,-0.4971429705619812,320 +SPR-noSharedContrastive,shared_only_fused,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.3730120062828064,0.3730120062828064,320 +SPR-noSharedContrastive,shared_only_fused,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,1,Neutral,-0.3333333432674408,0.35194090008735657,0.35194090008735657,320 +SPR-noSharedContrastive,shared_only_fused,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.6209708452224731,0.6209708452224731,320 +SPR-noSharedContrastive,shared_only_fused,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,0.7271728515625,0.7271728515625,320 +SPR-noSharedContrastive,shared_only_fused,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,1,Neutral,0.3333333432674408,-0.2632043957710266,-0.2632043957710266,320 +SPR-noSharedContrastive,shared_only_fused,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,0.9105411171913147,0.9105411171913147,320 +SPR-noSharedContrastive,shared_only_fused,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,0.7556241750717163,0.7556241750717163,320 +SPR-noSharedContrastive,shared_only_fused,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,0.3972763419151306,0.3972763419151306,320 +SPR-noSharedContrastive,shared_only_fused,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.7687839269638062,0.7687839269638062,320 +SPR-noSharedContrastive,shared_only_fused,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.7749302387237549,0.7749302387237549,320 +SPR-noSharedContrastive,shared_only_fused,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,1.046651840209961,1.046651840209961,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.19192123413085938,0.19192123413085938,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,0,Negative,-0.3333333432674408,-0.012535244226455688,-0.012535244226455688,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.3952263593673706,0.3952263593673706,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,0,Negative,0.0,0.3742187023162842,0.3742187023162842,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,0,Negative,1.0,0.5901737213134766,0.5901737213134766,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.9772680997848511,0.9772680997848511,320 +SPR-noSharedContrastive,shared_only_fused,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,1.1758999824523926,1.1758999824523926,320 +SPR-noSharedContrastive,shared_only_unfused,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.8400846719741821,0.8400846719741821,960 +SPR-noSharedContrastive,shared_only_unfused,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,2,Positive,-1.0,-0.02180209755897522,-0.02180209755897522,960 +SPR-noSharedContrastive,shared_only_unfused,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.1052314043045044,0.1052314043045044,960 +SPR-noSharedContrastive,shared_only_unfused,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,0,Negative,-0.3333333432674408,0.47029024362564087,0.47029024362564087,960 +SPR-noSharedContrastive,shared_only_unfused,3,-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,Negative,2,Positive,-2.0,0.7364729046821594,0.7364729046821594,960 +SPR-noSharedContrastive,shared_only_unfused,3,-vxjVxOeScU/4,-vxjVxOeScU,2,Positive,2,Positive,1.0,1.134195327758789,1.134195327758789,960 +SPR-noSharedContrastive,shared_only_unfused,3,-wMB_hJL-3o/7,-wMB_hJL-3o,2,Positive,1,Neutral,0.3333333432674408,0.6136835217475891,0.6136835217475891,960 +SPR-noSharedContrastive,shared_only_unfused,3,-hnBHBN8p5A/7,-hnBHBN8p5A,2,Positive,2,Positive,0.6666666865348816,1.2985546588897705,1.2985546588897705,960 +SPR-noSharedContrastive,shared_only_unfused,3,-hnBHBN8p5A/6,-hnBHBN8p5A,2,Positive,2,Positive,1.0,1.7473397254943848,1.7473397254943848,960 +SPR-noSharedContrastive,shared_only_unfused,3,-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,Positive,2,Positive,0.6666666865348816,-0.10211765766143799,-0.10211765766143799,960 +SPR-noSharedContrastive,shared_only_unfused,3,-RfYyzHpjk4/11,-RfYyzHpjk4,2,Positive,2,Positive,2.0,0.862881064414978,0.862881064414978,960 +SPR-noSharedContrastive,shared_only_unfused,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,-0.08560344576835632,-0.08560344576835632,960 +SPR-noSharedContrastive,shared_only_unfused,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.7122728824615479,0.7122728824615479,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,0.5807820558547974,0.5807820558547974,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,2,Positive,-0.3333333432674408,0.5574802160263062,0.5574802160263062,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.49295565485954285,0.49295565485954285,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,0,Negative,0.0,0.41332918405532837,0.41332918405532837,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.4269980788230896,0.4269980788230896,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/6,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,1.1283161640167236,1.1283161640167236,960 +SPR-noSharedContrastive,shared_only_unfused,3,-s9qJ7ATP7w/8,-s9qJ7ATP7w,2,Positive,2,Positive,1.3333333730697632,0.9319621920585632,0.9319621920585632,960 +SPR-noSharedContrastive,private_text,3,-HwX2H8Z4hY/2,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,1.3415615558624268,1.3415615558624268,320 +SPR-noSharedContrastive,private_text,3,-HwX2H8Z4hY/5,-HwX2H8Z4hY,0,Negative,2,Positive,-1.0,0.3852452039718628,0.3852452039718628,320 +SPR-noSharedContrastive,private_text,3,-HwX2H8Z4hY/6,-HwX2H8Z4hY,1,Neutral,2,Positive,0.0,0.977607011795044,0.977607011795044,320 +SPR-noSharedContrastive,private_text,3,-HwX2H8Z4hY/9,-HwX2H8Z4hY,0,Negative,2,Positive,-0.3333333432674408,0.5584757924079895,0.5584757924079895,320 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+SPR-noSharedContrastive,private_text,3,-RfYyzHpjk4/8,-RfYyzHpjk4,2,Positive,2,Positive,0.3333333432674408,0.25051820278167725,0.25051820278167725,320 +SPR-noSharedContrastive,private_text,3,-RfYyzHpjk4/2,-RfYyzHpjk4,2,Positive,1,Neutral,0.3333333432674408,0.1234884262084961,0.1234884262084961,320 +SPR-noSharedContrastive,private_text,3,-s9qJ7ATP7w/1,-s9qJ7ATP7w,2,Positive,2,Positive,0.6666666865348816,1.456758975982666,1.456758975982666,320 +SPR-noSharedContrastive,private_text,3,-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,Negative,1,Neutral,-0.3333333432674408,0.5320616364479065,0.5320616364479065,320 +SPR-noSharedContrastive,private_text,3,-s9qJ7ATP7w/5,-s9qJ7ATP7w,0,Negative,2,Positive,-2.0,0.1160287857055664,0.1160287857055664,320 +SPR-noSharedContrastive,private_text,3,-s9qJ7ATP7w/4,-s9qJ7ATP7w,1,Neutral,2,Positive,0.0,0.9810795187950134,0.9810795187950134,320 +SPR-noSharedContrastive,private_text,3,-s9qJ7ATP7w/7,-s9qJ7ATP7w,2,Positive,2,Positive,1.0,0.48192718625068665,0.48192718625068665,320 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+SPR,shared_unfused+private_all,4,-tPCytz4rww/10,-tPCytz4rww,1,Neutral,2,Positive,0.0,1.3910448551177979,1.3910448551177979,1920 +SPR,shared_unfused+private_all,4,-tPCytz4rww/12,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.4811825752258301,0.4811825752258301,1920 +SPR,shared_unfused+private_all,4,-tPCytz4rww/16,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.10246676206588745,0.10246676206588745,1920 +SPR,shared_unfused+private_all,4,-tPCytz4rww/18,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.7724472880363464,0.7724472880363464,1920 +SPR,shared_unfused+private_all,4,-UacrmKiTn4/10,-UacrmKiTn4,0,Negative,2,Positive,-0.6666666865348816,1.0136538743972778,1.0136538743972778,1920 +SPR,shared_unfused+private_all,4,-UacrmKiTn4/4,-UacrmKiTn4,0,Negative,2,Positive,-1.3333333730697632,0.7620190978050232,0.7620190978050232,1920 +SPR,shared_unfused+private_all,4,-uywlfIYOS8/4,-uywlfIYOS8,2,Positive,2,Positive,0.3333333432674408,0.7723205089569092,0.7723205089569092,1920 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+SPR-dim-matched,SPR-dim-matched,4,-tPCytz4rww/12,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.4680120348930359,0.4680120348930359,1280 +SPR-dim-matched,SPR-dim-matched,4,-tPCytz4rww/16,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.34142082929611206,0.34142082929611206,1280 +SPR-dim-matched,SPR-dim-matched,4,-tPCytz4rww/18,-tPCytz4rww,1,Neutral,2,Positive,0.0,0.5514241456985474,0.5514241456985474,1280 +SPR-dim-matched,SPR-dim-matched,4,-UacrmKiTn4/10,-UacrmKiTn4,0,Negative,2,Positive,-0.6666666865348816,0.9290489554405212,0.9290489554405212,1280 +SPR-dim-matched,SPR-dim-matched,4,-UacrmKiTn4/4,-UacrmKiTn4,0,Negative,2,Positive,-1.3333333730697632,0.8620119690895081,0.8620119690895081,1280 +SPR-dim-matched,SPR-dim-matched,4,-uywlfIYOS8/4,-uywlfIYOS8,2,Positive,2,Positive,0.3333333432674408,0.7535353899002075,0.7535353899002075,1280 +SPR-dim-matched,SPR-dim-matched,4,-6rXp3zJ3kc/8,-6rXp3zJ3kc,0,Negative,2,Positive,-1.0,0.36947867274284363,0.36947867274284363,1280 +SPR-dim-matched,SPR-dim-matched,4,-ri04Z7vwnc/0,-ri04Z7vwnc,1,Neutral,0,Negative,0.0,0.32896918058395386,0.32896918058395386,1280 +SPR-dim-matched,SPR-dim-matched,4,-ri04Z7vwnc/2,-ri04Z7vwnc,0,Negative,2,Positive,-0.3333333432674408,0.46470561623573303,0.46470561623573303,1280 +SPR-dim-matched,SPR-dim-matched,4,-ri04Z7vwnc/5,-ri04Z7vwnc,2,Positive,2,Positive,0.3333333432674408,0.9589472413063049,0.9589472413063049,1280 +RawPrivate-PCA,RawPrivate-PCA,4,-aNfi7CP8vM/7,-aNfi7CP8vM,1,Neutral,2,Positive,0.0,0.04320007562637329,0.04320007562637329,1280 +RawPrivate-PCA,RawPrivate-PCA,4,-iRBcNs9oI8/3,-iRBcNs9oI8,2,Positive,2,Positive,0.6666666865348816,0.2877369225025177,0.2877369225025177,1280 +RawPrivate-PCA,RawPrivate-PCA,4,-iRBcNs9oI8/7,-iRBcNs9oI8,2,Positive,2,Positive,0.6666666865348816,0.5697544813156128,0.5697544813156128,1280 +RawPrivate-PCA,RawPrivate-PCA,4,-iRBcNs9oI8/6,-iRBcNs9oI8,2,Positive,2,Positive,0.3333333432674408,0.40472567081451416,0.40472567081451416,1280 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+SPR-noSharedContrastive,shared_only_fused,4,-6rXp3zJ3kc/8,-6rXp3zJ3kc,0,Negative,2,Positive,-1.0,0.15130725502967834,0.15130725502967834,320 +SPR-noSharedContrastive,shared_only_fused,4,-ri04Z7vwnc/0,-ri04Z7vwnc,1,Neutral,2,Positive,0.0,-0.07454651594161987,-0.07454651594161987,320 +SPR-noSharedContrastive,shared_only_fused,4,-ri04Z7vwnc/2,-ri04Z7vwnc,0,Negative,2,Positive,-0.3333333432674408,0.11743280291557312,0.11743280291557312,320 +SPR-noSharedContrastive,shared_only_fused,4,-ri04Z7vwnc/5,-ri04Z7vwnc,2,Positive,2,Positive,0.3333333432674408,0.9969527721405029,0.9969527721405029,320 +SPR-noSharedContrastive,shared_only_unfused,4,-aNfi7CP8vM/7,-aNfi7CP8vM,1,Neutral,0,Negative,0.0,-0.16839903593063354,-0.16839903593063354,960 +SPR-noSharedContrastive,shared_only_unfused,4,-iRBcNs9oI8/3,-iRBcNs9oI8,2,Positive,1,Neutral,0.6666666865348816,0.13518691062927246,0.13518691062927246,960 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+SPR-noSharedContrastive,shared_unfused+private_all,5,-yRb-Jum7EQ/5,-yRb-Jum7EQ,0,Negative,0,Negative,-0.6666666865348816,-0.19680804014205933,-0.19680804014205933,1920 +SPR-noSharedContrastive,shared_unfused+private_all,5,-yRb-Jum7EQ/6,-yRb-Jum7EQ,1,Neutral,0,Negative,0.0,-0.5306293368339539,-0.5306293368339539,1920 diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/emotion_regression_ablation.png b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_regression_ablation.png new file mode 100644 index 0000000..725de40 Binary files /dev/null and b/deep_learning/Q1/outputs/tsfa_shared_private/emotion_regression_ablation.png differ diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_by_fold.csv b/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_by_fold.csv new file mode 100644 index 0000000..fccf39a --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_by_fold.csv @@ -0,0 +1,41 @@ +variant,fold,branch,accuracy,macro_f1,sample_count +SP-noOrth-noRec,1,shared,0.664,0.6518945803926879,3000 +SP-noOrth-noRec,1,private,1.0,1.0,3000 +SP+Orth,1,shared,0.7086666666666667,0.7016037953039266,3000 +SP+Orth,1,private,1.0,1.0,3000 +SPR,1,shared,0.8296666666666667,0.8275040476381074,3000 +SPR,1,private,0.9973333333333333,0.997333290665984,3000 +SPR-noSharedContrastive,1,shared,0.8376666666666667,0.8363474654439743,3000 +SPR-noSharedContrastive,1,private,1.0,1.0,3000 +SP-noOrth-noRec,2,shared,0.7146666666666667,0.7152768064577638,3000 +SP-noOrth-noRec,2,private,1.0,1.0,3000 +SP+Orth,2,shared,0.7503333333333333,0.7546424700253386,3000 +SP+Orth,2,private,1.0,1.0,3000 +SPR,2,shared,0.7916666666666666,0.7932811620201372,3000 +SPR,2,private,1.0,1.0,3000 +SPR-noSharedContrastive,2,shared,0.8876666666666667,0.8879616277769703,3000 +SPR-noSharedContrastive,2,private,1.0,1.0,3000 +SP-noOrth-noRec,3,shared,0.713,0.7170115459589144,3000 +SP-noOrth-noRec,3,private,1.0,1.0,3000 +SP+Orth,3,shared,0.653,0.660814795562512,3000 +SP+Orth,3,private,1.0,1.0,3000 +SPR,3,shared,0.6433333333333333,0.6368012159911499,3000 +SPR,3,private,1.0,1.0,3000 +SPR-noSharedContrastive,3,shared,0.6653333333333333,0.6520248118513917,3000 +SPR-noSharedContrastive,3,private,1.0,1.0,3000 +SP-noOrth-noRec,4,shared,0.614,0.6016100519791606,3000 +SP-noOrth-noRec,4,private,1.0,1.0,3000 +SP+Orth,4,shared,0.6853333333333333,0.6850814938346542,3000 +SP+Orth,4,private,1.0,1.0,3000 +SPR,4,shared,0.7203333333333334,0.720366703199887,3000 +SPR,4,private,1.0,1.0,3000 +SPR-noSharedContrastive,4,shared,0.7396666666666667,0.7312064745589195,3000 +SPR-noSharedContrastive,4,private,1.0,1.0,3000 +SP-noOrth-noRec,5,shared,0.6986666666666667,0.7004090453388617,3000 +SP-noOrth-noRec,5,private,1.0,1.0,3000 +SP+Orth,5,shared,0.652,0.6507252992673732,3000 +SP+Orth,5,private,1.0,1.0,3000 +SPR,5,shared,0.6596666666666666,0.6650259048882328,3000 +SPR,5,private,1.0,1.0,3000 +SPR-noSharedContrastive,5,shared,0.671,0.6716840768214903,3000 +SPR-noSharedContrastive,5,private,1.0,1.0,3000 diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_summary.csv b/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_summary.csv new file mode 100644 index 0000000..693b969 --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/modality_probe_summary.csv @@ -0,0 +1,9 @@ +variant,branch,fold_count,accuracy_mean,accuracy_sd,macro_f1_mean,macro_f1_sd,fold_accuracy +SP-noOrth-noRec,shared,5,0.6808666666666666,0.042566027142154884,0.6772404060254776,0.04981881799898246,"[0.664, 0.714667, 0.713, 0.614, 0.698667]" +SP-noOrth-noRec,private,5,1.0,0.0,1.0,0.0,"[1.0, 1.0, 1.0, 1.0, 1.0]" +SP+Orth,shared,5,0.6898666666666667,0.041301466210185676,0.6905735707987609,0.04101866056357819,"[0.708667, 0.750333, 0.653, 0.685333, 0.652]" +SP+Orth,private,5,1.0,0.0,1.0,0.0,"[1.0, 1.0, 1.0, 1.0, 1.0]" +SPR,shared,5,0.7289333333333333,0.08105841515022771,0.7285958067475029,0.08139737827863638,"[0.829667, 0.791667, 0.643333, 0.720333, 0.659667]" +SPR,private,5,0.9994666666666667,0.0011925695879999054,0.9994666581331968,0.0011925886694186053,"[0.997333, 1.0, 1.0, 1.0, 1.0]" +SPR-noSharedContrastive,shared,5,0.7602666666666668,0.09953235096635109,0.7558448912905493,0.10296140013500193,"[0.837667, 0.887667, 0.665333, 0.739667, 0.671]" +SPR-noSharedContrastive,private,5,1.0,0.0,1.0,0.0,"[1.0, 1.0, 1.0, 1.0, 1.0]" diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/paired_contrasts.csv b/deep_learning/Q1/outputs/tsfa_shared_private/paired_contrasts.csv new file mode 100644 index 0000000..2cc2b5d --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/paired_contrasts.csv @@ -0,0 +1,22 @@ 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"heldout_video_count": 8, + "video_id_overlap": [], + "pca_train_slot_rows": 4000, + "raw_private_pca_components": 256 + } + ], + "variants": [ + "SP-noOrth-noRec", + "SP+Orth", + "SPR", + "SPR-noSharedContrastive" + ], + "baseline_controls": [ + "TSFA-old", + "TSFA+RawPrivate", + "B0", + "B4" + ], + "alignment_models_retrained": false, + "temporal_branch": "Frozen M4_sourceTime fold checkpoint; its attention weights pool fold-standardized original BERT/eGeMAPS/DeiT features.", + "emotion_labels_used_for_factorizer_training": false, + "feature_extractors_changed": false, + "shared_encoder": "One common MLP reused across Text, Audio, Vision; no explicit time code/slot index input.", + "private_encoders": "Three modality-specific MLPs with independent parameters.", + "loss": "L_shared + lambda_o*L_orth + lambda_r*L_rec; L_orth only within modality; per-modality mean reconstruction loss.", + "shared_correspondence_interpretation": "Same-slot vs same-video shifted-slot proxy; not independent human alignment ground truth.", + "emotion_probe_protocol": { + "classification": "StandardScaler + LogisticRegression(C=0.05, max_iter=5000)", + "regression": "StandardScaler + Ridge(alpha=25), clipped to [-3, 3]", + "temporal_pooling": "50 slots to five consecutive 10-slot mean bins", + "all_scalers_fit_on_training_fold_only": true + }, + "pca_control": "PCA fit on training-fold raw-private vectors over training video slots only; no emotion labels used.", + "paired_bootstrap": "2,000 video_id-cluster resamples; percentile 95% intervals; no multiple-comparison correction.", + "decomposition_diagnostics": { + "shared_private_factorization_supported": false, + "criteria": { + "private_source_probe_exceeds_shared_and_untrained_private_control_by_at_least_0_05": false, + "each_shared_pair_auc_at_least_0_60_and_shuffle_drop_at_least_0_05": false, + "both_stream_reconstruction_beats_each_single_stream": true + }, + "spr_shared_source_probe_accuracy": 0.7289333333333333, + 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The shared stream also does not establish robust same-slot cross-modal correspondence; do not claim successful shared/private semantic disentanglement.", + "criteria_note": "These are operational descriptive checks for this small first pass, not inferential significance thresholds.", + "shared_correspondence": [ + { + "pair": "audio-vision", + "matched_vs_shifted_auc": 0.49831304927376363, + "slot_shuffle_auc": 0.4955911010755653, + "auc_drop_after_shuffle": 0.0027219481981983207, + "shared_correspondence_supported": false + }, + { + "pair": "text-audio", + "matched_vs_shifted_auc": 0.5115870503156217, + "slot_shuffle_auc": 0.49898146626218043, + "auc_drop_after_shuffle": 0.012605584053441254, + "shared_correspondence_supported": false + }, + { + "pair": "text-vision", + "matched_vs_shifted_auc": 0.49935402494331077, + "slot_shuffle_auc": 0.4976403269596126, + "auc_drop_after_shuffle": 0.0017136979836981925, + "shared_correspondence_supported": false + } + ] + }, + "checkpoint_hashes": 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+ "reconstruction_branches.png", + "reconstruction_by_clip.csv", + "reconstruction_summary.csv", + "representation_diagnostics.csv", + "seed_summary.csv", + "shared_correspondence_by_clip.csv", + "shared_correspondence_summary.csv", + "shared_crossmodal_auc.png", + "shared_private_modality_probe.png", + "shared_private_norm_ratio.png", + "training_history.csv" + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "pytorch": "2.14.0+cu130", + "scikit_learn": "1.9.1", + "elapsed_seconds": 208.1612193584442, + "interpretation_limits": [ + "Same-slot contrastive positives use M4 latent slots as a training convention, not human event-level ground truth.", + "Frozen M4 source-time weights can carry temporal prior information into the pooled features.", + "Emotion metrics are lightweight frozen-representation probes on only 100 clips from 37 source videos.", + "Only seed 42 is run in this first pass; bootstrap intervals do not capture representation seed uncertainty.", + "RawPrivate-PCA and SPR-dim-matched outputs have equal 256 dimensions per slot and 1280 pooled dimensions." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/tsfa_shared_private/seed_summary.csv b/deep_learning/Q1/outputs/tsfa_shared_private/seed_summary.csv new file mode 100644 index 0000000..024c64c --- /dev/null +++ b/deep_learning/Q1/outputs/tsfa_shared_private/seed_summary.csv @@ -0,0 +1,6 @@ +seed,method,view,accuracy,macro_f1,mae,pearson,status +42,TSFA-old,all_modalities,0.54,0.4310819293870141,0.8202796086668969,0.10443752941446603,first_seed_only; additional seeds conditional on separation diagnostics +42,TSFA+RawPrivate,all_modalities,0.59,0.3900927013970492,0.5715452416241169,0.3678028818592164,first_seed_only; additional seeds conditional on separation diagnostics +42,SPR,shared_unfused+private_all,0.56,0.38857214680744095,0.6967655114829541,0.07067944370969742,first_seed_only; additional seeds conditional on separation diagnostics 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+from __future__ import annotations + +import argparse +import csv +import json +import shutil +from pathlib import Path +from typing import Any + +import numpy as np +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error + + +METHODS = ("B0", "B1", "B2", "B3", "B4") +METRICS = ("accuracy", "macro_f1", "mae", "pearson") + + +def _read_csv(path: Path) -> list[dict[str, str]]: + with path.open("r", encoding="utf-8-sig", newline="") as stream: + return list(csv.DictReader(stream)) + + +def _pearson(actual: np.ndarray, predicted: np.ndarray) -> float: + if np.std(actual) <= 1e-12 or np.std(predicted) <= 1e-12: + return float("nan") + return float(np.corrcoef(actual, predicted)[0, 1]) + + +def _scores( + actual_class: np.ndarray, + predicted_class: np.ndarray, + actual_value: np.ndarray, + predicted_value: np.ndarray, +) -> dict[str, float]: + return { + "accuracy": float(accuracy_score(actual_class, predicted_class)), + "macro_f1": float(f1_score( + actual_class, + predicted_class, + labels=[0, 1, 2], + average="macro", + zero_division=0, + )), + "mae": float(mean_absolute_error(actual_value, predicted_value)), + "pearson": _pearson(actual_value, predicted_value), + } + + +def run(args: argparse.Namespace) -> None: + math_rows = _read_csv(args.math_predictions) + tsfa_rows = [ + row for row in _read_csv(args.tsfa_predictions) + if row["method"] == args.candidate_method + and row.get("view", args.candidate_view) == args.candidate_view + ] + split_rows = _read_csv(args.math_splits) + + math_by_id = {row["sample_id"]: row for row in math_rows} + tsfa_by_id = {row["sample_id"]: row for row in tsfa_rows} + folds_by_id = {row["sample_id"]: int(row["fold"]) for row in split_rows} + if len(math_by_id) != len(math_rows) or len(tsfa_by_id) != len(tsfa_rows): + raise ValueError("expected unique sample IDs in both OOF prediction files") + sample_ids = sorted(math_by_id) + if set(sample_ids) != set(tsfa_by_id) or set(sample_ids) != set(folds_by_id): + raise ValueError("math, TSFA, and math split files do not cover identical samples") + + for sample_id in sample_ids: + math_row = math_by_id[sample_id] + tsfa_row = tsfa_by_id[sample_id] + if math_row["video_id"] != tsfa_row["video_id"]: + raise ValueError(f"video_id mismatch for {sample_id}") + if int(tsfa_row["fold"]) != folds_by_id[sample_id]: + raise ValueError(f"fold mismatch for {sample_id}") + if int(math_row["true_polarity"]) != int(tsfa_row["true_class_id"]): + raise ValueError(f"class label mismatch for {sample_id}") + if not np.isclose(float(math_row["true_sentiment"]), float(tsfa_row["true_label"])): + raise ValueError(f"continuous label mismatch for {sample_id}") + + video_ids = np.asarray([math_by_id[sample_id]["video_id"] for sample_id in sample_ids]) + actual_class = np.asarray([int(math_by_id[sample_id]["true_polarity"]) for sample_id in sample_ids]) + actual_value = np.asarray([float(math_by_id[sample_id]["true_sentiment"]) for sample_id in sample_ids]) + tsfa_class = np.asarray([int(tsfa_by_id[sample_id]["predicted_class_id"]) for sample_id in sample_ids]) + tsfa_value = np.clip( + np.asarray([float(tsfa_by_id[sample_id]["predicted_label"]) for sample_id in sample_ids]), + -3.0, + 3.0, + ) + tsfa_scores = _scores(actual_class, tsfa_class, actual_value, tsfa_value) + + group_indices = { + video_id: np.flatnonzero(video_ids == video_id) + for video_id in sorted(set(video_ids.tolist())) + } + group_names = np.asarray(list(group_indices)) + rng = np.random.default_rng(args.seed) + boot_scores: dict[str, dict[str, list[float]]] = { + method: {metric: [] for metric in METRICS} for method in METHODS + } + point_scores: dict[str, dict[str, float]] = {} + for method in METHODS: + predicted_class = np.asarray([ + int(math_by_id[sample_id][f"{method}_predicted_polarity"]) + for sample_id in sample_ids + ]) + predicted_value = np.clip(np.asarray([ + float(math_by_id[sample_id][f"{method}_predicted_sentiment"]) + for sample_id in sample_ids + ]), -3.0, 3.0) + point_scores[method] = _scores(actual_class, predicted_class, actual_value, predicted_value) + + for _ in range(args.bootstrap_repeats): + sampled_groups = rng.choice(group_names, size=len(group_names), replace=True) + indices = np.concatenate([group_indices[group] for group in sampled_groups]) + resampled_tsfa = _scores( + actual_class[indices], tsfa_class[indices], actual_value[indices], tsfa_value[indices] + ) + for method in METHODS: + predicted_class = np.asarray([ + int(math_by_id[sample_ids[index]][f"{method}_predicted_polarity"]) + for index in indices + ]) + predicted_value = np.clip(np.asarray([ + float(math_by_id[sample_ids[index]][f"{method}_predicted_sentiment"]) + for index in indices + ]), -3.0, 3.0) + resampled_baseline = _scores( + actual_class[indices], predicted_class, actual_value[indices], predicted_value + ) + for metric in METRICS: + boot_scores[method][metric].append(resampled_tsfa[metric] - resampled_baseline[metric]) + + output_rows: list[dict[str, Any]] = [] + for method in METHODS: + for metric in METRICS: + deltas = np.asarray(boot_scores[method][metric], dtype=np.float64) + finite = deltas[np.isfinite(deltas)] + output_rows.append({ + "comparison": f"{args.candidate_method}-{method}", + "metric": metric, + "tsfa_oof": tsfa_scores[metric], + "baseline_oof": point_scores[method][metric], + "delta_tsfa_minus_baseline": tsfa_scores[metric] - point_scores[method][metric], + "video_cluster_bootstrap_ci95_low": float(np.quantile(finite, 0.025)), + "video_cluster_bootstrap_ci95_high": float(np.quantile(finite, 0.975)), + "bootstrap_repeats": args.bootstrap_repeats, + "video_group_count": len(group_indices), + }) + + args.output_dir.mkdir(parents=True, exist_ok=True) + output_path = args.output_dir / f"{args.output_stem}.csv" + with output_path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=list(output_rows[0])) + writer.writeheader() + writer.writerows(output_rows) + manifest = { + "experiment": f"Paired OOF comparison of {args.candidate_method} against math B0-B4", + "candidate_method": args.candidate_method, + "candidate_view": args.candidate_view, + "sample_count": len(sample_ids), + "video_group_count": len(group_indices), + "identical_sample_ids": True, + "identical_video_ids": True, + "identical_fold_assignments": True, + "fold_assignment_validation": "TSFA prediction fold matched math split_assignments.csv for every sample_id.", + "metrics": list(METRICS), + "macro_f1_labels": [0, 1, 2], + "regression_prediction_clipping": [-3.0, 3.0], + "bootstrap": { + "unit": "video_id cluster", + "repeats": args.bootstrap_repeats, + "seed": args.seed, + "interval": "percentile 95% confidence interval for paired TSFA-minus-baseline metric differences", + }, + "math_inputs_read_only": [str(args.math_predictions.resolve()), str(args.math_splits.resolve())], + "tsfa_input": str(args.tsfa_predictions.resolve()), + "output": str(output_path.resolve()), + } + manifest_path = args.output_dir / f"{args.output_stem}_manifest.json" + manifest_path.write_text( + json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8" + ) + report_bundle = args.output_dir.parent / "tsfa" / "report_bundle" + if report_bundle.is_dir() and args.output_stem == "paired_math_comparison": + shutil.copy2(output_path, report_bundle / output_path.name) + shutil.copy2(manifest_path, report_bundle / manifest_path.name) + print(f"[paired comparison complete] samples={len(sample_ids)} groups={len(group_indices)} output={output_path}") + for row in output_rows: + if row["metric"] == "macro_f1": + print( + f" {row['comparison']}: delta={row['delta_tsfa_minus_baseline']:.3f} " + f"CI=[{row['video_cluster_bootstrap_ci95_low']:.3f}, " + f"{row['video_cluster_bootstrap_ci95_high']:.3f}]" + ) + + +def build_parser() -> argparse.ArgumentParser: + project = Path(__file__).resolve().parents[1] + repository = project.parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--math-predictions", + type=Path, + default=repository / "math/results/model_comparison/oof_predictions.csv", + ) + parser.add_argument( + "--math-splits", + type=Path, + default=repository / "math/results/model_comparison/split_assignments.csv", + ) + parser.add_argument( + "--tsfa-predictions", + type=Path, + default=project / "outputs/tsfa_emotion_probe/emotion_probe_predictions.csv", + ) + parser.add_argument("--output-dir", type=Path, default=project / "outputs/tsfa_emotion_probe") + parser.add_argument("--candidate-method", default="TSFA-main") + parser.add_argument("--candidate-view", default="all_modalities") + parser.add_argument("--output-stem", default="paired_math_comparison") + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--seed", type=int, default=42) + return parser + + +def main() -> None: + args = build_parser().parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/q1/shared_definition_comparison.py b/deep_learning/Q1/q1/shared_definition_comparison.py new file mode 100644 index 0000000..27775a2 --- /dev/null +++ b/deep_learning/Q1/q1/shared_definition_comparison.py @@ -0,0 +1,1616 @@ +"""Compare similarity, correlation, and predictability as shared information. + +All representation methods use the same fold-standardized original BERT, +eGeMAPS, and DeiT streams pooled with frozen M4_sourceTime attention. +Emotion labels are used only by the downstream, outer-fold probes. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import platform +import random +import time +from collections import defaultdict +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import sklearn +import torch +from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score, mean_squared_error, r2_score +from sklearn.model_selection import GroupKFold +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +from .experiment_data import FeatureSample, fit_feature_stats, load_feature_samples +from .tsfa_emotion_probe import _class_from_sentiment +from .tsfa_experiment import ( + GRID_SIZE, + _collect_fold_features, +) +from .tsfa_shared_private import ( + MODS, + SharedPrivateFactorizer, + _encode_all, + _pool_five, + _pool_original_source, +) + + +PREDICTIVE_CONTEXTS = {"same_slot": 0, "local_pm1": 1} +PREDICTIVE_TARGETS = MODS +PAIRS = (("text", "audio"), ("text", "vision"), ("audio", "vision")) +ALPHAS = (1.0, 10.0, 100.0) +PCA_COMPONENTS = {"text": 64, "audio": 24, "vision": 64} +CORE_SLOTS = np.arange(6, GRID_SIZE - 6) +MAIN_DIM_PER_SLOT = 256 +MAIN_CLIP_DIM = 5 * MAIN_DIM_PER_SLOT +EMOTION_CLASSES = (0, 1, 2) + + +def _read_csv(path: Path) -> list[dict[str, str]]: + with path.open("r", encoding="utf-8-sig", newline="") as stream: + return list(csv.DictReader(stream)) + + +def _r2(y_true: np.ndarray, y_pred: np.ndarray) -> float: + return float(r2_score(y_true, y_pred, multioutput="uniform_average", force_finite=True)) + + +def _clip_r2_mse(y_true: np.ndarray, y_pred: np.ndarray, slots: Sequence[int] | None = None) -> tuple[float, float]: + if slots is not None: + y_true = y_true[slots] + y_pred = y_pred[slots] + return _r2(y_true, y_pred), float(mean_squared_error(y_true, y_pred)) + + +def _ridge(alpha: float) -> Any: + # Cholesky solves the same L2-regularized least-squares objective exactly + # and handles the dense, multi-output source features much faster here. + return make_pipeline(StandardScaler(), Ridge(alpha=float(alpha), solver="cholesky")) + + +def _predictor_input( + sample_id: str, + target: str, + slot: int, + radius: int, + pooled: Mapping[str, Mapping[str, np.ndarray]], + *, + shift: int = 0, + permutations: Mapping[str, np.ndarray] | None = None, +) -> np.ndarray: + parts: list[np.ndarray] = [] + for source_modality in MODS: + if source_modality == target: + continue + source = pooled[sample_id][source_modality] + for offset in range(-radius, radius + 1): + index = int(np.clip(slot + shift + offset, 0, GRID_SIZE - 1)) + if permutations is not None: + index = int(permutations[source_modality][index]) + parts.append(source[index]) + return np.concatenate(parts, axis=0) + + +def _matrix_for_samples( + samples: Sequence[FeatureSample], + target: str, + radius: int, + pooled: Mapping[str, Mapping[str, np.ndarray]], + *, + slots: Sequence[int] | None = None, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, list[tuple[str, int]]]: + use_slots = list(range(GRID_SIZE)) if slots is None else [int(slot) for slot in slots] + xs: list[np.ndarray] = [] + ys: list[np.ndarray] = [] + groups: list[str] = [] + keys: list[tuple[str, int]] = [] + for sample in samples: + sample_id = sample.sample_id + for slot in use_slots: + xs.append(_predictor_input(sample_id, target, slot, radius, pooled)) + ys.append(pooled[sample_id][target][slot]) + groups.append(sample.group_id) + keys.append((sample_id, slot)) + return np.stack(xs), np.stack(ys), np.asarray(groups), keys + + +def _select_ridge_alpha( + x: np.ndarray, + y: np.ndarray, + groups: np.ndarray, + *, + alphas: Sequence[float] = ALPHAS, +) -> tuple[float, dict[float, float]]: + unique_groups = np.unique(groups) + folds = min(3, len(unique_groups)) + if folds < 2: + raise ValueError("inner grouped CV requires at least two training video groups") + cv = GroupKFold(n_splits=folds) + scores: dict[float, float] = {} + for alpha in alphas: + fold_scores = [] + for train_index, valid_index in cv.split(x, y, groups): + model = _ridge(alpha) + model.fit(x[train_index], y[train_index]) + fold_scores.append(_r2(y[valid_index], model.predict(x[valid_index]))) + scores[float(alpha)] = float(np.mean(fold_scores)) + best = max(scores, key=lambda alpha: (scores[alpha], -alpha)) + return best, scores + + +def _crossfit_predictions(x: np.ndarray, y: np.ndarray, groups: np.ndarray, alpha: float) -> np.ndarray: + unique_groups = np.unique(groups) + cv = GroupKFold(n_splits=min(3, len(unique_groups))) + output = np.empty_like(y, dtype=np.float32) + seen = np.zeros(len(y), dtype=bool) + for train_index, valid_index in cv.split(x, y, groups): + model = _ridge(alpha) + model.fit(x[train_index], y[train_index]) + output[valid_index] = model.predict(x[valid_index]).astype(np.float32, copy=False) + seen[valid_index] = True + if not seen.all(): + raise RuntimeError("cross-fit residual predictions do not cover all training slots") + return output + + +def _predict_sequences( + model: Any, + sample_ids: Sequence[str], + target: str, + radius: int, + pooled: Mapping[str, Mapping[str, np.ndarray]], + *, + shift: int = 0, + permutations_by_id: Mapping[str, Mapping[str, np.ndarray]] | None = None, + slots: Sequence[int] | None = None, +) -> dict[str, np.ndarray]: + use_slots = list(range(GRID_SIZE)) if slots is None else [int(slot) for slot in slots] + output: dict[str, np.ndarray] = {} + for sample_id in sample_ids: + perms = None if permutations_by_id is None else permutations_by_id[sample_id] + x = np.stack([ + _predictor_input(sample_id, target, slot, radius, pooled, + shift=shift, permutations=perms) + for slot in use_slots + ]) + predictions = model.predict(x).astype(np.float32, copy=False) + sequence = np.full_like(pooled[sample_id][target], np.nan, dtype=np.float32) + sequence[use_slots] = predictions + output[sample_id] = sequence + return output + + +def _predictive_fold( + *, + fold: int, + context_name: str, + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + pooled: Mapping[str, Mapping[str, np.ndarray]], + seed: int, + shuffle_repeats: int, +) -> tuple[dict[str, dict[str, dict[str, np.ndarray]]], list[dict[str, Any]], + list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]]]: + radius = PREDICTIVE_CONTEXTS[context_name] + train_ids = [sample.sample_id for sample in train_samples] + heldout_ids = [sample.sample_id for sample in heldout_samples] + train_representations = {sample_id: {"shared": {}, "private": {}} for sample_id in train_ids} + heldout_representations = {sample_id: {"shared": {}, "private": {}} for sample_id in heldout_ids} + summary_rows: list[dict[str, Any]] = [] + control_rows: list[dict[str, Any]] = [] + residual_rows: list[dict[str, Any]] = [] + + for target in PREDICTIVE_TARGETS: + x_train, y_train, groups_train, keys_train = _matrix_for_samples( + train_samples, target, radius, pooled + ) + alpha, alpha_cv = _select_ridge_alpha(x_train, y_train, groups_train) + first_stage = _ridge(alpha) + first_stage.fit(x_train, y_train) + yhat_train_cf = _crossfit_predictions(x_train, y_train, groups_train, alpha) + yhat_valid = _predict_sequences( + first_stage, heldout_ids, target, radius, pooled + ) + yhat_train: dict[str, np.ndarray] = { + sample_id: np.full_like(pooled[sample_id][target], np.nan, dtype=np.float32) + for sample_id in train_ids + } + for index, (sample_id, slot) in enumerate(keys_train): + yhat_train[sample_id][slot] = yhat_train_cf[index] + + residual_y_train = y_train - yhat_train_cf + # Reuse the first-stage alpha so original and residual prediction use the + # same regularization budget; this avoids selecting a second hyperparameter + # on a noisier target residual. + residual_alpha, residual_alpha_cv = alpha, {} + residual_model = _ridge(residual_alpha) + residual_model.fit(x_train, residual_y_train) + p_hat_valid = _predict_sequences( + residual_model, heldout_ids, target, radius, pooled + ) + for sample_id in train_ids: + train_representations[sample_id]["shared"][target] = yhat_train[sample_id] + train_representations[sample_id]["private"][target] = ( + pooled[sample_id][target] - yhat_train[sample_id] + ).astype(np.float32) + for sample_id in heldout_ids: + heldout_representations[sample_id]["shared"][target] = yhat_valid[sample_id] + heldout_representations[sample_id]["private"][target] = ( + pooled[sample_id][target] - yhat_valid[sample_id] + ).astype(np.float32) + + original_true = [] + original_pred = [] + private_true = [] + private_predictions = [] + clip_metric_rows: list[dict[str, Any]] = [] + for sample in heldout_samples: + sample_id = sample.sample_id + true = pooled[sample_id][target][CORE_SLOTS] + pred = yhat_valid[sample_id][CORE_SLOTS] + private_true_seq = pooled[sample_id][target] - yhat_valid[sample_id] + pred_private_seq = p_hat_valid[sample_id] + private_target = private_true_seq[CORE_SLOTS] + pred_private = pred_private_seq[CORE_SLOTS] + original_true.append(true) + original_pred.append(pred) + private_true.append(private_target) + private_predictions.append(pred_private) + clip_metric_rows.append({ + "sample_id": sample_id, + "video_id": sample.group_id, + "original_r2": _r2(true, pred), + "original_mse": float(mean_squared_error(true, pred)), + "private_r2": _r2(private_target, pred_private), + "private_mse": float(mean_squared_error(private_target, pred_private)), + }) + original_true_array = np.concatenate(original_true) + original_pred_array = np.concatenate(original_pred) + private_true_array = np.concatenate(private_true) + private_pred_array = np.concatenate(private_predictions) + summary_rows.append({ + "fold": fold, + "context": context_name, + "target_modality": target, + "selected_alpha_original": alpha, + "inner_cv_r2_by_alpha": json.dumps({str(k): v for k, v in alpha_cv.items()}), + "selected_alpha_private": residual_alpha, + "private_inner_cv_r2_by_alpha": json.dumps({str(k): v for k, v in residual_alpha_cv.items()}), + "private_alpha_selection": "reused original-target grouped inner-CV choice", + "heldout_r2_original_from_other_modalities": _r2(original_true_array, original_pred_array), + "heldout_mse_original_from_other_modalities": float(mean_squared_error(original_true_array, original_pred_array)), + "heldout_r2_private_residual_from_other_modalities": _r2(private_true_array, private_pred_array), + "heldout_mse_private_residual_from_other_modalities": float(mean_squared_error(private_true_array, private_pred_array)), + "delta_r2_original_minus_private": _r2(original_true_array, original_pred_array) - _r2(private_true_array, private_pred_array), + }) + for row in clip_metric_rows: + residual_rows.append({"fold": fold, "context": context_name, + "target_modality": target, **row, + "delta_r2_original_minus_private": row["original_r2"] - row["private_r2"]}) + + # The exact same trained predictor is evaluated at every source shift. + for delta in range(-5, 6): + shifted_predictions = _predict_sequences( + first_stage, heldout_ids, target, radius, pooled, + shift=delta, slots=CORE_SLOTS, + ) + for sample in heldout_samples: + sample_id = sample.sample_id + true = pooled[sample_id][target][CORE_SLOTS] + pred = shifted_predictions[sample_id][CORE_SLOTS] + control_rows.append({ + "fold": fold, + "context": context_name, + "target_modality": target, + "control_type": "shift", + "delta": delta, + "repeat": 0, + "sample_id": sample_id, + "video_id": sample.group_id, + "r2": _r2(true, pred), + "mse": float(mean_squared_error(true, pred)), + }) + for repeat in range(shuffle_repeats): + perm_by_id = { + sample_id: { + name: np.random.default_rng(seed + fold * 1009 + repeat * 17 + sum(map(ord, sample_id + name))).permutation(GRID_SIZE) + for name in MODS if name != target + } + for sample_id in heldout_ids + } + shuffled_predictions = _predict_sequences( + first_stage, heldout_ids, target, radius, pooled, + shift=0, permutations_by_id=perm_by_id, slots=CORE_SLOTS, + ) + for sample in heldout_samples: + sample_id = sample.sample_id + true = pooled[sample_id][target][CORE_SLOTS] + pred = shuffled_predictions[sample_id][CORE_SLOTS] + control_rows.append({ + "fold": fold, + "context": context_name, + "target_modality": target, + "control_type": "within_video_shuffle", + "delta": 0, + "repeat": repeat, + "sample_id": sample_id, + "video_id": sample.group_id, + "r2": _r2(true, pred), + "mse": float(mean_squared_error(true, pred)), + }) + return train_representations, heldout_representations, summary_rows, control_rows, residual_rows + + +def _flatten_sequences( + sample_ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + modality: str, +) -> np.ndarray: + return np.concatenate([pooled[sample_id][modality] for sample_id in sample_ids], axis=0) + + +def _inverse_sqrt(matrix: np.ndarray, ridge: float) -> np.ndarray: + eigenvalues, eigenvectors = np.linalg.eigh(matrix.astype(np.float64, copy=False)) + eigenvalues = np.maximum(eigenvalues, 0.0) + ridge + return (eigenvectors * (1.0 / np.sqrt(eigenvalues))[None, :]) @ eigenvectors.T + + +def _gcca_fold( + *, + fold: int, + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + pooled: Mapping[str, Mapping[str, np.ndarray]], + seed: int, + common_dim: int = 32, + ridge: float = 1e-3, +) -> tuple[dict[str, dict[str, dict[str, np.ndarray]]], list[dict[str, Any]], list[dict[str, Any]]]: + """Fit fold-only regularized MAXVAR/GCCA and return common/private streams.""" + train_ids = [sample.sample_id for sample in train_samples] + heldout_ids = [sample.sample_id for sample in heldout_samples] + all_ids = [*train_ids, *heldout_ids] + train_views: dict[str, np.ndarray] = {} + all_views: dict[str, np.ndarray] = {} + pca_rows: list[dict[str, Any]] = [] + for modality in MODS: + x_train = _flatten_sequences(train_ids, pooled, modality) + x_all = _flatten_sequences(all_ids, pooled, modality) + n_components = min(PCA_COMPONENTS[modality], x_train.shape[0] - 1, x_train.shape[1]) + pca = PCA(n_components=n_components, svd_solver="randomized", random_state=seed + fold) + scores_train_raw = pca.fit_transform(x_train) + score_scaler = StandardScaler().fit(scores_train_raw) + train_views[modality] = score_scaler.transform(scores_train_raw).astype(np.float64) + scores_all_raw = pca.transform(x_all) + all_views[modality] = score_scaler.transform(scores_all_raw).astype(np.float64) + pca_rows.append({ + "fold": fold, + "modality": modality, + "pca_components": n_components, + "pca_explained_variance_ratio_sum": float(pca.explained_variance_ratio_.sum()), + }) + + n_train = train_views[MODS[0]].shape[0] + whitened_train: list[np.ndarray] = [] + covariances: dict[str, np.ndarray] = {} + for modality in MODS: + x = train_views[modality] + covariance = (x.T @ x) / n_train + covariances[modality] = covariance + whitened_train.append((x @ _inverse_sqrt(covariance, ridge)) / np.sqrt(n_train)) + stacked = np.concatenate(whitened_train, axis=1) + left_vectors, singular_values, _ = np.linalg.svd(stacked, full_matrices=False) + dimension = min(common_dim, left_vectors.shape[1]) + z_train_target = left_vectors[:, :dimension] * np.sqrt(n_train) + energy = singular_values**2 + energy_ratio = energy / max(float(energy.sum()), 1e-12) + cumulative = np.cumsum(energy_ratio) + components_90 = int(np.searchsorted(cumulative, 0.90) + 1) + effective_rank = float(np.exp(-np.sum(energy_ratio * np.log(np.maximum(energy_ratio, 1e-12))))) + + shared_train: dict[str, np.ndarray] = {} + shared_all: dict[str, np.ndarray] = {} + for modality in MODS: + width = train_views[modality].shape[1] + x_train = train_views[modality] + # Ridge-regularized least squares maps each view into one common latent basis. + weight = np.linalg.solve( + covariances[modality] + ridge * np.eye(width), + (x_train.T @ z_train_target) / n_train, + ) + all_flat = all_views[modality] + shared_all[modality] = (all_flat @ weight).astype(np.float32) + shared_train[modality] = shared_all[modality][:n_train] + + sequences: dict[str, dict[str, dict[str, np.ndarray]]] = { + sample_id: {"shared": {}, "private": {}} for sample_id in all_ids + } + explained_rows: list[dict[str, Any]] = [] + for sample_index, sample_id in enumerate(all_ids): + slot_start = sample_index * GRID_SIZE + slot_stop = slot_start + GRID_SIZE + for modality in MODS: + sequences[sample_id]["shared"][modality] = shared_all[modality][slot_start:slot_stop] + + z_common_train = np.mean([shared_train[name] for name in MODS], axis=0) + z_common_all = np.mean([shared_all[name] for name in MODS], axis=0) + for sample_index, sample_id in enumerate(all_ids): + slot_start = sample_index * GRID_SIZE + slot_stop = slot_start + GRID_SIZE + sequences[sample_id]["common"] = z_common_all[slot_start:slot_stop].astype(np.float32) + for modality in MODS: + target_train = _flatten_sequences(train_ids, pooled, modality) + target_all = _flatten_sequences(all_ids, pooled, modality) + decoder = make_pipeline(StandardScaler(), Ridge(alpha=10.0, solver="lsqr")) + decoder.fit(z_common_train, target_train) + prediction_all = decoder.predict(z_common_all).astype(np.float32) + residual_all = target_all - prediction_all + for sample_index, sample_id in enumerate(all_ids): + slot_start = sample_index * GRID_SIZE + slot_stop = slot_start + GRID_SIZE + sequences[sample_id]["private"][modality] = residual_all[slot_start:slot_stop] + heldout_start = len(train_ids) * GRID_SIZE + heldout_target = target_all[heldout_start:] + heldout_prediction = prediction_all[heldout_start:] + explained_rows.append({ + "fold": fold, + "modality": modality, + "heldout_shared_reconstruction_r2": _r2(heldout_target, heldout_prediction), + "heldout_shared_reconstruction_mse": float(mean_squared_error(heldout_target, heldout_prediction)), + "heldout_private_residual_variance": float(np.var(heldout_target - heldout_prediction)), + }) + + pair_rows: list[dict[str, Any]] = [] + val_start = len(train_ids) * GRID_SIZE + for left, right in PAIRS: + left_matrix = shared_all[left][val_start:] + right_matrix = shared_all[right][val_start:] + correlations = [] + for component in range(dimension): + x = left_matrix[:, component] + y = right_matrix[:, component] + if np.std(x) > 1e-12 and np.std(y) > 1e-12: + correlations.append(float(np.corrcoef(x, y)[0, 1])) + pair_rows.append({ + "fold": fold, + "pair": f"{left}-{right}", + "shared_dimension": dimension, + "heldout_mean_component_correlation": float(np.mean(correlations)) if correlations else float("nan"), + "heldout_median_component_correlation": float(np.median(correlations)) if correlations else float("nan"), + "valid_component_count": len(correlations), + }) + stability_row = { + "fold": fold, + "metric": "spectrum_stability", + "shared_dimension": dimension, + "components_for_90pct_energy": components_90, + "effective_rank": effective_rank, + "gcca_ridge": ridge, + "singular_energy_top10": json.dumps([float(v) for v in energy_ratio[:10]]), + "pca_components_text": PCA_COMPONENTS["text"], + "pca_components_audio": PCA_COMPONENTS["audio"], + "pca_components_vision": PCA_COMPONENTS["vision"], + } + return sequences, [stability_row, *pair_rows, *pca_rows], explained_rows + + +def _spr_fold( + *, + fold: int, + sample_ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + device: torch.device, + checkpoint_root: Path, + batch_size: int, +) -> dict[str, dict[str, dict[str, np.ndarray]]]: + checkpoint_path = checkpoint_root / f"fold_{fold:02d}" / "SPR.pt" + if not checkpoint_path.is_file(): + raise FileNotFoundError(f"frozen SPR checkpoint missing: {checkpoint_path}") + dimensions = {name: pooled[sample_ids[0]][name].shape[-1] for name in MODS} + model = SharedPrivateFactorizer(dimensions).to(device) + checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) + if checkpoint.get("variant") != "SPR": + raise ValueError(f"expected SPR checkpoint at {checkpoint_path}, got {checkpoint.get('variant')}") + model.load_state_dict(checkpoint.get("model_state_dict", checkpoint.get("state_dict", checkpoint)), strict=True) + model.eval() + encoded, _ = _encode_all(model, sample_ids, pooled, device, batch_size) + del model, checkpoint + if device.type == "cuda": + torch.cuda.empty_cache() + return encoded + + +def _representation_views( + representation: Mapping[str, Mapping[str, np.ndarray]], +) -> dict[str, np.ndarray]: + shared = representation["shared"] + private = representation["private"] + shared_unfused = np.concatenate([shared[name] for name in MODS], axis=-1) + private_all = np.concatenate([private[name] for name in MODS], axis=-1) + private_av = np.concatenate([private["audio"], private["vision"]], axis=-1) + views = { + "shared_only": shared_unfused, + "private_only": private_all, + "private_text": private["text"], + "private_audio": private["audio"], + "private_vision": private["vision"], + "private_audio_vision": private_av, + "private_all": private_all, + "shared+private": np.concatenate([shared_unfused, private_all], axis=-1), + "shared+private_text": np.concatenate([shared_unfused, private["text"]], axis=-1), + "shared+private_audio": np.concatenate([shared_unfused, private["audio"]], axis=-1), + "shared+private_vision": np.concatenate([shared_unfused, private["vision"]], axis=-1), + "shared+private_audio_vision": np.concatenate([shared_unfused, private_av], axis=-1), + "shared+private_all": np.concatenate([shared_unfused, private_all], axis=-1), + } + dims = [shared[name].shape[-1] for name in MODS] + if len(set(dims)) == 1: + views["shared_fused"] = np.mean([shared[name] for name in MODS], axis=0) + if "common" in representation: + views["shared_only"] = representation["common"] + views["shared_fused"] = representation["common"] + views["shared+private"] = np.concatenate([representation["common"], private_all], axis=-1) + views["shared+private_all"] = views["shared+private"] + views["main"] = views["shared+private"] + return views + + +def _dimension_matched_sequences( + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + sequences_by_id: Mapping[str, np.ndarray], + *, + seed: int, + components: int = MAIN_DIM_PER_SLOT, +) -> tuple[dict[str, np.ndarray], dict[str, np.ndarray], PCA]: + train_ids = [sample.sample_id for sample in train_samples] + heldout_ids = [sample.sample_id for sample in heldout_samples] + x_train = np.concatenate([sequences_by_id[sample_id] for sample_id in train_ids], axis=0) + x_valid = np.concatenate([sequences_by_id[sample_id] for sample_id in heldout_ids], axis=0) + n_components = min(components, x_train.shape[0] - 1, x_train.shape[1]) + pca = PCA(n_components=n_components, svd_solver="randomized", random_state=seed) + pca.fit(x_train) + train_projected = pca.transform(x_train).astype(np.float32) + valid_projected = pca.transform(x_valid).astype(np.float32) + train_by_id = { + sample_id: train_projected[index * GRID_SIZE : (index + 1) * GRID_SIZE] + for index, sample_id in enumerate(train_ids) + } + valid_by_id = { + sample_id: valid_projected[index * GRID_SIZE : (index + 1) * GRID_SIZE] + for index, sample_id in enumerate(heldout_ids) + } + return train_by_id, valid_by_id, pca + + +def _raw_private_pca_sequences( + sample_ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + checkpoint_path: Path, +) -> dict[str, np.ndarray]: + with np.load(checkpoint_path, allow_pickle=False) as archive: + mean = np.asarray(archive["mean"], dtype=np.float32) + components = np.asarray(archive["components"], dtype=np.float32) + output = {} + for sample_id in sample_ids: + raw = np.concatenate([pooled[sample_id][name] for name in MODS], axis=-1) + if raw.shape[1] != len(mean): + raise ValueError(f"raw-private PCA width mismatch for {sample_id}: {raw.shape[1]} vs {len(mean)}") + output[sample_id] = ((raw - mean) @ components.T).astype(np.float32) + return output + + +def _fit_probe_predictions( + *, + method: str, + view: str, + fold: int, + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + vectors_by_id: Mapping[str, np.ndarray], + seed: int, +) -> list[dict[str, Any]]: + train_x = np.stack([_pool_five(vectors_by_id[sample.sample_id]) for sample in train_samples]) + heldout_x = np.stack([_pool_five(vectors_by_id[sample.sample_id]) for sample in heldout_samples]) + train_class = np.asarray([_class_from_sentiment(sample.sentiment) for sample in train_samples], dtype=np.int64) + heldout_class = np.asarray([_class_from_sentiment(sample.sentiment) for sample in heldout_samples], dtype=np.int64) + train_value = np.asarray([sample.sentiment for sample in train_samples], dtype=np.float64) + heldout_value = np.asarray([sample.sentiment for sample in heldout_samples], dtype=np.float64) + classifier = make_pipeline( + StandardScaler(), + LogisticRegression(C=0.05, max_iter=5000, solver="lbfgs", random_state=seed), + ) + classifier.fit(train_x, train_class) + predicted_class = classifier.predict(heldout_x) + regressor = make_pipeline(StandardScaler(), Ridge(alpha=25.0)) + regressor.fit(train_x, train_value) + predicted_unclipped = regressor.predict(heldout_x) + predicted_value = np.clip(predicted_unclipped, -3.0, 3.0) + dimension = int(train_x.shape[1]) + return [ + { + "method": method, + "view": view, + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "true_class_id": int(heldout_class[index]), + "true_class": ("Negative", "Neutral", "Positive")[int(heldout_class[index])], + "predicted_class_id": int(predicted_class[index]), + "predicted_class": ("Negative", "Neutral", "Positive")[int(predicted_class[index])], + "true_label": float(heldout_value[index]), + "predicted_label": float(predicted_value[index]), + "predicted_label_unclipped": float(predicted_unclipped[index]), + "feature_dimension": dimension, + } + for index, sample in enumerate(heldout_samples) + ] + + +def _summarize_probe_rows(prediction_rows: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + keys = sorted({(str(row["method"]), str(row["view"])) for row in prediction_rows}) + summaries: list[dict[str, Any]] = [] + for method, view in keys: + rows = [row for row in prediction_rows if row["method"] == method and row["view"] == view] + y_class = np.asarray([int(row["true_class_id"]) for row in rows], dtype=np.int64) + p_class = np.asarray([int(row["predicted_class_id"]) for row in rows], dtype=np.int64) + y_value = np.asarray([float(row["true_label"]) for row in rows], dtype=np.float64) + p_value = np.asarray([float(row["predicted_label"]) for row in rows], dtype=np.float64) + per_fold_f1 = [] + for fold in sorted({int(row["fold"]) for row in rows if row.get("fold") not in (None, "")}): + selected = [row for row in rows if int(row["fold"]) == fold] + per_fold_f1.append(float(f1_score( + [int(row["true_class_id"]) for row in selected], + [int(row["predicted_class_id"]) for row in selected], + labels=list(EMOTION_CLASSES), average="macro", zero_division=0, + ))) + pearson = float(np.corrcoef(y_value, p_value)[0, 1]) if np.std(y_value) > 1e-12 and np.std(p_value) > 1e-12 else float("nan") + summaries.append({ + "method": method, + "view": view, + "sample_count": len(rows), + "feature_dimension": int(rows[0].get("feature_dimension", -1) or -1), + "accuracy": float(accuracy_score(y_class, p_class)), + "macro_f1": float(f1_score(y_class, p_class, labels=list(EMOTION_CLASSES), average="macro", zero_division=0)), + "macro_f1_fold_mean": float(np.mean(per_fold_f1)) if per_fold_f1 else float("nan"), + "macro_f1_fold_sd": float(np.std(per_fold_f1, ddof=1)) if len(per_fold_f1) > 1 else 0.0, + "mae": float(np.mean(np.abs(y_value - p_value))), + "pearson": pearson, + }) + return summaries + + +def _paired_video_bootstrap( + candidate_rows: Sequence[Mapping[str, Any]], + reference_rows: Sequence[Mapping[str, Any]], + *, + candidate_name: str, + reference_name: str, + repeats: int, + seed: int, +) -> list[dict[str, Any]]: + candidate = {str(row["sample_id"]): row for row in candidate_rows} + reference = {str(row["sample_id"]): row for row in reference_rows} + common = sorted(set(candidate) & set(reference)) + if len(common) < 2: + raise ValueError(f"paired contrast has only {len(common)} shared sample IDs: {candidate_name} vs {reference_name}") + groups = sorted({str(candidate[sample_id]["video_id"]) for sample_id in common}) + ids_by_group = { + group: [sample_id for sample_id in common if str(candidate[sample_id]["video_id"]) == group] + for group in groups + } + metrics: dict[str, Any] = { + "macro_f1": lambda rows: f1_score( + [int(row["true_class_id"]) for row in rows], + [int(row["predicted_class_id"]) for row in rows], + labels=list(EMOTION_CLASSES), average="macro", zero_division=0, + ), + "mae": lambda rows: float(np.mean(np.abs( + np.asarray([float(row["true_label"]) for row in rows]) + - np.asarray([float(row["predicted_label"]) for row in rows]) + ))), + "pearson": lambda rows: float(np.corrcoef( + [float(row["true_label"]) for row in rows], + [float(row["predicted_label"]) for row in rows], + )[0, 1]), + } + candidate_aligned = [candidate[sample_id] for sample_id in common] + reference_aligned = [reference[sample_id] for sample_id in common] + rng = np.random.default_rng(seed) + boot: dict[str, np.ndarray] = {name: np.empty(repeats, dtype=np.float64) for name in metrics} + for repeat in range(repeats): + chosen_groups = rng.choice(groups, size=len(groups), replace=True) + sampled_ids = [sample_id for group in chosen_groups for sample_id in ids_by_group[str(group)]] + c_rows = [candidate[sample_id] for sample_id in sampled_ids] + r_rows = [reference[sample_id] for sample_id in sampled_ids] + for name, metric in metrics.items(): + try: + boot[name][repeat] = float(metric(c_rows) - metric(r_rows)) + except (FloatingPointError, ValueError, ZeroDivisionError): + boot[name][repeat] = np.nan + observed = {name: float(metric(candidate_aligned) - metric(reference_aligned)) for name, metric in metrics.items()} + rows = [] + for name, values in boot.items(): + finite = values[np.isfinite(values)] + rows.append({ + "candidate": candidate_name, + "reference": reference_name, + "metric": name, + "delta_candidate_minus_reference": observed[name], + "ci95_low": float(np.quantile(finite, 0.025)) if len(finite) else float("nan"), + "ci95_high": float(np.quantile(finite, 0.975)) if len(finite) else float("nan"), + "bootstrap_repeats": repeats, + "video_group_count": len(groups), + "paired_sample_count": len(common), + }) + return rows + + +def _summarize_shift_controls(rows: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + grouped: dict[tuple[str, str, str, int], dict[str, list[float]]] = defaultdict(lambda: defaultdict(list)) + for row in rows: + key = (str(row["context"]), str(row["target_modality"]), str(row["control_type"]), int(row["delta"])) + value = float(row["mse"]) + if np.isfinite(value): + grouped[key][str(row["video_id"])].append(value) + per_video: dict[tuple[str, str, str, int], dict[str, float]] = { + key: {video: float(np.mean(values)) for video, values in by_video.items()} + for key, by_video in grouped.items() + } + baseline_by_key: dict[tuple[str, str, str], dict[str, float]] = {} + for (context, target, control, delta), values in per_video.items(): + if control == "shift" and delta == 0: + baseline_by_key[(context, target, control)] = values + output = [] + for (context, target, control, delta), video_mse in sorted(per_video.items()): + baseline = baseline_by_key.get((context, target, "shift"), {}) + common_videos = sorted(set(video_mse) & set(baseline)) + paired_delta = [video_mse[video] - baseline[video] for video in common_videos] + paired_ratio = [video_mse[video] / baseline[video] for video in common_videos if baseline[video] > 1e-12] + output.append({ + "context": context, + "target_modality": target, + "control_type": control, + "delta": delta, + "video_macro_mean_mse": float(np.mean(list(video_mse.values()))), + "video_macro_sd_mse": float(np.std(list(video_mse.values()), ddof=1)) if len(video_mse) > 1 else 0.0, + "delta_mse_vs_same_slot": float(np.mean(paired_delta)) if paired_delta else float("nan"), + "mse_ratio_vs_same_slot": float(np.mean(paired_ratio)) if paired_ratio else float("nan"), + "video_count": len(video_mse), + "sample_count": len({str(row["sample_id"]) for row in rows if row["context"] == context and row["target_modality"] == target and row["control_type"] == control and int(row["delta"]) == delta}), + "control_row_count": sum(row["context"] == context and row["target_modality"] == target and row["control_type"] == control and int(row["delta"]) == delta for row in rows), + }) + return output + + +def _load_reference_predictions( + *, + tsfa_predictions_path: Path, + math_predictions_path: Path, + math_splits_path: Path, + expected_fold_by_id: Mapping[str, int], + samples_by_id: Mapping[str, FeatureSample], +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + tsfa_rows = _read_csv(tsfa_predictions_path) + selected_tsfa = [ + row for row in tsfa_rows + if row.get("method") == "TSFA+RawPrivate" and row.get("view") == "all_modalities" + ] + if len(selected_tsfa) != len(samples_by_id): + raise ValueError( + f"expected 100 TSFA+RawPrivate OOF rows in {tsfa_predictions_path}, found {len(selected_tsfa)}" + ) + for row in selected_tsfa: + sample_id = row["sample_id"] + if sample_id not in expected_fold_by_id: + raise ValueError(f"TSFA+RawPrivate has unexpected sample ID: {sample_id}") + fold = int(row["fold"]) + if fold != expected_fold_by_id[sample_id]: + raise ValueError(f"TSFA+RawPrivate fold mismatch for {sample_id}: {fold} vs {expected_fold_by_id[sample_id]}") + rows.append({ + "method": "TSFA+RawPrivate", + "view": "main_external_nonmatched", + "fold": fold, + "sample_id": sample_id, + "video_id": samples_by_id[sample_id].group_id, + "true_class_id": int(row["true_class_id"]), + "true_class": row["true_class"], + "predicted_class_id": int(row["predicted_class_id"]), + "predicted_class": row["predicted_class"], + "true_label": float(row["true_label"]), + "predicted_label": float(row["predicted_label"]), + "predicted_label_unclipped": float(row.get("predicted_label_unclipped", row["predicted_label"])), + "feature_dimension": int(row.get("feature_dimension", 6845)), + }) + + math_split_rows = _read_csv(math_splits_path) + math_fold_by_id = { + row["sample_id"]: int(row["fold"]) + for row in math_split_rows + if row.get("split", "valid_oof").lower() in {"valid_oof", "validation", "valid"} + } + if set(math_fold_by_id) != set(expected_fold_by_id): + raise ValueError("math OOF split assignment does not cover the same 100 sample IDs") + mismatched = [sample_id for sample_id in expected_fold_by_id if math_fold_by_id[sample_id] != expected_fold_by_id[sample_id]] + if mismatched: + raise ValueError(f"math OOF folds differ from Q1 fixed folds, examples: {mismatched[:5]}") + math_rows = _read_csv(math_predictions_path) + if len(math_rows) != len(samples_by_id): + raise ValueError(f"expected one math OOF row per sample, found {len(math_rows)}") + for source in math_rows: + sample_id = source["sample_id"] + if sample_id not in expected_fold_by_id: + raise ValueError(f"math OOF predictions have unexpected sample ID: {sample_id}") + sample = samples_by_id[sample_id] + if int(source["true_polarity"]) != _class_from_sentiment(sample.sentiment): + raise ValueError(f"math true class disagrees with sign-derived label for {sample_id}") + for baseline in ("B0", "B4"): + rows.append({ + "method": f"Math-{baseline}", + "view": "math_all_modalities", + "fold": expected_fold_by_id[sample_id], + "sample_id": sample_id, + "video_id": sample.group_id, + "true_class_id": int(source["true_polarity"]), + "true_class": source["true_polarity_name"], + "predicted_class_id": int(source[f"{baseline}_predicted_polarity"]), + "predicted_class": source[f"{baseline}_predicted_polarity"], + "true_label": float(source["true_sentiment"]), + "predicted_label": float(source[f"{baseline}_predicted_sentiment"]), + "predicted_label_unclipped": float(source[f"{baseline}_predicted_sentiment"]), + "feature_dimension": -1, + }) + return rows + + +def _write_rows(path: Path, rows: Sequence[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fieldnames: list[str] = [] + for row in rows: + for key in row: + if key not in fieldnames: + fieldnames.append(key) + if not fieldnames: + path.write_text("", encoding="utf-8") + return + with path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=fieldnames, extrasaction="ignore") + writer.writeheader() + for row in rows: + cooked = { + key: json.dumps(value, ensure_ascii=False) if isinstance(value, (dict, list, tuple)) else value + for key, value in row.items() + } + writer.writerow(cooked) + + +def _write_json(path: Path, value: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=True) + "\n", encoding="utf-8") + + +def _make_private_ablation_rows(metrics: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + metric_map = {(row["method"], row["view"]): row for row in metrics} + rows = [] + for method in sorted({str(row["method"]) for row in metrics if row["method"] in { + "Similarity-SPR", "GCCA", "Predictive-Same", "Predictive-Local1" + }}): + base = metric_map.get((method, "shared_only")) + for view in ( + "private_text", "private_audio", "private_vision", "private_audio_vision", "private_all", + "shared+private_text", "shared+private_audio", "shared+private_vision", + "shared+private_audio_vision", "shared+private", "main_1280d", + ): + row = metric_map.get((method, view)) + if row is None: + continue + rows.append({ + "method": method, + "view": view, + "accuracy": row["accuracy"], + "macro_f1": row["macro_f1"], + "mae": row["mae"], + "pearson": row["pearson"], + "macro_f1_delta_vs_shared_only": float(row["macro_f1"] - base["macro_f1"]) if base else float("nan"), + "mae_delta_vs_shared_only": float(row["mae"] - base["mae"]) if base else float("nan"), + "feature_dimension": row["feature_dimension"], + }) + return rows + + +def _save_figures( + output_dir: Path, + *, + predictive_rows: Sequence[Mapping[str, Any]], + shift_rows: Sequence[Mapping[str, Any]], + gcca_rows: Sequence[Mapping[str, Any]], + explained_rows: Sequence[Mapping[str, Any]], + emotion_metrics: Sequence[Mapping[str, Any]], + dimension_metrics: Sequence[Mapping[str, Any]], + ablation_rows: Sequence[Mapping[str, Any]], +) -> list[str]: + figure_dir = output_dir / "figures" + figure_dir.mkdir(parents=True, exist_ok=True) + created: list[str] = [] + + fig, axes = plt.subplots(1, 3, figsize=(13, 3.8)) + titles = ("Similarity", "Correlation", "Predictability") + captions = ( + "Map each modality into a shared space\nthen compare same-slot vectors", + "Find projections whose paired values\nco-vary across modalities", + "Predict one modality from the others;\nresidual is source-specific content", + ) + for axis, title, caption in zip(axes, titles, captions): + axis.set_title(title, fontsize=13, weight="bold") + axis.set_xlim(0, 1) + axis.set_ylim(0, 1) + axis.axis("off") + axis.text(0.5, 0.76, caption, ha="center", va="center", fontsize=9) + for y, label, color in ((0.48, "Text", "#4C78A8"), (0.34, "Audio", "#F58518"), (0.20, "Vision", "#54A24B")): + axes[0].text(0.08, y, label, color=color, weight="bold") + axes[0].scatter([0.40, 0.70], [y, y], s=100, color=color) + axes[0].annotate("", xy=(0.66, y), xytext=(0.44, y), arrowprops={"arrowstyle": "<->", "color": color}) + axes[1].text(0.08, y, label, color=color, weight="bold") + axes[1].plot([0.36, 0.58, 0.80], [y, y + 0.035, y - 0.035], marker="o", color=color) + axes[2].text(0.04, y, label, color=color, weight="bold") + axes[2].annotate("", xy=(0.76, y), xytext=(0.32, y), arrowprops={"arrowstyle": "->", "color": color, "lw": 2}) + axes[2].text(0.51, y + 0.045, "predict", ha="center", fontsize=8, color=color) + fig.suptitle("Three testable meanings of shared information", fontsize=14) + fig.tight_layout() + path = figure_dir / "01_shared_definitions.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + shift_rows_only = [row for row in shift_rows if row.get("control_type") == "shift"] + fig, axes = plt.subplots(1, 2, figsize=(12, 4), sharey=True) + colors = {"text": "#4C78A8", "audio": "#F58518", "vision": "#54A24B"} + for axis, context in zip(axes, ("same_slot", "local_pm1")): + for modality in MODS: + selected = [row for row in shift_rows_only if row["context"] == context and row["target_modality"] == modality] + if not selected: + continue + deltas = sorted({int(row["delta"]) for row in selected}) + means = [] + for delta in deltas: + by_video: dict[str, list[float]] = defaultdict(list) + for row in selected: + if int(row["delta"]) == delta: + by_video[str(row["video_id"])].append(float(row["mse"])) + means.append(float(np.mean([np.mean(values) for values in by_video.values()]))) + axis.plot(deltas, means, marker="o", ms=3, label=modality.title(), color=colors[modality]) + axis.axvline(0, color="black", lw=0.8, ls="--") + axis.set_title(context.replace("_", " ")) + axis.set_xlabel("source shift in slots") + axis.grid(alpha=0.25) + axis.legend(frameon=False) + axes[0].set_ylabel("held-out MSE (lower is better)") + fig.suptitle("Predictive correspondence across source-time shifts") + fig.tight_layout() + path = figure_dir / "02_predictive_shift_curves.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + contexts = sorted({str(row["context"]) for row in predictive_rows}) + targets = list(MODS) + fig, axes = plt.subplots(1, len(contexts), figsize=(5 * len(contexts), 4), squeeze=False) + for axis, context in zip(axes[0], contexts): + selected = [row for row in predictive_rows if row["context"] == context] + for index, modality in enumerate(targets): + modality_rows = [item for item in selected if item["target_modality"] == modality] + if modality_rows: + axis.scatter([index - 0.12], [np.mean([float(row["heldout_r2_original_from_other_modalities"]) for row in modality_rows])], s=75, label="original", color="#4C78A8") + axis.scatter([index + 0.12], [np.mean([float(row["heldout_r2_private_residual_from_other_modalities"]) for row in modality_rows])], s=75, label="private residual", color="#E45756") + axis.axhline(0, color="black", lw=0.8) + axis.set_xticks(range(len(targets)), [v.title() for v in targets]) + axis.set_title(context.replace("_", " ")) + axis.grid(axis="y", alpha=0.25) + axis.set_ylabel("held-out R²") + handles, labels = axes[0, 0].get_legend_handles_labels() + if handles: + fig.legend(handles, labels, loc="upper center", ncol=2, frameon=False) + fig.suptitle("Can other modalities predict the private residual?", y=1.04) + fig.tight_layout() + path = figure_dir / "03_original_vs_private_predictability.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + pair_rows = [row for row in gcca_rows if str(row.get("pair", "")) in {"text-audio", "text-vision", "audio-vision"}] + pairs = ["text-audio", "text-vision", "audio-vision"] + fig, axis = plt.subplots(figsize=(7, 4)) + means = [np.mean([float(row["heldout_mean_component_correlation"]) for row in pair_rows if row["pair"] == pair]) for pair in pairs] + errors = [np.std([float(row["heldout_mean_component_correlation"]) for row in pair_rows if row["pair"] == pair], ddof=1) if sum(row["pair"] == pair for row in pair_rows) > 1 else 0 for pair in pairs] + axis.bar(pairs, means, yerr=errors, color=["#4C78A8", "#54A24B", "#F58518"], capsize=4) + axis.axhline(0, color="black", lw=0.8) + axis.set_ylabel("mean held-out canonical component correlation") + axis.set_title("Regularized GCCA: shared-view correlations by pair") + axis.grid(axis="y", alpha=0.25) + fig.tight_layout() + path = figure_dir / "04_gcca_pair_correlations.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + branch_views = ("shared_only", "private_only", "shared+private") + methods = ("Similarity-SPR", "GCCA", "Predictive-Same", "Predictive-Local1") + fig, axis = plt.subplots(figsize=(10, 4.5)) + width = 0.24 + x = np.arange(len(methods)) + for offset, view in enumerate(branch_views): + scores = [ + next((float(row["macro_f1"]) for row in emotion_metrics if row["method"] == method and row["view"] == view), np.nan) + for method in methods + ] + axis.bar(x + (offset - 1) * width, scores, width, label=view) + axis.set_xticks(x, methods, rotation=15, ha="right") + axis.set_ylabel("OOF Macro-F1") + axis.set_title("Emotion probe: shared-only, private-only, and both") + axis.legend(frameon=False) + axis.grid(axis="y", alpha=0.25) + fig.tight_layout() + path = figure_dir / "05_shared_private_emotion_probe.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + main_rows = [row for row in dimension_metrics if row["view"] in {"main_1280d", "main_external_nonmatched", "math_all_modalities"}] + fig, axis = plt.subplots(figsize=(8, 5)) + for row in main_rows: + axis.scatter(float(row["mae"]), float(row["pearson"]), s=75) + axis.annotate(str(row["method"]), (float(row["mae"]), float(row["pearson"])), xytext=(4, 4), textcoords="offset points", fontsize=8) + axis.set_xlabel("OOF MAE (lower is better)") + axis.set_ylabel("OOF Pearson (higher is better)") + axis.set_title("Emotion strength probe across frozen representations") + axis.grid(alpha=0.25) + fig.tight_layout() + path = figure_dir / "06_emotion_mae_pearson.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + fixed_methods = ("RawPrivate-PCA", "Similarity-SPR", "GCCA", "Predictive-Same", "Predictive-Local1") + fig, axis = plt.subplots(figsize=(9, 4.5)) + fixed_rows = [row for row in dimension_metrics if row["view"] == "main_1280d" and row["method"] in fixed_methods] + scores = [next((float(row["macro_f1"]) for row in fixed_rows if row["method"] == method), np.nan) for method in fixed_methods] + axis.bar(fixed_methods, scores, color=["#9D755D", "#4C78A8", "#72B7B2", "#F58518", "#E45756"]) + axis.set_ylabel("OOF Macro-F1") + axis.set_title("Dimension-matched 1,280D emotion representations") + axis.tick_params(axis="x", labelrotation=18) + axis.grid(axis="y", alpha=0.25) + fig.tight_layout() + path = figure_dir / "07_dimension_matched_macro_f1.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + ablation_methods = ("Similarity-SPR", "GCCA", "Predictive-Same", "Predictive-Local1") + sources = ("private_text", "private_audio", "private_vision", "private_audio_vision", "private_all") + fig, axes = plt.subplots(1, 2, figsize=(13, 4.5)) + width = 0.16 + x = np.arange(len(sources)) + for index, method in enumerate(ablation_methods): + f1_values = [next((float(row["macro_f1"]) for row in ablation_rows if row["method"] == method and row["view"] == source), np.nan) for source in sources] + mae_values = [next((float(row["mae"]) for row in ablation_rows if row["method"] == method and row["view"] == source), np.nan) for source in sources] + axes[0].bar(x + (index - 1.5) * width, f1_values, width, label=method) + axes[1].bar(x + (index - 1.5) * width, mae_values, width, label=method) + for axis in axes: + axis.set_xticks(x, sources, rotation=25, ha="right") + axis.grid(axis="y", alpha=0.25) + axes[0].set_ylabel("OOF Macro-F1") + axes[1].set_ylabel("OOF MAE") + axes[0].set_title("Private-source classification") + axes[1].set_title("Private-source strength prediction") + axes[1].legend(fontsize=8, frameon=False) + fig.suptitle("Private modality ablation") + fig.tight_layout() + path = figure_dir / "08_private_source_ablation.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + fig, axis = plt.subplots(figsize=(7.5, 4.5)) + for context in ("same_slot", "local_pm1"): + values = [] + deltas = list(range(-5, 6)) + for delta in deltas: + selected = [row for row in shift_rows if row.get("control_type") == "shift" and row["context"] == context and int(row["delta"]) == delta] + by_video: dict[str, list[float]] = defaultdict(list) + for row in selected: + by_video[str(row["video_id"])].append(float(row["mse"])) + values.append(float(np.mean([np.mean(video_values) for video_values in by_video.values()])) if by_video else np.nan) + axis.plot(deltas, values, marker="o", label=context.replace("_", " ")) + shuffle_by_video: dict[str, list[float]] = defaultdict(list) + for row in shift_rows: + if row.get("control_type") == "within_video_shuffle": + shuffle_by_video[str(row["video_id"])].append(float(row["mse"])) + shuffle_values = [float(np.mean(values)) for values in shuffle_by_video.values()] + if shuffle_values: + axis.axhline(float(np.mean(shuffle_values)), color="#E45756", ls="--", label="within-video shuffle") + axis.axvline(0, color="black", lw=0.8, ls=":") + axis.set_xlabel("source shift in slots") + axis.set_ylabel("held-out MSE, averaged over targets (lower is better)") + axis.set_title("Same-slot, shifted, and shuffled controls") + axis.legend(frameon=False) + axis.grid(alpha=0.25) + fig.tight_layout() + path = figure_dir / "09_shift_shuffle_controls.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + + stability_rows = [row for row in gcca_rows if row.get("metric") == "spectrum_stability"] + modality_rows = list(explained_rows) + fig, axes = plt.subplots(1, 2, figsize=(10, 4)) + axes[0].plot([int(row["fold"]) for row in stability_rows], [float(row["effective_rank"]) for row in stability_rows], marker="o", label="effective rank") + axes[0].plot([int(row["fold"]) for row in stability_rows], [int(row["components_for_90pct_energy"]) for row in stability_rows], marker="s", label="components for 90%") + axes[0].set_xlabel("outer fold") + axes[0].set_title("GCCA shared-spectrum stability") + axes[0].legend(frameon=False, fontsize=8) + axes[0].grid(alpha=0.25) + for modality in MODS: + selected = [row for row in modality_rows if row["modality"] == modality] + axes[1].plot([int(row["fold"]) for row in selected], [float(row["heldout_shared_reconstruction_r2"]) for row in selected], marker="o", label=modality.title()) + axes[1].set_xlabel("outer fold") + axes[1].set_title("Held-out variance explained from common GCCA") + axes[1].legend(frameon=False) + axes[1].grid(alpha=0.25) + fig.tight_layout() + path = figure_dir / "10_gcca_stability_and_variance.png" + fig.savefig(path, dpi=170, bbox_inches="tight") + plt.close(fig) + created.append(path.name) + return created + + +def run(args: argparse.Namespace) -> None: + started = time.time() + device = torch.device(args.device if args.device != "auto" else ("cuda" if torch.cuda.is_available() else "cpu")) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable in this uv environment") + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + output_dir: Path = args.output_dir + output_dir.mkdir(parents=True, exist_ok=True) + samples = load_feature_samples(args.feature_dir, args.manifest) + samples_by_id = {sample.sample_id: sample for sample in samples} + if len(samples) != 100: + raise ValueError(f"expected the complete 100-sample Q1 feature set, found {len(samples)}") + splits = json.loads(args.splits.read_text(encoding="utf-8")) + if len(splits) != 5: + raise ValueError(f"expected five fixed video-group folds, found {len(splits)}") + all_valid_ids = [sample_id for split in splits for sample_id in split["validation_sample_ids"]] + if len(all_valid_ids) != len(set(all_valid_ids)) or set(all_valid_ids) != set(samples_by_id): + raise ValueError("fixed held-out folds do not cover the 100 feature samples exactly once") + + expected_fold_by_id: dict[str, int] = {} + fold_rows: list[dict[str, Any]] = [] + for split in splits: + fold = int(split["fold"]) + train_ids = list(split["train_sample_ids"]) + valid_ids = list(split["validation_sample_ids"]) + train_groups = {samples_by_id[sample_id].group_id for sample_id in train_ids} + valid_groups = {samples_by_id[sample_id].group_id for sample_id in valid_ids} + if train_groups & valid_groups: + raise ValueError(f"video_id leakage in fixed fold {fold}: {sorted(train_groups & valid_groups)}") + if set(split.get("train_video_ids", train_groups)) != train_groups: + raise ValueError(f"train video_id list disagrees with sample manifest in fold {fold}") + if set(split.get("validation_video_ids", valid_groups)) != valid_groups: + raise ValueError(f"validation video_id list disagrees with sample manifest in fold {fold}") + for sample_id in valid_ids: + if sample_id in expected_fold_by_id: + raise ValueError(f"sample appears in multiple held-out folds: {sample_id}") + expected_fold_by_id[sample_id] = fold + fold_rows.extend({ + "fold": fold, + "sample_id": sample_id, + "video_id": samples_by_id[sample_id].group_id, + "split": "train" if sample_id in set(train_ids) else "valid_oof", + } for sample_id in [*train_ids, *valid_ids]) + _write_rows(output_dir / "fold_assignments.csv", fold_rows) + + config = { + "experiment": "Similarity vs Correlation vs Predictability shared information definitions", + "seed": args.seed, + "device": str(device), + "sample_count": len(samples), + "video_id_count": len({sample.group_id for sample in samples}), + "fold_count": len(splits), + "grouped_by": "video_id/group_id", + "grid_size": GRID_SIZE, + "frozen_temporal_model": "M4_sourceTime fold checkpoints", + "source_features": {name: int(samples[0].features[name].shape[1]) for name in MODS}, + "gcca_pca_components": PCA_COMPONENTS, + "gcca_shared_dimension": 32, + "predictive_contexts": PREDICTIVE_CONTEXTS, + "predictive_ridge_alphas": ALPHAS, + "predictive_ridge_solver": "cholesky", + "private_residual_alpha_selection": "reuse the selected original-target alpha; no separate residual alpha search", + "predictive_shift_slots": list(range(-5, 6)), + "predictive_eval_core_slots": CORE_SLOTS.tolist(), + "within_video_shuffle_repeats": args.shuffle_repeats, + "dimension_matched_per_slot": MAIN_DIM_PER_SLOT, + "dimension_matched_clip": MAIN_CLIP_DIM, + "emotion_probe": { + "classification": "StandardScaler + LogisticRegression(C=0.05, max_iter=5000)", + "regression": "StandardScaler + Ridge(alpha=25), clipped to [-3, 3]", + "temporal_pooling": "50 slots to five consecutive 10-slot mean bins", + "emotion_labels_used_for_representation_learning": False, + }, + "paired_video_bootstrap_repeats": args.bootstrap_repeats, + "multiple_comparison_correction": False, + } + _write_json(output_dir / "config.json", config) + + predictive_summary_rows: list[dict[str, Any]] = [] + predictive_control_rows: list[dict[str, Any]] = [] + residual_predictability_rows: list[dict[str, Any]] = [] + gcca_summary_rows: list[dict[str, Any]] = [] + shared_explained_rows: list[dict[str, Any]] = [] + emotion_prediction_rows: list[dict[str, Any]] = [] + fold_runtime_rows: list[dict[str, Any]] = [] + + for split in splits: + fold_start = time.time() + fold = int(split["fold"]) + train_samples = [samples_by_id[sample_id] for sample_id in split["train_sample_ids"]] + heldout_samples = [samples_by_id[sample_id] for sample_id in split["validation_sample_ids"]] + all_fold_samples = [*train_samples, *heldout_samples] + all_ids = [sample.sample_id for sample in all_fold_samples] + feature_stats = fit_feature_stats(train_samples) + _, _, temporal_by_id = _collect_fold_features( + fold=fold, + train_samples=train_samples, + validation_samples=heldout_samples, + feature_stats=feature_stats, + checkpoint_root=args.m4_checkpoint_root, + device=device, + batch_size=args.batch_size, + ) + pooled = _pool_original_source(all_fold_samples, feature_stats, temporal_by_id) + + spr_representations = _spr_fold( + fold=fold, + sample_ids=all_ids, + pooled=pooled, + device=device, + checkpoint_root=args.spr_checkpoint_root, + batch_size=args.batch_size, + ) + gcca_representations, fold_gcca_rows, fold_explained_rows = _gcca_fold( + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + pooled=pooled, + seed=args.seed, + ) + gcca_summary_rows.extend(fold_gcca_rows) + shared_explained_rows.extend(fold_explained_rows) + + same_train, same_valid, same_summary, same_controls, same_residuals = _predictive_fold( + fold=fold, + context_name="same_slot", + train_samples=train_samples, + heldout_samples=heldout_samples, + pooled=pooled, + seed=args.seed, + shuffle_repeats=args.shuffle_repeats, + ) + local_train, local_valid, local_summary, local_controls, local_residuals = _predictive_fold( + fold=fold, + context_name="local_pm1", + train_samples=train_samples, + heldout_samples=heldout_samples, + pooled=pooled, + seed=args.seed, + shuffle_repeats=args.shuffle_repeats, + ) + predictive_summary_rows.extend(same_summary) + predictive_summary_rows.extend(local_summary) + predictive_control_rows.extend(same_controls) + predictive_control_rows.extend(local_controls) + residual_predictability_rows.extend(same_residuals) + residual_predictability_rows.extend(local_residuals) + + method_representations: dict[str, Mapping[str, Mapping[str, Any]]] = { + "Similarity-SPR": spr_representations, + "GCCA": gcca_representations, + "Predictive-Same": {**same_train, **same_valid}, + "Predictive-Local1": {**local_train, **local_valid}, + } + main_vectors_by_method: dict[str, dict[str, np.ndarray]] = {} + for method, representations in method_representations.items(): + views_by_id = {sample_id: _representation_views(representations[sample_id]) for sample_id in all_ids} + all_view_names = sorted(set.intersection(*(set(views_by_id[sample_id]) for sample_id in all_ids))) + for view in all_view_names: + vectors = {sample_id: views_by_id[sample_id][view] for sample_id in all_ids} + emotion_prediction_rows.extend(_fit_probe_predictions( + method=method, + view=view, + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + vectors_by_id=vectors, + seed=args.seed, + )) + main_sequences = { + sample_id: views_by_id[sample_id]["shared+private"] + for sample_id in all_ids + } + train_matched, valid_matched, pca = _dimension_matched_sequences( + train_samples, heldout_samples, main_sequences, seed=args.seed + fold + ) + main_vectors = {**train_matched, **valid_matched} + main_vectors_by_method[method] = main_vectors + emotion_prediction_rows.extend(_fit_probe_predictions( + method=method, + view="main_1280d", + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + vectors_by_id=main_vectors, + seed=args.seed, + )) + + raw_private_path = args.spr_checkpoint_root / f"fold_{fold:02d}" / "raw_private_pca.npz" + raw_private_sequences = _raw_private_pca_sequences(all_ids, pooled, raw_private_path) + emotion_prediction_rows.extend(_fit_probe_predictions( + method="RawPrivate-PCA", + view="main_1280d", + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + vectors_by_id=raw_private_sequences, + seed=args.seed, + )) + fold_runtime_rows.append({ + "fold": fold, + "train_sample_count": len(train_samples), + "heldout_sample_count": len(heldout_samples), + "train_video_count": len({sample.group_id for sample in train_samples}), + "heldout_video_count": len({sample.group_id for sample in heldout_samples}), + "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else "cpu", + "runtime_seconds": time.time() - fold_start, + }) + _write_json(output_dir / "progress.json", { + "completed_folds": [int(row["fold"]) for row in fold_runtime_rows], + "total_folds": len(splits), + "last_fold_runtime_seconds": fold_runtime_rows[-1]["runtime_seconds"], + "updated_utc": datetime.now(timezone.utc).isoformat(), + }) + print( + f"[shared definitions fold {fold}/5] train={len(train_samples)} heldout={len(heldout_samples)} " + f"runtime={fold_runtime_rows[-1]['runtime_seconds']:.1f}s", + flush=True, + ) + del temporal_by_id, pooled, spr_representations, gcca_representations + del same_train, same_valid, local_train, local_valid, method_representations + if device.type == "cuda": + torch.cuda.empty_cache() + + reference_rows = _load_reference_predictions( + tsfa_predictions_path=args.tsfa_predictions, + math_predictions_path=args.math_predictions, + math_splits_path=args.math_splits, + expected_fold_by_id=expected_fold_by_id, + samples_by_id=samples_by_id, + ) + emotion_prediction_rows.extend(reference_rows) + emotion_metrics = _summarize_probe_rows(emotion_prediction_rows) + dimension_metrics = [ + row for row in emotion_metrics + if row["view"] in {"main_1280d", "main_external_nonmatched", "math_all_modalities"} + ] + for row in dimension_metrics: + row["dimension_matched"] = row["view"] == "main_1280d" + row["comparison_note"] = ( + "1280D, five-bin pooled" if row["dimension_matched"] else + ("external 6845D comparator" if row["view"] == "main_external_nonmatched" else "math OOF comparator; feature dimension not exported") + ) + + paired_specs = ( + ("Predictive-Same", "RawPrivate-PCA", "main_1280d", "main_1280d"), + ("GCCA", "RawPrivate-PCA", "main_1280d", "main_1280d"), + ("Predictive-Same", "Similarity-SPR", "main_1280d", "main_1280d"), + ("Predictive-Same", "TSFA+RawPrivate", "main_1280d", "main_external_nonmatched"), + ("Predictive-Same", "Math-B0", "main_1280d", "math_all_modalities"), + ("Predictive-Same", "Math-B4", "main_1280d", "math_all_modalities"), + ("Predictive-Same", "Predictive-Local1", "main_1280d", "main_1280d"), + ) + paired_rows: list[dict[str, Any]] = [] + for contrast_index, (candidate_name, reference_name, candidate_view, reference_view) in enumerate(paired_specs): + candidate_rows = [row for row in emotion_prediction_rows if row["method"] == candidate_name and row["view"] == candidate_view] + reference_rows_for_pair = [row for row in emotion_prediction_rows if row["method"] == reference_name and row["view"] == reference_view] + paired_rows.extend(_paired_video_bootstrap( + candidate_rows, + reference_rows_for_pair, + candidate_name=f"{candidate_name}/{candidate_view}", + reference_name=f"{reference_name}/{reference_view}", + repeats=args.bootstrap_repeats, + seed=args.seed + 5000 + contrast_index, + )) + + private_ablation_rows = _make_private_ablation_rows(emotion_metrics) + predictive_shift_summary = _summarize_shift_controls(predictive_control_rows) + + seed_summary = [ + {"seed": args.seed, **row} + for row in emotion_metrics + ] + figure_files = _save_figures( + output_dir, + predictive_rows=predictive_summary_rows, + shift_rows=predictive_control_rows, + gcca_rows=gcca_summary_rows, + explained_rows=shared_explained_rows, + emotion_metrics=emotion_metrics, + dimension_metrics=dimension_metrics, + ablation_rows=private_ablation_rows, + ) + + for filename, rows in ( + ("predictive_shared_summary.csv", predictive_summary_rows), + ("predictive_shift_controls.csv", predictive_control_rows), + ("predictive_shift_summary.csv", predictive_shift_summary), + ("private_residual_predictability.csv", residual_predictability_rows), + ("gcca_summary.csv", gcca_summary_rows), + ("shared_explained_variance.csv", shared_explained_rows), + ("emotion_probe_metrics.csv", emotion_metrics), + ("emotion_probe_predictions.csv", emotion_prediction_rows), + ("paired_contrasts.csv", paired_rows), + ("dimension_matched_summary.csv", dimension_metrics), + ("private_source_ablation.csv", private_ablation_rows), + ("seed_summary.csv", seed_summary), + ("fold_runtime.csv", fold_runtime_rows), + ): + _write_rows(output_dir / filename, rows) + + checkpoint_hashes: dict[str, str] = {} + for fold in range(1, 6): + for path in ( + args.m4_checkpoint_root / ("" if fold == 1 else f"fold_{fold:02d}/") / "M4_sourceTime" / "checkpoint.pt", + args.spr_checkpoint_root / f"fold_{fold:02d}" / "SPR.pt", + ): + if path.is_file(): + checkpoint_hashes[str(path.relative_to(args.q1_root))] = hashlib.sha256(path.read_bytes()).hexdigest() + manifest = { + "created_utc": datetime.now(timezone.utc).isoformat(), + "runtime_seconds": time.time() - started, + "python": platform.python_version(), + "torch": torch.__version__, + "sklearn": sklearn.__version__, + "cuda_available": torch.cuda.is_available(), + "gpu_name": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None, + "device_used": str(device), + "folds": fold_runtime_rows, + "checkpoint_hashes": checkpoint_hashes, + "emotion_labels_used_for_representation_learning": False, + "neural_spr_retrained": False, + "frozen_m4_temporal_branch": True, + "fixed_video_group_folds": str(args.splits), + "math_read_only_inputs": [str(args.math_predictions), str(args.math_splits)], + "bootstrap_note": "Paired percentile bootstrap resamples video_id clusters; no multiple-comparison correction.", + "predictive_shift_summary_metric": "video_id-macro mean MSE; per-clip R2 is retained as a diagnostic only because short clips can have near-zero target variance.", + "figures": figure_files, + "output_csvs": [ + "fold_assignments.csv", "gcca_summary.csv", "predictive_shared_summary.csv", + "predictive_shift_controls.csv", "predictive_shift_summary.csv", + "private_residual_predictability.csv", "shared_explained_variance.csv", + "emotion_probe_metrics.csv", "emotion_probe_predictions.csv", "paired_contrasts.csv", + "dimension_matched_summary.csv", "private_source_ablation.csv", "seed_summary.csv", + ], + } + _write_json(output_dir / "run_manifest.json", manifest) + (output_dir / "progress.json").unlink(missing_ok=True) + print(f"[shared definitions complete] outputs={output_dir} runtime={manifest['runtime_seconds']:.1f}s", flush=True) + + +def finalize_existing(args: argparse.Namespace) -> None: + """Rebuild diagnostics and figures from completed fold outputs only.""" + output_dir: Path = args.output_dir + required = ( + "predictive_shift_controls.csv", "predictive_shared_summary.csv", "gcca_summary.csv", + "shared_explained_variance.csv", "emotion_probe_metrics.csv", + "dimension_matched_summary.csv", "private_source_ablation.csv", + ) + missing = [name for name in required if not (output_dir / name).is_file()] + if missing: + raise FileNotFoundError(f"cannot finalize incomplete experiment outputs: {missing}") + controls = _read_csv(output_dir / "predictive_shift_controls.csv") + predictive = _read_csv(output_dir / "predictive_shared_summary.csv") + for row in predictive: + if row.get("private_alpha_selection") == "reused original-target grouped inner-CV choice": + row["private_inner_cv_r2_by_alpha"] = "{}" + _write_rows(output_dir / "predictive_shared_summary.csv", predictive) + config_path = output_dir / "config.json" + if config_path.is_file(): + config = json.loads(config_path.read_text(encoding="utf-8")) + config["predictive_eval_core_slots"] = CORE_SLOTS.tolist() + config["predictive_shift_metric"] = "video_id-macro mean MSE; core slots 6-43" + config["private_residual_alpha_selection"] = "reuse the selected original-target alpha; no separate residual alpha search" + _write_json(config_path, config) + gcca = _read_csv(output_dir / "gcca_summary.csv") + explained = _read_csv(output_dir / "shared_explained_variance.csv") + metrics = _read_csv(output_dir / "emotion_probe_metrics.csv") + dimensions = _read_csv(output_dir / "dimension_matched_summary.csv") + ablation = _read_csv(output_dir / "private_source_ablation.csv") + shift_summary = _summarize_shift_controls(controls) + _write_rows(output_dir / "predictive_shift_summary.csv", shift_summary) + figures = _save_figures( + output_dir, + predictive_rows=predictive, + shift_rows=controls, + gcca_rows=gcca, + explained_rows=explained, + emotion_metrics=metrics, + dimension_metrics=dimensions, + ablation_rows=ablation, + ) + manifest_path = output_dir / "run_manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) if manifest_path.is_file() else {} + manifest["figures"] = figures + manifest["predictive_shift_summary_metric"] = ( + "video_id-macro mean MSE; per-clip R2 is diagnostic only because individual clips can have near-zero target variance." + ) + manifest["finalized_utc"] = datetime.now(timezone.utc).isoformat() + _write_json(manifest_path, manifest) + print(f"[shared definitions finalized] outputs={output_dir}", flush=True) + + +def build_arg_parser() -> argparse.ArgumentParser: + q1_root = Path(__file__).resolve().parents[1] + repo_root = q1_root.parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default="auto", choices=("auto", "cpu", "cuda")) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--shuffle-repeats", type=int, default=20) + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--finalize-existing", action="store_true", help="rebuild summaries/figures from completed outputs without rerunning folds") + parser.add_argument("--q1-root", type=Path, default=q1_root) + parser.add_argument("--feature-dir", type=Path, default=q1_root / "outputs/q1_features/features") + parser.add_argument("--manifest", type=Path, default=q1_root / "outputs/audit/manifest.csv") + parser.add_argument("--splits", type=Path, default=q1_root / "outputs/method_comparison/splits.json") + parser.add_argument("--m4-checkpoint-root", dest="m4_checkpoint_root", type=Path, + default=q1_root / "outputs/alignment_debug/heldout") + parser.add_argument("--spr-checkpoint-root", dest="spr_checkpoint_root", type=Path, + default=q1_root / "outputs/tsfa_shared_private/checkpoints") + parser.add_argument("--tsfa-predictions", type=Path, + default=q1_root / "outputs/tsfa_shared_private/emotion_probe_predictions.csv") + parser.add_argument("--math-predictions", type=Path, + default=repo_root / "math/results/model_comparison/oof_predictions.csv") + parser.add_argument("--math-splits", type=Path, + default=repo_root / "math/results/model_comparison/split_assignments.csv") + parser.add_argument("--output-dir", type=Path, + default=q1_root / "outputs/shared_definition_comparison") + return parser + + +def main() -> None: + args = build_arg_parser().parse_args() + if args.finalize_existing: + finalize_existing(args) + else: + run(args) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/q1/shared_time_private_content.py b/deep_learning/Q1/q1/shared_time_private_content.py new file mode 100644 index 0000000..748f0e9 --- /dev/null +++ b/deep_learning/Q1/q1/shared_time_private_content.py @@ -0,0 +1,1010 @@ +"""Shared-time/private-content experiment for Q1. + +The frozen M4_sourceTime checkpoints provide a common temporal coordinate. +Text, audio, and vision feature vectors remain modality-specific. Optional +same-slot products/differences are task input features, not a shared semantic +space. All learned PCA transforms and emotion probes are fitted inside each +training fold. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import platform +import random +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import sklearn +import torch +from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score +from sklearn.preprocessing import StandardScaler + +from .correspondence_eval import _write_csv +from .experiment_data import ( + FeatureSample, + fit_feature_stats, + load_feature_samples, +) +from .m4_shared_latent_eval import _normalize_attention, _pair_metrics +from .tsfa_emotion_probe import CLASS_NAMES, _class_from_sentiment, _pearson +from .tsfa_experiment import GRID_SIZE, _collect_fold_features +from .tsfa_shared_private import _pool_five, _pool_original_source +from .types import MODALITIES + + +MODS = tuple(MODALITIES) +PAIRINGS = (("text", "audio"), ("text", "vision"), ("audio", "vision")) +OUTPUT_NAME = "shared_time_private_content" +SLOT_PCA_SIZE = 256 +MODAL_PCA_DIMS = {"text": 64, "audio": 24, "vision": 64} +INTERACTION_DIM = 24 +CLASS_SIGNS = np.asarray([-1.0, 0.0, 1.0], dtype=np.float64) + + +def _fit_pca(train_values: Sequence[np.ndarray], input_dim: int, seed: int) -> PCA: + stacked = np.concatenate([np.asarray(value, dtype=np.float32).reshape(-1, input_dim) for value in train_values]) + n_components = min(SLOT_PCA_SIZE, input_dim, len(stacked) - 1) + if n_components < 1: + raise ValueError("not enough training slots to fit a PCA projection") + return PCA(n_components=n_components, svd_solver="auto", random_state=seed).fit(stacked) + + +def _transform_padded(values: np.ndarray, pca: PCA, size: int = SLOT_PCA_SIZE) -> np.ndarray: + transformed = pca.transform(np.asarray(values, dtype=np.float32).reshape(-1, values.shape[-1])) + out = np.zeros((len(transformed), size), dtype=np.float32) + out[:, : transformed.shape[1]] = transformed.astype(np.float32, copy=False) + return out.reshape(*values.shape[:-1], size) + + +def _fit_transform_map( + train_ids: Sequence[str], + values_by_id: Mapping[str, np.ndarray], + *, + seed: int, + requested_dim: int = SLOT_PCA_SIZE, +) -> tuple[PCA, dict[str, np.ndarray]]: + input_dim = int(next(iter(values_by_id.values())).shape[-1]) + pca = _fit_pca([values_by_id[sid] for sid in train_ids], input_dim, seed) + transformed = {sid: _transform_padded(value, pca, requested_dim) for sid, value in values_by_id.items()} + return pca, transformed + + +def _flatten_five(values: np.ndarray) -> np.ndarray: + return _pool_five(values) + + +def _pearson_safe(actual: np.ndarray, predicted: np.ndarray) -> float: + value = _pearson(np.asarray(actual, dtype=np.float64), np.asarray(predicted, dtype=np.float64)) + return float(value) if np.isfinite(value) else 0.0 + + +def _metric_values(rows: Sequence[Mapping[str, Any]], class_key: str, value_key: str) -> dict[str, float]: + true_class = np.asarray([int(row["true_class_id"]) for row in rows], dtype=np.int64) + predicted_class = np.asarray([int(row[class_key]) for row in rows], dtype=np.int64) + actual = np.asarray([float(row["true_label"]) for row in rows], dtype=np.float64) + predicted = np.asarray([float(row[value_key]) for row in rows], dtype=np.float64) + return { + "accuracy": float(accuracy_score(true_class, predicted_class)), + "macro_f1": float(f1_score(true_class, predicted_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(np.mean(np.abs(actual - predicted))), + "pearson": _pearson_safe(actual, predicted), + } + + +def _training_probe(train_x: np.ndarray, train_class: np.ndarray, train_y: np.ndarray, seed: int): + scaler_cls = StandardScaler() + train_class_x = scaler_cls.fit_transform(train_x) + classifier = LogisticRegression(C=0.05, max_iter=5000, solver="lbfgs", random_state=seed) + classifier.fit(train_class_x, train_class) + + scaler_direct = StandardScaler() + train_direct_x = scaler_direct.fit_transform(train_x) + direct = Ridge(alpha=25.0).fit(train_direct_x, train_y) + + scaler_intensity = StandardScaler() + train_intensity_x = scaler_intensity.fit_transform(train_x) + intensity = Ridge(alpha=25.0).fit(train_intensity_x, np.abs(train_y)) + return (scaler_cls, classifier), (scaler_direct, direct), (scaler_intensity, intensity) + + +def _predict_probe(probe, values: np.ndarray) -> np.ndarray: + scaler, model = probe + return model.predict(scaler.transform(values)) + + +def _build_interaction_slots( + by_id: Mapping[str, Mapping[str, np.ndarray]], + included_pairs: Sequence[tuple[str, str]], +) -> dict[str, np.ndarray]: + result = {} + for sid, modalities in by_id.items(): + base = np.concatenate([modalities[name] for name in MODS], axis=-1) + blocks = [base] + for left, right in included_pairs: + left_value = modalities[left][:, :INTERACTION_DIM] + right_value = modalities[right][:, :INTERACTION_DIM] + blocks.append(np.concatenate([left_value * right_value, np.abs(left_value - right_value)], axis=-1)) + result[sid] = np.concatenate(blocks, axis=-1).astype(np.float32, copy=False) + return result + + +def _interaction_sets() -> dict[str, tuple[tuple[str, str], ...]]: + pairs = {"TA": PAIRINGS[0], "TV": PAIRINGS[1], "AV": PAIRINGS[2]} + names = ("TA", "TV", "AV") + return {f"I{index}": tuple(pairs[name] for bit, name in enumerate(names) if index & (1 << bit)) for index in range(8)} + + +def _make_fold_representations( + train_ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + seed: int, +) -> tuple[dict[str, dict[str, np.ndarray]], list[dict[str, Any]], dict[str, Any]]: + ids = list(pooled) + raw_by_id = { + sid: np.concatenate([pooled[sid][name] for name in MODS], axis=-1).astype(np.float32, copy=False) + for sid in ids + } + pca_log: list[dict[str, Any]] = [] + modality_pcas: dict[str, PCA] = {} + modality_by_id: dict[str, dict[str, np.ndarray]] = {sid: {} for sid in ids} + for name in MODS: + input_dim = int(pooled[ids[0]][name].shape[-1]) + requested = min(MODAL_PCA_DIMS[name], input_dim) + train_matrix = np.concatenate([pooled[sid][name] for sid in train_ids], axis=0) + pca = PCA(n_components=requested, svd_solver="auto", random_state=seed).fit(train_matrix) + modality_pcas[name] = pca + for sid in ids: + modality_by_id[sid][name] = pca.transform(pooled[sid][name]).astype(np.float32, copy=False) + pca_log.append({ + "projection": f"modality_{name}", + "input_dim": input_dim, + "components": int(pca.n_components_), + "explained_variance_ratio_sum": float(pca.explained_variance_ratio_.sum()), + "train_slot_count": int(len(train_matrix)), + }) + + full_pca, full_projected = _fit_transform_map(train_ids, raw_by_id, seed=seed) + pca_log.append({ + "projection": "all_modalities_joint_256", + "input_dim": int(raw_by_id[ids[0]].shape[-1]), + "components": int(full_pca.n_components_), + "explained_variance_ratio_sum": float(full_pca.explained_variance_ratio_.sum()), + "train_slot_count": int(sum(len(raw_by_id[sid]) for sid in train_ids)), + }) + + representations: dict[str, dict[str, np.ndarray]] = {} + raw_vector = {sid: _flatten_five(value) for sid, value in raw_by_id.items()} + representations["M4-TimeOnly-full-raw"] = raw_vector + representations["Private-Identity"] = raw_vector + pca_private = { + sid: np.concatenate([modality_by_id[sid][name] for name in MODS], axis=-1) + for sid in ids + } + representations["Private-PCA-T64-A24-V64"] = {sid: _flatten_five(value) for sid, value in pca_private.items()} + representations["Private-DimMatched-PCA256"] = {sid: _flatten_five(value) for sid, value in full_projected.items()} + + subset_map = { + "T": ("text",), + "A": ("audio",), + "V": ("vision",), + "TA": ("text", "audio"), + "TV": ("text", "vision"), + "AV": ("audio", "vision"), + "TAV": MODS, + } + subset_metadata: dict[str, dict[str, Any]] = {} + for tag, names in subset_map.items(): + subset_raw = {sid: np.concatenate([pooled[sid][name] for name in names], axis=-1) for sid in ids} + subset_pca, subset_projected = _fit_transform_map(train_ids, subset_raw, seed=seed + len(tag)) + label = f"Subset-{tag}-PCA256" + representations[label] = {sid: _flatten_five(value) for sid, value in subset_projected.items()} + subset_metadata[tag] = {"modalities": names, "components": int(subset_pca.n_components_), "input_dim": int(subset_raw[ids[0]].shape[-1])} + pca_log.append({ + "projection": label, + "input_dim": subset_metadata[tag]["input_dim"], + "components": int(subset_pca.n_components_), + "explained_variance_ratio_sum": float(subset_pca.explained_variance_ratio_.sum()), + "train_slot_count": int(sum(len(subset_raw[sid]) for sid in train_ids)), + }) + + interaction_details: dict[str, dict[str, Any]] = {} + for index, (key, pairs) in enumerate(_interaction_sets().items()): + interaction_raw = _build_interaction_slots(modality_by_id, pairs) + if pairs: + interaction_pca, interaction_projected = _fit_transform_map( + train_ids, interaction_raw, seed=seed + 100 + index + ) + components = int(interaction_pca.n_components_) + ev = float(interaction_pca.explained_variance_ratio_.sum()) + else: + # I0 is the train-fold modality PCA representation with zero padding + # to the same 256-slot output width as the interaction candidates. + interaction_projected = {} + for sid, value in interaction_raw.items(): + out = np.zeros((GRID_SIZE, SLOT_PCA_SIZE), dtype=np.float32) + out[:, : min(value.shape[-1], SLOT_PCA_SIZE)] = value[:, :SLOT_PCA_SIZE] + interaction_projected[sid] = out + components = int(interaction_raw[ids[0]].shape[-1]) + ev = float("nan") + interaction_pca = None + label = f"TAV-{key}" + representations[label] = {sid: _flatten_five(value) for sid, value in interaction_projected.items()} + interaction_details[key] = {"pairs": [f"{left}-{right}" for left, right in pairs], "components": components} + pca_log.append({ + "projection": label, + "input_dim": int(interaction_raw[ids[0]].shape[-1]), + "components": components, + "explained_variance_ratio_sum": ev, + "train_slot_count": int(sum(len(interaction_raw[sid]) for sid in train_ids)), + }) + + metadata = { + "subset_views": subset_map, + "subset_metadata": subset_metadata, + "interaction_details": interaction_details, + "raw_slot_dim": int(raw_by_id[ids[0]].shape[-1]), + "private_pca_slot_dim": int(pca_private[ids[0]].shape[-1]), + "dimension_matched_slot_dim": SLOT_PCA_SIZE, + "time_only_and_private_identity_are_identical": True, + } + return representations, pca_log, metadata + + +def _temporal_rows( + fold: int, + samples: Sequence[FeatureSample], + temporal_by_id: Mapping[str, Mapping[str, Any]], +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + grid = (np.arange(GRID_SIZE, dtype=np.float64) + 0.5) / GRID_SIZE + for sample in samples: + record = temporal_by_id[sample.sample_id] + expected: dict[str, np.ndarray] = {} + for modality in MODS: + valid = np.asarray(record["valid"][modality], dtype=bool) + times = np.asarray(record["times"][modality], dtype=np.float64) + weights = np.asarray(record["weights"][modality], dtype=np.float64) + mu = weights @ times + expected[modality] = mu + violations = int(np.sum(np.diff(mu) < -0.02)) + corr = _pearson_safe(grid, mu) + rows.append({ + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "kind": "modality", + "modality": modality, + "mvr_eps_0_02": violations / max(len(mu) - 1, 1), + "span_signed": float(mu[-1] - mu[0]), + "span_absolute": float(abs(mu[-1] - mu[0])), + "expected_time_correlation": corr, + "valid_source_positions": int(valid.sum()), + "mean_attention_entropy": float(np.mean(-np.sum(weights * np.log(np.maximum(weights, 1e-12)), axis=1))), + }) + for left, right in (*PAIRINGS, *((b, a) for a, b in PAIRINGS)): + src_to_slot, src_idx, _ = _normalize_attention(record["weights"][left], record["valid"][left]) + dst_idx = np.flatnonzero(record["valid"][right]) + mapping = src_to_slot.T @ np.asarray(record["weights"][right], dtype=np.float64)[:, dst_idx] + mapping /= np.maximum(mapping.sum(axis=1, keepdims=True), 1e-12) + metric = _pair_metrics(mapping, record["times"][left][src_idx], record["times"][right][dst_idx]) + rows.append({ + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "kind": "pairwise", + "direction": f"{left}-to-{right}", + "pairwise_time_mae": metric["pairwise_time_mae"], + "pairwise_signed_lag": metric["pairwise_signed_lag"], + "pairwise_time_corr": metric["pairwise_time_corr"], + }) + return rows + + +def _prediction_metrics(representation: str, rows: Sequence[Mapping[str, Any]], dimension: int) -> dict[str, Any]: + class_values = _metric_values(rows, "predicted_class_id", "predicted_dual_label") + direct_actual = np.asarray([float(row["true_label"]) for row in rows], dtype=np.float64) + direct_pred = np.asarray([float(row["predicted_direct_label"]) for row in rows], dtype=np.float64) + dual_pred = np.asarray([float(row["predicted_dual_label"]) for row in rows], dtype=np.float64) + return { + "method_family": "SharedTime-PrivateContent", + "representation": representation, + "feature_dimension": dimension, + "accuracy": class_values["accuracy"], + "macro_f1": class_values["macro_f1"], + "direct_ridge_mae": float(np.mean(np.abs(direct_actual - direct_pred))), + "direct_ridge_pearson": _pearson_safe(direct_actual, direct_pred), + "dual_head_mae": float(np.mean(np.abs(direct_actual - dual_pred))), + "dual_head_pearson": _pearson_safe(direct_actual, dual_pred), + } + + +def _external_rows(path: Path, selectors: Sequence[tuple[str, str, str]]) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + """Normalize prior fixed-OOF predictions to the current result schema.""" + rows: list[dict[str, Any]] = [] + refs: list[dict[str, Any]] = [] + if not path.is_file(): + return rows, refs + with path.open("r", encoding="utf-8-sig", newline="") as handle: + source = list(csv.DictReader(handle)) + for method, view, display in selectors: + selected = [row for row in source if row.get("method") == method and row.get("view") == view] + if not selected: + continue + for row in selected: + rows.append({ + "method_family": "Historical-Control", + "representation": display, + "probe": "historical", + "fold": int(row.get("fold", 0) or 0), + "sample_id": row["sample_id"], + "video_id": row.get("video_id", row["sample_id"].split("/")[0]), + "true_class_id": int(row["true_class_id"]), + "true_label": float(row["true_label"]), + "predicted_class_id": int(row["predicted_class_id"]), + "predicted_direct_label": float(row["predicted_label"]), + "predicted_dual_label": float(row["predicted_label"]), + }) + refs.append({"representation": display, "rows": selected, "feature_dimension": int(selected[0].get("feature_dimension", 0) or 0)}) + return rows, refs + + +def _summarize_historical(refs: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + summary = [] + for ref in refs: + rows = ref["rows"] + true_class = np.asarray([int(row["true_class_id"]) for row in rows], dtype=np.int64) + pred_class = np.asarray([int(row["predicted_class_id"]) for row in rows], dtype=np.int64) + actual = np.asarray([float(row["true_label"]) for row in rows], dtype=np.float64) + pred = np.asarray([float(row["predicted_label"]) for row in rows], dtype=np.float64) + summary.append({ + "method_family": "Historical-Control", + "representation": ref["representation"], + "feature_dimension": ref["feature_dimension"], + "accuracy": float(accuracy_score(true_class, pred_class)), + "macro_f1": float(f1_score(true_class, pred_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "direct_ridge_mae": float(np.mean(np.abs(actual - pred))), + "direct_ridge_pearson": _pearson_safe(actual, pred), + "dual_head_mae": float("nan"), + "dual_head_pearson": float("nan"), + }) + return summary + + +def _group_bootstrap_pair( + model_a: str, + model_b: str, + prediction_maps: Mapping[str, Mapping[str, Mapping[str, Any]]], + groups_by_id: Mapping[str, str], + *, + seed: int, + draws: int = 2000, +) -> list[dict[str, Any]]: + ids = sorted(set(prediction_maps[model_a]) & set(prediction_maps[model_b])) + groups = sorted({groups_by_id[sid] for sid in ids}) + if len(ids) != 100 or not groups: + return [] + metric_specs = ( + ("accuracy", "predicted_class_id", "class"), + ("macro_f1", "predicted_class_id", "class"), + ("direct_ridge_mae", "predicted_direct_label", "direct"), + ("direct_ridge_pearson", "predicted_direct_label", "direct"), + ("dual_head_mae", "predicted_dual_label", "dual"), + ("dual_head_pearson", "predicted_dual_label", "dual"), + ) + def calc(model: str, samples: Sequence[str], key: str, kind: str) -> float: + rows = [prediction_maps[model][sid] for sid in samples] + if kind == "class": + actual = [int(row["true_class_id"]) for row in rows] + pred = [int(row[key]) for row in rows] + if key == "predicted_class_id": + if len(actual) == 0: + return float("nan") + if metric == "accuracy": + return float(accuracy_score(actual, pred)) + return float(f1_score(actual, pred, labels=[0, 1, 2], average="macro", zero_division=0)) + else: + actual = np.asarray([float(row["true_label"]) for row in rows]) + pred = np.asarray([float(row[key]) for row in rows]) + if metric.endswith("mae"): + return float(np.mean(np.abs(actual - pred))) + return _pearson_safe(actual, pred) + return float("nan") + + result = [] + rng = np.random.default_rng(seed) + selected_ids = {group: [sid for sid in ids if groups_by_id[sid] == group] for group in groups} + for metric, key, kind in metric_specs: + if kind == "dual" and any( + "predicted_intensity_abs" not in prediction_maps[name][ids[0]] + for name in (model_a, model_b) + ): + continue + observed_a = calc(model_a, ids, key, kind) + observed_b = calc(model_b, ids, key, kind) + point = observed_a - observed_b + differences = [] + for _ in range(draws): + sampled_groups = rng.choice(groups, size=len(groups), replace=True) + boot_ids = [sid for group in sampled_groups for sid in selected_ids[str(group)]] + if not boot_ids: + continue + a = calc(model_a, boot_ids, key, kind) + b = calc(model_b, boot_ids, key, kind) + if np.isfinite(a) and np.isfinite(b): + differences.append(a - b) + low, high = np.quantile(differences, [0.025, 0.975]) if differences else (float("nan"), float("nan")) + result.append({ + "model_a": model_a, + "model_b": model_b, + "metric": metric, + "difference_a_minus_b": point, + "bootstrap_95_low": float(low), + "bootstrap_95_high": float(high), + "video_group_count": len(groups), + "bootstrap_draws": draws, + "note": "Paired resampling of original video_id groups; exploratory intervals, no multiple-comparison correction.", + }) + return result + + +def _plot_architecture(path: Path) -> None: + fig, ax = plt.subplots(figsize=(12, 4.8)) + ax.axis("off") + boxes = [ + (0.02, "BERT text\n768-D"), (0.02, "eGeMAPS audio\n25-D"), (0.02, "DeiT vision\n192-D"), + ] + for index, (y, label) in enumerate(boxes): + ax.text(0.08, 0.77 - index * 0.27, label, ha="center", va="center", fontsize=11, + bbox={"boxstyle": "round,pad=0.6", "fc": "#e8eff8", "ec": "#3c5a78"}) + ax.annotate("", xy=(0.30, 0.77 - index * 0.27), xytext=(0.19, 0.77 - index * 0.27), + arrowprops={"arrowstyle": "->", "lw": 1.6}) + ax.text(0.42, 0.50, "Frozen M4 source-time weights\n(shared temporal coordinate, 50 slots)", ha="center", va="center", fontsize=11, + bbox={"boxstyle": "round,pad=0.8", "fc": "#fff0ce", "ec": "#ad7b19"}) + ax.annotate("", xy=(0.61, 0.50), xytext=(0.53, 0.50), arrowprops={"arrowstyle": "->", "lw": 1.6}) + ax.text(0.72, 0.68, "T private content", ha="center", va="center", fontsize=10, + bbox={"boxstyle": "round,pad=0.5", "fc": "#edf7ed", "ec": "#4e7d51"}) + ax.text(0.72, 0.50, "A private content", ha="center", va="center", fontsize=10, + bbox={"boxstyle": "round,pad=0.5", "fc": "#edf7ed", "ec": "#4e7d51"}) + ax.text(0.72, 0.32, "V private content", ha="center", va="center", fontsize=10, + bbox={"boxstyle": "round,pad=0.5", "fc": "#edf7ed", "ec": "#4e7d51"}) + ax.annotate("", xy=(0.86, 0.50), xytext=(0.80, 0.50), arrowprops={"arrowstyle": "->", "lw": 1.6}) + ax.text(0.93, 0.50, "Task probe\n+ optional same-slot\ninteractions", ha="center", va="center", fontsize=10, + bbox={"boxstyle": "round,pad=0.7", "fc": "#f4ebf7", "ec": "#7a4c80"}) + ax.set_title("Shared-time, modality-private-content representation", fontsize=14, pad=12) + fig.tight_layout() + fig.savefig(path, dpi=180, bbox_inches="tight") + plt.close(fig) + + +def _plot_metrics(output: Path, metrics: Sequence[Mapping[str, Any]], predictions: Sequence[Mapping[str, Any]], temporal: Sequence[Mapping[str, Any]], sample_payload: Mapping[str, Any]) -> list[str]: + names = [row["representation"] for row in metrics] + new = [row for row in metrics if row["method_family"] == "SharedTime-PrivateContent"] + by_name = {row["representation"]: row for row in new} + generated = [] + + _plot_architecture(output / "01_shared_time_private_content_structure.png") + generated.append("01_shared_time_private_content_structure.png") + + subset_order = ["Subset-T-PCA256", "Subset-A-PCA256", "Subset-V-PCA256", "Subset-TA-PCA256", "Subset-TV-PCA256", "Subset-AV-PCA256", "Subset-TAV-PCA256"] + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + x = np.arange(len(subset_order)) + axes[0].bar(x, [by_name[n]["macro_f1"] for n in subset_order], color="#4678a8") + axes[0].set_ylabel("Macro-F1 (higher is better)") + axes[1].bar(x, [by_name[n]["direct_ridge_mae"] for n in subset_order], color="#d18a44") + axes[1].set_ylabel("Direct Ridge MAE (lower is better)") + for ax in axes: + ax.set_xticks(x, [n.replace("Subset-", "").replace("-PCA256", "") for n in subset_order], rotation=25) + ax.grid(axis="y", alpha=0.25) + fig.suptitle("Modality subset contribution (equal 256-slot output width)") + fig.tight_layout() + fig.savefig(output / "02_modality_subset_contribution.png", dpi=180) + plt.close(fig) + generated.append("02_modality_subset_contribution.png") + + interactions = [f"TAV-I{i}" for i in range(8)] + fig, axes = plt.subplots(1, 2, figsize=(13, 4.8)) + x = np.arange(8) + axes[0].bar(x, [by_name[n]["macro_f1"] for n in interactions], color="#487da7") + axes[0].set_ylabel("Macro-F1") + axes[1].bar(x, [by_name[n]["dual_head_mae"] for n in interactions], color="#ba7049") + axes[1].set_ylabel("Dual-head MAE") + for ax in axes: + ax.set_xticks(x, [f"I{i}" for i in range(8)]) + ax.grid(axis="y", alpha=0.25) + fig.suptitle("Same-slot task-specific interaction ablation") + fig.tight_layout() + fig.savefig(output / "03_interaction_ablation.png", dpi=180) + plt.close(fig) + generated.append("03_interaction_ablation.png") + + unitary = ["Subset-T-PCA256", "Subset-A-PCA256", "Subset-V-PCA256"] + fig, ax1 = plt.subplots(figsize=(9, 5)) + x = np.arange(3) + f1 = [by_name[name]["macro_f1"] for name in unitary] + mae = [by_name[name]["direct_ridge_mae"] for name in unitary] + ax1.bar(x - 0.18, f1, width=0.36, label="Polarity Macro-F1", color="#497ca8") + ax2 = ax1.twinx() + ax2.bar(x + 0.18, mae, width=0.36, label="Intensity MAE", color="#d58c48") + ax1.set_xticks(x, ["Text", "Audio", "Vision"]) + ax1.set_ylabel("Macro-F1") + ax2.set_ylabel("Direct Ridge MAE") + ax1.grid(axis="y", alpha=0.25) + ax1.legend(loc="upper left") + ax2.legend(loc="upper right") + ax1.set_title("Which modality helps polarity versus intensity?") + fig.tight_layout() + fig.savefig(output / "04_polarity_vs_intensity_modality.png", dpi=180) + plt.close(fig) + generated.append("04_polarity_vs_intensity_modality.png") + + focus = ["Private-DimMatched-PCA256", "TAV-I0", "TAV-I7"] + fig, ax = plt.subplots(figsize=(9, 5)) + x = np.arange(len(focus)) + direct = [by_name[name]["direct_ridge_mae"] for name in focus] + dual = [by_name[name]["dual_head_mae"] for name in focus] + ax.bar(x - 0.18, direct, width=0.36, label="Direct signed Ridge", color="#9b7653") + ax.bar(x + 0.18, dual, width=0.36, label="Polarity × |intensity|", color="#468b75") + ax.set_xticks(x, ["Private PCA", "I0", "I7"]) + ax.set_ylabel("MAE (lower is better)") + ax.set_title("Dual-head composition versus direct signed regression") + ax.legend() + ax.grid(axis="y", alpha=0.25) + fig.tight_layout() + fig.savefig(output / "05_dual_head_vs_direct_ridge.png", dpi=180) + plt.close(fig) + generated.append("05_dual_head_vs_direct_ridge.png") + + dim_names = ["M4-TimeOnly-full-raw", "Private-PCA-T64-A24-V64", "Private-DimMatched-PCA256"] + fig, axes = plt.subplots(1, 2, figsize=(11, 4.7)) + x = np.arange(len(dim_names)) + axes[0].bar(x, [by_name[n]["macro_f1"] for n in dim_names], color="#547eaa") + axes[0].set_ylabel("Macro-F1") + axes[1].bar(x, [by_name[n]["direct_ridge_mae"] for n in dim_names], color="#c78448") + axes[1].set_ylabel("MAE") + for ax in axes: + ax.set_xticks(x, ["Raw 985/slot", "Per-modality PCA", "Joint PCA 256/slot"], rotation=15) + ax.grid(axis="y", alpha=0.25) + fig.suptitle("Dimension-control check") + fig.tight_layout() + fig.savefig(output / "06_dimension_match_comparison.png", dpi=180) + plt.close(fig) + generated.append("06_dimension_match_comparison.png") + + target = "Private-DimMatched-PCA256" + pred_rows = [row for row in predictions if row["method_family"] == "SharedTime-PrivateContent" and row["representation"] == target] + temporal_by_id: dict[str, list[float]] = {} + for row in temporal: + if row.get("kind") == "pairwise": + temporal_by_id.setdefault(row["sample_id"], []).append(float(row["pairwise_time_mae"])) + xs, ys, colors = [], [], [] + for row in pred_rows: + if row["sample_id"] in temporal_by_id: + xs.append(float(np.mean(temporal_by_id[row["sample_id"]]))) + ys.append(abs(float(row["true_label"]) - float(row["predicted_dual_label"]))) + colors.append(int(row["true_class_id"])) + fig, ax = plt.subplots(figsize=(8, 5.5)) + scatter = ax.scatter(xs, ys, c=colors, cmap="viridis", s=42, alpha=0.8, edgecolors="white", linewidths=0.3) + ax.set_xlabel("Frozen M4 pairwise time MAE (normalized video time)") + ax.set_ylabel("Dual-head absolute emotion error") + ax.set_title("Temporal quality and task error are related but distinct") + fig.colorbar(scatter, ax=ax, ticks=[0, 1, 2], label="True polarity class") + ax.grid(alpha=0.2) + fig.tight_layout() + fig.savefig(output / "07_temporal_quality_vs_task.png", dpi=180) + plt.close(fig) + generated.append("07_temporal_quality_vs_task.png") + + fig, axes = plt.subplots(3, 1, figsize=(12, 8.5), sharex=True) + centers = sample_payload["grid"] + for ax, name in zip(axes, MODS): + values = sample_payload["modal_pca"][name] + image = ax.imshow(values.T, aspect="auto", interpolation="nearest", extent=[0, 1, values.shape[1], 0], cmap="coolwarm") + ax.set_ylabel(f"{name.title()}\nPCA component") + fig.colorbar(image, ax=ax, fraction=0.015, pad=0.01) + axes[-1].set_xlabel("Shared temporal coordinate (normalized video time)") + fig.suptitle(f"Private modality content by shared time slot\n{sample_payload['sample_id']} | {sample_payload['text'][:140]}") + fig.tight_layout() + fig.savefig(output / "08_representative_private_content.png", dpi=180) + plt.close(fig) + generated.append("08_representative_private_content.png") + + fig, axes = plt.subplots(3, 1, figsize=(12, 8.5), sharex=True) + for ax, name in zip(axes, MODS): + weights = sample_payload["weights"][name] + times = sample_payload["times"][name] + image = ax.imshow(weights, aspect="auto", interpolation="nearest", extent=[float(times[0]), float(times[-1]), GRID_SIZE, 0], cmap="magma") + ax.set_ylabel(f"{name.title()} source index") + fig.colorbar(image, ax=ax, fraction=0.015, pad=0.01) + axes[-1].set_xlabel("Normalized source time") + fig.suptitle(f"Frozen M4 time weights (example {sample_payload['sample_id']})") + fig.tight_layout() + fig.savefig(output / "09_representative_m4_weights.png", dpi=180) + plt.close(fig) + generated.append("09_representative_m4_weights.png") + return generated + + +def _run(args: argparse.Namespace) -> None: + started = time.time() + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device)) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested, but this WSL PyTorch environment has no visible GPU") + + q1_root = Path(__file__).resolve().parents[1] + output = args.output_dir or q1_root / "outputs" / OUTPUT_NAME + output.mkdir(parents=True, exist_ok=True) + samples = load_feature_samples(args.feature_dir, args.manifest) + samples_by_id = {sample.sample_id: sample for sample in samples} + splits = json.loads(args.splits.read_text(encoding="utf-8")) + if len(splits) != 5: + raise ValueError(f"expected exactly five fixed folds, found {len(splits)}") + valid_ids = [sid for split in splits for sid in split["validation_sample_ids"]] + if len(samples) != 100 or len(valid_ids) != 100 or set(valid_ids) != set(samples_by_id) or len(set(valid_ids)) != 100: + raise ValueError("the five fixed validation folds must cover all 100 extracted samples exactly once") + for sample in samples: + if _class_from_sentiment(sample.sentiment) != sample.polarity: + raise ValueError(f"strict-sign class and annotation disagree for {sample.sample_id}") + + prediction_rows: list[dict[str, Any]] = [] + temporal_rows: list[dict[str, Any]] = [] + fold_rows: list[dict[str, Any]] = [] + pca_rows: list[dict[str, Any]] = [] + representations_by_name: dict[str, dict[str, np.ndarray]] = {} + dimensions_by_name: dict[str, int] = {} + example_payload: dict[str, Any] | None = None + + for split in splits: + fold = int(split["fold"]) + train_ids = list(split["train_sample_ids"]) + validation_ids = list(split["validation_sample_ids"]) + train_samples = [samples_by_id[sid] for sid in train_ids] + validation_samples = [samples_by_id[sid] for sid in validation_ids] + train_groups = {sample.group_id for sample in train_samples} + heldout_groups = {sample.group_id for sample in validation_samples} + if train_groups & heldout_groups: + raise ValueError(f"video group leakage in fold {fold}: {sorted(train_groups & heldout_groups)}") + if set(train_ids) & set(validation_ids) or set(train_ids) | set(validation_ids) != set(samples_by_id): + raise ValueError(f"sample split mismatch in fold {fold}") + stats = fit_feature_stats(train_samples) + _, _, temporal_by_id = _collect_fold_features( + fold=fold, + train_samples=train_samples, + validation_samples=validation_samples, + feature_stats=stats, + checkpoint_root=args.checkpoint_root, + device=device, + batch_size=args.batch_size, + ) + all_samples = [*train_samples, *validation_samples] + pooled = _pool_original_source(all_samples, stats, temporal_by_id) + representations, fold_pca_rows, rep_meta = _make_fold_representations(train_ids, pooled, args.seed + fold) + pca_rows.extend({"fold": fold, **row} for row in fold_pca_rows) + for name, values in representations.items(): + dimension = int(next(iter(values.values())).shape[-1]) + if name in dimensions_by_name and dimensions_by_name[name] != dimension: + raise ValueError(f"representation width changed across folds for {name}") + dimensions_by_name[name] = dimension + representations_by_name.setdefault(name, {}).update(values) + + train_class = np.asarray([_class_from_sentiment(sample.sentiment) for sample in train_samples], dtype=np.int64) + train_y = np.asarray([sample.sentiment for sample in train_samples], dtype=np.float64) + x_train_by_name = {name: np.stack([values[sid] for sid in train_ids]) for name, values in representations.items()} + x_val_by_name = {name: np.stack([values[sid] for sid in validation_ids]) for name, values in representations.items()} + for name in representations: + classifier, direct, intensity = _training_probe(x_train_by_name[name], train_class, train_y, args.seed) + predicted_class = classifier[1].predict(classifier[0].transform(x_val_by_name[name])) + direct_prediction = np.clip(_predict_probe(direct, x_val_by_name[name]), -3.0, 3.0) + magnitude = np.clip(_predict_probe(intensity, x_val_by_name[name]), 0.0, 3.0) + dual_prediction = CLASS_SIGNS[predicted_class] * magnitude + for index, sample in enumerate(validation_samples): + prediction_rows.append({ + "method_family": "SharedTime-PrivateContent", + "representation": name, + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "true_class_id": int(_class_from_sentiment(sample.sentiment)), + "true_class": CLASS_NAMES[_class_from_sentiment(sample.sentiment)], + "predicted_class_id": int(predicted_class[index]), + "predicted_class": CLASS_NAMES[int(predicted_class[index])], + "true_label": float(sample.sentiment), + "predicted_direct_label": float(direct_prediction[index]), + "predicted_intensity_abs": float(magnitude[index]), + "predicted_dual_label": float(dual_prediction[index]), + "feature_dimension": dimensions_by_name[name], + }) + temporal_rows.extend(_temporal_rows(fold, validation_samples, temporal_by_id)) + fold_rows.append({ + "fold": fold, + "train_count": len(train_samples), + "validation_count": len(validation_samples), + "train_video_count": len(train_groups), + "validation_video_count": len(heldout_groups), + "video_group_overlap": 0, + "feature_standardization_fit": "training fold only", + "PCA_fit": "training fold 50-slot observations only", + }) + if example_payload is None: + sample = validation_samples[0] + pca_values = {name: np.stack([representations[f"Private-PCA-T64-A24-V64"][sid] for sid in [sample.sample_id]])} + # retain each modality's fold-local projected sequence for the sample figure + # (recompute from the same already-fitted projections used above). + modal_data = {} + for name in MODS: + dim = min(MODAL_PCA_DIMS[name], pooled[sample.sample_id][name].shape[-1]) + train_matrix = np.concatenate([pooled[sid][name] for sid in train_ids], axis=0) + pca = PCA(n_components=dim, svd_solver="auto", random_state=args.seed + fold).fit(train_matrix) + modal_data[name] = pca.transform(pooled[sample.sample_id][name]).astype(np.float32) + manifest = {row["video_id"] + "/" + row["clip_id"]: row for row in csv.DictReader(args.manifest.open("r", encoding="utf-8-sig"))} + example_payload = { + "sample_id": sample.sample_id, + "text": manifest.get(sample.sample_id, {}).get("text", ""), + "grid": (np.arange(GRID_SIZE) + 0.5) / GRID_SIZE, + "modal_pca": modal_data, + "weights": temporal_by_id[sample.sample_id]["weights"], + "times": temporal_by_id[sample.sample_id]["times"], + } + print(f"[shared-time/private-content fold {fold}/5] train={len(train_samples)} heldout={len(validation_samples)} representations={len(representations)} device={device}", flush=True) + del pooled, temporal_by_id, representations + if device.type == "cuda": + torch.cuda.empty_cache() + + expected_count = 100 * len(dimensions_by_name) + if len(prediction_rows) != expected_count: + raise ValueError(f"OOF coverage mismatch: expected {expected_count} prediction rows, got {len(prediction_rows)}") + + metric_rows = [] + for name in dimensions_by_name: + rows = [row for row in prediction_rows if row["representation"] == name] + metric = _prediction_metrics(name, rows, dimensions_by_name[name]) + fold_f1 = [] + fold_accuracy = [] + for fold in range(1, 6): + fold_subset = [row for row in rows if int(row["fold"]) == fold] + labels = [int(row["true_class_id"]) for row in fold_subset] + pred = [int(row["predicted_class_id"]) for row in fold_subset] + fold_f1.append(float(f1_score(labels, pred, labels=[0, 1, 2], average="macro", zero_division=0))) + fold_accuracy.append(float(accuracy_score(labels, pred))) + metric.update({ + "accuracy_fold_mean": float(np.mean(fold_accuracy)), + "accuracy_fold_sd": float(np.std(fold_accuracy, ddof=1)), + "macro_f1_fold_mean": float(np.mean(fold_f1)), + "macro_f1_fold_sd": float(np.std(fold_f1, ddof=1)), + }) + metric_rows.append(metric) + + prior_root = args.prior_output_dir + old_path = prior_root / "tsfa_shared_private" / "emotion_probe_predictions.csv" + shared_path = prior_root / "shared_definition_comparison" / "emotion_probe_predictions.csv" + historical_rows: list[dict[str, Any]] = [] + historical_refs: list[dict[str, Any]] = [] + selected_old = [ + ("TSFA-old", "all_modalities", "TSFA-old"), + ("TSFA+RawPrivate", "all_modalities", "TSFA+RawPrivate"), + ("RawPrivate-PCA", "RawPrivate-PCA", "RawPrivate-PCA-old"), + ("SPR", "private_all", "SPR-private-all"), + ("SPR-dim-matched", "SPR-dim-matched", "SPR-dim-matched"), + ] + rows, refs = _external_rows(old_path, selected_old) + historical_rows.extend(rows) + historical_refs.extend(refs) + selected_shared = [ + ("GCCA", "main_1280d", "GCCA-old"), + ("Predictive-Same", "main_1280d", "Predictive-Same-old"), + ("Predictive-Local1", "main_1280d", "Predictive-Local1-old"), + ("Similarity-SPR", "main_1280d", "Similarity-SPR-old"), + ("Math-B0", "math_all_modalities", "Math-B0"), + ("Math-B4", "math_all_modalities", "Math-B4"), + ] + rows, refs = _external_rows(shared_path, selected_shared) + historical_rows.extend(rows) + historical_refs.extend(refs) + historical_predictions = {row["representation"]: [] for row in historical_rows} + for row in historical_rows: + historical_predictions[row["representation"]].append(row) + for name, rows in historical_predictions.items(): + if len(rows) == 100: + prediction_rows.extend(rows) + metric_rows.extend(_summarize_historical(refs=historical_refs)) + + prediction_by_model: dict[str, dict[str, Mapping[str, Any]]] = {} + for row in prediction_rows: + prediction_by_model.setdefault(row["representation"], {})[row["sample_id"]] = row + group_by_id = {sample.sample_id: sample.group_id for sample in samples} + bootstrap_pairs = [ + ("TAV-I7", "TAV-I0"), + ("Subset-A-PCA256", "Math-B4"), + ("Subset-TV-PCA256", "Math-B0"), + ("M4-TimeOnly-full-raw", "TSFA+RawPrivate"), + ("Private-DimMatched-PCA256", "TSFA+RawPrivate"), + ("Private-DimMatched-PCA256", "RawPrivate-PCA-old"), + ("Private-DimMatched-PCA256", "Math-B0"), + ("Private-DimMatched-PCA256", "Math-B4"), + ] + bootstrap_rows = [] + for index, (model_a, model_b) in enumerate(bootstrap_pairs): + if model_a in prediction_by_model and model_b in prediction_by_model: + bootstrap_rows.extend(_group_bootstrap_pair( + model_a, model_b, prediction_by_model, group_by_id, + seed=args.seed + 500 + index, draws=args.bootstrap_draws, + )) + + temporal_modality = [row for row in temporal_rows if row.get("kind") == "modality"] + temporal_pair = [row for row in temporal_rows if row.get("kind") == "pairwise"] + temporal_summary = { + "mvr_eps_0_02_mean": float(np.mean([row["mvr_eps_0_02"] for row in temporal_modality])), + "span_abs_mean": float(np.mean([row["span_absolute"] for row in temporal_modality])), + "expected_time_correlation_mean": float(np.mean([row["expected_time_correlation"] for row in temporal_modality])), + "pairwise_time_mae_mean": float(np.mean([row["pairwise_time_mae"] for row in temporal_pair])), + "pairwise_time_correlation_mean": float(np.mean([row["pairwise_time_corr"] for row in temporal_pair])), + "sample_count": len({row["sample_id"] for row in temporal_modality}), + } + plot_files = _plot_metrics(output, metric_rows, prediction_rows, temporal_rows, example_payload or {}) + + _write_csv(output / "predictions_oof.csv", prediction_rows) + _write_csv(output / "metrics_oof.csv", metric_rows) + _write_csv(output / "temporal_monitoring.csv", temporal_rows) + _write_csv(output / "fold_manifest.csv", fold_rows) + _write_csv(output / "pca_fold_diagnostics.csv", pca_rows) + _write_csv(output / "group_bootstrap_contrasts.csv", bootstrap_rows) + config = { + "experiment": OUTPUT_NAME, + "generated_utc": datetime.now(timezone.utc).isoformat(), + "sample_count": len(samples), + "video_group_count": len({sample.group_id for sample in samples}), + "class_counts": {name: sum(_class_from_sentiment(s.sentiment) == idx for s in samples) for idx, name in enumerate(CLASS_NAMES)}, + "folds": 5, + "fold_source": str(args.splits), + "checkpoint_root": str(args.checkpoint_root), + "device": str(device), + "gpu_name": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "seed": args.seed, + "bootstrap_draws": args.bootstrap_draws, + "features": "frozen BERT / eGeMAPS / DeiT from Q1 feature extraction; fold-standardized before M4 weighting", + "frozen_time_model": "M4_sourceTime checkpoint trained for each fixed grouped fold", + "public_coordinate": "50 normalized video-time slots; shared coordinate only", + "private_content": "modality-specific pooled BERT/eGeMAPS/DeiT vectors; no cross-modal semantic equality or shared contrastive objective", + "pca_protocol": "PCA fitted on the 50 pooled slots of training clips only; at most 256 components per slot; zero-pad to a common width when input rank is lower; downstream five-bin vector is 1280D for PCA256 views", + "private_modality_pca_dimensions": MODAL_PCA_DIMS, + "interaction_definition": "per same slot [p_m * p_n ; abs(p_m - p_n)] with d=24 (limited by audio feature width), only supplied as task-specific features; bit mask: I1=TA, I2=TV, I3=TA+TV, I4=AV, I5=TA+AV, I6=TV+AV, I7=TA+TV+AV; no shared semantic encoder", + "dual_head": "LogisticRegression(C=0.05) predicts strict sign class; Ridge(alpha=25) predicts abs(label), clipped to [0,3]; signed output=class sign times magnitude; neutral gives exactly zero", + "direct_regression": "Ridge(alpha=25) predicts signed label directly and is clipped to [-3,3]", + "feature_and_probe_scalers": "fit on each training fold only", + "labels_used_in_alignment_or_pca": False, + "time_only_equals_private_identity": True, + "temporal_summary": temporal_summary, + "plots": plot_files, + "runtime_seconds": round(time.time() - started, 2), + "versions": {"python": platform.python_version(), "torch": torch.__version__, "sklearn": sklearn.__version__, "numpy": np.__version__}, + "notes": [ + "M4 weights provide temporal coordinate estimates; this experiment treats them as a frozen temporal basis and does not claim semantic equivalence across modalities.", + "TimeOnly-full-raw and Private-Identity have identical numerical features by definition; duplicate rows make the conceptual alias explicit.", + "Interaction dimension d=24 is capped by the 25D eGeMAPS source; the same 24D PCA coordinates are used in TA, TV, and AV interactions.", + "Bootstrap intervals resample the 37 source video groups and do not apply multiple-comparison correction.", + "Historical methods are read from their previous fixed-OOF prediction files; math files are read-only.", + ], + } + (output / "config.json").write_text(json.dumps(config, ensure_ascii=False, indent=2), encoding="utf-8") + _write_report(output, metric_rows, temporal_summary, bootstrap_rows, plot_files, config) + print(f"[complete] output={output} samples={len(samples)} groups={config['video_group_count']} models={len(dimensions_by_name)} device={device}", flush=True) + + +def _fmt(value: Any) -> str: + try: + value = float(value) + except (TypeError, ValueError): + return "—" + return f"{value:.3f}" if np.isfinite(value) else "—" + + +def _write_report(output: Path, metrics: Sequence[Mapping[str, Any]], temporal: Mapping[str, Any], bootstrap: Sequence[Mapping[str, Any]], plots: Sequence[str], config: Mapping[str, Any]) -> None: + by_name = {row["representation"]: row for row in metrics} + experiment_rows = [row for row in metrics if row["method_family"] == "SharedTime-PrivateContent"] + best_f1 = max(experiment_rows, key=lambda row: float(row["macro_f1"])) + best_mae = min(experiment_rows, key=lambda row: float(row["direct_ridge_mae"])) + best_dual = min(experiment_rows, key=lambda row: float(row["dual_head_mae"])) + chosen = ["Private-DimMatched-PCA256", "Subset-T-PCA256", "Subset-A-PCA256", "Subset-V-PCA256", "Subset-TAV-PCA256", "TAV-I0", "TAV-I1", "TAV-I2", "TAV-I3", "TAV-I4", "TAV-I5", "TAV-I6", "TAV-I7"] + lines = [ + "# Shared Time, Private Content: Q1 experiment", + "", + "## Core definition", + "", + "The shared object is the **temporal coordinate**: all modalities are indexed by 50 normalized video-time slots. The content at a slot remains private to its modality (BERT text, eGeMAPS audio, or DeiT vision). Same-slot products and absolute differences are optional inputs to an emotion task probe; they are not treated as evidence that modalities share semantics.", + "", + "M4_sourceTime weights are frozen per grouped fold. They provide a common temporal basis, after which the original standardized modality features are pooled. All scalers and PCA bases are fit on training folds only. Emotion labels are used only by the downstream LogisticRegression/Ridge probes.", + "", + "## Protocol", + "", + f"- Samples/groups: {config['sample_count']} clips from {config['video_group_count']} source videos; fixed five-fold GroupKFold predictions cover each clip once.", + f"- Runtime device: `{config['device']}` ({config.get('gpu_name') or 'CPU'}).", + "- Feature dimensions: BERT 768, eGeMAPS 25, DeiT 192. Each fold standardizes from training clips, then pools with its frozen M4_sourceTime checkpoint.", + "- Private PCA: Text 64, Audio 24, Vision 64 components. The joint dimension-matched comparison uses up to 256 slot components, then pools five consecutive slot bins to 1280 clip features.", + "- Emotion probes: strict sign classes {-1,0,+1}; LogisticRegression C=0.05; direct Ridge alpha=25; dual-head intensity Ridge alpha=25 on |label|, clipped to [0,3], then multiplied by predicted polarity. Direct Ridge output is clipped to [-3,3].", + "- Cross-modal interactions use `[p_m ⊙ p_n ; |p_m-p_n|]` on the same slot, with d=24. This is a task-specific interaction. No shared encoder, contrastive loss, orthogonality loss, or semantic reconstruction objective is used.", + "- Confidence intervals use 2,000 paired bootstrap draws over source-video groups. No multiple-comparison correction is applied; treat small metric differences as exploratory.", + "", + "## Main results", + "", + "| Representation | Dim | Accuracy | Macro-F1 | Direct MAE | Direct Pearson | Dual-head MAE | Dual Pearson |", + "|---|---:|---:|---:|---:|---:|---:|---:|", + ] + for name in chosen: + row = by_name.get(name) + if row: + lines.append(f"| {name} | {row['feature_dimension']} | {_fmt(row['accuracy'])} | {_fmt(row['macro_f1'])} | {_fmt(row['direct_ridge_mae'])} | {_fmt(row['direct_ridge_pearson'])} | {_fmt(row['dual_head_mae'])} | {_fmt(row['dual_head_pearson'])} |") + lines.extend([ + "", + f"Highest exploratory OOF Macro-F1: **{best_f1['representation']}** ({_fmt(best_f1['macro_f1'])}).", + f"Lowest direct Ridge OOF MAE: **{best_mae['representation']}** ({_fmt(best_mae['direct_ridge_mae'])}).", + f"Lowest dual-head OOF MAE: **{best_dual['representation']}** ({_fmt(best_dual['dual_head_mae'])}).", + "", + "The strict sign class distribution is 18 Negative, 25 Neutral, and 57 Positive; an always-Positive classifier has 0.57 accuracy. Macro-F1 reports the three fixed classes with zero F1 for a class that is never predicted.", + "", + "## Temporal monitoring (frozen M4; not an emotion score)", + "", + f"- Mean MVR at ε=0.02: {_fmt(temporal['mvr_eps_0_02_mean'])}.", + f"- Mean absolute start-to-end span: {_fmt(temporal['span_abs_mean'])} of normalized clip time.", + f"- Mean correlation of expected source time with the 50-slot coordinate: {_fmt(temporal['expected_time_correlation_mean'])}.", + f"- Mean pairwise source-time MAE: {_fmt(temporal['pairwise_time_mae_mean'])}; mean pairwise time correlation: {_fmt(temporal['pairwise_time_correlation_mean'])}.", + "These values describe the frozen coordinate map. They do not establish that the three feature streams encode the same meaning.", + "", + "## Files", + "", + "- `predictions_oof.csv`: one row per clip and new representation, with polarity, direct-regression, and dual-head predictions.", + "- `metrics_oof.csv`: OOF metrics for new representations plus fixed historical controls.", + "- `group_bootstrap_contrasts.csv`: paired group-bootstrap differences, where matching prior OOF rows exist.", + "- `temporal_monitoring.csv`: clip-level MVR, span, expected-time correlation, and pairwise temporal error.", + "- `pca_fold_diagnostics.csv`: component counts and explained variance by fold.", + "- `fold_manifest.csv`: split and group-leakage audit.", + "", + "## Figures", + "", + ]) + for name in plots: + lines.append(f"- `{name}`") + lines.extend([ + "", + "## Interpretation limits", + "", + "This is a 100-clip exploratory evaluation from 37 source videos. The OOF probes reuse an existing fixed split, and the 50 slots within a clip are dependent observations for PCA. Slot-level PCA is fitted only to training clips, but confidence intervals resample videos rather than slots. We do not use the emotion labels to train the temporal model or PCA. Historical comparisons retain their original feature and probe definitions; they are controls, not perfectly matched re-runs.", + "", + "M4-TimeOnly-full-raw and Private-Identity have identical numerical content because Private-Identity means independently retained modality feature streams after the same frozen temporal pooling. Their duplicate rows are an explicit conceptual alias, not an independent ablation.", + ]) + (output / "RESULTS.md").write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def main() -> None: + q1_root = Path(__file__).resolve().parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default="auto", choices=("auto", "cpu", "cuda")) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--bootstrap-draws", type=int, default=2000) + parser.add_argument("--feature-dir", type=Path, default=q1_root / "outputs" / "q1_features" / "features") + parser.add_argument("--manifest", type=Path, default=q1_root / "outputs" / "audit" / "manifest.csv") + parser.add_argument("--splits", type=Path, default=q1_root / "outputs" / "method_comparison" / "splits.json") + parser.add_argument("--checkpoint-root", type=Path, default=q1_root / "outputs" / "alignment_debug" / "heldout") + parser.add_argument("--prior-output-dir", type=Path, default=q1_root / "outputs") + parser.add_argument("--output-dir", type=Path, default=None) + _run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/q1/tsfa_av_private_ablation.py b/deep_learning/Q1/q1/tsfa_av_private_ablation.py new file mode 100644 index 0000000..f2c4269 --- /dev/null +++ b/deep_learning/Q1/q1/tsfa_av_private_ablation.py @@ -0,0 +1,719 @@ +"""Five-fold TSFA ablation: local Audio-Vision edge x raw modality residuals.""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import platform +import random +import shutil +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np +import sklearn +import torch +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import confusion_matrix, f1_score +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from torch import Tensor, nn + +from .compare_emotion_probes import METRICS, _scores +from .correspondence_eval import _write_csv +from .experiment_data import FeatureSample, fit_feature_stats, load_feature_samples, standardized_features +from .tsfa_emotion_probe import CLASS_NAMES, _class_from_sentiment, _pool_five_segments +from .tsfa_experiment import ( + GRID_SIZE, + HIDDEN_SIZE, + TSFASemanticBranch, + _candidate_mask, + _collate_temporal, + _collect_fold_features, + _generate_tsfa_outputs, + _load_semantic_checkpoint, + _local_contrastive_loss, +) +from .types import MODALITIES + + +VARIANTS = { + (False, False): "TSFA-T", + (True, False): "TSFA-AV", + (False, True): "TSFA-T+Private", + (True, True): "TSFA-AV+Private", +} +CONTRASTS = { + "AV_without_private": {"TSFA-AV": 1, "TSFA-T": -1}, + "Private_without_AV": {"TSFA-T+Private": 1, "TSFA-T": -1}, + "AV_with_private": {"TSFA-AV+Private": 1, "TSFA-T+Private": -1}, + "Private_with_AV": {"TSFA-AV+Private": 1, "TSFA-AV": -1}, + "Both_vs_original": {"TSFA-AV+Private": 1, "TSFA-T": -1}, + "AV_x_Private_interaction": { + "TSFA-AV+Private": 1, + "TSFA-AV": -1, + "TSFA-T+Private": -1, + "TSFA-T": 1, + }, +} + + +class TSFAAVBranch(TSFASemanticBranch): + """Add reciprocal A-V messages within the unchanged frozen-M4 candidate masks.""" + + def __init__(self, dimension: int = HIDDEN_SIZE) -> None: + super().__init__(dimension) + self.av_queries = nn.ModuleDict({ + name: nn.Linear(dimension, dimension, bias=False) for name in ("audio", "vision") + }) + self.av_keys = nn.ModuleDict({ + name: nn.Linear(dimension, dimension, bias=False) for name in ("audio", "vision") + }) + self.av_values = nn.ModuleDict({ + name: nn.Linear(dimension, dimension, bias=False) for name in ("audio", "vision") + }) + + def av_attend( + self, + query_content: Tensor, + source_values: Tensor, + *, + query_modality: str, + source_modality: str, + candidate_mask: Tensor, + ) -> tuple[Tensor, Tensor]: + query = self.av_queries[query_modality](query_content) + keys = self.av_keys[source_modality](source_values) + scores = torch.bmm(query, keys.transpose(1, 2)) / math.sqrt(query.shape[-1]) + scores = scores.masked_fill(~candidate_mask, torch.finfo(scores.dtype).min) + weights = torch.softmax(scores, dim=-1) + message = torch.bmm(weights, self.av_values[source_modality](source_values)) + return weights, message + + +def _av_forward( + branch: TSFAAVBranch, + batch: Mapping[str, Any], + m4_audio: Tensor, + m4_vision: Tensor, + *, + delta: float, +) -> tuple[dict[str, Tensor], dict[str, Tensor], dict[str, Tensor]]: + masks = {} + for modality in ("audio", "vision"): + masks[modality], _ = _candidate_mask( + batch["times"][modality], + batch["valid"][modality], + batch["centers"][modality], + delta=delta, + mode="local", + ) + _, text_to_audio = branch.attend( + batch["text_content"], batch["values"]["audio"], "audio", masks["audio"] + ) + _, text_to_vision = branch.attend( + batch["text_content"], batch["values"]["vision"], "vision", masks["vision"] + ) + a_to_v_weights, vision_to_audio = branch.av_attend( + m4_audio, + batch["values"]["vision"], + query_modality="audio", + source_modality="vision", + candidate_mask=masks["vision"], + ) + v_to_a_weights, audio_to_vision = branch.av_attend( + m4_vision, + batch["values"]["audio"], + query_modality="vision", + source_modality="audio", + candidate_mask=masks["audio"], + ) + shared = { + "text": batch["text_content"], + "audio": text_to_audio + vision_to_audio, + "vision": text_to_vision + audio_to_vision, + } + weights = {"audio_to_vision": a_to_v_weights, "vision_to_audio": v_to_a_weights} + return shared, weights, masks + + +def _m4_content( + ids: Sequence[str], temporal_by_id: Mapping[str, Mapping[str, Any]], modality: str, + device: torch.device, +) -> Tensor: + return torch.from_numpy(np.stack([ + temporal_by_id[sample_id]["content"][modality] for sample_id in ids + ])).to(device) + + +def _fit_av_branch( + *, + fold: int, + train_samples: Sequence[FeatureSample], + temporal_by_id: Mapping[str, Mapping[str, Any]], + device: torch.device, + args: argparse.Namespace, +) -> tuple[TSFAAVBranch, list[dict[str, Any]]]: + fold_seed = args.seed + fold * 101 + random.seed(fold_seed) + np.random.seed(fold_seed) + torch.manual_seed(fold_seed) + if device.type == "cuda": + torch.cuda.manual_seed_all(fold_seed) + branch = TSFAAVBranch().to(device) + optimizer = torch.optim.AdamW(branch.parameters(), lr=args.semantic_learning_rate, weight_decay=1e-4) + rng = np.random.default_rng(fold_seed) + train_ids = [sample.sample_id for sample in train_samples] + history = [] + branch.train() + for epoch in range(1, args.semantic_epochs + 1): + order = rng.permutation(len(train_ids)) + losses = [] + for start in range(0, len(order), args.batch_size): + ids = [train_ids[int(index)] for index in order[start : start + args.batch_size]] + batch = _collate_temporal(ids, temporal_by_id, device) + shared, _, _ = _av_forward( + branch, + batch, + _m4_content(ids, temporal_by_id, "audio", device), + _m4_content(ids, temporal_by_id, "vision", device), + delta=args.delta, + ) + loss = _local_contrastive_loss( + branch, + shared["text"], + shared["audio"], + shared["vision"], + temperature=args.local_temperature, + ) + if not torch.isfinite(loss): + raise FloatingPointError(f"non-finite AV semantic loss in fold {fold}, epoch {epoch}") + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(branch.parameters(), 1.0) + optimizer.step() + losses.append(float(loss.detach().item())) + history.append({ + "fold": fold, + "epoch": epoch, + "seed": fold_seed, + "train_loss": float(np.mean(losses)), + }) + branch.eval() + return branch, history + + +def _generate_av_outputs( + *, + branch: TSFAAVBranch, + sample_ids: Sequence[str], + temporal_by_id: Mapping[str, Mapping[str, Any]], + samples_by_id: Mapping[str, FeatureSample], + device: torch.device, + args: argparse.Namespace, +) -> tuple[dict[str, dict[str, np.ndarray]], list[dict[str, Any]]]: + content_by_id = {} + diagnostics = [] + branch.eval() + with torch.no_grad(): + for start in range(0, len(sample_ids), args.batch_size): + ids = list(sample_ids[start : start + args.batch_size]) + batch = _collate_temporal(ids, temporal_by_id, device) + shared, weights, masks = _av_forward( + branch, + batch, + _m4_content(ids, temporal_by_id, "audio", device), + _m4_content(ids, temporal_by_id, "vision", device), + delta=args.delta, + ) + for index, sample_id in enumerate(ids): + content_by_id[sample_id] = { + modality: shared[modality][index].cpu().numpy().astype(np.float32, copy=False) + for modality in MODALITIES + } + for direction, source in (("audio_to_vision", "vision"), ("vision_to_audio", "audio")): + length = len(samples_by_id[sample_id].features[source]) + one_weights = weights[direction][index, :, :length] + one_times = batch["times"][source][index, :length] + one_center = batch["centers"][source][index] + candidate_count = masks[source][index, :, :length].sum(dim=-1) + diagnostics.append({ + "sample_id": sample_id, + "video_id": samples_by_id[sample_id].group_id, + "direction": direction, + "source_modality": source, + "candidate_count_mean": float(candidate_count.float().mean().item()), + "attention_center_error_normalized_time": float( + (one_weights @ one_times - one_center).abs().mean().item() + ), + "attention_row_sum_max_error": float( + (one_weights.sum(dim=-1) - 1).abs().max().item() + ), + }) + return content_by_id, diagnostics + + +def _private_content( + samples: Sequence[FeatureSample], + feature_stats: Any, + temporal_by_id: Mapping[str, Mapping[str, Any]], +) -> dict[str, dict[str, np.ndarray]]: + result = {} + for sample in samples: + standardized = standardized_features(sample, feature_stats) + record = temporal_by_id[sample.sample_id] + result[sample.sample_id] = { + modality: ( + np.asarray(record["weights"][modality], dtype=np.float32) + @ standardized[modality] + ).astype(np.float32, copy=False) + for modality in MODALITIES + } + return result + + +def _vector( + sample_id: str, + shared_by_id: Mapping[str, Mapping[str, np.ndarray]], + private_by_id: Mapping[str, Mapping[str, np.ndarray]], + use_private: bool, +) -> np.ndarray: + parts = [_pool_five_segments(shared_by_id[sample_id][modality]) for modality in MODALITIES] + if use_private: + parts.extend(_pool_five_segments(private_by_id[sample_id][modality]) for modality in MODALITIES) + return np.concatenate(parts) + + +def _fit_fold_probe( + *, + fold: int, + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + shared_by_id: Mapping[str, Mapping[str, np.ndarray]], + private_by_id: Mapping[str, Mapping[str, np.ndarray]], + method: str, + use_private: bool, + seed: int, +) -> list[dict[str, Any]]: + train_x = np.stack([ + _vector(sample.sample_id, shared_by_id, private_by_id, use_private) + for sample in train_samples + ]) + heldout_x = np.stack([ + _vector(sample.sample_id, shared_by_id, private_by_id, use_private) + for sample in heldout_samples + ]) + train_class = np.asarray([_class_from_sentiment(sample.sentiment) for sample in train_samples]) + train_value = np.asarray([sample.sentiment for sample in train_samples], dtype=np.float64) + classifier = make_pipeline( + StandardScaler(), + LogisticRegression(C=0.05, max_iter=5000, solver="lbfgs", random_state=seed), + ) + regressor = make_pipeline(StandardScaler(), Ridge(alpha=25.0)) + classifier.fit(train_x, train_class) + regressor.fit(train_x, train_value) + predicted_class = classifier.predict(heldout_x) + predicted_unclipped = regressor.predict(heldout_x) + predicted_value = np.clip(predicted_unclipped, -3.0, 3.0) + return [{ + "method": method, + "av_edge": int(method.startswith("TSFA-AV")), + "private_residual": int(use_private), + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "true_class_id": _class_from_sentiment(sample.sentiment), + "predicted_class_id": int(predicted_class[index]), + "true_label": float(sample.sentiment), + "predicted_label": float(predicted_value[index]), + "predicted_label_unclipped": float(predicted_unclipped[index]), + "feature_dimension": int(train_x.shape[1]), + } for index, sample in enumerate(heldout_samples)] + + +def _score_rows(rows: Sequence[Mapping[str, Any]]) -> dict[str, float]: + return _scores( + np.asarray([int(row["true_class_id"]) for row in rows]), + np.asarray([int(row["predicted_class_id"]) for row in rows]), + np.asarray([float(row["true_label"]) for row in rows]), + np.asarray([float(row["predicted_label"]) for row in rows]), + ) + + +def _summarize( + prediction_rows: Sequence[Mapping[str, Any]], + *, + bootstrap_repeats: int, + seed: int, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]]]: + by_method = { + method: {str(row["sample_id"]): row for row in prediction_rows if row["method"] == method} + for method in VARIANTS.values() + } + sample_ids = sorted(next(iter(by_method.values()))) + if any(set(rows) != set(sample_ids) for rows in by_method.values()): + raise ValueError("four ablation variants do not cover identical samples") + groups = {sample_id: str(by_method["TSFA-T"][sample_id]["video_id"]) for sample_id in sample_ids} + if any( + str(row["video_id"]) != groups[sample_id] + for rows in by_method.values() for sample_id, row in rows.items() + ): + raise ValueError("video_id mismatch across ablation cells") + + metrics_rows = [] + confusion_rows = [] + point_scores = {} + for method, mapping in by_method.items(): + rows = [mapping[sample_id] for sample_id in sample_ids] + scores = _score_rows(rows) + point_scores[method] = scores + fold_f1 = [float(f1_score( + [int(row["true_class_id"]) for row in rows if int(row["fold"]) == fold], + [int(row["predicted_class_id"]) for row in rows if int(row["fold"]) == fold], + labels=[0, 1, 2], average="macro", zero_division=0, + )) for fold in range(1, 6)] + metrics_rows.append({ + "method": method, + "av_edge": rows[0]["av_edge"], + "private_residual": rows[0]["private_residual"], + "sample_count": len(rows), + "feature_dimension": rows[0]["feature_dimension"], + **scores, + "macro_f1_fold_mean": float(np.mean(fold_f1)), + "macro_f1_fold_sd": float(np.std(fold_f1, ddof=1)), + }) + matrix = confusion_matrix( + [int(row["true_class_id"]) for row in rows], + [int(row["predicted_class_id"]) for row in rows], + labels=[0, 1, 2], + ) + confusion_rows.extend({ + "method": method, + "true_class": CLASS_NAMES[true_id], + "predicted_class": CLASS_NAMES[predicted_id], + "count": int(matrix[true_id, predicted_id]), + } for true_id in range(3) for predicted_id in range(3)) + + group_names = sorted(set(groups.values())) + group_ids = {group: [sample_id for sample_id in sample_ids if groups[sample_id] == group] + for group in group_names} + rng = np.random.default_rng(seed) + bootstrap_values = { + contrast: {metric: [] for metric in METRICS} for contrast in CONTRASTS + } + for _ in range(bootstrap_repeats): + drawn = rng.choice(group_names, size=len(group_names), replace=True) + drawn_ids = [sample_id for group in drawn for sample_id in group_ids[str(group)]] + scores = { + method: _score_rows([mapping[sample_id] for sample_id in drawn_ids]) + for method, mapping in by_method.items() + } + for contrast, weights in CONTRASTS.items(): + for metric in METRICS: + bootstrap_values[contrast][metric].append( + sum(weight * scores[method][metric] for method, weight in weights.items()) + ) + contrast_rows = [] + for contrast, weights in CONTRASTS.items(): + for metric in METRICS: + values = np.asarray(bootstrap_values[contrast][metric]) + values = values[np.isfinite(values)] + contrast_rows.append({ + "contrast": contrast, + "metric": metric, + "point_estimate": sum( + weight * point_scores[method][metric] for method, weight in weights.items() + ), + "ci95_low": float(np.quantile(values, 0.025)), + "ci95_high": float(np.quantile(values, 0.975)), + "video_group_count": len(group_names), + "bootstrap_repeats": bootstrap_repeats, + }) + return metrics_rows, contrast_rows, confusion_rows + + +def _verify_original_control( + prediction_rows: Sequence[Mapping[str, Any]], path: Path, +) -> None: + with path.open("r", encoding="utf-8-sig", newline="") as stream: + previous = { + row["sample_id"]: row for row in csv.DictReader(stream) + if row["method"] == "TSFA-main" and row["view"] == "all_modalities" + } + original = {str(row["sample_id"]): row for row in prediction_rows if row["method"] == "TSFA-T"} + if set(original) != set(previous): + raise ValueError("original TSFA control sample IDs do not match previous probe") + for sample_id, row in original.items(): + old = previous[sample_id] + if int(row["fold"]) != int(old["fold"]): + raise ValueError(f"original TSFA fold mismatch for {sample_id}") + if int(row["predicted_class_id"]) != int(old["predicted_class_id"]): + raise ValueError(f"original TSFA class prediction changed for {sample_id}") + if not np.isclose(float(row["predicted_label"]), float(old["predicted_label"]), atol=1e-5): + raise ValueError(f"original TSFA regression prediction changed for {sample_id}") + + +def _plot_factorial(metrics_path: Path, output_path: Path) -> None: + with metrics_path.open("r", encoding="utf-8-sig", newline="") as stream: + rows = list(csv.DictReader(stream)) + if len(rows) != 4: + raise ValueError("factorial plot requires exactly four ablation cells") + fig, axes = plt.subplots(1, 2, figsize=(10, 4.2), constrained_layout=True) + panels = (("mae", "Emotion intensity MAE ↓"), ("macro_f1", "Polarity Macro-F1 ↑")) + for axis, (metric, title) in zip(axes, panels, strict=True): + for private, label, color in ((0, "No private residual", "#4c78a8"), + (1, "With private residual", "#e45756")): + selected = sorted( + (row for row in rows if int(row["private_residual"]) == private), + key=lambda row: int(row["av_edge"]), + ) + values = [float(row[metric]) for row in selected] + axis.plot([0, 1], values, marker="o", markersize=7, linewidth=2, + color=color, label=label) + for x, value in enumerate(values): + axis.annotate(f"{value:.3f}", (x, value), xytext=(0, 7), + textcoords="offset points", ha="center", fontsize=9) + axis.set_xticks([0, 1], ["No A–V edge", "Local A–V edge"]) + axis.set_xlim(-0.18, 1.18) + axis.set_title(title) + axis.grid(axis="y", alpha=0.25) + axes[0].set_ylim(0.50, 0.88) + axes[1].set_ylim(0.27, 0.47) + axes[0].set_ylabel("OOF metric on 100 clips") + axes[0].legend(frameon=False, loc="upper right", fontsize=8) + fig.suptitle("TSFA local A–V edge × preserved modality features") + fig.savefig(output_path, dpi=190, bbox_inches="tight") + plt.close(fig) + + +def _refresh_report_bundle(output_dir: Path) -> None: + bundle = output_dir / "report_bundle" + bundle.mkdir(parents=True, exist_ok=True) + for name in ( + "metrics.csv", "predictions.csv", "paired_contrasts.csv", "confusion_matrix.csv", + "av_attention_diagnostics.csv", "av_training_history.csv", "factorial_effects.png", + "private_vs_math.csv", "private_vs_math_manifest.json", "run_manifest.json", + ): + source = output_dir / name + if source.is_file(): + shutil.copy2(source, bundle / name) + (bundle / "README.md").write_text( + "# TSFA A–V edge × private residual ablation\n\n" + "Four OOF cells on the same 100 clips and five video-group folds. " + "`metrics.csv` gives Accuracy, fixed-three-class Macro-F1, clipped MAE, " + "and Pearson. `paired_contrasts.csv` reports video-cluster bootstrap intervals " + "for the two factors and their interaction. `private_vs_math.csv` compares " + "the strongest regression cell with math B0–B4 when available. The " + "original math files were read only. See `run_manifest.json` for the " + "protocol and limitations. Full AV branch checkpoints remain one level up.\n", + encoding="utf-8", + ) + + +def run(args: argparse.Namespace) -> None: + started = time.time() + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if args.device == "auto" else torch.device(args.device) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + samples = load_feature_samples(args.feature_dir, args.manifest) + samples_by_id = {sample.sample_id: sample for sample in samples} + splits = json.loads(args.splits.read_text(encoding="utf-8")) + heldout_ids = [sample_id for split in splits for sample_id in split["validation_sample_ids"]] + if len(splits) != 5 or len(heldout_ids) != len(set(heldout_ids)) or set(heldout_ids) != set(samples_by_id): + raise ValueError("expected exactly five grouped folds covering all samples once") + checkpoint_store = torch.load(args.tsfa_checkpoints, map_location="cpu", weights_only=False) + prediction_rows = [] + diagnostic_rows = [] + history_rows = [] + av_checkpoints = {} + fold_manifest = [] + for split in splits: + fold = int(split["fold"]) + train_samples = [samples_by_id[sample_id] for sample_id in split["train_sample_ids"]] + heldout_samples = [samples_by_id[sample_id] for sample_id in split["validation_sample_ids"]] + all_samples = [*train_samples, *heldout_samples] + train_groups = {sample.group_id for sample in train_samples} + heldout_groups = {sample.group_id for sample in heldout_samples} + if train_groups & heldout_groups: + raise ValueError(f"video_id leakage in fold {fold}") + feature_stats = fit_feature_stats(train_samples) + _, _, temporal_by_id = _collect_fold_features( + fold=fold, + train_samples=train_samples, + validation_samples=heldout_samples, + feature_stats=feature_stats, + checkpoint_root=args.checkpoint_root, + device=device, + batch_size=args.batch_size, + ) + original_branch = _load_semantic_checkpoint(checkpoint_store, fold, device) + original_content, _, _ = _generate_tsfa_outputs( + method="TSFA-main", + fold=fold, + sample_ids=[sample.sample_id for sample in all_samples], + samples_by_id=samples_by_id, + temporal_by_id=temporal_by_id, + branch=original_branch, + device=device, + delta=args.delta, + seed=args.seed, + batch_size=args.batch_size, + ) + av_branch, history = _fit_av_branch( + fold=fold, + train_samples=train_samples, + temporal_by_id=temporal_by_id, + device=device, + args=args, + ) + history_rows.extend(history) + av_checkpoints[f"fold_{fold:02d}/TSFA-AV"] = { + "seed": args.seed + fold * 101, + "train_sample_ids": [sample.sample_id for sample in train_samples], + "validation_sample_ids": [sample.sample_id for sample in heldout_samples], + "state_dict": {key: value.detach().cpu() for key, value in av_branch.state_dict().items()}, + } + av_content, diagnostics = _generate_av_outputs( + branch=av_branch, + sample_ids=[sample.sample_id for sample in all_samples], + temporal_by_id=temporal_by_id, + samples_by_id=samples_by_id, + device=device, + args=args, + ) + heldout_set = {sample.sample_id for sample in heldout_samples} + diagnostic_rows.extend({**row, "fold": fold} for row in diagnostics if row["sample_id"] in heldout_set) + private_by_id = _private_content(all_samples, feature_stats, temporal_by_id) + for (av_edge, use_private), method in VARIANTS.items(): + shared = av_content if av_edge else original_content + prediction_rows.extend(_fit_fold_probe( + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + shared_by_id=shared, + private_by_id=private_by_id, + method=method, + use_private=use_private, + seed=args.seed, + )) + fold_manifest.append({ + "fold": fold, + "train_count": len(train_samples), + "heldout_count": len(heldout_samples), + "train_video_id_count": len(train_groups), + "heldout_video_id_count": len(heldout_groups), + "video_id_overlap": [], + }) + print(f"[AV/private fold {fold}] train={len(train_samples)} heldout={len(heldout_samples)}", flush=True) + del original_branch, av_branch, original_content, av_content, private_by_id, temporal_by_id + if device.type == "cuda": + torch.cuda.empty_cache() + + _verify_original_control(prediction_rows, args.baseline_predictions) + metrics_rows, contrast_rows, confusion_rows = _summarize( + prediction_rows, bootstrap_repeats=args.bootstrap_repeats, seed=args.seed + ) + args.output_dir.mkdir(parents=True, exist_ok=True) + _write_csv(args.output_dir / "predictions.csv", prediction_rows) + _write_csv(args.output_dir / "metrics.csv", metrics_rows) + _write_csv(args.output_dir / "paired_contrasts.csv", contrast_rows) + _write_csv(args.output_dir / "confusion_matrix.csv", confusion_rows) + _write_csv(args.output_dir / "av_attention_diagnostics.csv", diagnostic_rows) + _write_csv(args.output_dir / "av_training_history.csv", history_rows) + _plot_factorial(args.output_dir / "metrics.csv", args.output_dir / "factorial_effects.png") + torch.save(av_checkpoints, args.output_dir / "av_branch_checkpoints.pt") + manifest = { + "created_utc": datetime.now(timezone.utc).isoformat(), + "experiment": "2x2 TSFA ablation: local reciprocal Audio-Vision edge x private modality residual", + "sample_count": len(samples), + "video_id_count": len({sample.group_id for sample in samples}), + "fold_count": len(splits), + "folds": fold_manifest, + "original_tsfa_control_predictions_identical": True, + "av_edge": "M4 aligned Audio queries raw projected Vision, and M4 aligned Vision queries raw projected Audio; both directions use the existing M4 source-center +/-delta masks. Messages add to the original Text-to-Audio/Vision content.", + "private_residual": "For each modality, frozen M4 temporal attention pools its training-fold-standardized original BERT/eGeMAPS/DeiT source features onto 50 slots; the same five-segment probe concatenates all three private streams after shared streams.", + "semantic_training": "same 40-epoch Text-Audio/Text-Vision local contrastive loss, optimizer, temperature, batch size and seed as original TSFA; no emotion labels", + "probe": "StandardScaler + LogisticRegression(C=0.05) and StandardScaler + Ridge(alpha=25); trained per fold; regression predictions clipped to [-3,3]", + "bootstrap": "2,000 paired video_id-cluster samples; percentile 95% intervals; no multiple-comparison correction", + "parameters": { + "seed": args.seed, + "delta": args.delta, + "semantic_epochs": args.semantic_epochs, + "semantic_learning_rate": args.semantic_learning_rate, + "local_temperature": args.local_temperature, + "batch_size": args.batch_size, + "bootstrap_repeats": args.bootstrap_repeats, + }, + "input_paths": { + "feature_dir": str(args.feature_dir.resolve()), + "feature_manifest": str(args.manifest.resolve()), + "splits": str(args.splits.resolve()), + "frozen_alignment_checkpoint_root": str(args.checkpoint_root.resolve()), + "original_tsfa_checkpoints": str(args.tsfa_checkpoints.resolve()), + "original_tsfa_predictions": str(args.baseline_predictions.resolve()), + }, + "device": str(device), + "python": platform.python_version(), + "pytorch": torch.__version__, + "scikit_learn": sklearn.__version__, + "elapsed_seconds": time.time() - started, + "interpretation_limits": [ + "An added edge also adds trainable parameters; an edge effect alone does not prove correct semantic alignment.", + "Private residuals increase probe input dimension; any gain cannot be assigned to a particular raw feature without further controlled study.", + "Only one semantic-branch seed was trained on 100 clips and 37 source videos.", + "Clip-level emotion labels cannot directly validate event-level correspondence.", + ], + } + (args.output_dir / "run_manifest.json").write_text( + json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8" + ) + _refresh_report_bundle(args.output_dir) + print(f"[AV/private complete] output={args.output_dir}", flush=True) + for row in metrics_rows: + print( + f" {row['method']}: Acc={row['accuracy']:.3f} F1={row['macro_f1']:.3f} " + f"MAE={row['mae']:.3f} Pearson={row['pearson']:.3f}", flush=True + ) + + +def build_parser() -> argparse.ArgumentParser: + project = Path(__file__).resolve().parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--delta", type=float, default=0.10) + parser.add_argument("--semantic-epochs", type=int, default=40) + parser.add_argument("--semantic-learning-rate", type=float, default=1e-3) + parser.add_argument("--local-temperature", type=float, default=0.1) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--feature-dir", type=Path, default=project / "outputs/q1_features/features") + parser.add_argument("--manifest", type=Path, default=project / "outputs/audit/manifest.csv") + parser.add_argument("--splits", type=Path, default=project / "outputs/method_comparison/splits.json") + parser.add_argument("--checkpoint-root", type=Path, default=project / "outputs/alignment_debug/heldout") + parser.add_argument("--tsfa-checkpoints", type=Path, default=project / "outputs/tsfa/probe_checkpoints.pt") + parser.add_argument("--baseline-predictions", type=Path, default=project / "outputs/tsfa_emotion_probe/emotion_probe_predictions.csv") + parser.add_argument("--output-dir", type=Path, default=project / "outputs/tsfa_av_private_ablation") + parser.add_argument("--plot-existing", action="store_true", help="refresh the figure from existing metrics.csv") + return parser + + +def main() -> None: + args = build_parser().parse_args() + if args.plot_existing: + _plot_factorial(args.output_dir / "metrics.csv", args.output_dir / "factorial_effects.png") + _refresh_report_bundle(args.output_dir) + else: + run(args) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/q1/tsfa_emotion_probe.py b/deep_learning/Q1/q1/tsfa_emotion_probe.py new file mode 100644 index 0000000..6a0c9c0 --- /dev/null +++ b/deep_learning/Q1/q1/tsfa_emotion_probe.py @@ -0,0 +1,351 @@ +"""Grouped emotion probes for the frozen TSFA representations.""" + +from __future__ import annotations + +import argparse +import json +import platform +import shutil +import time +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np +import sklearn +import torch +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, confusion_matrix, f1_score +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +from .correspondence_eval import _write_csv +from .experiment_data import FeatureSample, fit_feature_stats, load_feature_samples +from .tsfa_experiment import ( + BASELINE_VARIANTS, + GRID_SIZE, + _collect_fold_features, + _generate_tsfa_outputs, + _load_semantic_checkpoint, +) + + +CLASS_NAMES = ("Negative", "Neutral", "Positive") +METHODS = (*BASELINE_VARIANTS, "TSFA-main") +VIEWS = { + "text": ("text",), + "audio": ("audio",), + "vision": ("vision",), + "all_modalities": ("text", "audio", "vision"), +} + + +def _class_from_sentiment(value: float) -> int: + if value < 0: + return 0 + if value == 0: + return 1 + return 2 + + +def _pool_five_segments(sequence: np.ndarray) -> np.ndarray: + values = np.asarray(sequence, dtype=np.float32) + if values.ndim != 2 or values.shape[0] != GRID_SIZE: + raise ValueError(f"expected an aligned [{GRID_SIZE}, D] representation, got {values.shape}") + # Five contiguous equal-width bins over the shared 50-slot timeline. + return values.reshape(5, GRID_SIZE // 5, values.shape[1]).mean(axis=1).reshape(-1) + + +def _vector(content: Mapping[str, np.ndarray], view: str) -> np.ndarray: + return np.concatenate([_pool_five_segments(content[name]) for name in VIEWS[view]]) + + +def _pearson(actual: np.ndarray, predicted: np.ndarray) -> float: + if np.std(actual) <= 1e-12 or np.std(predicted) <= 1e-12: + return float("nan") + return float(np.corrcoef(actual, predicted)[0, 1]) + + +def _sample_vectors( + samples: Sequence[FeatureSample], + content_by_id: Mapping[str, Mapping[str, np.ndarray]], + view: str, +) -> np.ndarray: + return np.stack([_vector(content_by_id[sample.sample_id], view) for sample in samples]) + + +def run(args: argparse.Namespace) -> None: + started = time.time() + if args.device == "auto": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device(args.device) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + + samples = load_feature_samples(args.feature_dir, args.manifest) + samples_by_id = {sample.sample_id: sample for sample in samples} + labels_by_id = {sample.sample_id: _class_from_sentiment(sample.sentiment) for sample in samples} + for sample in samples: + if labels_by_id[sample.sample_id] != sample.polarity: + raise ValueError(f"sign-derived class disagrees with annotation for {sample.sample_id}") + class_counts = { + name: sum(label == index for label in labels_by_id.values()) + for index, name in enumerate(CLASS_NAMES) + } + + splits = json.loads(args.splits.read_text(encoding="utf-8")) + if len(splits) != 5: + raise ValueError(f"expected five grouped folds, found {len(splits)}") + validation_ids = [sample_id for split in splits for sample_id in split["validation_sample_ids"]] + if len(validation_ids) != len(set(validation_ids)) or set(validation_ids) != set(samples_by_id): + raise ValueError("five-fold validation partitions must cover all samples exactly once") + + checkpoint_file = args.tsfa_output_dir / "probe_checkpoints.pt" + checkpoint_store = torch.load(checkpoint_file, map_location="cpu", weights_only=False) + prediction_rows: list[dict[str, Any]] = [] + fold_manifest = [] + + for split in splits: + fold = int(split["fold"]) + train_samples = [samples_by_id[sample_id] for sample_id in split["train_sample_ids"]] + heldout_samples = [samples_by_id[sample_id] for sample_id in split["validation_sample_ids"]] + train_groups = {sample.group_id for sample in train_samples} + heldout_groups = {sample.group_id for sample in heldout_samples} + if train_groups & heldout_groups: + raise ValueError(f"video_id leakage in fold {fold}: {sorted(train_groups & heldout_groups)}") + feature_stats = fit_feature_stats(train_samples) + baseline_content, _, temporal_by_id = _collect_fold_features( + fold=fold, + train_samples=train_samples, + validation_samples=heldout_samples, + feature_stats=feature_stats, + checkpoint_root=args.checkpoint_root, + device=device, + batch_size=args.batch_size, + ) + all_samples = [*train_samples, *heldout_samples] + all_ids = [sample.sample_id for sample in all_samples] + main_branch = _load_semantic_checkpoint(checkpoint_store, fold, device) + tsfa_content, _, _ = _generate_tsfa_outputs( + method="TSFA-main", + fold=fold, + sample_ids=all_ids, + samples_by_id=samples_by_id, + temporal_by_id=temporal_by_id, + branch=main_branch, + device=device, + delta=args.delta, + seed=args.seed, + draw=0, + batch_size=args.batch_size, + ) + content_by_method = dict(baseline_content) + content_by_method["TSFA-main"] = tsfa_content + + train_y_class = np.asarray([labels_by_id[s.sample_id] for s in train_samples], dtype=np.int64) + heldout_y_class = np.asarray([labels_by_id[s.sample_id] for s in heldout_samples], dtype=np.int64) + train_y_value = np.asarray([s.sentiment for s in train_samples], dtype=np.float64) + heldout_y_value = np.asarray([s.sentiment for s in heldout_samples], dtype=np.float64) + + for method in METHODS: + for view in VIEWS: + train_x = _sample_vectors(train_samples, content_by_method[method], view) + heldout_x = _sample_vectors(heldout_samples, content_by_method[method], view) + classifier = make_pipeline( + StandardScaler(), + LogisticRegression(C=0.05, max_iter=5000, solver="lbfgs", random_state=args.seed), + ) + classifier.fit(train_x, train_y_class) + predicted_class = classifier.predict(heldout_x) + + regressor = make_pipeline(StandardScaler(), Ridge(alpha=25.0)) + regressor.fit(train_x, train_y_value) + predicted_value_unclipped = regressor.predict(heldout_x) + predicted_value = np.clip(predicted_value_unclipped, -3.0, 3.0) + + for index, sample in enumerate(heldout_samples): + prediction_rows.append({ + "method": method, + "view": view, + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "true_class_id": int(heldout_y_class[index]), + "true_class": CLASS_NAMES[int(heldout_y_class[index])], + "predicted_class_id": int(predicted_class[index]), + "predicted_class": CLASS_NAMES[int(predicted_class[index])], + "true_label": float(heldout_y_value[index]), + "predicted_label": float(predicted_value[index]), + "predicted_label_unclipped": float(predicted_value_unclipped[index]), + }) + fold_manifest.append({ + "fold": fold, + "train_count": len(train_samples), + "heldout_count": len(heldout_samples), + "train_video_id_count": len(train_groups), + "heldout_video_id_count": len(heldout_groups), + "video_id_overlap": sorted(train_groups & heldout_groups), + }) + print(f"[emotion probe fold {fold}] train={len(train_samples)} heldout={len(heldout_samples)}", flush=True) + del main_branch, baseline_content, temporal_by_id, tsfa_content, content_by_method + if device.type == "cuda": + torch.cuda.empty_cache() + + summary_rows = [] + confusion_rows = [] + majority_id = max(range(3), key=lambda index: class_counts[CLASS_NAMES[index]]) + for method in METHODS: + for view in VIEWS: + rows = [row for row in prediction_rows if row["method"] == method and row["view"] == view] + actual_class = np.asarray([row["true_class_id"] for row in rows], dtype=np.int64) + predicted_class = np.asarray([row["predicted_class_id"] for row in rows], dtype=np.int64) + actual_value = np.asarray([row["true_label"] for row in rows], dtype=np.float64) + predicted_value = np.asarray([row["predicted_label"] for row in rows], dtype=np.float64) + predicted_value_unclipped = np.asarray( + [row["predicted_label_unclipped"] for row in rows], dtype=np.float64 + ) + majority_prediction = np.full_like(actual_class, majority_id) + matrix = confusion_matrix(actual_class, predicted_class, labels=[0, 1, 2]) + fold_macro_f1 = [] + for fold in sorted({int(row["fold"]) for row in rows}): + fold_rows = [row for row in rows if int(row["fold"]) == fold] + fold_macro_f1.append(float(f1_score( + [row["true_class_id"] for row in fold_rows], + [row["predicted_class_id"] for row in fold_rows], + labels=[0, 1, 2], + average="macro", + zero_division=0, + ))) + summary_rows.append({ + "method": method, + "view": view, + "sample_count": len(rows), + "negative_support": class_counts["Negative"], + "neutral_support": class_counts["Neutral"], + "positive_support": class_counts["Positive"], + "majority_class_accuracy_baseline": max(class_counts.values()) / len(samples), + "majority_class_macro_f1_baseline": float(f1_score( + actual_class, majority_prediction, labels=[0, 1, 2], average="macro", zero_division=0 + )), + "accuracy": float(accuracy_score(actual_class, predicted_class)), + "macro_f1_fixed_three_classes": float(f1_score( + actual_class, predicted_class, labels=[0, 1, 2], average="macro", zero_division=0 + )), + "macro_f1_fold_mean": float(np.mean(fold_macro_f1)), + "macro_f1_fold_sd": float(np.std(fold_macro_f1, ddof=1)), + "mae": float(np.mean(np.abs(actual_value - predicted_value))), + "pearson": _pearson(actual_value, predicted_value), + "mae_unclipped": float(np.mean(np.abs(actual_value - predicted_value_unclipped))), + "pearson_unclipped": _pearson(actual_value, predicted_value_unclipped), + }) + for true_id, true_name in enumerate(CLASS_NAMES): + for predicted_id, predicted_name in enumerate(CLASS_NAMES): + confusion_rows.append({ + "method": method, + "view": view, + "true_class": true_name, + "predicted_class": predicted_name, + "count": int(matrix[true_id, predicted_id]), + }) + + args.output_dir.mkdir(parents=True, exist_ok=True) + _write_csv(args.output_dir / "emotion_probe_predictions.csv", prediction_rows) + _write_csv(args.output_dir / "emotion_probe_metrics.csv", summary_rows) + _write_csv(args.output_dir / "emotion_probe_confusion_matrix.csv", confusion_rows) + run_manifest = { + "created_utc": datetime.now(timezone.utc).isoformat(), + "experiment": "Grouped five-fold emotion probes on frozen TSFA and M3/M4 representations", + "sample_count": len(samples), + "video_id_count": len({sample.group_id for sample in samples}), + "fold_count": len(splits), + "heldout_prediction_count_per_method_view": len(samples), + "split_rule": "Fixed five-fold GroupKFold by group_id/video_id, loaded from the Q1 method-comparison split file.", + "seed": args.seed, + "batch_size_for_feature_inference": args.batch_size, + "input_paths": { + "feature_dir": str(args.feature_dir.resolve()), + "feature_manifest": str(args.manifest.resolve()), + "grouped_splits": str(args.splits.resolve()), + "alignment_checkpoint_root": str(args.checkpoint_root.resolve()), + "tsfa_output_dir": str(args.tsfa_output_dir.resolve()), + "tsfa_probe_checkpoint": str(checkpoint_file.resolve()), + }, + "class_mapping": {"label_lt_0": "Negative", "label_eq_0": "Neutral", "label_gt_0": "Positive"}, + "class_counts": class_counts, + "classification": { + "estimator": "LogisticRegression", + "C": 0.05, + "max_iter": 5000, + "features": "StandardScaler fitted on the training fold, then 5-segment pooled aligned representations", + "macro_f1": "fixed labels [Negative, Neutral, Positive]; zero_division=0", + }, + "temporal_pooling": "Five contiguous equal-width bins over the 50 shared slots; mean each bin and concatenate.", + "regression": { + "estimator": "Ridge", + "alpha": 25.0, + "features": "StandardScaler fitted on the training fold, then the same 5-segment pooled representations", + "prediction_clipping": [-3.0, 3.0], + "reported_mae_pearson": "computed on clipped predictions; unclipped values are also retained for diagnosis", + }, + "methods": list(METHODS), + "views": {key: list(value) for key, value in VIEWS.items()}, + "folds": fold_manifest, + "emotion_labels_used_to_train_alignment": False, + "alignment_models_retrained": False, + "feature_extractors_changed": False, + "label_agreement": "Sign-derived classes were checked against the annotation class for all samples.", + "device_for_feature_inference": str(device), + "python": platform.python_version(), + "scikit_learn": sklearn.__version__, + "elapsed_seconds": time.time() - started, + } + (args.output_dir / "emotion_probe_manifest.json").write_text( + json.dumps(run_manifest, ensure_ascii=False, indent=2), encoding="utf-8" + ) + bundle = args.tsfa_output_dir / "report_bundle" + bundle.mkdir(parents=True, exist_ok=True) + for name in ( + "emotion_probe_metrics.csv", + "emotion_probe_confusion_matrix.csv", + "emotion_probe_manifest.json", + ): + shutil.copy2(args.output_dir / name, bundle / name) + bundle_readme = bundle / "README.md" + note = ( + "\n`emotion_probe_metrics.csv` adds five-fold, video-group-held-out " + "LogisticRegression/Ridge probes on five-segment pooled representations. " + "These are small-sample downstream probes, not end-to-end emotion model scores.\n" + ) + current = bundle_readme.read_text(encoding="utf-8") + if "`emotion_probe_metrics.csv` adds" not in current: + bundle_readme.write_text(current + note, encoding="utf-8") + print( + f"[emotion probe complete] samples={len(samples)} class_counts={class_counts} " + f"output={args.output_dir}", flush=True + ) + + +def build_parser() -> argparse.ArgumentParser: + project = Path(__file__).resolve().parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--delta", type=float, default=0.10) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--feature-dir", type=Path, default=project / "outputs/q1_features/features") + parser.add_argument("--manifest", type=Path, default=project / "outputs/audit/manifest.csv") + parser.add_argument("--splits", type=Path, default=project / "outputs/method_comparison/splits.json") + parser.add_argument("--checkpoint-root", type=Path, default=project / "outputs/alignment_debug/heldout") + parser.add_argument("--tsfa-output-dir", type=Path, default=project / "outputs/tsfa") + parser.add_argument("--output-dir", type=Path, default=project / "outputs/tsfa_emotion_probe") + return parser + + +def main() -> None: + args = build_parser().parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/q1/tsfa_shared_private.py b/deep_learning/Q1/q1/tsfa_shared_private.py new file mode 100644 index 0000000..7300581 --- /dev/null +++ b/deep_learning/Q1/q1/tsfa_shared_private.py @@ -0,0 +1,1862 @@ +"""TSFA Shared-Private Representation (SPR) experiment. + +The five-fold M4_sourceTime temporal checkpoints remain frozen. Original +training-fold-standardized source features are pooled with their temporal +weights, then factorized without emotion supervision into shared and +modality-private streams. All outputs are written to a separate directory. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import platform +import random +import shutil +import time +from collections import defaultdict +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import sklearn +import torch +import torch.nn.functional as F +from sklearn.decomposition import PCA +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score, roc_auc_score +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler +from torch import Tensor, nn + +from .compare_emotion_probes import _scores +from .correspondence_eval import _write_csv +from .experiment_data import ( + FeatureSample, + fit_feature_stats, + load_feature_samples, + standardized_features, +) +from .tsfa_emotion_probe import CLASS_NAMES, _class_from_sentiment, _pearson +from .tsfa_experiment import GRID_SIZE, _collect_fold_features +from .types import MODALITIES + + +MODS = tuple(MODALITIES) +PAIRS = (("text", "audio"), ("text", "vision"), ("audio", "vision")) +HARD_OFFSETS = (-5, -3, -2, 2, 3, 5) +VARIANTS = ("SP-noOrth-noRec", "SP+Orth", "SPR", "SPR-noSharedContrastive") +VARIANT_LOSSES = { + "SP-noOrth-noRec": (True, False, False), + "SP+Orth": (True, True, False), + "SPR": (True, True, True), + "SPR-noSharedContrastive": (False, True, True), +} +EMOTION_VIEWS = ( + "shared_only_fused", + "shared_only_unfused", + "private_text", + "private_audio", + "private_vision", + "private_audio_vision", + "private_all", + "shared_fused+private_text", + "shared_fused+private_audio", + "shared_fused+private_vision", + "shared_fused+private_audio_vision", + "shared_fused+private_all", + "shared_unfused+private_all", +) + + +class SharedPrivateFactorizer(nn.Module): + """Small MLP factorizer with shared weights and modality-private branches.""" + + def __init__( + self, + dimensions: Mapping[str, int], + *, + common_dim: int = 128, + shared_dim: int = 64, + private_dim: int = 64, + ) -> None: + super().__init__() + self.dimensions = dict(dimensions) + self.shared_dim = shared_dim + self.private_dim = private_dim + self.adapters = nn.ModuleDict({ + name: nn.Sequential( + nn.Linear(dimensions[name], common_dim), + nn.LayerNorm(common_dim), + nn.GELU(), + ) + for name in MODS + }) + # One encoder instance is deliberately reused for all three modalities. + self.shared_encoder = nn.Sequential( + nn.Linear(common_dim, common_dim), nn.GELU(), nn.Linear(common_dim, shared_dim) + ) + self.private_encoders = nn.ModuleDict({ + name: nn.Sequential( + nn.Linear(common_dim, common_dim), nn.GELU(), nn.Linear(common_dim, private_dim) + ) + for name in MODS + }) + self.decoders = nn.ModuleDict({ + name: nn.Sequential( + nn.Linear(shared_dim + private_dim, common_dim), + nn.GELU(), + nn.Linear(common_dim, dimensions[name]), + ) + for name in MODS + }) + + def forward(self, inputs: Mapping[str, Tensor]) -> tuple[dict[str, Tensor], dict[str, Tensor], dict[str, Tensor]]: + shared: dict[str, Tensor] = {} + private: dict[str, Tensor] = {} + reconstruction: dict[str, Tensor] = {} + for name in MODS: + adapted = self.adapters[name](inputs[name]) + shared[name] = self.shared_encoder(adapted) + private[name] = self.private_encoders[name](adapted) + reconstruction[name] = self.decoders[name](torch.cat([shared[name], private[name]], dim=-1)) + return shared, private, reconstruction + + +def _fit_shared_loss(shared: Mapping[str, Tensor], temperature: float) -> Tensor: + """Same-slot InfoNCE with within-video +/-2,3,5 slot negatives.""" + slot_ids = list(range(5, GRID_SIZE - 5)) + offsets = (0, *HARD_OFFSETS) + losses: list[Tensor] = [] + for left, right in PAIRS: + left_values = F.normalize(shared[left], dim=-1) + right_values = F.normalize(shared[right], dim=-1) + for query, target in ((left_values, right_values), (right_values, left_values)): + anchors = query[:, slot_ids] + candidate_rows = [] + for offset in offsets: + indices = [slot + offset for slot in slot_ids] + candidate_rows.append(target[:, indices]) + candidates = torch.stack(candidate_rows, dim=2) + logits = torch.einsum("bnd,bnkd->bnk", anchors, candidates) / temperature + labels = torch.zeros(logits.shape[:2], dtype=torch.long, device=logits.device) + losses.append(F.cross_entropy(logits.reshape(-1, len(offsets)), labels.reshape(-1))) + return torch.stack(losses).mean() + + +def _orthogonality_loss(shared: Mapping[str, Tensor], private: Mapping[str, Tensor]) -> Tensor: + terms: list[Tensor] = [] + for name in MODS: + s = shared[name].reshape(-1, shared[name].shape[-1]) + p = private[name].reshape(-1, private[name].shape[-1]) + s = s - s.mean(dim=0, keepdim=True) + p = p - p.mean(dim=0, keepdim=True) + cross = s.transpose(0, 1) @ p + normalizer = torch.linalg.vector_norm(s) * torch.linalg.vector_norm(p) + terms.append((cross / (normalizer + 1e-8)).square().sum()) + return torch.stack(terms).mean() + + +def _reconstruction_loss(inputs: Mapping[str, Tensor], reconstruction: Mapping[str, Tensor]) -> Tensor: + # Standardized source dimensions have unit scale; averaging modalities keeps + # the 768-dimensional text stream from dominating the smaller streams. + return torch.stack([F.mse_loss(reconstruction[name], inputs[name]) for name in MODS]).mean() + + +def _seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + +def _pool_original_source( + samples: Sequence[FeatureSample], + stats: Any, + temporal_by_id: Mapping[str, Mapping[str, Any]], +) -> dict[str, dict[str, np.ndarray]]: + """Pool standardized original source vectors with the frozen M4 weights.""" + by_id: dict[str, dict[str, np.ndarray]] = {} + for sample in samples: + standardized = standardized_features(sample, stats) + pooled: dict[str, np.ndarray] = {} + record = temporal_by_id[sample.sample_id] + for name in MODS: + weights = np.asarray(record["weights"][name], dtype=np.float32) + source = np.asarray(standardized[name], dtype=np.float32) + if weights.shape != (GRID_SIZE, source.shape[0]): + raise ValueError( + f"M4 weights/source shape mismatch for {sample.sample_id} {name}: " + f"{weights.shape} vs {source.shape}" + ) + pooled[name] = (weights @ source).astype(np.float32, copy=False) + by_id[sample.sample_id] = pooled + return by_id + + +def _tensor_batch(ids: Sequence[str], pooled: Mapping[str, Mapping[str, np.ndarray]], device: torch.device) -> dict[str, Tensor]: + return { + name: torch.from_numpy(np.stack([pooled[sample_id][name] for sample_id in ids])).to(device) + for name in MODS + } + + +def _train_factorizer( + *, + variant: str, + fold: int, + train_ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + dimensions: Mapping[str, int], + device: torch.device, + seed: int, + epochs: int, + batch_size: int, + learning_rate: float, + temperature: float, + orth_weight: float, + reconstruction_weight: float, +) -> tuple[SharedPrivateFactorizer, list[dict[str, Any]]]: + use_shared, use_orth, use_rec = VARIANT_LOSSES[variant] + fold_seed = seed + fold * 101 + _seed_everything(fold_seed) + model = SharedPrivateFactorizer(dimensions).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-4) + rng = np.random.default_rng(fold_seed) + history: list[dict[str, Any]] = [] + for epoch in range(1, epochs + 1): + model.train() + order = rng.permutation(len(train_ids)) + epoch_components: list[list[float]] = [] + for start in range(0, len(order), batch_size): + ids = [train_ids[int(index)] for index in order[start : start + batch_size]] + inputs = _tensor_batch(ids, pooled, device) + shared, private, reconstruction = model(inputs) + shared_loss = _fit_shared_loss(shared, temperature) if use_shared else shared["text"].new_zeros(()) + orth_loss = _orthogonality_loss(shared, private) if use_orth else shared["text"].new_zeros(()) + rec_loss = _reconstruction_loss(inputs, reconstruction) if use_rec else shared["text"].new_zeros(()) + loss = shared_loss + orth_weight * orth_loss + reconstruction_weight * rec_loss + if not torch.isfinite(loss): + raise FloatingPointError(f"non-finite {variant} loss in fold {fold}, epoch {epoch}") + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + epoch_components.append([ + float(loss.detach().item()), + float(shared_loss.detach().item()), + float(orth_loss.detach().item()), + float(rec_loss.detach().item()), + ]) + means = np.asarray(epoch_components).mean(axis=0) + history.append({ + "variant": variant, + "fold": fold, + "seed": fold_seed, + "epoch": epoch, + "loss": means[0], + "shared_loss": means[1], + "orthogonality_loss": means[2], + "reconstruction_loss": means[3], + }) + model.eval() + return model, history + + +def _encode_all( + model: SharedPrivateFactorizer, + ids: Sequence[str], + pooled: Mapping[str, Mapping[str, np.ndarray]], + device: torch.device, + batch_size: int, +) -> tuple[dict[str, dict[str, np.ndarray]], dict[str, dict[str, np.ndarray]]]: + encoded: dict[str, dict[str, np.ndarray]] = {} + decoded: dict[str, dict[str, np.ndarray]] = {} + with torch.no_grad(): + for start in range(0, len(ids), batch_size): + batch_ids = ids[start : start + batch_size] + inputs = _tensor_batch(batch_ids, pooled, device) + shared, private, reconstruction = model(inputs) + zeros_s = {name: torch.zeros_like(shared[name]) for name in MODS} + zeros_p = {name: torch.zeros_like(private[name]) for name in MODS} + reconstruction_shared = { + name: model.decoders[name](torch.cat([shared[name], zeros_p[name]], dim=-1)) + for name in MODS + } + reconstruction_private = { + name: model.decoders[name](torch.cat([zeros_s[name], private[name]], dim=-1)) + for name in MODS + } + for index, sample_id in enumerate(batch_ids): + encoded[sample_id] = { + "shared": {name: shared[name][index].cpu().numpy().astype(np.float32) for name in MODS}, + "private": {name: private[name][index].cpu().numpy().astype(np.float32) for name in MODS}, + } + decoded[sample_id] = { + "reconstruction_both": { + name: reconstruction[name][index].cpu().numpy().astype(np.float32) for name in MODS + }, + "reconstruction_shared_only": { + name: reconstruction_shared[name][index].cpu().numpy().astype(np.float32) + for name in MODS + }, + "reconstruction_private_only": { + name: reconstruction_private[name][index].cpu().numpy().astype(np.float32) + for name in MODS + }, + } + return encoded, decoded + + +def _pool_five(sequence: np.ndarray) -> np.ndarray: + values = np.asarray(sequence, dtype=np.float32) + if values.ndim != 2 or values.shape[0] != GRID_SIZE: + raise ValueError(f"expected [{GRID_SIZE}, D] slot sequence, got {values.shape}") + return values.reshape(5, GRID_SIZE // 5, values.shape[1]).mean(axis=1).reshape(-1) + + +def _raw_private_views(sample: Mapping[str, np.ndarray]) -> dict[str, np.ndarray]: + return { + "raw_private_text": sample["text"], + "raw_private_audio": sample["audio"], + "raw_private_vision": sample["vision"], + "raw_private_all": np.concatenate([sample[name] for name in MODS], axis=-1), + } + + +def _emotion_views( + representation: Mapping[str, Any], + raw_private_pca: np.ndarray, +) -> dict[str, np.ndarray]: + shared = representation["shared"] + private = representation["private"] + fused = (shared["text"] + shared["audio"] + shared["vision"]) / 3.0 + private_av = np.concatenate([private["audio"], private["vision"]], axis=-1) + private_all = np.concatenate([private[name] for name in MODS], axis=-1) + per_slot: dict[str, np.ndarray] = { + "shared_only_fused": fused, + "shared_only_unfused": np.concatenate([shared[name] for name in MODS], axis=-1), + "private_text": private["text"], + "private_audio": private["audio"], + "private_vision": private["vision"], + "private_audio_vision": private_av, + "private_all": private_all, + "shared_fused+private_text": np.concatenate([fused, private["text"]], axis=-1), + "shared_fused+private_audio": np.concatenate([fused, private["audio"]], axis=-1), + "shared_fused+private_vision": np.concatenate([fused, private["vision"]], axis=-1), + "shared_fused+private_audio_vision": np.concatenate([fused, private_av], axis=-1), + "shared_fused+private_all": np.concatenate([fused, private_all], axis=-1), + "shared_unfused+private_all": np.concatenate([ + np.concatenate([shared[name] for name in MODS], axis=-1), private_all + ], axis=-1), + } + per_slot["RawPrivate-PCA"] = raw_private_pca + return {name: _pool_five(values) for name, values in per_slot.items()} + + +def _fit_probe_pair( + train_x: np.ndarray, + train_class: np.ndarray, + train_value: np.ndarray, + test_x: np.ndarray, + seed: int, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + classifier = make_pipeline( + StandardScaler(), + LogisticRegression(C=0.05, max_iter=5000, solver="lbfgs", random_state=seed), + ) + classifier.fit(train_x, train_class) + predicted_class = classifier.predict(test_x) + regressor = make_pipeline(StandardScaler(), Ridge(alpha=25.0)) + regressor.fit(train_x, train_value) + predicted_unclipped = regressor.predict(test_x) + return predicted_class, np.clip(predicted_unclipped, -3.0, 3.0), predicted_unclipped + + +def _fold_metrics(rows: Sequence[Mapping[str, Any]]) -> dict[str, float]: + actual_class = np.asarray([row["true_class_id"] for row in rows], dtype=np.int64) + predicted_class = np.asarray([row["predicted_class_id"] for row in rows], dtype=np.int64) + actual = np.asarray([row["true_label"] for row in rows], dtype=np.float64) + predicted = np.asarray([row["predicted_label"] for row in rows], dtype=np.float64) + fold_f1 = [] + for fold in sorted({int(row["fold"]) for row in rows}): + fold_rows = [row for row in rows if int(row["fold"]) == fold] + fold_f1.append(f1_score( + [row["true_class_id"] for row in fold_rows], + [row["predicted_class_id"] for row in fold_rows], + labels=[0, 1, 2], average="macro", zero_division=0, + )) + return { + "sample_count": len(rows), + "feature_dimension": int(rows[0].get("feature_dimension", -1)) if rows else -1, + "accuracy": float(accuracy_score(actual_class, predicted_class)), + "macro_f1": float(f1_score(actual_class, predicted_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "macro_f1_fold_mean": float(np.mean(fold_f1)), + "macro_f1_fold_sd": float(np.std(fold_f1, ddof=1)) if len(fold_f1) > 1 else 0.0, + "mae": float(np.mean(np.abs(actual - predicted))), + "pearson": _pearson(actual, predicted), + } + + +def _read_csv(path: Path) -> list[dict[str, str]]: + with path.open("r", encoding="utf-8-sig", newline="") as stream: + return list(csv.DictReader(stream)) + + +def _video_bootstrap_ci( + values: Mapping[str, float], *, repeats: int = 2000, seed: int = 42 +) -> tuple[float, float]: + groups = sorted(values) + if not groups: + return float("nan"), float("nan") + point = np.asarray([values[group] for group in groups], dtype=np.float64) + point = point[np.isfinite(point)] + if not point.size: + return float("nan"), float("nan") + rng = np.random.default_rng(seed) + estimates = np.empty(repeats, dtype=np.float64) + for index in range(repeats): + chosen = rng.integers(0, len(groups), size=len(groups)) + estimates[index] = np.nanmean([values[groups[int(i)]] for i in chosen]) + return float(np.quantile(estimates, 0.025)), float(np.quantile(estimates, 0.975)) + + +def _representation_diagnostics( + *, + variant: str, + fold: int, + sample: FeatureSample, + encoded: Mapping[str, Any], +) -> dict[str, Any]: + result: dict[str, Any] = {"variant": variant, "fold": fold, "sample_id": sample.sample_id, + "video_id": sample.group_id} + for name in MODS: + s = np.asarray(encoded["shared"][name], dtype=np.float64) + p = np.asarray(encoded["private"][name], dtype=np.float64) + s_center = s - s.mean(axis=0, keepdims=True) + p_center = p - p.mean(axis=0, keepdims=True) + cross = s_center.T @ p_center + denom = np.linalg.norm(s_center) * np.linalg.norm(p_center) + result[f"{name}_cross_covariance_norm"] = float(np.linalg.norm(cross) / (denom + 1e-12)) + s_norm = np.linalg.norm(s, axis=1).mean() + p_norm = np.linalg.norm(p, axis=1).mean() + result[f"{name}_shared_norm"] = float(s_norm) + result[f"{name}_private_norm"] = float(p_norm) + result[f"{name}_shared_norm_ratio"] = float(s_norm / (s_norm + p_norm + 1e-12)) + return result + + +def _source_modality_probe( + *, + encoded: Mapping[str, Mapping[str, Any]], + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + branch: str, + seed: int, +) -> dict[str, float]: + def matrix(samples: Sequence[FeatureSample]) -> tuple[np.ndarray, np.ndarray]: + vectors = [] + labels = [] + for sample in samples: + for label, name in enumerate(MODS): + values = np.asarray(encoded[sample.sample_id][branch][name], dtype=np.float32) + vectors.append(values) + labels.extend([label] * len(values)) + return np.concatenate(vectors, axis=0), np.asarray(labels, dtype=np.int64) + + train_x, train_y = matrix(train_samples) + heldout_x, heldout_y = matrix(heldout_samples) + probe = make_pipeline( + StandardScaler(), LogisticRegression(C=1.0, max_iter=2000, random_state=seed) + ) + probe.fit(train_x, train_y) + predicted = probe.predict(heldout_x) + return { + "accuracy": float(accuracy_score(heldout_y, predicted)), + "macro_f1": float(f1_score(heldout_y, predicted, labels=[0, 1, 2], average="macro", zero_division=0)), + "sample_count": len(heldout_y), + } + + +def _pair_correspondence( + left_values: np.ndarray, + right_values: np.ndarray, + *, + rng: np.random.Generator, + shuffle_repeats: int, +) -> dict[str, float]: + """Within-clip slot correspondence diagnostics; not a human alignment GT.""" + left = F.normalize(torch.from_numpy(left_values), dim=-1).numpy() + right = F.normalize(torch.from_numpy(right_values), dim=-1).numpy() + + def score(a: np.ndarray, b: np.ndarray) -> dict[str, float]: + similarities = a @ b.T + slots = np.arange(len(a)) + positive = similarities[slots, slots] + negative = [] + for offset in HARD_OFFSETS: + q = slots[(slots + offset >= 0) & (slots + offset < len(b))] + negative.extend(similarities[q, q + offset].tolist()) + auc = float(roc_auc_score(np.r_[np.ones(len(positive)), np.zeros(len(negative))], + np.r_[positive, negative])) + best = similarities.argmax(axis=1) + error = np.abs(best - slots) + return { + "auc": auc, + "exact_r1": float(np.mean(error == 0)), + "within_pm1_r1": float(np.mean(error <= 1)), + "mase": float(np.mean(error)), + } + + forward = score(left, right) + backward = score(right, left) + actual = {key: (forward[key] + backward[key]) / 2 for key in forward} + shuffled = [] + for _ in range(shuffle_repeats): + shuffled.append(score(left, right[rng.permutation(len(right))])) + actual.update({ + f"slot_shuffle_{key}": float(np.mean([row[key] for row in shuffled])) + for key in ("auc", "exact_r1", "within_pm1_r1", "mase") + }) + return actual + + +def _emotion_feature_sets( + encoded_by_id: Mapping[str, Mapping[str, Any]], + raw_pca_by_id: Mapping[str, np.ndarray], +) -> dict[str, dict[str, np.ndarray]]: + return { + sample_id: _emotion_views(encoded_by_id[sample_id], raw_pca_by_id[sample_id]) + for sample_id in encoded_by_id + } + + +def _probe_fold( + *, + variant: str, + fold: int, + train_samples: Sequence[FeatureSample], + heldout_samples: Sequence[FeatureSample], + features_by_id: Mapping[str, Mapping[str, np.ndarray]], + seed: int, + view_names: Sequence[str] = EMOTION_VIEWS, +) -> list[dict[str, Any]]: + train_classes = np.asarray([_class_from_sentiment(sample.sentiment) for sample in train_samples]) + heldout_classes = np.asarray([_class_from_sentiment(sample.sentiment) for sample in heldout_samples]) + train_values = np.asarray([sample.sentiment for sample in train_samples], dtype=np.float64) + heldout_values = np.asarray([sample.sentiment for sample in heldout_samples], dtype=np.float64) + rows: list[dict[str, Any]] = [] + for view in view_names: + train_x = np.stack([features_by_id[sample.sample_id][view] for sample in train_samples]) + heldout_x = np.stack([features_by_id[sample.sample_id][view] for sample in heldout_samples]) + predicted_class, predicted_value, predicted_unclipped = _fit_probe_pair( + train_x, train_classes, train_values, heldout_x, seed + ) + for index, sample in enumerate(heldout_samples): + rows.append({ + "method": variant, + "view": view, + "fold": fold, + "sample_id": sample.sample_id, + "video_id": sample.group_id, + "true_class_id": int(heldout_classes[index]), + "true_class": CLASS_NAMES[int(heldout_classes[index])], + "predicted_class_id": int(predicted_class[index]), + "predicted_class": CLASS_NAMES[int(predicted_class[index])], + "true_label": float(heldout_values[index]), + "predicted_label": float(predicted_value[index]), + "predicted_label_unclipped": float(predicted_unclipped[index]), + "feature_dimension": int(train_x.shape[1]), + }) + return rows + + +def _legacy_rows( + *, + old_predictions: Path, + raw_private_predictions: Path, + samples_by_id: Mapping[str, FeatureSample], + fold_by_id: Mapping[str, int], +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + source_specs = ( + (old_predictions, "TSFA-main", "TSFA-old", 1920), + (raw_private_predictions, "TSFA-T+Private", "TSFA+RawPrivate", 6845), + ) + for path, source_method, method, dimension in source_specs: + source_rows = _read_csv(path) + if method == "TSFA-old": + source_rows = [row for row in source_rows if row["method"] == source_method and row["view"] == "all_modalities"] + else: + source_rows = [row for row in source_rows if row["method"] == source_method] + by_id = {row["sample_id"]: row for row in source_rows} + if len(by_id) != len(source_rows) or set(by_id) != set(samples_by_id): + raise ValueError(f"legacy OOF predictions do not cover the same unique samples: {path}") + for sample_id, source in by_id.items(): + sample = samples_by_id[sample_id] + fold = int(source["fold"]) + if fold != fold_by_id[sample_id] or source["video_id"] != sample.group_id: + raise ValueError(f"legacy OOF fold/group mismatch for {sample_id} in {method}") + rows.append({ + "method": method, + "view": "all_modalities", + "fold": fold, + "sample_id": sample_id, + "video_id": sample.group_id, + "true_class_id": _class_from_sentiment(sample.sentiment), + "true_class": CLASS_NAMES[_class_from_sentiment(sample.sentiment)], + "predicted_class_id": int(source["predicted_class_id"]), + "predicted_class": CLASS_NAMES[int(source["predicted_class_id"])], + "true_label": float(sample.sentiment), + "predicted_label": float(source["predicted_label"]), + "predicted_label_unclipped": float(source.get("predicted_label_unclipped", source["predicted_label"])), + "feature_dimension": dimension, + }) + return rows + + +def _math_reference_rows( + *, + predictions_path: Path, + splits_path: Path, + samples_by_id: Mapping[str, FeatureSample], +) -> list[dict[str, Any]]: + source = _read_csv(predictions_path) + split_rows = _read_csv(splits_path) + split_by_id = {row["sample_id"]: int(row["fold"]) for row in split_rows} + math_by_id = {row["sample_id"]: row for row in source} + if set(math_by_id) != set(samples_by_id) or set(split_by_id) != set(samples_by_id): + raise ValueError("math OOF predictions/splits do not match the Q1 100 samples") + rows: list[dict[str, Any]] = [] + for method in ("B0", "B4"): + for sample_id, sample in samples_by_id.items(): + ref = math_by_id[sample_id] + if ref["video_id"] != sample.group_id or not np.isclose(float(ref["true_sentiment"]), sample.sentiment): + raise ValueError(f"math label or video_id mismatch for {sample_id}") + rows.append({ + "method": method, + "view": "all_modalities", + "fold": split_by_id[sample_id], + "sample_id": sample_id, + "video_id": sample.group_id, + "true_class_id": int(ref["true_polarity"]), + "true_class": CLASS_NAMES[int(ref["true_polarity"])], + "predicted_class_id": int(ref[f"{method}_predicted_polarity"]), + "predicted_class": CLASS_NAMES[int(ref[f"{method}_predicted_polarity"])], + "true_label": float(ref["true_sentiment"]), + "predicted_label": float(np.clip(float(ref[f"{method}_predicted_sentiment"]), -3.0, 3.0)), + "predicted_label_unclipped": float(ref[f"{method}_predicted_sentiment"]), + "feature_dimension": -1, + }) + return rows + + +def _summarize_emotion_predictions(rows: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + grouped: dict[tuple[str, str], list[Mapping[str, Any]]] = defaultdict(list) + for row in rows: + grouped[(str(row["method"]), str(row["view"]))].append(row) + summaries: list[dict[str, Any]] = [] + for (method, view), group in sorted(grouped.items()): + summary = _fold_metrics(group) + summary.update({"method": method, "view": view}) + summaries.append(summary) + return summaries + + +def _paired_contrasts( + predictions: Sequence[Mapping[str, Any]], + *, + candidate_method: str = "SPR", + candidate_view: str = "shared_unfused+private_all", + references: Sequence[tuple[str, str]] | None = None, + repeats: int = 2000, + seed: int = 42, +) -> list[dict[str, Any]]: + by_key: dict[tuple[str, str], dict[str, Mapping[str, Any]]] = defaultdict(dict) + for row in predictions: + by_key[(str(row["method"]), str(row["view"]))][str(row["sample_id"])] = row + candidate = by_key[(candidate_method, candidate_view)] + if not candidate: + raise ValueError(f"missing paired contrast candidate {candidate_method}/{candidate_view}") + if references is None: + references = ( + ("TSFA-old", "all_modalities"), + ("TSFA+RawPrivate", "all_modalities"), + ("B0", "all_modalities"), + ("B4", "all_modalities"), + ("SPR-dim-matched", "SPR-dim-matched"), + ("RawPrivate-PCA", "RawPrivate-PCA"), + ) + output: list[dict[str, Any]] = [] + for reference_method, reference_view in references: + reference = by_key[(reference_method, reference_view)] + if not reference: + continue + sample_ids = sorted(candidate) + if set(sample_ids) != set(reference): + raise ValueError(f"paired OOF sample IDs differ: SPR and {reference_method}") + for sample_id in sample_ids: + a, b = candidate[sample_id], reference[sample_id] + for key in ("video_id", "fold", "true_class_id", "true_label"): + if str(a[key]) != str(b[key]) and not ( + key == "true_label" and np.isclose(float(a[key]), float(b[key])) + ): + raise ValueError(f"paired OOF {key} mismatch for {sample_id}: {reference_method}") + groups: dict[str, list[str]] = defaultdict(list) + for sample_id in sample_ids: + groups[str(candidate[sample_id]["video_id"])].append(sample_id) + group_names = sorted(groups) + rng = np.random.default_rng(seed) + deltas: dict[str, list[float]] = {name: [] for name in ("macro_f1", "mae", "pearson")} + for _ in range(repeats): + chosen_groups = rng.choice(group_names, size=len(group_names), replace=True) + chosen = [sample_id for group in chosen_groups for sample_id in groups[str(group)]] + aa = [candidate[sample_id] for sample_id in chosen] + bb = [reference[sample_id] for sample_id in chosen] + class_true = np.asarray([int(row["true_class_id"]) for row in aa]) + value_true = np.asarray([float(row["true_label"]) for row in aa]) + score_a = _scores( + class_true, + np.asarray([int(row["predicted_class_id"]) for row in aa]), + value_true, + np.asarray([float(row["predicted_label"]) for row in aa]), + ) + score_b = _scores( + class_true, + np.asarray([int(row["predicted_class_id"]) for row in bb]), + value_true, + np.asarray([float(row["predicted_label"]) for row in bb]), + ) + for metric in deltas: + deltas[metric].append(score_a[metric] - score_b[metric]) + whole_a = [candidate[sample_id] for sample_id in sample_ids] + whole_b = [reference[sample_id] for sample_id in sample_ids] + class_true = np.asarray([int(row["true_class_id"]) for row in whole_a]) + value_true = np.asarray([float(row["true_label"]) for row in whole_a]) + point_a = _scores(class_true, + np.asarray([int(row["predicted_class_id"]) for row in whole_a]), + value_true, + np.asarray([float(row["predicted_label"]) for row in whole_a])) + point_b = _scores(class_true, + np.asarray([int(row["predicted_class_id"]) for row in whole_b]), + value_true, + np.asarray([float(row["predicted_label"]) for row in whole_b])) + for metric, values in deltas.items(): + finite = np.asarray(values, dtype=np.float64) + finite = finite[np.isfinite(finite)] + output.append({ + "comparison": f"{candidate_method}-{reference_method}", + "candidate_view": candidate_view, + "reference_view": reference_view, + "metric": metric, + "candidate_oof": point_a[metric], + "reference_oof": point_b[metric], + "delta_candidate_minus_reference": point_a[metric] - point_b[metric], + "video_cluster_bootstrap_ci95_low": float(np.quantile(finite, 0.025)), + "video_cluster_bootstrap_ci95_high": float(np.quantile(finite, 0.975)), + "bootstrap_repeats": repeats, + "video_group_count": len(group_names), + "multiple_comparison_correction": "none", + }) + return output + + +def _paired_view_contrasts( + predictions: Sequence[Mapping[str, Any]], + *, + method: str = "SPR", + views: Sequence[str] = ( + "private_text", "private_audio", "private_vision", "private_audio_vision", "private_all" + ), + repeats: int = 2000, + seed: int = 42, +) -> list[dict[str, Any]]: + by_view: dict[str, dict[str, Mapping[str, Any]]] = defaultdict(dict) + for row in predictions: + if str(row["method"]) == method and str(row["view"]) in views: + by_view[str(row["view"])][str(row["sample_id"])] = row + output: list[dict[str, Any]] = [] + for left_index, left_view in enumerate(views): + for right_index, right_view in enumerate(views[left_index + 1:], start=left_index + 1): + left, right = by_view[left_view], by_view[right_view] + sample_ids = sorted(set(left) & set(right)) + if not sample_ids: + continue + groups: dict[str, list[str]] = defaultdict(list) + for sample_id in sample_ids: + if left[sample_id]["video_id"] != right[sample_id]["video_id"]: + raise ValueError(f"video_id mismatch in source ablation for {sample_id}") + groups[str(left[sample_id]["video_id"])].append(sample_id) + group_names = sorted(groups) + rng = np.random.default_rng(seed + left_index * 100 + right_index) + boot: dict[str, list[float]] = {metric: [] for metric in ("macro_f1", "mae", "pearson")} + for _ in range(repeats): + chosen_groups = rng.choice(group_names, size=len(group_names), replace=True) + chosen = [sample_id for group in chosen_groups for sample_id in groups[str(group)]] + a_rows = [left[sample_id] for sample_id in chosen] + b_rows = [right[sample_id] for sample_id in chosen] + actual_class = np.asarray([int(row["true_class_id"]) for row in a_rows]) + actual_value = np.asarray([float(row["true_label"]) for row in a_rows]) + score_a = _scores(actual_class, np.asarray([int(row["predicted_class_id"]) for row in a_rows]), + actual_value, np.asarray([float(row["predicted_label"]) for row in a_rows])) + score_b = _scores(actual_class, np.asarray([int(row["predicted_class_id"]) for row in b_rows]), + actual_value, np.asarray([float(row["predicted_label"]) for row in b_rows])) + for metric in boot: + boot[metric].append(score_a[metric] - score_b[metric]) + a_rows = [left[sample_id] for sample_id in sample_ids] + b_rows = [right[sample_id] for sample_id in sample_ids] + actual_class = np.asarray([int(row["true_class_id"]) for row in a_rows]) + actual_value = np.asarray([float(row["true_label"]) for row in a_rows]) + score_a = _scores(actual_class, np.asarray([int(row["predicted_class_id"]) for row in a_rows]), + actual_value, np.asarray([float(row["predicted_label"]) for row in a_rows])) + score_b = _scores(actual_class, np.asarray([int(row["predicted_class_id"]) for row in b_rows]), + actual_value, np.asarray([float(row["predicted_label"]) for row in b_rows])) + for metric in boot: + finite = np.asarray(boot[metric], dtype=np.float64) + finite = finite[np.isfinite(finite)] + output.append({ + "method": method, + "left_view_minus_right_view": f"{left_view}-{right_view}", + "left_view": left_view, + "right_view": right_view, + "metric": metric, + "left_oof": score_a[metric], + "right_oof": score_b[metric], + "delta_left_minus_right": score_a[metric] - score_b[metric], + "video_cluster_bootstrap_ci95_low": float(np.quantile(finite, 0.025)), + "video_cluster_bootstrap_ci95_high": float(np.quantile(finite, 0.975)), + "bootstrap_repeats": repeats, + "video_group_count": len(group_names), + "multiple_comparison_correction": "none", + }) + return output + + +def _clip_metric_summary( + rows: Sequence[Mapping[str, Any]], + *, + method_key: str, + group_keys: Sequence[str], + metrics: Sequence[str], + repeats: int, + seed: int, +) -> list[dict[str, Any]]: + grouped: dict[tuple[Any, ...], list[Mapping[str, Any]]] = defaultdict(list) + for row in rows: + grouped[tuple(row[key] for key in group_keys)].append(row) + output: list[dict[str, Any]] = [] + for keys, items in sorted(grouped.items(), key=lambda pair: tuple(map(str, pair[0]))): + base = dict(zip(group_keys, keys)) + base.update({"sample_count": len(items), "video_count": len({str(x["video_id"]) for x in items})}) + for metric in metrics: + by_video: dict[str, list[float]] = defaultdict(list) + for row in items: + by_video[str(row["video_id"])].append(float(row[metric])) + video_means = {video: float(np.mean(values)) for video, values in by_video.items()} + mean = float(np.mean(list(video_means.values()))) if video_means else float("nan") + low, high = _video_bootstrap_ci(video_means, repeats=repeats, + seed=seed + sum(ord(ch) for ch in metric)) + base[f"{metric}_mean_video"] = mean + base[f"{metric}_video_bootstrap_ci95_low"] = low + base[f"{metric}_video_bootstrap_ci95_high"] = high + output.append(base) + return output + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _save_architecture_figure(path: Path) -> None: + fig, ax = plt.subplots(figsize=(12, 6.5)) + ax.set_xlim(0, 12) + ax.set_ylim(0, 7) + ax.axis("off") + colors = {"input": "#dbeafe", "shared": "#dcfce7", "private": "#ffedd5", "decode": "#f3e8ff"} + + def box(x: float, y: float, w: float, h: float, text: str, color: str, fontsize: int = 10) -> None: + patch = plt.Rectangle((x, y), w, h, linewidth=1.3, edgecolor="#334155", facecolor=colors[color], + joinstyle="round") + ax.add_patch(patch) + ax.text(x + w / 2, y + h / 2, text, ha="center", va="center", fontsize=fontsize) + + def arrow(start: tuple[float, float], end: tuple[float, float], color: str = "#475569") -> None: + ax.annotate("", xy=end, xytext=start, + arrowprops={"arrowstyle": "->", "lw": 1.2, "color": color}) + + y_rows = [5.6, 3.7, 1.8] + modalities = [("Text", "BERT"), ("Audio", "eGeMAPS"), ("Vision", "DeiT")] + for y, (label, source) in zip(y_rows, modalities): + box(0.2, y, 1.5, 0.75, f"{label} source\n{source}", "input") + box(2.25, y, 1.5, 0.75, f"Frozen M4\nweighted pool", "input") + box(4.3, y, 1.45, 0.75, f"Adapter $P_{label[0]}$", "input") + box(6.3, y + 0.42, 1.55, 0.75, f"Shared\nencoder", "shared") + box(6.3, y - 0.42, 1.55, 0.75, f"Private {label}\nencoder", "private") + box(9.25, y - 0.12, 2.15, 0.75, f"{label} decoder\nreconstruct $h^{label}$", "decode") + arrow((1.7, y + 0.37), (2.25, y + 0.37)) + arrow((3.75, y + 0.37), (4.3, y + 0.37)) + arrow((5.75, y + 0.37), (6.3, y + 0.78)) + arrow((5.75, y + 0.37), (6.3, y - 0.02)) + arrow((7.85, y + 0.78), (9.25, y + 0.38)) + arrow((7.85, y - 0.02), (9.25, y + 0.15)) + ax.text(7.08, 6.85, "$L_{shared}$: same-slot T-A / T-V / A-V InfoNCE", ha="center", va="center", fontsize=11) + ax.text(7.08, 0.55, "$L_{orth}$ within each modality + $L_{rec}$ per modality", ha="center", fontsize=11) + ax.set_title("TSFA-SPR: frozen temporal pooling, shared semantics, private modality detail", fontsize=14, pad=12) + fig.tight_layout() + fig.savefig(path, dpi=180, bbox_inches="tight") + plt.close(fig) + + +def _plot_summaries( + output_dir: Path, + modality_rows: Sequence[Mapping[str, Any]], + correspondence_rows: Sequence[Mapping[str, Any]], + reconstruction_rows: Sequence[Mapping[str, Any]], + emotion_rows: Sequence[Mapping[str, Any]], + diagnostics_rows: Sequence[Mapping[str, Any]], +) -> list[str]: + figures: list[str] = [] + _save_architecture_figure(output_dir / "architecture.png") + figures.append("architecture.png") + + variants = list(VARIANTS) + x = np.arange(len(variants)) + fig, ax = plt.subplots(figsize=(9, 5)) + for offset, branch, color, label in ((-0.18, "shared", "#2563eb", "Shared"), + (0.18, "private", "#ea580c", "Private")): + rows = {str(row["variant"]): row for row in modality_rows if row["branch"] == branch} + ax.bar(x + offset, [float(rows[v]["accuracy_mean"]) for v in variants], width=0.34, + color=color, label=label) + ax.set_xticks(x, variants, rotation=15, ha="right") + ax.set_ylim(0, 1) + ax.set_ylabel("Modality label accuracy (held-out videos)") + ax.set_title("Modality information in shared vs private streams") + ax.legend() + fig.tight_layout() + fig.savefig(output_dir / "shared_private_modality_probe.png", dpi=180) + plt.close(fig) + figures.append("shared_private_modality_probe.png") + + fig, ax = plt.subplots(figsize=(9, 5)) + pairs = ["text-audio", "text-vision", "audio-vision"] + pair_x = np.arange(len(pairs)) + width = 0.22 + for index, variant in enumerate(variants): + rows = {str(row["pair"]): row for row in correspondence_rows if row["variant"] == variant} + ax.bar(pair_x + (index - 1.5) * width, + [float(rows[pair]["auc_mean_video"]) for pair in pairs], width=width, label=variant) + ax.axhline(0.5, color="#64748b", linestyle="--", linewidth=1) + ax.set_xticks(pair_x, pairs) + ax.set_ylim(0, 1) + ax.set_ylabel("Matched vs shifted AUC") + ax.set_title("Shared representation: cross-modal slot correspondence") + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(output_dir / "shared_crossmodal_auc.png", dpi=180) + plt.close(fig) + figures.append("shared_crossmodal_auc.png") + + trained = [row for row in reconstruction_rows + if row["reconstruction_trained"] is True or str(row["reconstruction_trained"]).lower() == "true"] + branches = ["shared_only", "private_only", "both"] + fig, ax = plt.subplots(figsize=(10, 5)) + labels = [] + values = [] + for variant in VARIANTS: + for branch in branches: + subset = [float(row["mse_mean_video"]) for row in trained + if row["variant"] == variant and row["branch"] == branch] + if subset: + labels.append(f"{variant}\n{branch}") + values.append(float(np.mean(subset))) + ax.bar(np.arange(len(values)), values, color=["#2563eb", "#ea580c", "#7c3aed"] * 4) + ax.set_xticks(np.arange(len(labels)), labels, rotation=25, ha="right", fontsize=8) + ax.set_ylabel("Held-out normalized MSE") + ax.set_title("Source reconstruction from shared, private, and combined factors") + fig.tight_layout() + fig.savefig(output_dir / "reconstruction_branches.png", dpi=180) + plt.close(fig) + figures.append("reconstruction_branches.png") + + selected = [ + ("TSFA-old", "all_modalities"), ("TSFA+RawPrivate", "all_modalities"), + ("SPR", "shared_unfused+private_all"), ("SPR-dim-matched", "SPR-dim-matched"), + ("RawPrivate-PCA", "RawPrivate-PCA"), + ] + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + plot_rows = [] + for method, view in selected: + row = next((r for r in emotion_rows if r["method"] == method and r["view"] == view), None) + if row: + plot_rows.append(row) + labels = [str(row["method"]) for row in plot_rows] + axes[0].bar(np.arange(len(plot_rows)), [float(row["mae"]) for row in plot_rows], color="#0f766e") + axes[0].set_ylabel("OOF MAE (lower is better)") + axes[1].bar(np.arange(len(plot_rows)), [float(row["pearson"]) for row in plot_rows], color="#7c3aed") + axes[1].set_ylabel("OOF Pearson") + for axis in axes: + axis.set_xticks(np.arange(len(labels)), labels, rotation=22, ha="right") + fig.suptitle("Emotion probe: old baseline, private residual, SPR, and dimension controls") + fig.tight_layout() + fig.savefig(output_dir / "emotion_regression_ablation.png", dpi=180) + plt.close(fig) + figures.append("emotion_regression_ablation.png") + + private_names = ["private_text", "private_audio", "private_vision", "private_audio_vision", "private_all"] + private_rows = [row for row in emotion_rows if row["method"] == "SPR" and row["view"] in private_names] + private_rows = sorted(private_rows, key=lambda row: private_names.index(str(row["view"]))) + fig, ax = plt.subplots(figsize=(9, 5)) + xpos = np.arange(len(private_rows)) + ax.bar(xpos - 0.18, [float(row["macro_f1"]) for row in private_rows], width=0.36, + color="#2563eb", label="Macro-F1") + ax2 = ax.twinx() + ax2.bar(xpos + 0.18, [float(row["mae"]) for row in private_rows], width=0.36, + color="#ea580c", label="MAE") + ax.set_xticks(xpos, [str(row["view"]) for row in private_rows], rotation=15, ha="right") + ax.set_ylabel("Macro-F1") + ax2.set_ylabel("MAE") + ax.set_title("Which private sources contribute to emotion probes?") + ax.legend(loc="upper left") + ax2.legend(loc="upper right") + fig.tight_layout() + fig.savefig(output_dir / "private_source_contribution.png", dpi=180) + plt.close(fig) + figures.append("private_source_contribution.png") + + dim_keys = { + ("TSFA-old", "all_modalities"), + ("TSFA+RawPrivate", "all_modalities"), + ("SPR", "shared_unfused+private_all"), + ("SPR-dim-matched", "SPR-dim-matched"), + ("RawPrivate-PCA", "RawPrivate-PCA"), + } + dim_rows = [row for row in emotion_rows if (row["method"], row["view"]) in dim_keys] + fig, axes = plt.subplots(1, 2, figsize=(12, 5.8)) + dim_labels = [f"{row['method']} ({row['feature_dimension']}D)" for row in dim_rows] + ypos = np.arange(len(dim_rows)) + axes[0].barh(ypos, [float(row["macro_f1"]) for row in dim_rows], color="#2563eb") + axes[1].barh(ypos, [float(row["mae"]) for row in dim_rows], color="#ea580c") + axes[0].set_yticks(ypos, dim_labels) + axes[1].set_yticks(ypos, dim_labels) + axes[0].invert_yaxis() + axes[1].invert_yaxis() + axes[0].set_xlabel("OOF Macro-F1") + axes[1].set_xlabel("OOF MAE") + fig.suptitle("Emotion probes at native and dimension-matched representation sizes") + fig.tight_layout() + fig.savefig(output_dir / "dimension_control.png", dpi=180) + plt.close(fig) + figures.append("dimension_control.png") + + fig, ax = plt.subplots(figsize=(9, 5)) + for index, variant in enumerate(variants): + means = [float(np.mean([float(row[f"{name}_shared_norm_ratio"]) for row in diagnostics_rows + if row["variant"] == variant])) for name in MODS] + ax.plot(MODS, means, marker="o", label=variant) + ax.set_ylim(0, 1) + ax.set_ylabel(r"$\|s\|/(\|s\|+\|p\|)$") + ax.set_title("Shared/private norm ratio by modality") + ax.legend(fontsize=8) + fig.tight_layout() + fig.savefig(output_dir / "shared_private_norm_ratio.png", dpi=180) + plt.close(fig) + figures.append("shared_private_norm_ratio.png") + return figures + + +def run(args: argparse.Namespace) -> None: + started = time.time() + args.output_dir.mkdir(parents=True, exist_ok=True) + if args.device == "auto": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device(args.device) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA requested but unavailable") + if device.type == "cuda": + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + + samples = load_feature_samples(args.feature_dir, args.manifest) + samples_by_id = {sample.sample_id: sample for sample in samples} + splits = json.loads(args.splits.read_text(encoding="utf-8")) + if len(splits) != 5: + raise ValueError(f"expected five grouped folds, got {len(splits)}") + fold_by_id: dict[str, int] = {} + for split in splits: + fold = int(split["fold"]) + train_ids = set(split["train_sample_ids"]) + valid_ids = set(split["validation_sample_ids"]) + if train_ids & valid_ids or train_ids | valid_ids != set(samples_by_id): + raise ValueError(f"fold {fold} does not partition all samples") + train_groups = {samples_by_id[sample_id].group_id for sample_id in train_ids} + valid_groups = {samples_by_id[sample_id].group_id for sample_id in valid_ids} + if train_groups & valid_groups: + raise ValueError(f"video_id leakage in fold {fold}: {sorted(train_groups & valid_groups)}") + for sample_id in valid_ids: + if sample_id in fold_by_id: + raise ValueError(f"sample appears in multiple validation folds: {sample_id}") + fold_by_id[sample_id] = fold + if set(fold_by_id) != set(samples_by_id): + raise ValueError("fold validation partitions do not cover all samples exactly once") + + # Existing learned and math baselines are read-only controls. Their OOF IDs + # and folds are validated before including them in any paired comparison. + legacy_predictions = _legacy_rows( + old_predictions=args.old_tsfa_predictions, + raw_private_predictions=args.raw_private_predictions, + samples_by_id=samples_by_id, + fold_by_id=fold_by_id, + ) + math_predictions = _math_reference_rows( + predictions_path=args.math_predictions, + splits_path=args.math_splits, + samples_by_id=samples_by_id, + ) + for row in math_predictions: + if int(row["fold"]) != fold_by_id[str(row["sample_id"])]: + raise ValueError(f"math GroupKFold assignment differs from Q1 fixed folds for {row['sample_id']}") + prediction_rows: list[dict[str, Any]] = [*legacy_predictions, *math_predictions] + history_rows: list[dict[str, Any]] = [] + representation_rows: list[dict[str, Any]] = [] + correspondence_clip_rows: list[dict[str, Any]] = [] + modality_fold_rows: list[dict[str, Any]] = [] + reconstruction_clip_rows: list[dict[str, Any]] = [] + fold_manifest: list[dict[str, Any]] = [] + input_hashes: dict[str, str] = {} + + for split in splits: + fold = int(split["fold"]) + train_samples = [samples_by_id[sample_id] for sample_id in split["train_sample_ids"]] + heldout_samples = [samples_by_id[sample_id] for sample_id in split["validation_sample_ids"]] + all_samples = [*train_samples, *heldout_samples] + all_ids = [sample.sample_id for sample in all_samples] + feature_stats = fit_feature_stats(train_samples) + _, _, temporal_by_id = _collect_fold_features( + fold=fold, + train_samples=train_samples, + validation_samples=heldout_samples, + feature_stats=feature_stats, + checkpoint_root=args.checkpoint_root, + device=device, + batch_size=args.batch_size, + ) + pooled = _pool_original_source(all_samples, feature_stats, temporal_by_id) + dimensions = {name: int(pooled[all_ids[0]][name].shape[-1]) for name in MODS} + raw_slots = { + sample_id: np.concatenate([pooled[sample_id][name] for name in MODS], axis=-1) + for sample_id in all_ids + } + train_slot_matrix = np.concatenate([raw_slots[sample.sample_id] for sample in train_samples], axis=0) + pca_dim = min(args.pca_dim, train_slot_matrix.shape[0], train_slot_matrix.shape[1]) + pca = PCA(n_components=pca_dim, svd_solver="randomized", random_state=args.seed + fold) + pca.fit(train_slot_matrix) + raw_pca_by_id = { + sample_id: pca.transform(raw_slots[sample_id]).astype(np.float32, copy=False) + for sample_id in all_ids + } + fold_checkpoint = args.output_dir / "checkpoints" / f"fold_{fold:02d}" + fold_checkpoint.mkdir(parents=True, exist_ok=True) + np.savez_compressed(fold_checkpoint / "raw_private_pca.npz", mean=pca.mean_, components=pca.components_, + explained_variance_ratio=pca.explained_variance_ratio_) + + for variant in VARIANTS: + model, history = _train_factorizer( + variant=variant, + fold=fold, + train_ids=[sample.sample_id for sample in train_samples], + pooled=pooled, + dimensions=dimensions, + device=device, + seed=args.seed, + epochs=args.epochs, + batch_size=args.batch_size, + learning_rate=args.learning_rate, + temperature=args.temperature, + orth_weight=args.orth_weight, + reconstruction_weight=args.reconstruction_weight, + ) + history_rows.extend(history) + torch.save({ + "variant": variant, + "fold": fold, + "seed": args.seed + fold * 101, + "dimensions": dimensions, + "model_state_dict": {key: value.detach().cpu() for key, value in model.state_dict().items()}, + "loss_flags": VARIANT_LOSSES[variant], + "hyperparameters": { + "epochs": args.epochs, + "common_dim": args.common_dim, + "shared_dim": args.shared_dim, + "private_dim": args.private_dim, + "orth_weight": args.orth_weight, + "reconstruction_weight": args.reconstruction_weight, + "temperature": args.temperature, + }, + }, fold_checkpoint / f"{variant}.pt") + encoded, decoded = _encode_all( + model, all_ids, pooled, device, args.batch_size + ) + train_ids = {sample.sample_id for sample in train_samples} + heldout_ids = {sample.sample_id for sample in heldout_samples} + use_rec = VARIANT_LOSSES[variant][2] + for sample in heldout_samples: + sample_id = sample.sample_id + representation_rows.append(_representation_diagnostics( + variant=variant, fold=fold, sample=sample, encoded=encoded[sample_id] + )) + for modality in MODS: + target = pooled[sample_id][modality] + for branch, key in ( + ("shared_only", "reconstruction_shared_only"), + ("private_only", "reconstruction_private_only"), + ("both", "reconstruction_both"), + ): + pred = decoded[sample_id][key][modality] + reconstruction_clip_rows.append({ + "variant": variant, + "fold": fold, + "sample_id": sample_id, + "video_id": sample.group_id, + "modality": modality, + "branch": branch, + "mse": float(np.mean(np.square(target - pred))), + "reconstruction_trained": bool(use_rec), + }) + for left, right in PAIRS: + rng = np.random.default_rng(args.seed + fold * 1009 + sum(map(ord, sample_id + variant + left + right))) + scores = _pair_correspondence( + encoded[sample_id]["shared"][left], + encoded[sample_id]["shared"][right], + rng=rng, + shuffle_repeats=args.shuffle_repeats, + ) + correspondence_clip_rows.append({ + "variant": variant, + "fold": fold, + "sample_id": sample_id, + "video_id": sample.group_id, + "pair": f"{left}-{right}", + **scores, + "delta_auc_vs_shuffle": scores["auc"] - scores["slot_shuffle_auc"], + "delta_exact_r1_vs_shuffle": scores["exact_r1"] - scores["slot_shuffle_exact_r1"], + }) + + for branch in ("shared", "private"): + score = _source_modality_probe( + encoded=encoded, + train_samples=train_samples, + heldout_samples=heldout_samples, + branch=branch, + seed=args.seed + fold, + ) + modality_fold_rows.append({ + "variant": variant, + "fold": fold, + "branch": branch, + **score, + }) + + feature_sets = _emotion_feature_sets(encoded, raw_pca_by_id) + prediction_rows.extend(_probe_fold( + variant=variant, + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + features_by_id=feature_sets, + seed=args.seed, + )) + raw_pca_features = { + sample_id: {"RawPrivate-PCA": _pool_five(raw_pca_by_id[sample_id])} + for sample_id in all_ids + } + if variant == "SPR": + prediction_rows.extend(_probe_fold( + variant="SPR-dim-matched", + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + features_by_id={ + sample_id: {"SPR-dim-matched": feature_sets[sample_id]["shared_fused+private_all"]} + for sample_id in all_ids + }, + seed=args.seed, + view_names=("SPR-dim-matched",), + )) + prediction_rows.extend(_probe_fold( + variant="RawPrivate-PCA", + fold=fold, + train_samples=train_samples, + heldout_samples=heldout_samples, + features_by_id=raw_pca_features, + seed=args.seed, + view_names=("RawPrivate-PCA",), + )) + del model, encoded, decoded, feature_sets, raw_pca_features + if device.type == "cuda": + torch.cuda.empty_cache() + print(f"[TSFA-SPR fold {fold}] {variant} complete", flush=True) + + m4_checkpoint = args.checkpoint_root / ("M4_sourceTime/checkpoint.pt" if fold == 1 else f"fold_{fold:02d}/M4_sourceTime/checkpoint.pt") + if m4_checkpoint.is_file(): + input_hashes[f"fold_{fold:02d}/M4_sourceTime"] = _sha256(m4_checkpoint) + fold_manifest.append({ + "fold": fold, + "train_count": len(train_samples), + "heldout_count": len(heldout_samples), + "train_video_count": len({sample.group_id for sample in train_samples}), + "heldout_video_count": len({sample.group_id for sample in heldout_samples}), + "video_id_overlap": [], + "pca_train_slot_rows": int(train_slot_matrix.shape[0]), + "raw_private_pca_components": int(pca_dim), + }) + del pooled, temporal_by_id, raw_slots, raw_pca_by_id, pca + if device.type == "cuda": + torch.cuda.empty_cache() + print(f"[TSFA-SPR fold {fold}] completed: train={len(train_samples)} heldout={len(heldout_samples)}", flush=True) + + # The RawPrivate-PCA and SPR dimension-matched views are separate methods; + # this alias allows their paired OOF rows to be verified and joined directly. + summaries = _summarize_emotion_predictions(prediction_rows) + by_method_view = {(row["method"], row["view"]): row for row in summaries} + private_source_rows = [ + row for row in summaries + if row["method"] == "SPR" and row["view"] in { + "private_text", "private_audio", "private_vision", "private_audio_vision", "private_all", + "shared_only_fused", "shared_only_unfused", "shared_unfused+private_all", + "shared_fused+private_all", + } + ] + dimension_keys = ( + ("TSFA-old", "all_modalities"), + ("TSFA+RawPrivate", "all_modalities"), + ("SPR", "shared_unfused+private_all"), + ("SPR-dim-matched", "SPR-dim-matched"), + ("RawPrivate-PCA", "RawPrivate-PCA"), + ) + dimension_rows = [] + for key in dimension_keys: + if key in by_method_view: + dimension_rows.append({**by_method_view[key], + "per_slot_dimension": int(by_method_view[key]["feature_dimension"] // 5), + "dimension_control_note": ( + "matched learned fused-shared + all-private" if key[0] == "SPR-dim-matched" + else "training-fold PCA on raw private slots" if key[0] == "RawPrivate-PCA" + else "existing baseline; native dimension" + )}) + paired_rows = _paired_contrasts( + prediction_rows, + repeats=args.bootstrap_repeats, + seed=args.seed, + ) + paired_rows.extend(_paired_contrasts( + prediction_rows, + candidate_method="SPR-dim-matched", + candidate_view="SPR-dim-matched", + references=(("RawPrivate-PCA", "RawPrivate-PCA"),), + repeats=args.bootstrap_repeats, + seed=args.seed, + )) + + correspondence_summary = _clip_metric_summary( + correspondence_clip_rows, + method_key="variant", + group_keys=("variant", "pair"), + metrics=("auc", "exact_r1", "within_pm1_r1", "mase", "slot_shuffle_auc", + "slot_shuffle_exact_r1", "delta_auc_vs_shuffle", "delta_exact_r1_vs_shuffle"), + repeats=args.bootstrap_repeats, + seed=args.seed, + ) + reconstruction_summary = _clip_metric_summary( + reconstruction_clip_rows, + method_key="variant", + group_keys=("variant", "modality", "branch", "reconstruction_trained"), + metrics=("mse",), + repeats=args.bootstrap_repeats, + seed=args.seed, + ) + modality_summary: list[dict[str, Any]] = [] + for variant in VARIANTS: + for branch in ("shared", "private"): + items = [row for row in modality_fold_rows if row["variant"] == variant and row["branch"] == branch] + modality_summary.append({ + "variant": variant, + "branch": branch, + "fold_count": len(items), + "accuracy_mean": float(np.mean([row["accuracy"] for row in items])), + "accuracy_sd": float(np.std([row["accuracy"] for row in items], ddof=1)), + "macro_f1_mean": float(np.mean([row["macro_f1"] for row in items])), + "macro_f1_sd": float(np.std([row["macro_f1"] for row in items], ddof=1)), + "fold_accuracy": json.dumps([round(float(row["accuracy"]), 6) for row in items]), + }) + + seed_rows = [] + for method, view in dimension_keys: + if (method, view) in by_method_view: + row = by_method_view[(method, view)] + seed_rows.append({"seed": args.seed, "method": method, "view": view, + "accuracy": row["accuracy"], "macro_f1": row["macro_f1"], + "mae": row["mae"], "pearson": row["pearson"], + "status": "first_seed_only; additional seeds conditional on separation diagnostics"}) + + figures = _plot_summaries( + args.output_dir, + modality_summary, + correspondence_summary, + reconstruction_summary, + summaries, + representation_rows, + ) + + args.output_dir.mkdir(parents=True, exist_ok=True) + _write_csv(args.output_dir / "training_history.csv", history_rows) + _write_csv(args.output_dir / "representation_diagnostics.csv", representation_rows) + _write_csv(args.output_dir / "shared_correspondence_by_clip.csv", correspondence_clip_rows) + _write_csv(args.output_dir / "shared_correspondence_summary.csv", correspondence_summary) + _write_csv(args.output_dir / "modality_probe_by_fold.csv", modality_fold_rows) + _write_csv(args.output_dir / "modality_probe_summary.csv", modality_summary) + _write_csv(args.output_dir / "reconstruction_by_clip.csv", reconstruction_clip_rows) + _write_csv(args.output_dir / "reconstruction_summary.csv", reconstruction_summary) + _write_csv(args.output_dir / "emotion_probe_predictions.csv", prediction_rows) + _write_csv(args.output_dir / "emotion_probe_metrics.csv", summaries) + _write_csv(args.output_dir / "paired_contrasts.csv", paired_rows) + _write_csv(args.output_dir / "private_source_ablation.csv", private_source_rows) + _write_csv(args.output_dir / "dimension_control_summary.csv", dimension_rows) + _write_csv(args.output_dir / "seed_summary.csv", seed_rows) + + spr_modality = {row["branch"]: row for row in modality_summary if row["variant"] == "SPR"} + spr_correspondence = [row for row in correspondence_summary if row["variant"] == "SPR"] + spr_reconstruction = [row for row in reconstruction_summary + if row["variant"] == "SPR" and row["reconstruction_trained"]] + private_source_accuracy = float(spr_modality["private"]["accuracy_mean"]) + shared_source_accuracy = float(spr_modality["shared"]["accuracy_mean"]) + mean_shared_auc = float(np.mean([row["auc_mean_video"] for row in spr_correspondence])) + rec_by_branch = { + branch: float(np.mean([row["mse_mean_video"] for row in spr_reconstruction if row["branch"] == branch])) + for branch in ("shared_only", "private_only", "both") + } + split_supported = bool( + private_source_accuracy > shared_source_accuracy + and private_source_accuracy - shared_source_accuracy >= args.modality_gap_threshold + and mean_shared_auc > 0.5 + and rec_by_branch["both"] < min(rec_by_branch["shared_only"], rec_by_branch["private_only"]) + ) + config = { + "experiment": "TSFA-SPR: Temporal-Shared-Private Representation", + "seed": args.seed, + "folds": 5, + "grouped_by": "video_id/group_id", + "grid_size": GRID_SIZE, + "feature_dimensions": {name: int(samples[0].features[name].shape[1]) for name in MODS}, + "common_dim": args.common_dim, + "shared_dim": args.shared_dim, + "private_dim": args.private_dim, + "batch_size": args.batch_size, + "epochs": args.epochs, + "learning_rate": args.learning_rate, + "temperature": args.temperature, + "orthogonality_weight": args.orth_weight, + "reconstruction_weight": args.reconstruction_weight, + "contrastive_pairs": [list(pair) for pair in PAIRS], + "hard_negative_offsets": list(HARD_OFFSETS), + "loss_variants": VARIANT_LOSSES, + "emotion_probe": { + "classification": "StandardScaler + LogisticRegression(C=0.05, max_iter=5000)", + "regression": "StandardScaler + Ridge(alpha=25), clipped to [-3, 3]", + "temporal_pooling": "50 slots to five consecutive 10-slot mean bins", + "all_scalers_fit_on_training_fold_only": True, + }, + "dimension_controls": { + "SPR_main": "unfused shared 3x64 plus private 3x64 = 384 per slot; 1920 after five-bin pooling", + "SPR_dim_matched": "mean fused shared 64 plus all private 3x64 = 256 per slot; 1280 after pooling", + "RawPrivate_PCA": f"train-fold PCA from raw private 985-d slot vectors to {args.pca_dim} per slot; 1280 after pooling if 256 components", + }, + "emotion_labels_used_in_factorizer_training": False, + "frozen_m4_temporal_branch": True, + "explicit_time_code_or_slot_index_in_shared_encoder": False, + "bootstrap_repeats": args.bootstrap_repeats, + } + (args.output_dir / "config.json").write_text(json.dumps(config, ensure_ascii=False, indent=2), encoding="utf-8") + manifest = { + "created_utc": datetime.now(timezone.utc).isoformat(), + "experiment": "TSFA-SPR: Temporal-Shared-Private Representation", + "sample_count": len(samples), + "video_id_count": len({sample.group_id for sample in samples}), + "fold_count": len(splits), + "folds": fold_manifest, + "variants": list(VARIANTS), + "baseline_controls": ["TSFA-old", "TSFA+RawPrivate", "B0", "B4"], + "alignment_models_retrained": False, + "temporal_branch": "Frozen M4_sourceTime fold checkpoint; its attention weights pool fold-standardized original BERT/eGeMAPS/DeiT features.", + "emotion_labels_used_for_factorizer_training": False, + "feature_extractors_changed": False, + "shared_encoder": "One common MLP reused across Text, Audio, Vision; no explicit time code/slot index input.", + "private_encoders": "Three modality-specific MLPs with independent parameters.", + "loss": "L_shared + lambda_o*L_orth + lambda_r*L_rec; L_orth only within modality; per-modality mean reconstruction loss.", + "shared_correspondence_interpretation": "Same-slot vs same-video shifted-slot proxy; not independent human alignment ground truth.", + "emotion_probe_protocol": config["emotion_probe"], + "pca_control": "PCA fit on training-fold raw-private vectors over training video slots only; no emotion labels used.", + "paired_bootstrap": "2,000 video_id-cluster resamples; percentile 95% intervals; no multiple-comparison correction.", + "decomposition_diagnostics": { + "SPR_shared_modality_probe_accuracy": shared_source_accuracy, + "SPR_private_modality_probe_accuracy": private_source_accuracy, + "SPR_private_minus_shared_modality_accuracy": private_source_accuracy - shared_source_accuracy, + "SPR_mean_shared_crossmodal_auc": mean_shared_auc, + "SPR_reconstruction_mse": rec_by_branch, + "shared_private_separation_supported_by_predeclared_diagnostics": split_supported, + }, + "checkpoint_hashes": input_hashes, + "input_paths": { + "features": str(args.feature_dir.resolve()), + "feature_manifest": str(args.manifest.resolve()), + "grouped_splits": str(args.splits.resolve()), + "frozen_m4_checkpoint_root": str(args.checkpoint_root.resolve()), + "math_predictions_read_only": str(args.math_predictions.resolve()), + "math_splits_read_only": str(args.math_splits.resolve()), + "old_tsfa_predictions": str(args.old_tsfa_predictions.resolve()), + "raw_private_predictions": str(args.raw_private_predictions.resolve()), + }, + "input_sha256": { + "feature_manifest": _sha256(args.manifest), + "grouped_splits": _sha256(args.splits), + "math_predictions": _sha256(args.math_predictions), + "math_splits": _sha256(args.math_splits), + "old_tsfa_predictions": _sha256(args.old_tsfa_predictions), + "raw_private_predictions": _sha256(args.raw_private_predictions), + **input_hashes, + }, + "outputs": [ + "training_history.csv", "representation_diagnostics.csv", "shared_correspondence_summary.csv", + "modality_probe_summary.csv", "reconstruction_summary.csv", "emotion_probe_metrics.csv", + "emotion_probe_predictions.csv", "paired_contrasts.csv", "private_source_ablation.csv", + "dimension_control_summary.csv", "seed_summary.csv", *figures, + ], + "device": str(device), + "gpu_name": torch.cuda.get_device_name(device) if device.type == "cuda" else None, + "python": platform.python_version(), + "pytorch": torch.__version__, + "scikit_learn": sklearn.__version__, + "elapsed_seconds": time.time() - started, + "interpretation_limits": [ + "Same-slot contrastive positives use M4 latent slots as a training convention, not human event-level ground truth.", + "Frozen M4 source-time weights can carry temporal prior information into the pooled features.", + "Emotion metrics are lightweight frozen-representation probes on only 100 clips from 37 source videos.", + "Only seed 42 is run in this first pass; bootstrap intervals do not capture representation seed uncertainty.", + "RawPrivate-PCA and SPR-dim-matched outputs have equal 256 dimensions per slot and 1280 pooled dimensions.", + ], + } + (args.output_dir / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + final_factorization_supported = _finalize_existing(args) + print(f"[TSFA-SPR complete] samples={len(samples)} device={device} output={args.output_dir}", flush=True) + print(f" separation diagnostics support factorization: {final_factorization_supported}", flush=True) + for key in dimension_keys: + if key in by_method_view: + row = by_method_view[key] + print(f" {key[0]}/{key[1]}: F1={row['macro_f1']:.3f}, MAE={row['mae']:.3f}, Pearson={row['pearson']:.3f}, d={row['feature_dimension']}", flush=True) + + +def _write_readme_section( + output_dir: Path, + config: Mapping[str, Any], + manifest: Mapping[str, Any], + summaries: Sequence[Mapping[str, Any]], + paired_rows: Sequence[Mapping[str, Any]], + dimension_rows: Sequence[Mapping[str, Any]], + private_rows: Sequence[Mapping[str, Any]], +) -> None: + metrics_lookup = {(row["method"], row["view"]): row for row in summaries} + main = metrics_lookup.get(("SPR", "shared_unfused+private_all"), {}) + dimensions = {row["method"]: row for row in dimension_rows} + private_best_mae = min( + (row for row in private_rows if row["method"] == "SPR" and row["view"].startswith("private_")), + key=lambda row: float(row["mae"]), + default={}, + ) + lines = [ + "# TSFA-SPR run notes", + "", + "This directory contains the first seed (42) of the temporal shared/private factorization experiment.", + "The M4_sourceTime checkpoint remains frozen. Factorizer losses use only source features and M4 slot relationships; emotion labels are used only by the held-out probes.", + "", + "## Main probe", + "", + f"- SPR shared-unfused + all-private: Accuracy {main.get('accuracy', float('nan')):.3f}, Macro-F1 {main.get('macro_f1', float('nan')):.3f}, MAE {main.get('mae', float('nan')):.3f}, Pearson {main.get('pearson', float('nan')):.3f}.", + f"- Best private-only MAE view: {private_best_mae.get('view', 'n/a')} (MAE {private_best_mae.get('mae', float('nan')):.3f}).", + "", + "## Dimension controls", + "", + ] + for name in ("SPR", "SPR-dim-matched", "RawPrivate-PCA", "TSFA-old", "TSFA+RawPrivate"): + row = dimensions.get(name) + if row: + lines.append(f"- {name}: dimension {row['feature_dimension']}; Macro-F1 {row['macro_f1']:.3f}; MAE {row['mae']:.3f}; Pearson {row['pearson']:.3f}.") + lines.extend([ + "", + "## Output map", + "", + "- `training_history.csv`: per-fold, per-epoch loss terms.", + "- `representation_diagnostics.csv`: held-out shared/private norms and normalized cross-covariance by modality.", + "- `shared_correspondence_summary.csv`: same-slot vs shifted-slot correspondence and slot-shuffle control.", + "- `modality_probe_summary.csv`: modality-source classification from frozen shared/private vectors.", + "- `reconstruction_summary.csv`: held-out standardized-source reconstruction from shared, private, and combined streams.", + "- `emotion_probe_metrics.csv` and `emotion_probe_predictions.csv`: five-fold grouped sentiment probes.", + "- `paired_contrasts.csv`: video-cluster paired bootstrap against TSFA-old, TSFA+RawPrivate, math B0/B4, and dimension controls.", + "- `checkpoints/`: fold-specific factorizer weights and train-fold PCA components.", + "", + "## Caution", + "", + "Same-slot AUC/retrieval tests agreement with the M4 slot convention, not independent human event alignment. A claim of successful shared/private separation requires the modality-source, covariance, reconstruction, and slot-shuffle diagnostics to agree; emotion score gains alone are insufficient.", + ]) + (output_dir / "README.md").write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def _finalize_existing(args: argparse.Namespace) -> bool: + """Refresh derived summaries/plots without retraining any factorizer.""" + output_dir = args.output_dir + manifest_path = output_dir / "run_manifest.json" + if not manifest_path.is_file(): + raise FileNotFoundError(f"no existing TSFA-SPR run manifest: {manifest_path}") + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + modality_rows = _read_csv(output_dir / "modality_probe_summary.csv") + correspondence_rows = _read_csv(output_dir / "shared_correspondence_summary.csv") + reconstruction_rows = _read_csv(output_dir / "reconstruction_summary.csv") + diagnostic_rows = _read_csv(output_dir / "representation_diagnostics.csv") + emotion_rows = _read_csv(output_dir / "emotion_probe_metrics.csv") + prediction_rows = _read_csv(output_dir / "emotion_probe_predictions.csv") + paired_rows = _read_csv(output_dir / "paired_contrasts.csv") + dimension_rows = _read_csv(output_dir / "dimension_control_summary.csv") + private_rows = _read_csv(output_dir / "private_source_ablation.csv") + paired_rows = [row for row in paired_rows + if row["comparison"] != "SPR-dim-matched-RawPrivate-PCA"] + paired_rows.extend(_paired_contrasts( + prediction_rows, + candidate_method="SPR-dim-matched", + candidate_view="SPR-dim-matched", + references=(("RawPrivate-PCA", "RawPrivate-PCA"),), + repeats=args.bootstrap_repeats, + seed=args.seed, + )) + _write_csv(output_dir / "paired_contrasts.csv", paired_rows) + + spr_probe = {row["branch"]: row for row in modality_rows if row["variant"] == "SPR"} + untrained_private_probe = next( + (row for row in modality_rows + if row["variant"] == "SP-noOrth-noRec" and row["branch"] == "private"), + None, + ) + spr_pairs = [row for row in correspondence_rows if row["variant"] == "SPR"] + pair_checks = [] + for row in spr_pairs: + auc = float(row["auc_mean_video"]) + shuffled_auc = float(row["slot_shuffle_auc_mean_video"]) + pair_checks.append({ + "pair": row["pair"], + "matched_vs_shifted_auc": auc, + "slot_shuffle_auc": shuffled_auc, + "auc_drop_after_shuffle": auc - shuffled_auc, + "shared_correspondence_supported": auc >= 0.60 and auc - shuffled_auc >= 0.05, + }) + spr_reconstruction = [row for row in reconstruction_rows + if row["variant"] == "SPR" and str(row["reconstruction_trained"]).lower() == "true"] + rec_by_branch = { + branch: float(np.mean([float(row["mse_mean_video"]) for row in spr_reconstruction + if row["branch"] == branch])) + for branch in ("shared_only", "private_only", "both") + } + shared_accuracy = float(spr_probe["shared"]["accuracy_mean"]) + private_accuracy = float(spr_probe["private"]["accuracy_mean"]) + untrained_private_accuracy = ( + float(untrained_private_probe["accuracy_mean"]) if untrained_private_probe else float("nan") + ) + source_separation = ( + private_accuracy - shared_accuracy >= 0.05 + and private_accuracy - untrained_private_accuracy >= 0.05 + ) + shared_correspondence = bool(pair_checks) and all(row["shared_correspondence_supported"] for row in pair_checks) + combined_reconstruction = rec_by_branch["both"] < min(rec_by_branch["shared_only"], rec_by_branch["private_only"]) + factorization_supported = source_separation and shared_correspondence and combined_reconstruction + cross_covariance = { + modality: float(np.mean([float(row[f"{modality}_cross_covariance_norm"]) for row in diagnostic_rows + if row["variant"] == "SPR"])) + for modality in MODS + } + norm_ratio = { + modality: float(np.mean([float(row[f"{modality}_shared_norm_ratio"]) for row in diagnostic_rows + if row["variant"] == "SPR"])) + for modality in MODS + } + assessment = { + "shared_private_factorization_supported": factorization_supported, + "criteria": { + "private_source_probe_exceeds_shared_and_untrained_private_control_by_at_least_0_05": source_separation, + "each_shared_pair_auc_at_least_0_60_and_shuffle_drop_at_least_0_05": shared_correspondence, + "both_stream_reconstruction_beats_each_single_stream": combined_reconstruction, + }, + "spr_shared_source_probe_accuracy": shared_accuracy, + "spr_private_source_probe_accuracy": private_accuracy, + "untrained_private_control_source_probe_accuracy": untrained_private_accuracy, + "spr_private_probe_gain_over_untrained_control": private_accuracy - untrained_private_accuracy, + "private_minus_shared_source_probe_accuracy": private_accuracy - shared_accuracy, + "shared_pair_checks": pair_checks, + "reconstruction_mse_by_branch": rec_by_branch, + "normalized_cross_covariance_by_modality": cross_covariance, + "shared_norm_ratio_by_modality": norm_ratio, + "interpretation": ( + "The shared/private branches are distinguishable by source classification, but the no-private-loss control already identifies modality perfectly, so this is not evidence that training learned private semantic content. The shared stream also does not establish robust same-slot cross-modal correspondence; do not claim successful shared/private semantic disentanglement." + if not factorization_supported else + "All operational diagnostics passed; the result supports further validation, not a claim of human event-level alignment." + ), + "criteria_note": "These are operational descriptive checks for this small first pass, not inferential significance thresholds.", + } + (output_dir / "decomposition_assessment.json").write_text( + json.dumps(assessment, ensure_ascii=False, indent=2), encoding="utf-8" + ) + manifest["decomposition_diagnostics"] = { + **assessment, + "shared_correspondence": pair_checks, + } + manifest["outputs"] = sorted(set(manifest.get("outputs", [])) | { + "private_source_paired_contrasts.csv", "decomposition_assessment.json", "README.md", + "shared_correspondence_by_clip.csv", "modality_probe_by_fold.csv", "reconstruction_by_clip.csv", + }) + manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + + view_contrasts = _paired_view_contrasts( + prediction_rows, repeats=args.bootstrap_repeats, seed=args.seed + ) + _write_csv(output_dir / "private_source_paired_contrasts.csv", view_contrasts) + # Regenerate the dimension-control panel after adding all native-size controls. + _plot_summaries(output_dir, modality_rows, correspondence_rows, reconstruction_rows, + emotion_rows, diagnostic_rows) + + by_metric = {(row["comparison"], row["metric"]): row for row in paired_rows} + best_f1 = max((row for row in private_rows if row["view"].startswith("private_")), + key=lambda row: float(row["macro_f1"]), default={}) + best_mae = min((row for row in private_rows if row["view"].startswith("private_")), + key=lambda row: float(row["mae"]), default={}) + emotion_lookup = {(row["method"], row["view"]): row for row in emotion_rows} + dim_lookup = {row["method"]: row for row in dimension_rows} + main_row = emotion_lookup[("SPR", "shared_unfused+private_all")] + auc_text = ", ".join( + f"{row['pair']}={float(row['matched_vs_shifted_auc']):.3f}" for row in pair_checks + ) + lines = [ + "# TSFA-SPR：时序—共享—私有分解", + "", + "## 首轮结果（seed 42）", + "", + "M4_sourceTime 的五折 temporal checkpoints 已冻结；共享/私有因子器只用折内标准化的原始 BERT、eGeMAPS、DeiT 特征及 M4 权重训练，没有使用情感标签。Probe 口径沿用现有五折 GroupKFold、五段顺序池化、LogisticRegression(C=0.05)、Ridge(alpha=25) 与 [-3,3] 限幅。", + "", + f"SPR 主表示（unfused shared + all private）：Accuracy {float(main_row['accuracy']):.3f}，Macro-F1 {float(main_row['macro_f1']):.3f}，MAE {float(main_row['mae']):.3f}，Pearson {float(main_row['pearson']):.3f}。", + f"来源模态 probe：shared accuracy {shared_accuracy:.3f}，SPR private accuracy {private_accuracy:.3f}；但不含 private 训练损失的 SP-noOrth-noRec 控制也达到 {untrained_private_accuracy:.3f}，说明该识别率可能来自模态专属分支/特征分布，不能当成已学会私有语义。shared 三组匹配 AUC 为 {auc_text}，slot shuffle 后几乎不下降。", + f"合并重构 MSE {rec_by_branch['both']:.3f}，shared-only {rec_by_branch['shared_only']:.3f}，private-only {rec_by_branch['private_only']:.3f}。", + "", + f"诊断结论：**{'支持' if factorization_supported else '不支持'}已成功实现 shared/private 语义解耦**。合并分支能改善源特征重构，但 shared 跨模态同槽检索约为机会水平，且 shuffle 控制没有明显下降；private 来源识别在随机未训练控制中也已饱和。因此不应把本轮写成成功的 shared/private 语义对齐。", + "", + "## 私有来源探针", + "", + f"- private-only Macro-F1 最高：{best_f1.get('view', 'n/a')}（{float(best_f1.get('macro_f1', float('nan'))):.3f}）。", + f"- private-only MAE 最低：{best_mae.get('view', 'n/a')}（{float(best_mae.get('mae', float('nan'))):.3f}）。", + "- Audio 的单独分类探针 Macro-F1 点估计领先，Vision 的单独回归 probe MAE 点估计最低；video-group paired 区间跨 0，不能据此确定模态贡献差异。", + "", + "## 维度控制和基线", + "", + ] + for name in ("TSFA-old", "TSFA+RawPrivate", "SPR", "SPR-dim-matched", "RawPrivate-PCA"): + row = dim_lookup.get(name) + if row: + lines.append(f"- {name}: d={row['feature_dimension']} pooled，Macro-F1 {float(row['macro_f1']):.3f}，MAE {float(row['mae']):.3f}，Pearson {float(row['pearson']):.3f}。") + main_vs_raw = by_metric.get(("SPR-RawPrivate-PCA", "mae")) + matched_vs_raw = by_metric.get(("SPR-dim-matched-RawPrivate-PCA", "mae")) + if main_vs_raw: + lines.append(f"- SPR 主表示相对 RawPrivate-PCA 的 MAE 差为 {float(main_vs_raw['delta_candidate_minus_reference']):+.3f}，视频组 bootstrap 95% CI [{float(main_vs_raw['video_cluster_bootstrap_ci95_low']):+.3f}, {float(main_vs_raw['video_cluster_bootstrap_ci95_high']):+.3f}]。" + " RawPrivate-PCA 有较低 MAE，维度匹配后没有证据支持 learned shared/private 分解带来强度回归增益。") + if matched_vs_raw: + lines.append(f"- 直接比较同为 1280 维 pooled 的 SPR-dim-matched 与 RawPrivate-PCA，MAE 差为 {float(matched_vs_raw['delta_candidate_minus_reference']):+.3f}，95% CI [{float(matched_vs_raw['video_cluster_bootstrap_ci95_low']):+.3f}, {float(matched_vs_raw['video_cluster_bootstrap_ci95_high']):+.3f}]。" + " 这是控制输出维数后的成对对照。") + lines.extend([ + "", + "RawPrivate-PCA 将每个 slot 的 985 维原始私有特征仅用训练折做 PCA 到 256 维；与 fused-shared + all-private 的 SPR-dim-matched 都是 256 维/slot、1280 维/clip。RawPrivate-PCA 的低 MAE 说明可压缩 raw residual 已足以保留强度信息，但分类和 Pearson 并未同步领先。", + "", + "## 文件", + "", + "- `emotion_probe_metrics.csv` / `emotion_probe_predictions.csv`：全部五折 OOF probe。", + "- `paired_contrasts.csv`：SPR 主表示对 TSFA-old、TSFA+RawPrivate、math B0/B4 和维度对照的 2,000 次 video_id paired bootstrap；未做多重比较校正。", + "- `private_source_ablation.csv` 与 `private_source_paired_contrasts.csv`：模态来源 probe 及来源间配对区间。", + "- `decomposition_assessment.json`：shared/private 诊断判定和逐项依据。", + "- `representation_diagnostics.csv`、`shared_correspondence_summary.csv`、`modality_probe_summary.csv`、`reconstruction_summary.csv`：结构诊断明细。", + "- `architecture.png` 及其余七张 `.png`：实验结构、对应、重构、probe 与维度控制图。", + "", + "## 解释范围", + "", + "当前只有一个表示学习 seed，样本为 100 条 clips / 37 个来源视频；bootstrap 区间反映来源视频抽样不确定性，不含 seed 不确定性。same-slot 检索依赖 M4 槽位约定，并非人工事件级对齐真值。分类与回归来自独立轻量 probe。", + "", + "## 下一步", + "", + "暂不升级现有 TSFA 为正式 TSFA-SPR。最值得先做的是： (1) 检查同槽对比目标是否能在独立视频内建立可打乱验证的 shared correspondence；(2) 若共享诊断改善，再运行 seed 3407 和 2026，确认模态来源与情感 probe 差异是否稳定。", + ]) + (output_dir / "README.md").write_text("\n".join(lines) + "\n", encoding="utf-8") + print(f"[TSFA-SPR finalize] factorization_supported={factorization_supported}; assessment={output_dir / 'decomposition_assessment.json'}") + return factorization_supported + + +def build_parser() -> argparse.ArgumentParser: + project = Path(__file__).resolve().parents[1] + repository = project.parents[1] + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--epochs", type=int, default=40) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--learning-rate", type=float, default=1e-3) + parser.add_argument("--temperature", type=float, default=0.1) + parser.add_argument("--common-dim", type=int, default=128) + parser.add_argument("--shared-dim", type=int, default=64) + parser.add_argument("--private-dim", type=int, default=64) + parser.add_argument("--orth-weight", type=float, default=0.1) + parser.add_argument("--reconstruction-weight", type=float, default=1.0) + parser.add_argument("--pca-dim", type=int, default=256) + parser.add_argument("--shuffle-repeats", type=int, default=20) + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--modality-gap-threshold", type=float, default=0.05) + parser.add_argument("--feature-dir", type=Path, default=project / "outputs/q1_features/features") + parser.add_argument("--manifest", type=Path, default=project / "outputs/audit/manifest.csv") + parser.add_argument("--splits", type=Path, default=project / "outputs/method_comparison/splits.json") + parser.add_argument("--checkpoint-root", type=Path, default=project / "outputs/alignment_debug/heldout") + parser.add_argument("--output-dir", type=Path, default=project / "outputs/tsfa_shared_private") + parser.add_argument("--old-tsfa-predictions", type=Path, + default=project / "outputs/tsfa_emotion_probe/emotion_probe_predictions.csv") + parser.add_argument("--raw-private-predictions", type=Path, + default=project / "outputs/tsfa_av_private_ablation/predictions.csv") + parser.add_argument("--math-predictions", type=Path, + default=repository / "math/results/model_comparison/oof_predictions.csv") + parser.add_argument("--math-splits", type=Path, + default=repository / "math/results/model_comparison/split_assignments.csv") + parser.add_argument("--finalize-existing", action="store_true", + help="refresh derived diagnostics and report files without retraining") + return parser + + +def main() -> None: + args = build_parser().parse_args() + if args.finalize_existing: + _finalize_existing(args) + else: + run(args) + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q1/方法说明_零基础版.md b/deep_learning/Q1/方法说明_零基础版.md index 05dc61b..6d0bd90 100644 --- a/deep_learning/Q1/方法说明_零基础版.md +++ b/deep_learning/Q1/方法说明_零基础版.md @@ -134,3 +134,13 @@ uv run python -m q1.extract_features --output-dir outputs/q1_features 为了知道“附近”是否真的有用，还做了两种对照：一种把同样宽的窗口随机放置，另一种让模型看完整段视频。七种方法使用相同的 100 条视频、相同的 BERT 文字特征、音频特征和 DeiT 图像特征；同一原视频的片段在同一折里一起作为训练或留出数据。总共做 5 折，每条样本都轮到一次留出评价。 结果显示，TSFA 比只用 M4 时更容易把同一位置的**文字与声音**对应起来，但画面相关的改善很小。随机窗口和看完整段视频的方案在“内容相似度”指标上甚至更高，却经常把视频前后位置弄乱。TSFA 的局部时间窗口主要帮助模型保持先后顺序。这里的“同一位置”由时间槽定义，还不是人工确认的同一事件，因此目前不能说三种模态已经准确理解了彼此。详细数字与图见 [RESULTS.md](RESULTS.md) 的 TSFA 小节。 + +我们也把连续情感分数按正负号分成三类，做了一个单独的小测。TSFA 三模态预测的 Accuracy 为 `0.54`，略低于总是猜样本最多的 Positive 类所得到的 `0.57`；它的 Macro-F1 为 `0.431`，高于这个简单猜测的 `0.242`。这表示它能识别一部分少数类别，但在连续分数回归上的误差仍偏大，所以不能据此认为 TSFA 已经是成熟的情感识别模型。 + +## 新增检查：让声音与画面交流，并保留各自的原始线索 + +原来的 TSFA 像是让文字分别询问声音和画面：“你们哪些部分与我对应？”这可能漏掉声音和表情之间的直接关系，也可能在挑选“与文字对应的部分”时丢掉有用的语调或画面线索。我们分别试了两件事：让声音和画面在同一小段时间内直接交换信息；以及在挑选后的信息之外，额外保留各模态未经这次语义挑选的特征。于是得到“两个开关各开或关”的四种组合。 + +四种组合仍使用原来的 100 条视频、BERT/eGeMAPS/DeiT 特征、五折留出方式和同一个简单情感预测器。原版的连续情感分数平均误差为 `0.820`；只加声音与画面的直接联系后为 `0.803`，变化很小；只保留原模态线索后降至 `0.572`；两者都加则为 `0.579`。原版预测的正负类别 Macro-F1 为 `0.431`；四种组合里，原版仍最高。按来源视频成组比较后，保留原模态线索带来的回归改善比较明确，新增的声音—画面联系暂时没有显示稳定改善。 + +这项结果说明原版 TSFA 交给情感预测器的信息可能不够完整。额外保留的线索同时包含文字、声音和画面，所以目前还不能说改进一定来自语调或表情；新组合的输入数字也更多。详细四组结果、配对区间和图见 [RESULTS.md](RESULTS.md) 的“A–V 局部边 × 原模态残差消融”一节。 diff --git a/deep_learning/Q2/.gitignore b/deep_learning/Q2/.gitignore new file mode 100644 index 0000000..f0ccc32 --- /dev/null +++ b/deep_learning/Q2/.gitignore @@ -0,0 +1,3 @@ +.venv/ +__pycache__/ +*.py[cod] diff --git a/deep_learning/Q2/README.md b/deep_learning/Q2/README.md new file mode 100644 index 0000000..4cfeaf8 --- /dev/null +++ b/deep_learning/Q2/README.md @@ -0,0 +1,39 @@ +# Q2/Q3 algorithm selection built on Q1 alignment + +## Decision about reusing Q1 + +The transferable part of Q1 is its explicit time correspondence and observation mask: features from different modalities share ordered positions, missing values are accompanied by masks, and a position can be traced to source time. That interface is useful for both Q2 local-gap handling and Q3 evidence localization. + +The exact Q1 B1 extraction cannot be rerun over the 4,850 Attachment 2 training examples. Attachment 2 supplies precomputed aligned and unaligned feature tensors, but not the source audio/video or CTC word-time posteriors for the full training set. Its `aligned_50.pkl` also has 50 wordpiece positions and no Q1 `time_bounds_s`; those positions must not be described as the 50 equal-duration physical-time bins exported by `final/Q1`. + +Accordingly, the Q2 experiment uses the official aligned feature set as its shared wordpiece axis, and compares it with a fixed equal-window pooling control made from the official unaligned audio/vision sequences. This is a downstream alignment-utility check, not a claim that B1 was recomputed on Attachment 2. The official train/validation split is retained; test labels are not used. + +## Q2 candidates + +All candidates use identical training examples, train-only median/MAD scaling, joint polarity/intensity objectives, and 15 validation corruptions (three contiguous missing rates by five modality patterns). + +| Candidate | Fusion rule | What it tests | +| --- | --- | --- | +| `concat` | Project each modality, concatenate features and availability flags, then run a bidirectional GRU | Strong, simple early-fusion baseline | +| `gate` | Learn per-slot modality weights, mask unavailable modalities, then run a bidirectional GRU | Whether explicit reliability-aware fusion handles local gaps | +| `crossattn` | Apply masked cross-modal attention over the 50 shared slots, then temporal pooling | Whether contextual cross-modal exchange improves robustness | + +The report keeps Macro-F1, MAE, and Pearson separate. The default selection is Macro-F1-first across local corruption conditions; MAE and Pearson remain explicit tradeoffs, not terms in a constructed total score. The selected architecture is also trained on fixed-window-resampled features as an alignment control. A separate validation control shifts audio and vision by 1–10 positions to measure sensitivity to cross-modal timing. + +## Q3 explanation selection + +The selected Q2 model is frozen. Integrated Gradients and five-slot grouped occlusion are compared on held-out Attachment 2 validation clips using deletion comprehensiveness, sufficiency, and local rank stability. Attachment 4 has original videos and transcripts, so B1's CTC hard word-time procedure can be applied to those 20 clips to map high-importance wordpiece positions back to seconds. The saved Attachment 4 pickle files do not include `time_bounds_s`; explanations therefore retain both the model slot and the CTC-derived word interval, with alignment quality recorded. + +## Run + +The project environment is managed by `uv` and installs the CUDA 13.0 PyTorch build: + +```bash +cd deep_learning/Q2 +uv sync +uv run python -m q2.train_compare +cd ../Q3 +uv run --project ../Q2 python -m q3.explain_selection +``` + +The main outputs are written to `outputs/algorithm_selection/`; plots, CSV metrics, run metadata, and checkpoints stay under this directory. The source data, `math`, and `final/Q1` are read-only inputs. diff --git a/deep_learning/Q2/RESULTS.md b/deep_learning/Q2/RESULTS.md new file mode 100644 index 0000000..3d3ea8b --- /dev/null +++ b/deep_learning/Q2/RESULTS.md @@ -0,0 +1,54 @@ +# Q2 algorithm selection results + +## Q1 alignment transfer decision + +Q1 B1 aligns BERT word features and audio/vision observations with hard CTC word intervals, projects observed features onto a 0.1-second common grid, exports 50 equal-duration physical-time bins, and keeps observation masks. For Q2, the shared ordered axis and explicit masks transfer directly: a local gap stays a local gap after alignment and can be represented without inventing feature values. + +The exact B1 extraction was not recomputed over Attachment 2. The official 4,850-row feature package contains precomputed aligned and unaligned tensors, but no full-set source audio/video or word-time posterior. Its `aligned_50.pkl` has 50 wordpiece positions and no per-slot `time_bounds_s`; those positions are not Q1's 50 equal-duration bins. This experiment therefore trains on the official aligned features and compares them with an equal-window audio/vision resampling control. The comparison tests the value of an aligned ordered representation for the downstream Q2 task; it does not claim to reproduce B1 on all 4,850 clips. + +## Data and protocol + +- Attachment 2 official split: 3,395 training clips and 728 validation clips. Their source-video ID sets do not overlap. +- Each official aligned example has 50 positions with Text 768-D, Audio 74-D, Vision 35-D features and modality observation masks. +- Attachment 2 test labels were not used. +- The three candidates shared train-only median/MAD normalization, the joint polarity/intensity objective, and training-time contiguous block masking. +- Validation corruption covered 10%, 20%, and 30% of 50 positions for Text, Audio, Vision, Audio+Vision, and all three modalities. This is a wordpiece-position proxy for a continuous time gap; full-set second-level timestamps are not supplied. +- Each candidate was run with seeds 42, 3407, and 2026. Reported `±` values are seed standard deviations over the fixed official validation set and deterministic corruption draws; they are not confidence intervals over new videos. + +## Fusion comparison + +| Model | Clean Accuracy | Clean Macro-F1 | Corrupt Accuracy, mean | Corrupt Macro-F1, mean | Worst condition Macro-F1 | Corrupt MAE | Corrupt Pearson | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| Early concatenation + BiGRU | 0.626 ± 0.013 | 0.580 ± 0.018 | 0.623 ± 0.011 | **0.575 ± 0.017** | **0.516** | **0.640 ± 0.004** | 0.607 ± 0.004 | +| Reliability gate + BiGRU | 0.621 ± 0.011 | 0.570 ± 0.019 | 0.617 ± 0.011 | 0.565 ± 0.018 | 0.498 | 0.643 ± 0.008 | **0.610 ± 0.008** | +| Masked cross-modal attention | 0.604 ± 0.012 | 0.540 ± 0.049 | 0.601 ± 0.009 | 0.539 ± 0.047 | 0.462 | 0.649 ± 0.024 | 0.592 ± 0.016 | + +Early concatenation has the best mean corrupted Macro-F1 and MAE. The gate has slightly higher Pearson, so the metrics do not collapse to one score. Cross-modal attention is lower and more variable at this sample size. It is not selected for the next Q2 stage. + +For the selected concatenation model, the hardest tested case is 30% Text masking: Macro-F1 0.547 and MAE 0.665, compared with clean Macro-F1 0.580 and MAE 0.636. Audio-only or Vision-only masking has a smaller effect in these runs. This is evidence about this feature set and these simulated spans; it does not establish a universal modality ranking. + +## Alignment utility control + +The same concatenation model was trained either on the supplied aligned wordpiece features or on equal-window-resampled audio/vision features from the official unaligned tensors. + +| Representation | Clean Macro-F1 | Corrupt Macro-F1 | Corrupt MAE | Corrupt Pearson | +| --- | ---: | ---: | ---: | ---: | +| Supplied word-aligned 50 positions | 0.580 ± 0.018 | **0.575 ± 0.017** | **0.640 ± 0.004** | **0.607 ± 0.004** | +| Equal-window resampled unaligned input | 0.501 ± 0.014 | 0.504 ± 0.016 | 0.665 ± 0.005 | 0.577 ± 0.010 | + +On the selected model, shifting Audio and Vision by 1–10 positions changed aligned Macro-F1 from 0.580 to 0.557. That is a modest timing-sensitivity signal; it does not prove the model uses precise physical-time correspondence. Together with the fixed-window comparison, the result supports retaining the supplied aligned sequence for Q2. + +## Selected Q2 direction + +Continue with mask-aware early concatenation plus a bidirectional GRU, using local block masking during training. Keep the reliability gate as an ablation because its Pearson is slightly higher. Revisit cross-attention only if a later run has stronger evidence and enough data to control overfitting. + +## Reproducible artifacts + +- [Model and representation summary](outputs/algorithm_selection/summary.csv) +- [Metrics by missing type and rate](outputs/algorithm_selection/validation_metrics_by_condition.csv) +- [Aligned versus fixed-window and temporal-shift controls](outputs/algorithm_selection/alignment_transfer_ablation.csv) +- [Training/data audit and run manifest](outputs/algorithm_selection/data_audit.json), [run manifest](outputs/algorithm_selection/run_manifest.json) +- [Validation plot](outputs/algorithm_selection/missing_rate_comparison.png) +- [Selected seed-42 checkpoint](outputs/algorithm_selection/models/aligned/concat/model_best.pt) + +The fitted checkpoint is for algorithm selection, not the final Attachment 3 submission model. The final model should be trained on train+validation after the architecture and thresholds are frozen. diff --git a/deep_learning/Q2/outputs/algorithm_selection/aligned_robust_stats.npz b/deep_learning/Q2/outputs/algorithm_selection/aligned_robust_stats.npz new file mode 100644 index 0000000..3d7956d Binary files /dev/null and b/deep_learning/Q2/outputs/algorithm_selection/aligned_robust_stats.npz differ diff --git a/deep_learning/Q2/outputs/algorithm_selection/alignment_transfer_ablation.csv b/deep_learning/Q2/outputs/algorithm_selection/alignment_transfer_ablation.csv new file mode 100644 index 0000000..2697f09 --- /dev/null +++ b/deep_learning/Q2/outputs/algorithm_selection/alignment_transfer_ablation.csv @@ -0,0 +1,7 @@ +method,representation,condition,n_valid,n_seeds,accuracy,accuracy_sd,macro_f1,macro_f1_sd,mae,mae_sd,pearson,pearson_sd,missing_rate +concat,provided_word_aligned_50,clean,728,3,0.6259157509157509,0.012689016905266455,0.5803030257575642,0.01849482950112713,0.6362011035283407,0.0035474183737517766,0.6131094378711319,0.003049322677391937, +concat,provided_word_aligned_50,audio_vision_shifted_1_to_10_slots,728,3,0.6144688644688645,0.017174907814570563,0.5566710058858901,0.02417454695632849,0.6264231006304423,0.003071737263575265,0.610966440919508,0.00045089311901344093, +concat,provided_word_aligned_50,all_local_corruption_mean,728,3,0.6226800976800977,0.011084117339613314,0.5751264735191365,0.016846851540436875,0.6402280900213454,0.004374268275822229,0.6068152054284395,0.004485336212715804,0.20000000000000004 +concat,equal_window_resampled_unaligned,clean,728,3,0.586996336996337,0.010491244722884272,0.5005019574320758,0.013925639871727626,0.6615431904792786,0.005397772426619935,0.581966026863303,0.009642037378255953, 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"test_labels_used": false, + "alignment_transfer_limit": "The official aligned_50 data use a 50-slot wordpiece sequence with no per-slot seconds or stored Q1 B1 time_bounds. The fixed-window comparison is a downstream alignment control, not a re-run of Q1 B1 on the full dataset.", + "python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]", + "torch": "2.14.0+cu130", + "numpy": "2.5.3", + "created_unix": 1790237371.5417986 +} \ No newline at end of file diff --git a/deep_learning/Q2/outputs/algorithm_selection/selected_method.txt b/deep_learning/Q2/outputs/algorithm_selection/selected_method.txt new file mode 100644 index 0000000..8d7be13 --- /dev/null +++ b/deep_learning/Q2/outputs/algorithm_selection/selected_method.txt @@ -0,0 +1 @@ +Macro-F1-first validation selection: concat. 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+concat,equal_window_resampled_unaligned,2026,text,0.2,728,0.570054945054945,0.4938343318957674,0.6707639694213867,0.5459273537200591 +concat,equal_window_resampled_unaligned,2026,audio,0.2,728,0.5934065934065934,0.5230327607731714,0.6592981815338135,0.5755735913907287 +concat,equal_window_resampled_unaligned,2026,vision,0.2,728,0.5906593406593407,0.5211672989417647,0.6658343076705933,0.5720666083948681 +concat,equal_window_resampled_unaligned,2026,audio_vision,0.2,728,0.5989010989010989,0.5276783237584716,0.6637739539146423,0.583433626473117 +concat,equal_window_resampled_unaligned,2026,all_modalities,0.2,728,0.5851648351648352,0.5131525873114892,0.6693573594093323,0.5605391525623993 +concat,equal_window_resampled_unaligned,2026,text,0.3,728,0.5508241758241759,0.4775661528696233,0.6854801177978516,0.5184409269913169 +concat,equal_window_resampled_unaligned,2026,audio,0.3,728,0.5989010989010989,0.5333065606332371,0.6566126942634583,0.576381050540554 +concat,equal_window_resampled_unaligned,2026,vision,0.3,728,0.592032967032967,0.5197981617020956,0.6703022122383118,0.5691319241770694 +concat,equal_window_resampled_unaligned,2026,audio_vision,0.3,728,0.6016483516483516,0.5344217204682321,0.6686394214630127,0.5850381809653504 +concat,equal_window_resampled_unaligned,2026,all_modalities,0.3,728,0.5728021978021978,0.4906531284411469,0.6887523531913757,0.5344899699772931 +concat,equal_window_resampled_unaligned,2026,audio_vision_shifted_1_to_10_slots,0.0,728,0.5851648351648352,0.5171187040239319,0.6643120646476746,0.5689291630270369 diff --git a/deep_learning/Q2/pyproject.toml b/deep_learning/Q2/pyproject.toml new file mode 100644 index 0000000..e91a7a8 --- /dev/null +++ b/deep_learning/Q2/pyproject.toml @@ -0,0 +1,19 @@ +[project] +name = "deep-learning-q2-q3-selection" +version = "0.1.0" +requires-python = ">=3.14" +dependencies = [ + "matplotlib>=3.11.2", + "numpy>=2.5.3", + "scikit-learn>=1.9.1", + "torch>=2.14.0", + "transformers>=5.17.0", +] + +[tool.uv.sources] +torch = { index = "pytorch" } + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/deep_learning/Q2/q2/__init__.py b/deep_learning/Q2/q2/__init__.py new file mode 100644 index 0000000..56d18d5 --- /dev/null +++ b/deep_learning/Q2/q2/__init__.py @@ -0,0 +1 @@ +"""Q2 robustness and Q3 explanation-selection experiments.""" diff --git a/deep_learning/Q2/q2/data.py b/deep_learning/Q2/q2/data.py new file mode 100644 index 0000000..18fd1b3 --- /dev/null +++ b/deep_learning/Q2/q2/data.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[3] +ATTACHMENT2 = ROOT / "E题数据" / "附件2-数据集特征文件" +MODALITIES = ("text", "audio", "vision") + + +@dataclass +class Split: + x: tuple[np.ndarray, np.ndarray, np.ndarray] + mask: np.ndarray # N x T x 3 + y_cls: np.ndarray + y_reg: np.ndarray + ids: list[str] + + @property + def n(self) -> int: + return len(self.y_cls) + + @property + def steps(self) -> int: + return int(self.x[0].shape[1]) + + +@dataclass +class RobustStats: + center: tuple[np.ndarray, np.ndarray, np.ndarray] + scale: tuple[np.ndarray, np.ndarray, np.ndarray] + + def save(self, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + path, + text_center=self.center[0], text_scale=self.scale[0], + audio_center=self.center[1], audio_scale=self.scale[1], + vision_center=self.center[2], vision_scale=self.scale[2], + ) + + @classmethod + def load(cls, path: Path) -> "RobustStats": + with np.load(path) as data: + return cls( + tuple(data[f"{m}_center"].astype(np.float32) for m in MODALITIES), + tuple(data[f"{m}_scale"].astype(np.float32) for m in MODALITIES), + ) + + +def _unpickle(path: Path) -> dict[str, Any]: + with path.open("rb") as stream: + return pickle.load(stream, encoding="latin1") + + +def _ids_and_targets(part: dict[str, Any]) -> tuple[list[str], np.ndarray, np.ndarray]: + ids = [str(x) for x in part["id"]] + y_cls = np.asarray(part["classification_labels"], dtype=np.int64).reshape(-1) + y_reg = np.asarray(part["regression_labels"], dtype=np.float32).reshape(-1) + return ids, y_cls, y_reg + + +def _text_mask(part: dict[str, Any]) -> np.ndarray: + tokens = np.asarray(part["text_bert"]) + if tokens.ndim != 3 or tokens.shape[1] < 2: + raise ValueError(f"unexpected text_bert shape: {tokens.shape}") + # MOSEI text_bert rows are input_ids, input_mask, segment_ids. + return tokens[:, 1, :].astype(bool) + + +def load_aligned(path: Path | None = None) -> dict[str, Split]: + path = path or ATTACHMENT2 / "aligned_50.pkl" + raw = _unpickle(path) + result: dict[str, Split] = {} + for name in ("train", "valid"): + part = raw[name] + xs = tuple(np.asarray(part[m], dtype=np.float32) for m in MODALITIES) + masks = [ + _text_mask(part), + np.any(np.isfinite(xs[1]) & (xs[1] != 0), axis=-1), + np.any(np.isfinite(xs[2]) & (xs[2] != 0), axis=-1), + ] + mask = np.stack(masks, axis=-1) + ids, y_cls, y_reg = _ids_and_targets(part) + if any(x.shape[1] != 50 for x in xs): + raise ValueError(f"{name} aligned feature tensors must have 50 slots") + result[name] = Split(xs, mask, y_cls, y_reg, ids) + train_videos = {x.split("$_$", 1)[0] for x in result["train"].ids} + valid_videos = {x.split("$_$", 1)[0] for x in result["valid"].ids} + overlap = train_videos & valid_videos + if overlap: + raise ValueError(f"official train/valid split leaks {len(overlap)} source video ids") + return result + + +def _resample_rows_to_50(values: np.ndarray, lengths: list[int] | np.ndarray) -> tuple[np.ndarray, np.ndarray]: + n, source_steps, dim = values.shape + output = np.zeros((n, 50, dim), dtype=np.float32) + mask = np.zeros((n, 50), dtype=bool) + lengths_arr = np.asarray(lengths, dtype=np.int64).reshape(-1) + for i in range(n): + length = int(np.clip(lengths_arr[i], 0, source_steps)) + if length == 0: + continue + source = np.nan_to_num(values[i, :length], nan=0.0, posinf=0.0, neginf=0.0) + observed = np.any(source != 0, axis=-1) + for j in range(50): + left = int(np.floor(j * length / 50)) + right = max(left + 1, int(np.ceil((j + 1) * length / 50))) + right = min(right, length) + use = observed[left:right] + if use.any(): + output[i, j] = source[left:right][use].mean(axis=0) + mask[i, j] = True + return output, mask + + +def load_fixed_window(path: Path | None = None) -> dict[str, Split]: + """Build a matched 50-slot equal-window control from the unaligned file.""" + path = path or ATTACHMENT2 / "unaligned_50.pkl" + raw = _unpickle(path) + result: dict[str, Split] = {} + for name in ("train", "valid"): + part = raw[name] + text = np.asarray(part["text"], dtype=np.float32) + audio, audio_mask = _resample_rows_to_50(part["audio"], part["audio_lengths"]) + vision, vision_mask = _resample_rows_to_50(part["vision"], part["vision_lengths"]) + text_mask = _text_mask(part) + xs = (text, audio, vision) + mask = np.stack((text_mask, audio_mask, vision_mask), axis=-1) + ids, y_cls, y_reg = _ids_and_targets(part) + result[name] = Split(xs, mask, y_cls, y_reg, ids) + return result + + +def fit_robust_stats(split: Split) -> RobustStats: + centers: list[np.ndarray] = [] + scales: list[np.ndarray] = [] + for modality in range(3): + observed = split.mask[:, :, modality].reshape(-1) + values = split.x[modality].reshape(-1, split.x[modality].shape[-1])[observed] + if not len(values): + raise ValueError(f"no observed values for {MODALITIES[modality]}") + values = np.nan_to_num(values, nan=0.0, posinf=0.0, neginf=0.0) + center = np.median(values, axis=0) + mad = np.median(np.abs(values - center), axis=0) + scale = 1.4826 * mad + std = np.std(values, axis=0) + scale = np.where(scale > 1e-6, scale, std) + scale = np.where(scale > 1e-6, scale, 1.0) + centers.append(center.astype(np.float32)) + scales.append(scale.astype(np.float32)) + return RobustStats(tuple(centers), tuple(scales)) + + +def apply_robust_stats(split: Split, stats: RobustStats) -> Split: + xs: list[np.ndarray] = [] + for modality in range(3): + values = (split.x[modality] - stats.center[modality]) / stats.scale[modality] + values = np.nan_to_num(values, nan=0.0, posinf=0.0, neginf=0.0) + values *= split.mask[:, :, modality, None] + xs.append(values.astype(np.float32, copy=False)) + return Split(tuple(xs), split.mask.copy(), split.y_cls, split.y_reg, split.ids) + + +def corrupt_masks( + base: np.ndarray, + ratio: float, + modalities: tuple[int, ...], + seed: int, +) -> np.ndarray: + result = base.copy() + rng = np.random.default_rng(seed) + n, steps, _ = result.shape + width = max(1, min(steps, int(round(ratio * steps)))) + starts = rng.integers(0, steps - width + 1, size=n) + for row, start in enumerate(starts.tolist()): + result[row, start:start + width, list(modalities)] = False + return result + + +def augment_masks(base: np.ndarray, rng: np.random.Generator) -> np.ndarray: + result = base.copy() + n, steps, _ = result.shape + for row in range(n): + if rng.random() >= 0.85: + continue + count = int(rng.integers(1, 4)) + modalities = rng.choice(3, size=count, replace=False) + ratio = float(rng.choice((0.10, 0.20, 0.30))) + width = max(1, int(round(ratio * steps))) + start = int(rng.integers(0, steps - width + 1)) + result[row, start:start + width, modalities] = False + return result + + +def shift_audio_vision(split: Split, seed: int, max_shift: int = 10) -> Split: + rng = np.random.default_rng(seed) + xs = [x.copy() for x in split.x] + masks = split.mask.copy() + for row in range(split.n): + for modality in (1, 2): + shift = int(rng.integers(1, max_shift + 1)) + if rng.random() < 0.5: + shift = -shift + xs[modality][row] = np.roll(xs[modality][row], shift, axis=0) + masks[row, :, modality] = np.roll(masks[row, :, modality], shift) + return Split(tuple(xs), masks, split.y_cls, split.y_reg, split.ids) diff --git a/deep_learning/Q2/q2/finalize_summary.py b/deep_learning/Q2/q2/finalize_summary.py new file mode 100644 index 0000000..b015d47 --- /dev/null +++ b/deep_learning/Q2/q2/finalize_summary.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +import argparse +import csv +from pathlib import Path + +from .train_compare import _plot, _summary, _write_csv + + +def main() -> None: + parser = argparse.ArgumentParser(description="Rebuild Q2 summary tables from saved validation predictions") + parser.add_argument("--output-dir", default=str(Path(__file__).resolve().parents[1] / "outputs" / "algorithm_selection")) + args = parser.parse_args() + output = Path(args.output_dir) + with (output / "validation_metrics_by_condition.csv").open(encoding="utf-8-sig", newline="") as stream: + rows = list(csv.DictReader(stream)) + for row in rows: + for key in ("missing_rate", "accuracy", "macro_f1", "mae", "pearson", "n_valid"): + row[key] = float(row[key]) + row["seed"] = int(row["seed"]) + summary = _summary(rows) + _write_csv(output / "summary.csv", summary) + aligned = [row for row in summary if row["representation"] == "provided_word_aligned_50"] + _plot(aligned, rows, output / "missing_rate_comparison.png") + print(f"rebuilt summary table and plot from {len(rows)} saved validation rows") + + +if __name__ == "__main__": + main() diff --git a/deep_learning/Q2/q2/models.py b/deep_learning/Q2/q2/models.py new file mode 100644 index 0000000..5c725d8 --- /dev/null +++ b/deep_learning/Q2/q2/models.py @@ -0,0 +1,109 @@ +from __future__ import annotations + +import torch +from torch import nn + + +class AlignedFusionModel(nn.Module): + def __init__( + self, + kind: str, + dims: tuple[int, int, int], + steps: int = 50, + hidden: int = 128, + dropout: float = 0.15, + ) -> None: + super().__init__() + if kind not in {"concat", "gate", "crossattn"}: + raise ValueError(f"unknown model kind: {kind}") + self.kind = kind + self.hidden = hidden + self.projections = nn.ModuleList( + nn.Sequential(nn.Linear(size, hidden), nn.GELU(), nn.LayerNorm(hidden)) + for size in dims + ) + self.position = nn.Parameter(torch.randn(1, steps, hidden) * 0.02) + self.modality = nn.Parameter(torch.randn(1, 1, 3, hidden) * 0.02) + self.dropout = nn.Dropout(dropout) + + if kind == "concat": + self.fusion = nn.Sequential( + nn.Linear(hidden * 3 + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout) + ) + elif kind == "gate": + self.gate_score = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.Tanh(), nn.Linear(hidden // 2, 1)) + self.fusion = nn.Sequential( + nn.Linear(hidden + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout) + ) + else: + layer = nn.TransformerEncoderLayer( + d_model=hidden, + nhead=4, + dim_feedforward=hidden * 2, + dropout=dropout, + activation="gelu", + batch_first=True, + norm_first=True, + ) + self.cross_encoder = nn.TransformerEncoder(layer, num_layers=2, enable_nested_tensor=False) + self.fusion = nn.Sequential( + nn.Linear(hidden + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout) + ) + + self.temporal = nn.GRU( + input_size=hidden, + hidden_size=hidden // 2, + num_layers=1, + batch_first=True, + bidirectional=True, + ) + self.head = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.GELU(), nn.Dropout(dropout)) + self.classifier = nn.Linear(hidden // 2, 3) + self.regressor = nn.Linear(hidden // 2, 1) + + def forward(self, xs: tuple[torch.Tensor, torch.Tensor, torch.Tensor], masks: torch.Tensor): + masks = masks.bool() + pos = self.position[:, :masks.shape[1]] + encoded = [] + for modality, (projection, x) in enumerate(zip(self.projections, xs)): + token = projection(x) + token = token + pos + self.modality[:, :, modality, :] + token = token * masks[:, :, modality, None] + encoded.append(token) + stack = torch.stack(encoded, dim=2) # B x T x M x D + availability = masks.to(stack.dtype) + gate_weights = None + + if self.kind == "concat": + fused = self.fusion(torch.cat((stack.flatten(2), availability), dim=-1)) + elif self.kind == "gate": + scores = self.gate_score(stack).squeeze(-1) + scores = scores.masked_fill(~masks, -1e4) + gate_weights = torch.softmax(scores, dim=-1) * availability + gate_weights = gate_weights / gate_weights.sum(dim=-1, keepdim=True).clamp_min(1e-8) + weighted = (stack * gate_weights[..., None]).sum(dim=2) + fused = self.fusion(torch.cat((weighted, availability), dim=-1)) + else: + batch, steps, modalities, hidden = stack.shape + flat = stack.reshape(batch, steps * modalities, hidden) + valid = masks.reshape(batch, steps * modalities).clone() + empty = ~valid.any(dim=1) + if empty.any(): + valid[empty, 0] = True + flat[empty, 0] = 0.0 + attended = self.cross_encoder(flat, src_key_padding_mask=~valid) + attended = attended.reshape(batch, steps, modalities, hidden) + observed_count = availability.sum(dim=2, keepdim=True) + pooled = (attended * availability[..., None]).sum(dim=2) / observed_count.clamp_min(1.0) + fused = self.fusion(torch.cat((pooled, availability), dim=-1)) + + temporal, _ = self.temporal(self.dropout(fused)) + time_weight = masks.any(dim=-1).to(temporal.dtype) + empty_time = time_weight.sum(dim=1, keepdim=True) <= 0 + if empty_time.any(): + time_weight[empty_time.squeeze(1), 0] = 1.0 + pooled = (temporal * time_weight[..., None]).sum(dim=1) / time_weight.sum(dim=1, keepdim=True).clamp_min(1.0) + hidden = self.head(pooled) + logits = self.classifier(hidden) + intensity = 3.0 * torch.tanh(self.regressor(hidden).squeeze(-1)) + return {"logits": logits, "intensity": intensity, "gate": gate_weights} diff --git a/deep_learning/Q2/q2/train_compare.py b/deep_learning/Q2/q2/train_compare.py new file mode 100644 index 0000000..c8d76bf --- /dev/null +++ b/deep_learning/Q2/q2/train_compare.py @@ -0,0 +1,490 @@ +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import random +import shutil +import time +from collections import Counter +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error +from torch import nn + +from .data import ( + ATTACHMENT2, + ROOT, + MODALITIES, + RobustStats, + Split, + apply_robust_stats, + augment_masks, + corrupt_masks, + fit_robust_stats, + load_aligned, + load_fixed_window, + shift_audio_vision, +) +from .models import AlignedFusionModel + + +PATTERNS = { + "text": (0,), + "audio": (1,), + "vision": (2,), + "audio_vision": (1, 2), + "all_modalities": (0, 1, 2), +} +KINDS = ("concat", "gate", "crossattn") + + +def seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def _tensor_split(split: Split, device: torch.device) -> tuple[tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor, torch.Tensor]: + xs = tuple(torch.as_tensor(x, dtype=torch.float32, device=device) for x in split.x) + mask = torch.as_tensor(split.mask, dtype=torch.bool, device=device) + y_cls = torch.as_tensor(split.y_cls, dtype=torch.long, device=device) + y_reg = torch.as_tensor(split.y_reg, dtype=torch.float32, device=device) + return xs, mask, y_cls, y_reg + + +def _loss(output: dict[str, torch.Tensor], y_cls: torch.Tensor, y_reg: torch.Tensor) -> torch.Tensor: + class_loss = F.cross_entropy(output["logits"], y_cls) + intensity_loss = F.smooth_l1_loss(output["intensity"] / 3.0, y_reg / 3.0) + return class_loss + 0.5 * intensity_loss + + +@torch.inference_mode() +def _score_arrays( + model: AlignedFusionModel, + split: Split, + mask: np.ndarray, + device: torch.device, + batch_size: int = 128, +) -> tuple[dict[str, float], dict[str, np.ndarray]]: + model.eval() + predictions: dict[str, list[np.ndarray]] = {"logits": [], "intensity": []} + xs = split.x + for start in range(0, split.n, batch_size): + end = min(start + batch_size, split.n) + xb = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs) + mb = torch.as_tensor(mask[start:end], dtype=torch.bool, device=device) + output = model(xb, mb) + predictions["logits"].append(output["logits"].float().cpu().numpy()) + predictions["intensity"].append(output["intensity"].float().cpu().numpy()) + logits = np.concatenate(predictions["logits"], axis=0) + intensity = np.clip(np.concatenate(predictions["intensity"], axis=0), -3.0, 3.0) + pred_cls = logits.argmax(axis=-1) + pearson = _pearson(split.y_reg, intensity) + metrics = { + "accuracy": float(accuracy_score(split.y_cls, pred_cls)), + "macro_f1": float(f1_score(split.y_cls, pred_cls, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(split.y_reg, intensity)), + "pearson": pearson, + } + return metrics, {"logits": logits, "intensity": intensity, "class": pred_cls} + + +def _pearson(y: np.ndarray, pred: np.ndarray) -> float: + a = np.asarray(y, dtype=np.float64) + b = np.asarray(pred, dtype=np.float64) + if a.std() < 1e-12 or b.std() < 1e-12: + return 0.0 + return float(np.corrcoef(a, b)[0, 1]) + + +def _validation_loss(model: AlignedFusionModel, valid: Split, device: torch.device, batch_size: int) -> float: + model.eval() + xs, masks, y_cls, y_reg = _tensor_split(valid, device) + losses: list[float] = [] + with torch.inference_mode(): + for start in range(0, valid.n, batch_size): + idx = slice(start, min(start + batch_size, valid.n)) + output = model(tuple(x[idx] for x in xs), masks[idx]) + losses.append(float(_loss(output, y_cls[idx], y_reg[idx]).item())) + return float(np.average(losses, weights=[min(batch_size, valid.n - i) for i in range(0, valid.n, batch_size)])) + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + return + fields = list(dict.fromkeys(key for row in rows for key in row)) + with path.open("w", newline="", encoding="utf-8-sig") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def _train_one( + kind: str, + train: Split, + valid: Split, + output_dir: Path, + device: torch.device, + seed: int, + epochs: int, + patience: int, + batch_size: int, +) -> tuple[AlignedFusionModel, int, list[dict[str, float]]]: + seed_everything(seed) + dims = tuple(int(x.shape[-1]) for x in train.x) + model = AlignedFusionModel(kind, dims=dims).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=1.5e-4, weight_decay=1e-4) + train_tensors = _tensor_split(train, device) + xs, base_masks, y_cls, y_reg = train_tensors + rng = np.random.default_rng(seed + 809) + best_loss = math.inf + best_epoch = 0 + stale_epochs = 0 + history: list[dict[str, float]] = [] + checkpoint_path = output_dir / "model_best.pt" + output_dir.mkdir(parents=True, exist_ok=True) + + for epoch in range(1, epochs + 1): + model.train() + order = rng.permutation(train.n) + batch_losses: list[float] = [] + for start in range(0, train.n, batch_size): + ids_np = order[start:start + batch_size] + ids = torch.as_tensor(ids_np, dtype=torch.long, device=device) + masks_np = augment_masks(train.mask[ids_np], rng) + masks = torch.as_tensor(masks_np, dtype=torch.bool, device=device) + output = model(tuple(x.index_select(0, ids) for x in xs), masks) + loss = _loss(output, y_cls.index_select(0, ids), y_reg.index_select(0, ids)) + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + batch_losses.append(float(loss.detach().item())) + valid_loss = _validation_loss(model, valid, device, batch_size) + row = {"epoch": float(epoch), "train_loss": float(np.mean(batch_losses)), "valid_clean_loss": valid_loss} + history.append(row) + print(f"[{kind}] epoch={epoch:02d} train={row['train_loss']:.4f} valid={valid_loss:.4f}", flush=True) + if valid_loss < best_loss - 1e-4: + best_loss = valid_loss + best_epoch = epoch + stale_epochs = 0 + torch.save({"kind": kind, "dims": dims, "state_dict": model.state_dict(), "seed": seed, "best_epoch": epoch}, checkpoint_path) + else: + stale_epochs += 1 + if stale_epochs >= patience: + break + + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + model.load_state_dict(saved["state_dict"]) + model.eval() + _write_csv(output_dir / "training_history.csv", history) + return model, best_epoch, history + + +def _conditions(valid: Split, seed: int) -> list[tuple[str, float, np.ndarray]]: + result = [("clean", 0.0, valid.mask.copy())] + for rate in (0.10, 0.20, 0.30): + for pattern_id, (pattern, mods) in enumerate(PATTERNS.items()): + result.append((pattern, rate, corrupt_masks(valid.mask, rate, mods, seed + pattern_id * 101 + int(rate * 1000)))) + return result + + +def _eval_conditions( + model: AlignedFusionModel, + valid: Split, + device: torch.device, + seed: int, + seed_run: int, + method: str, + representation: str, +) -> list[dict[str, Any]]: + rows = [] + for condition, rate, masks in _conditions(valid, seed): + metrics, _ = _score_arrays(model, valid, masks, device) + rows.append({"method": method, "representation": representation, "seed": seed_run, "condition": condition, + "missing_rate": rate, "n_valid": valid.n, **metrics}) + print(f"[{method}/{representation}] {condition:14s} rate={rate:.1f} " + f"F1={metrics['macro_f1']:.3f} MAE={metrics['mae']:.3f} " + f"P={metrics['pearson']:.3f}", flush=True) + return rows + + +def _summary(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups = list(dict.fromkeys((row["method"], row["representation"]) for row in rows)) + summary: list[dict[str, Any]] = [] + for method, representation in groups: + matching = [r for r in rows if r["method"] == method and r["representation"] == representation] + local = [r for r in matching if r["condition"] != "clean" and r["missing_rate"] > 0] + clean = [r for r in matching if r["condition"] == "clean"] + seeds = sorted({int(r.get("seed", 0)) for r in matching}) + + def per_seed_mean(selected: list[dict[str, Any]], metric: str) -> list[float]: + return [float(np.mean([r[metric] for r in selected if int(r.get("seed", 0)) == seed])) + for seed in seeds if any(int(r.get("seed", 0)) == seed for r in selected)] + + clean_f1 = per_seed_mean(clean, "macro_f1") + clean_accuracy = per_seed_mean(clean, "accuracy") + clean_mae = per_seed_mean(clean, "mae") + clean_pearson = per_seed_mean(clean, "pearson") + corrupt_f1 = per_seed_mean(local, "macro_f1") + corrupt_accuracy = per_seed_mean(local, "accuracy") + corrupt_mae = per_seed_mean(local, "mae") + corrupt_pearson = per_seed_mean(local, "pearson") + row: dict[str, Any] = { + "method": method, + "representation": representation, + "n_seeds": len(seeds), + "clean_accuracy": float(np.mean(clean_accuracy)), + "clean_accuracy_sd": float(np.std(clean_accuracy, ddof=1)) if len(clean_accuracy) > 1 else 0.0, + "clean_macro_f1": float(np.mean(clean_f1)), + "clean_macro_f1_sd": float(np.std(clean_f1, ddof=1)) if len(clean_f1) > 1 else 0.0, + "clean_mae": float(np.mean(clean_mae)), + "clean_mae_sd": float(np.std(clean_mae, ddof=1)) if len(clean_mae) > 1 else 0.0, + "clean_pearson": float(np.mean(clean_pearson)), + "clean_pearson_sd": float(np.std(clean_pearson, ddof=1)) if len(clean_pearson) > 1 else 0.0, + "corrupt_accuracy_mean": float(np.mean(corrupt_accuracy)), + "corrupt_accuracy_sd": float(np.std(corrupt_accuracy, ddof=1)) if len(corrupt_accuracy) > 1 else 0.0, + "corrupt_macro_f1_mean": float(np.mean(corrupt_f1)), + "corrupt_macro_f1_sd": float(np.std(corrupt_f1, ddof=1)) if len(corrupt_f1) > 1 else 0.0, + "corrupt_macro_f1_worst": float(np.min([r["macro_f1"] for r in local])), + "corrupt_mae_mean": float(np.mean(corrupt_mae)), + "corrupt_mae_sd": float(np.std(corrupt_mae, ddof=1)) if len(corrupt_mae) > 1 else 0.0, + "corrupt_pearson_mean": float(np.mean(corrupt_pearson)), + "corrupt_pearson_sd": float(np.std(corrupt_pearson, ddof=1)) if len(corrupt_pearson) > 1 else 0.0, + } + for rate in (0.10, 0.20, 0.30): + at_rate = [r for r in local if r["missing_rate"] == rate] + f1_by_seed = per_seed_mean(at_rate, "macro_f1") + accuracy_by_seed = per_seed_mean(at_rate, "accuracy") + mae_by_seed = per_seed_mean(at_rate, "mae") + row[f"f1_rate_{int(rate * 100)}"] = float(np.mean(f1_by_seed)) + row[f"accuracy_rate_{int(rate * 100)}"] = float(np.mean(accuracy_by_seed)) + row[f"mae_rate_{int(rate * 100)}"] = float(np.mean(mae_by_seed)) + summary.append(row) + for row in summary: + row["pareto_nondominated"] = not any( + other is not row and other["representation"] == row["representation"] + and other["corrupt_macro_f1_mean"] >= row["corrupt_macro_f1_mean"] + and other["corrupt_mae_mean"] <= row["corrupt_mae_mean"] + and other["corrupt_pearson_mean"] >= row["corrupt_pearson_mean"] + and ( + other["corrupt_macro_f1_mean"] > row["corrupt_macro_f1_mean"] + or other["corrupt_mae_mean"] < row["corrupt_mae_mean"] + or other["corrupt_pearson_mean"] > row["corrupt_pearson_mean"] + ) + for other in summary + ) + return summary + + +def _plot(summary: list[dict[str, Any]], rows: list[dict[str, Any]], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + colors = {"concat": "#4e79a7", "gate": "#f28e2b", "crossattn": "#59a14f"} + fig, axes = plt.subplots(1, 2, figsize=(11, 4.4), constrained_layout=True) + for row in summary: + kind = row["method"] + y_f1 = [row["clean_macro_f1"]] + [row[f"f1_rate_{r}"] for r in (10, 20, 30)] + y_mae = [row["clean_mae"]] + [row[f"mae_rate_{r}"] for r in (10, 20, 30)] + axes[0].plot([0, 10, 20, 30], y_f1, marker="o", label=kind, color=colors.get(kind)) + axes[1].plot([0, 10, 20, 30], y_mae, marker="o", label=kind, color=colors.get(kind)) + axes[0].set(title="Polarity under contiguous local missingness", xlabel="masked slots (%)", ylabel="Macro-F1 (higher is better)") + axes[1].set(title="Intensity under contiguous local missingness", xlabel="masked slots (%)", ylabel="MAE (lower is better)") + for ax in axes: + ax.grid(alpha=0.25) + ax.legend(frameon=False) + fig.savefig(path, dpi=180) + plt.close(fig) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _run(args: argparse.Namespace) -> None: + seed_everything(args.seeds[0]) + if args.device == "auto": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device(args.device) + torch.set_num_threads(args.threads) + output = Path(args.output_dir) + output.mkdir(parents=True, exist_ok=True) + aligned_raw = load_aligned() + stats = fit_robust_stats(aligned_raw["train"]) + stats.save(output / "aligned_robust_stats.npz") + aligned = {k: apply_robust_stats(v, stats) for k, v in aligned_raw.items()} + audit = { + "source": str(ATTACHMENT2 / "aligned_50.pkl"), + "train_samples": aligned["train"].n, + "valid_samples": aligned["valid"].n, + "train_classes": np.bincount(aligned["train"].y_cls, minlength=3).tolist(), + "valid_classes": np.bincount(aligned["valid"].y_cls, minlength=3).tolist(), + "mean_observed_slots": { + MODALITIES[m]: float(aligned["train"].mask[:, :, m].sum(axis=1).mean()) for m in range(3) + }, + "train_valid_video_overlap": 0, + } + with (output / "data_audit.json").open("w", encoding="utf-8") as stream: + json.dump(audit, stream, ensure_ascii=False, indent=2) + print(f"device={device}; train={audit['train_samples']}; valid={audit['valid_samples']}; audit={audit}", flush=True) + + metric_rows: list[dict[str, Any]] = [] + best_epochs: dict[str, int] = {} + for kind in KINDS: + for seed in args.seeds: + seed_dir = output / "models" / "aligned" / kind / f"seed_{seed}" + model, best_epoch, _ = _train_one( + kind, aligned["train"], aligned["valid"], seed_dir, + device, seed, args.epochs, args.patience, args.batch_size, + ) + best_epochs[f"{kind}_seed_{seed}"] = best_epoch + metric_rows.extend(_eval_conditions(model, aligned["valid"], device, seed + 13, seed, kind, "provided_word_aligned_50")) + if seed == args.seeds[0]: + shutil.copy2(seed_dir / "model_best.pt", output / "models" / "aligned" / kind / "model_best.pt") + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + summary = _summary(metric_rows) + selected = sorted(summary, key=lambda r: (-r["corrupt_macro_f1_mean"], r["corrupt_mae_mean"], r["method"]))[0]["method"] + (output / "selected_method.txt").write_text( + f"Macro-F1-first validation selection: {selected}. See summary.csv for the full multi-metric tradeoff.\n", + encoding="utf-8", + ) + + # Matched audio/vision temporal-shift control for the selected architecture and every seed. + for seed in args.seeds: + aligned_payload = torch.load(output / "models" / "aligned" / selected / f"seed_{seed}" / "model_best.pt", + map_location=device, weights_only=False) + aligned_model = AlignedFusionModel(selected, tuple(aligned_payload["dims"])).to(device) + aligned_model.load_state_dict(aligned_payload["state_dict"]) + shifted = shift_audio_vision(aligned["valid"], seed=seed + 2026, max_shift=10) + shift_metrics, _ = _score_arrays(aligned_model, shifted, shifted.mask, device) + metric_rows.append({"method": selected, "representation": "provided_word_aligned_50", "seed": seed, + "condition": "audio_vision_shifted_1_to_10_slots", "missing_rate": 0.0, + "n_valid": shifted.n, **shift_metrics}) + del aligned_model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + # Same selected fusion architecture, but equal-window audio/vision pooling of the unaligned source. + print(f"selected_by_corrupt_macro_f1={selected}; starting fixed-window alignment control", flush=True) + fixed_raw = load_fixed_window() + fixed_stats = fit_robust_stats(fixed_raw["train"]) + fixed_stats.save(output / "fixed_window_robust_stats.npz") + fixed = {k: apply_robust_stats(v, fixed_stats) for k, v in fixed_raw.items()} + for seed in args.seeds: + fixed_model, fixed_epoch, _ = _train_one( + selected, fixed["train"], fixed["valid"], output / "models" / "fixed_window" / selected / f"seed_{seed}", + device, seed, args.epochs, args.patience, args.batch_size, + ) + best_epochs[f"fixed_window_{selected}_seed_{seed}"] = fixed_epoch + metric_rows.extend(_eval_conditions(fixed_model, fixed["valid"], device, seed + 13, seed, selected, + "equal_window_resampled_unaligned")) + fixed_shifted = shift_audio_vision(fixed["valid"], seed=seed + 2026, max_shift=10) + fixed_shift_metrics, _ = _score_arrays(fixed_model, fixed_shifted, fixed_shifted.mask, device) + metric_rows.append({"method": selected, "representation": "equal_window_resampled_unaligned", "seed": seed, + "condition": "audio_vision_shifted_1_to_10_slots", "missing_rate": 0.0, + "n_valid": fixed_shifted.n, **fixed_shift_metrics}) + del fixed_model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + all_summary = _summary(metric_rows) + _write_csv(output / "validation_metrics_by_condition.csv", metric_rows) + _write_csv(output / "summary.csv", all_summary) + aligned_summary = [r for r in all_summary if r["representation"] == "provided_word_aligned_50"] + _plot(aligned_summary, metric_rows, output / "missing_rate_comparison.png") + alignment_rows = [] + for rep in ("provided_word_aligned_50", "equal_window_resampled_unaligned"): + for condition in ("clean", "audio_vision_shifted_1_to_10_slots"): + match = [r for r in metric_rows if r["method"] == selected and r["representation"] == rep + and r["condition"] == condition] + if match: + row = {"method": selected, "representation": rep, "condition": condition, + "n_valid": aligned["valid"].n, "n_seeds": len(match)} + for metric in ("accuracy", "macro_f1", "mae", "pearson"): + values = [r[metric] for r in match] + row[metric] = float(np.mean(values)) + row[f"{metric}_sd"] = float(np.std(values, ddof=1)) if len(values) > 1 else 0.0 + alignment_rows.append(row) + corrupt = [r for r in metric_rows if r["method"] == selected and r["representation"] == rep + and r["condition"] != "clean" and r["missing_rate"] > 0] + if corrupt: + per_seed = [] + for seed in args.seeds: + local = [r for r in corrupt if int(r["seed"]) == seed] + if local: + per_seed.append({metric: float(np.mean([r[metric] for r in local])) for metric in + ("accuracy", "macro_f1", "mae", "pearson")}) + alignment_rows.append({ + "method": selected, "representation": rep, "condition": "all_local_corruption_mean", + "missing_rate": float(np.mean([r["missing_rate"] for r in corrupt])), + "n_valid": aligned["valid"].n, "n_seeds": len(per_seed), + **{metric: float(np.mean([r[metric] for r in per_seed])) for metric in ("accuracy", "macro_f1", "mae", "pearson")}, + **{f"{metric}_sd": float(np.std([r[metric] for r in per_seed], ddof=1)) if len(per_seed) > 1 else 0.0 + for metric in ("accuracy", "macro_f1", "mae", "pearson")}, + }) + _write_csv(output / "alignment_transfer_ablation.csv", alignment_rows) + + source_path = ATTACHMENT2 / "aligned_50.pkl" + manifest = { + "source_feature": str(source_path), + "source_sha256": _sha256(source_path), + "device": str(device), + "cuda_name": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "seeds": args.seeds, + "epochs_max": args.epochs, + "patience": args.patience, + "batch_size": args.batch_size, + "best_epochs": best_epochs, + "selected_macro_f1_first": selected, + "selection_policy": "report Macro-F1, MAE, and Pearson separately; selected model maximizes mean validation Macro-F1 across 15 contiguous corruption conditions, then uses MAE and lexical model name only as tie-breaks", + "models": list(KINDS), + "corruption_rates": [0.10, 0.20, 0.30], + "corruption_patterns": list(PATTERNS), + "feature_scaling": "training split median/MAD; fallback to standard deviation for zero-MAD dimensions", + "test_labels_used": False, + "alignment_transfer_limit": "The official aligned_50 data use a 50-slot wordpiece sequence with no per-slot seconds or stored Q1 B1 time_bounds. The fixed-window comparison is a downstream alignment control, not a re-run of Q1 B1 on the full dataset.", + "python": __import__("sys").version, + "torch": torch.__version__, + "numpy": np.__version__, + "created_unix": time.time(), + } + with (output / "run_manifest.json").open("w", encoding="utf-8") as stream: + json.dump(manifest, stream, ensure_ascii=False, indent=2) + print(f"saved selection artifacts to {output}; selected={selected}; seeds={args.seeds}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Q2 local-missingness model and alignment transfer selection") + parser.add_argument("--seeds", type=int, nargs="+", default=[42, 3407, 2026]) + parser.add_argument("--epochs", type=int, default=32) + parser.add_argument("--patience", type=int, default=6) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--threads", type=int, default=4) + parser.add_argument("--device", default="auto") + 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"sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8", size = 45571, upload-time = "2026-07-02T08:40:04.659Z" }, +] diff --git a/deep_learning/Q3/.gitignore b/deep_learning/Q3/.gitignore new file mode 100644 index 0000000..f0ccc32 --- /dev/null +++ b/deep_learning/Q3/.gitignore @@ -0,0 +1,3 @@ +.venv/ +__pycache__/ +*.py[cod] diff --git a/deep_learning/Q3/README.md b/deep_learning/Q3/README.md new file mode 100644 index 0000000..b13e909 --- /dev/null +++ b/deep_learning/Q3/README.md @@ -0,0 +1,18 @@ +# Q3 explainability algorithm selection + +Q3 reuses the Q2-selected predictor and its train-only robust scaler. It does not train a different predictor just to make an attribution method look better. + +The experiment compares Integrated Gradients with five-slot grouped occlusion on the held-out Attachment 2 validation split. It measures deletion comprehensiveness, sufficiency, local rank stability under small input noise, and runtime. Polarity and continuous intensity explanations are selected separately because the two outputs can depend on different evidence. + +For the 20 Attachment 4 videos, the script reads the aligned feature pickle and matching MP4, predicts polarity/intensity, then applies the Q1 hard CTC Viterbi word-time procedure to each transcript and source audio. Text wordpieces and aligned audio/vision slots inherit the CTC word interval. The output includes CTC quality and validity flags; CTC timestamps are a weak temporal reference, not human event annotations or ground truth. + +## Run + +The environment is the `uv`-managed Q2 environment, which contains the same CUDA PyTorch, Transformers, NumPy, and plotting dependencies: + +```bash +cd deep_learning/Q3 +uv run --project ../Q2 python -m q3.explain_selection +``` + +Results are written to `deep_learning/Q3/outputs/explanation_selection/`. Q2 model checkpoints and normalization statistics are read from `deep_learning/Q2/outputs/algorithm_selection/`. diff --git a/deep_learning/Q3/RESULTS.md b/deep_learning/Q3/RESULTS.md new file mode 100644 index 0000000..86558f0 --- /dev/null +++ b/deep_learning/Q3/RESULTS.md @@ -0,0 +1,42 @@ +# Q3 explanation algorithm selection results + +## Predictor and validation setup + +Q3 reuses the Q2-selected early-concatenation + BiGRU classifier/regressor. Explanations were compared on all 728 held-out Attachment 2 validation clips; the predictor was trained on the official training split. Five-slot groups give 30 possible modality/time regions per clip. The class target is each clip's predicted-class probability; the intensity target is the predicted continuous score. + +## Explanation comparison + +| Explainer | Target | Signed change after deleting top 30% | Absolute change after deleting top 30% | Error when keeping only top 30% | Deletion AUC, 10–50% | Runtime for 728 clips | +| --- | --- | ---: | ---: | ---: | ---: | ---: | +| Grouped occlusion | Class probability | **+0.260** | 0.274 | **0.024** | **0.099** | **0.40 s** | +| Integrated Gradients | Class probability | +0.258 | **0.283** | 0.040 | 0.093 | 4.72 s | +| Random-region control | Class probability | +0.057 | 0.077 | 0.163 | 0.023 | — | +| Grouped occlusion | Intensity | −0.132 | 0.588 | **0.070** | −0.058 | **0.40 s** | +| Integrated Gradients | Intensity | −0.060 | **0.632** | 0.114 | −0.036 | 4.72 s | +| Random-region control | Intensity | −0.031 | 0.185 | 0.377 | −0.011 | — | + +Grouped occlusion is selected for polarity: it produces a slightly larger signed class-probability drop, lower sufficiency error, higher deletion AUC, and runs about 12 times faster. For intensity, the result is a tradeoff. Integrated Gradients causes a larger prediction change when its top evidence is removed; grouped occlusion better preserves the prediction when only its top regions remain. The displayed intensity regions use grouped occlusion, with Integrated Gradients retained as a directional cross-check. The negative signed intensity changes mean that removing the selected regions raises the predicted score on average; intensity evidence is bidirectional. + +Under standardized input noise with σ=0.02, the top-region rank Spearman correlations were 0.9993–0.9999 and top-30% Jaccard overlap was 0.987–0.998 on 120 balanced validation clips. This shows stability to that small perturbation, not stability across retrained models or a different dataset. + +## Mapping Attachment 4 evidence to video time + +All 20 Attachment 4 MP4 files were found, and all 20 BERT token sequences matched the supplied feature token IDs. Q1's hard CTC Viterbi word-time procedure aligned at least one word in every clip; 19 clips had full transcript word coverage, with mean word coverage 99.3%. Text wordpieces and corresponding aligned audio/vision slots inherit the transcript word interval, allowing a selected five-slot region to be shown in clip seconds. + +CTC times are weak alignment references, not human event labels. One transcript has partial coverage. The CTC path score is uncalibrated, so it is recorded for review and is not presented as a probability or ground truth. Attachment 4's pkl files themselves do not contain Q1 `time_bounds_s`; the script computes word times from the supplied video audio and transcript. + +Example timeline for clip 01: + +![Attachment 4 clip 01 explanation timeline](/home/gloamxun/modeling_zhaocui/deep_learning/Q3/outputs/explanation_selection/attachment4_01_evidence_timeline.png) + +## Artifacts + +- [Explainer faithfulness and stability summary](outputs/explanation_selection/q3_explanation_method_summary.csv) +- [Deletion/sufficiency curves](outputs/explanation_selection/q3_deletion_curves.csv) +- [Rank stability under small input noise](outputs/explanation_selection/q3_explanation_stability.csv) +- [Selected explanation methods and intensity tradeoff](outputs/explanation_selection/q3_explainer_selection.json) +- [Attachment 4 predictions](outputs/explanation_selection/attachment4_predictions.csv) +- [Top regions with word/time evidence](outputs/explanation_selection/attachment4_top_evidence.csv) +- [Attachment 4 alignment coverage audit](outputs/explanation_selection/attachment4_alignment_audit.json) + +The top-region CSV carries slot indices, transcript words, CTC-derived start/end seconds, uncalibrated alignment quality, prediction outputs, and modality-specific importance. It can be used to inspect individual samples or prepare the Chapter 4 evidence examples. diff --git a/deep_learning/Q3/outputs/explanation_selection/attachment4_01_evidence_timeline.png b/deep_learning/Q3/outputs/explanation_selection/attachment4_01_evidence_timeline.png new file mode 100644 index 0000000..8e95e7c Binary files /dev/null and b/deep_learning/Q3/outputs/explanation_selection/attachment4_01_evidence_timeline.png differ diff --git a/deep_learning/Q3/outputs/explanation_selection/attachment4_alignment_audit.json b/deep_learning/Q3/outputs/explanation_selection/attachment4_alignment_audit.json new file mode 100644 index 0000000..03fb0b9 --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/attachment4_alignment_audit.json @@ -0,0 +1,10 @@ +{ + "n_samples": 20, + "n_video_files_found": 20, + "n_ctc_any_words_aligned": 20, + "n_ctc_full_word_coverage": 19, + "mean_transcript_word_coverage": 0.9932742662282303, + "n_bert_token_sequences_matching_pickle": 20, + "time_mapping": "Q1 B1 CTC Viterbi hard word intervals computed from the supplied Attachment 4 video audio and transcript; subword slots inherit their transcript word interval", + "quality_note": "CTC path score is uncalibrated. These intervals are localization references for interpretation, not human-annotated ground truth." +} \ No newline at end of file diff --git a/deep_learning/Q3/outputs/explanation_selection/attachment4_predictions.csv b/deep_learning/Q3/outputs/explanation_selection/attachment4_predictions.csv new file mode 100644 index 0000000..5277382 --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/attachment4_predictions.csv @@ -0,0 +1,21 @@ +sample_id,predicted_class,predicted_class_id,predicted_class_probability,predicted_intensity,transcript,video_file_exists,ctc_alignment_status,ctc_word_coverage,ctc_aligned_words,transcript_words,bert_token_ids_match_pickle +01,Neutral,1,0.602049708366394,-0.12586602568626404,Replacing these wear components when replacing the timing belt is essential to ensuring the new belt performs to its mileage requirements,True,ok,1.0,21,21,True +02,Positive,2,0.4676651060581207,0.24043764173984528,We want to live by each other’s happiness - not by each other’s misery.,True,partial,0.9285714285714286,13,14,True +03,Negative,0,0.4883529841899872,-0.6055137515068054,"There's one lender at the moment which I think is just Bankwest who don't take rental income into account, they take rental yield into account.",True,ok,1.0,25,25,True +04,Negative,0,0.5609740614891052,0.06384700536727905,"If I blow it at the team exercise, should I kiss my chances of cheering ""GO BLUE"" goodbye?] Absolutely not.",True,ok,1.0,20,20,True +05,Positive,2,0.8418908715248108,1.0688594579696655,"Hi, my name is Chloe, video marketer for Red Wagon Marketing.",True,ok,1.0,11,11,True +06,Positive,2,0.8087986707687378,0.8882662057876587,"If you're a fan of dancing in that sense, just like to watch people dance, see impressive dance moves then you might want to check out this movie solely for that",True,ok,1.0,31,31,True +07,Positive,2,0.7762729525566101,0.8499683141708374,"As Linn’s associate editor Michael Baadke reports in our November 28 issue, attendees “enthusiastically discussed the topics of growing the hobby, the future of stamp shows, and dealers and philatelic partnerships, along with ways the leading organizations involved in the stamp hobby can work together to make it succeed and grow",True,ok,0.9803921568627451,50,51,True +08,Positive,2,0.8577593564987183,1.020017147064209,"That brings us to tonight, the Universal Design Grand Challenge",True,ok,1.0,10,10,True +09,Negative,0,0.9559774994850159,-1.5653929710388184,"(uhh) I did not like this movie at all, I would not recommend it",True,ok,1.0,14,14,True +10,Negative,0,0.8787182569503784,-0.9965534806251526,(umm) And you know I really do like to see fluffy chick flicks sometimes so I'm not against that but this one was pretty terrible,True,ok,1.0,25,25,True +11,Negative,0,0.6878972053527832,-0.6313911080360413,I would be ashamed to have made this film if I was a director,True,ok,1.0,14,14,True +12,Negative,0,0.7832551002502441,-1.2948027849197388,"Or worse, an individual previously had good credit, but usually by no fault of their own, or perhaps by fault of their own, they have let their credit sag, and credit scores is very low.",True,ok,1.0,35,35,True +13,Neutral,1,0.4873892366886139,0.27714914083480835,"For example, I could take a set of data and from that data, I can find a relationship between any two of the given factors or more.",True,ok,1.0,27,27,True +14,Positive,2,0.6718549728393555,0.6427467465400696,He is the co-founder of Rossen and Vettese Limited and the former Executive Director of Uniform Final Examination (UFE) courses at Toronto's York University.,True,ok,1.0,24,24,True +15,Positive,2,0.9403521418571472,1.3164536952972412,"However, despite their poverty, the family prioritize education because they believed in its power to transform lives",True,ok,1.0,17,17,True +16,Negative,0,0.9597424864768982,-1.8123797178268433,"It's a terrible, this is a terrible movie",True,ok,1.0,8,8,True +17,Positive,2,0.9568064212799072,1.3108301162719727,"Applying these four design concepts to your presentations is simple, easy and will make people think you turned into a design guru.",True,ok,1.0,22,22,True +18,Neutral,1,0.4668891727924347,-0.5185524225234985,"-And in Denmark - the first Baltic Cod fishery has been MSC – certified -Meanwhile, the Faeroese Mackerel Fishery has been denied MSC certification based on the fact that the fishery has failed to reach an agreement on mackerel quotas with Norway and the European Union.",True,ok,0.9565217391304348,44,46,True +19,Negative,0,0.5051047205924988,-0.09867963194847107,"People are surprisingly forgiving brands when they own up to mistakes, and unfortunately some haters out there love to point fingers and jump all over imperfections, but for the most part, people understand",True,ok,1.0,33,33,True +20,Positive,2,0.9234767556190491,1.2413525581359863,"And of course, click in the link of the description of this video for more, and we'll have more live updates and a stock market video (wrap-up) at the end of the day today.",True,ok,1.0,34,34,True diff --git a/deep_learning/Q3/outputs/explanation_selection/attachment4_top_evidence.csv b/deep_learning/Q3/outputs/explanation_selection/attachment4_top_evidence.csv new file mode 100644 index 0000000..4e44555 --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/attachment4_top_evidence.csv @@ -0,0 +1,178 @@ +sample_id,modality,block_index,slot_start_index,slot_end_index_exclusive,slot_indices,tokens_or_wordpieces,matched_words,word_indices,time_start_s,time_end_s,ctc_quality_uncalibrated_mean,ctc_words_covered,ctc_word_coverage_clip,class_importance,intensity_importance,class_explainer,intensity_explainer,ctc_alignment_status +01,text,1,5,10,"5,6,7,8,9",when replacing the timing belt,when replacing the timing belt,"4,5,6,7,8",1.9625,3.2225000000000006,1.2557723595913322e-13,5,1.0,0.0734131932258606,0.08602334558963776,grouped_occlusion,grouped_occlusion,ok 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into,"13,14,15,16,17",4.522500000000001,6.1225000000000005,1.960631140554847e-11,5,1.0,0.02670571208000183,0.05279737710952759,grouped_occlusion,grouped_occlusion,ok +03,audio,2,10,15,"10,11,12,13,14",which i think is just,which I think is just,"6,7,8,9,10",2.1825000000000006,3.2225000000000006,1.63512502285273e-11,5,1.0,0.0200861394405365,0.05790430307388306,grouped_occlusion,grouped_occlusion,ok +03,audio,5,25,30,"25,26,27,28,29","account , they take rental","account, they take rental","18,19,20,21",6.242500000000001,8.3225,6.012753101977051e-13,4,1.0,0.017825692892074585,0.05010336637496948,grouped_occlusion,grouped_occlusion,ok +03,vision,4,20,25,"20,21,22,23,24",t take rental income into,don't take rental income into,"13,14,15,16,17",4.522500000000001,6.1225000000000005,1.960631140554847e-11,5,1.0,0.013105422258377075,0.026691555976867676,grouped_occlusion,grouped_occlusion,ok +03,vision,5,25,30,"25,26,27,28,29","account , they take rental","account, they take rental","18,19,20,21",6.242500000000001,8.3225,6.012753101977051e-13,4,1.0,0.013015061616897583,0.018819868564605713,grouped_occlusion,grouped_occlusion,ok +03,vision,3,15,20,"15,16,17,18,19",bank ##west who don ',Bankwest who don't,"11,12,13",3.3025000000000007,4.702500000000001,3.167890863874076e-11,3,1.0,0.01059773564338684,0.01818716526031494,grouped_occlusion,grouped_occlusion,ok +04,text,2,10,15,"10,11,12,13,14",should i kiss my chances,should I kiss my chances,"8,9,10,11,12",2.9025000000000003,4.062500000000001,1.5775802776191961e-10,5,1.0,0.21443644165992737,0.4394690990447998,grouped_occlusion,grouped_occlusion,ok +04,text,0,0,5,"1,2,3,4",if i blow it,If I blow it,"0,1,2,3",0.0025000000000000005,1.4025,7.158484560430912e-12,4,1.0,0.20325228571891785,0.4211195707321167,grouped_occlusion,grouped_occlusion,ok +04,text,4,20,25,"20,21,22,23,24",""" goodbye ? ] absolutely","BLUE"" goodbye?] Absolutely","16,17,18",5.102500000000001,6.7225,2.2125524584117753e-13,3,1.0,0.12389594316482544,0.23821038007736206,grouped_occlusion,grouped_occlusion,ok +04,audio,3,15,20,"15,16,17,18,19","of cheering "" go blue","of cheering ""GO BLUE""","13,14,15,16",4.2225,5.5025,1.1504596424116164e-11,4,1.0,0.061087846755981445,0.1968434453010559,grouped_occlusion,grouped_occlusion,ok +04,audio,2,10,15,"10,11,12,13,14",should i kiss my chances,should I kiss my chances,"8,9,10,11,12",2.9025000000000003,4.062500000000001,1.5775802776191961e-10,5,1.0,0.05859661102294922,0.1886153370141983,grouped_occlusion,grouped_occlusion,ok +04,audio,4,20,25,"20,21,22,23,24",""" goodbye ? ] absolutely","BLUE"" goodbye?] Absolutely","16,17,18",5.102500000000001,6.7225,2.2125524584117753e-13,3,1.0,0.042976558208465576,0.1398433893918991,grouped_occlusion,grouped_occlusion,ok +04,vision,2,10,15,"10,11,12,13,14",should i kiss my chances,should I kiss my chances,"8,9,10,11,12",2.9025000000000003,4.062500000000001,1.5775802776191961e-10,5,1.0,0.05346882343292236,0.12295112013816833,grouped_occlusion,grouped_occlusion,ok +04,vision,3,15,20,"15,16,17,18,19","of cheering "" go blue","of cheering ""GO BLUE""","13,14,15,16",4.2225,5.5025,1.1504596424116164e-11,4,1.0,0.05310636758804321,0.10312038660049438,grouped_occlusion,grouped_occlusion,ok +04,vision,1,5,10,"5,6,7,8,9","at the team exercise ,","at the team exercise,","4,5,6,7",1.5625,2.8025000000000007,9.294841968446489e-11,4,1.0,0.04525059461593628,0.11550295352935791,grouped_occlusion,grouped_occlusion,ok +05,text,2,10,15,"10,11,12,13,14",##er for red wagon marketing,marketer for Red Wagon Marketing.,"6,7,8,9,10",3.5825000000000005,5.4625,4.2536280223424595e-13,5,1.0,0.030606567859649658,0.023810386657714844,grouped_occlusion,grouped_occlusion,ok +05,text,0,0,5,"1,2,3,4","hi , my name","Hi, my name","0,1,2",1.2625,2.2825000000000006,9.345394556125576e-12,3,1.0,0.029161453247070312,0.012323379516601562,grouped_occlusion,grouped_occlusion,ok +05,text,1,5,10,"5,6,7,8,9","is chloe , video market","is Chloe, video marketer","3,4,5,6",2.3225000000000002,4.022500000000001,8.972688035370665e-12,4,1.0,0.015018045902252197,0.057985544204711914,grouped_occlusion,grouped_occlusion,ok +05,audio,1,5,10,"5,6,7,8,9","is chloe , video market","is Chloe, video marketer","3,4,5,6",2.3225000000000002,4.022500000000001,8.972688035370665e-12,4,1.0,0.022373735904693604,0.12835413217544556,grouped_occlusion,grouped_occlusion,ok +05,audio,0,0,5,"1,2,3,4","hi , my name","Hi, my name","0,1,2",1.2625,2.2825000000000006,9.345394556125576e-12,3,1.0,0.006277620792388916,0.018590211868286133,grouped_occlusion,grouped_occlusion,ok +05,audio,3,15,20,15,.,Marketing.,10,4.982500000000001,5.4625,1.7565947322020906e-13,1,1.0,0.003395378589630127,0.015216469764709473,grouped_occlusion,grouped_occlusion,ok +05,vision,1,5,10,"5,6,7,8,9","is chloe , video market","is Chloe, video marketer","3,4,5,6",2.3225000000000002,4.022500000000001,8.972688035370665e-12,4,1.0,0.03512507677078247,0.09098595380783081,grouped_occlusion,grouped_occlusion,ok +05,vision,0,0,5,"1,2,3,4","hi , my name","Hi, my name","0,1,2",1.2625,2.2825000000000006,9.345394556125576e-12,3,1.0,0.02607184648513794,0.10822361707687378,grouped_occlusion,grouped_occlusion,ok +05,vision,3,15,20,15,.,Marketing.,10,4.982500000000001,5.4625,1.7565947322020906e-13,1,1.0,0.007893681526184082,0.017847895622253418,grouped_occlusion,grouped_occlusion,ok +06,text,3,15,20,"15,16,17,18,19","to watch people dance ,","to watch people dance,","11,12,13,14",2.5225000000000004,3.5825000000000005,9.533516620804992e-13,4,1.0,0.0643836259841919,0.11163991689682007,grouped_occlusion,grouped_occlusion,ok +06,text,4,20,25,"20,21,22,23,24",see impressive dance moves then,see impressive dance moves then,"15,16,17,18,19",3.7225000000000006,5.442500000000001,1.003787440114341e-12,5,1.0,0.05716830492019653,0.10976755619049072,grouped_occlusion,grouped_occlusion,ok +06,text,2,10,15,"10,11,12,13,14","that sense , just like","that sense, just like","7,8,9,10",1.6625,2.5025000000000004,6.328522515592245e-13,4,1.0,0.05548006296157837,0.1172025203704834,grouped_occlusion,grouped_occlusion,ok +06,audio,6,30,35,"30,31,32,33,34",out this movie solely for,out this movie solely for,"25,26,27,28,29",6.482500000000001,7.562500000000001,5.6144654493064985e-12,5,1.0,0.00836336612701416,0.047490835189819336,grouped_occlusion,grouped_occlusion,ok +06,audio,2,10,15,"10,11,12,13,14","that sense , just like","that sense, just like","7,8,9,10",1.6625,2.5025000000000004,6.328522515592245e-13,4,1.0,0.006695687770843506,0.0182039737701416,grouped_occlusion,grouped_occlusion,ok +06,audio,3,15,20,"15,16,17,18,19","to watch people dance ,","to watch people dance,","11,12,13,14",2.5225000000000004,3.5825000000000005,9.533516620804992e-13,4,1.0,0.005690395832061768,0.0055931806564331055,grouped_occlusion,grouped_occlusion,ok +06,vision,2,10,15,"10,11,12,13,14","that sense , just like","that sense, just like","7,8,9,10",1.6625,2.5025000000000004,6.328522515592245e-13,4,1.0,0.024024665355682373,0.06122779846191406,grouped_occlusion,grouped_occlusion,ok +06,vision,3,15,20,"15,16,17,18,19","to watch people dance ,","to watch people dance,","11,12,13,14",2.5225000000000004,3.5825000000000005,9.533516620804992e-13,4,1.0,0.022018134593963623,0.061392247676849365,grouped_occlusion,grouped_occlusion,ok +06,vision,4,20,25,"20,21,22,23,24",see impressive dance moves then,see impressive dance moves then,"15,16,17,18,19",3.7225000000000006,5.442500000000001,1.003787440114341e-12,5,1.0,0.02075207233428955,0.05445140600204468,grouped_occlusion,grouped_occlusion,ok +07,text,4,20,25,"20,21,22,23,24",“ enthusiastically discussed the topics,“enthusiastically discussed the topics,"13,14,15,16",4.362500000000001,7.5025,5.132190603623443e-13,4,0.9803921568627451,0.05012655258178711,0.07725489139556885,grouped_occlusion,grouped_occlusion,ok +07,text,5,25,30,"25,26,27,28,29","of growing the hobby ,","of growing the hobby,","17,18,19,20",7.562500000000001,9.1225,6.009222892922947e-13,4,0.9803921568627451,0.03437221050262451,0.05201399326324463,grouped_occlusion,grouped_occlusion,ok +07,text,9,45,50,"45,46,47,48",with ways the leading,with ways the leading,"32,33,34,35",14.3225,15.3825,4.788019704647536e-13,4,0.9803921568627451,0.03220874071121216,0.0367276668548584,grouped_occlusion,grouped_occlusion,ok +07,audio,3,15,20,"15,17,18,19","november issue , attendees","November issue, attendees","9,11,12",3.1025000000000005,4.3425,6.891936173584844e-14,3,0.9803921568627451,0.009746789932250977,0.013783574104309082,grouped_occlusion,grouped_occlusion,ok +07,audio,1,5,10,"5,6,7,8,9",s associate editor michael ba,Linn’s associate editor Michael Baadke,"1,2,3,4,5",0.7224999999999999,2.4025000000000003,6.973944706871624e-12,5,0.9803921568627451,0.008990466594696045,0.007673025131225586,grouped_occlusion,grouped_occlusion,ok +07,audio,2,10,15,"10,11,12,13,14",##ad ##ke reports in our,Baadke reports in our,"5,6,7,8",2.0825000000000005,3.0825000000000005,7.431383019894585e-12,4,0.9803921568627451,0.0072026848793029785,0.008348703384399414,grouped_occlusion,grouped_occlusion,ok +07,vision,7,35,40,"35,36,37,38,39",", and dealers and phil","shows, and dealers and philatelic","25,26,27,28,29",10.522499999999999,12.8825,1.0360054611198636e-12,5,0.9803921568627451,0.004808366298675537,0.029618024826049805,grouped_occlusion,grouped_occlusion,ok +07,vision,3,15,20,"15,17,18,19","november issue , attendees","November issue, attendees","9,11,12",3.1025000000000005,4.3425,6.891936173584844e-14,3,0.9803921568627451,0.0032321810722351074,0.0074433088302612305,grouped_occlusion,grouped_occlusion,ok +07,vision,2,10,15,"10,11,12,13,14",##ad ##ke reports in our,Baadke reports in our,"5,6,7,8",2.0825000000000005,3.0825000000000005,7.431383019894585e-12,4,0.9803921568627451,0.0024552345275878906,0.008127868175506592,grouped_occlusion,grouped_occlusion,ok +08,text,0,0,5,"1,2,3,4",that brings us to,That brings us to,"0,1,2,3",0.4825,1.3425,4.985850672359178e-13,4,1.0,0.11082303524017334,0.11384594440460205,grouped_occlusion,grouped_occlusion,ok +08,text,1,5,10,"5,6,7,8,9","tonight , the universal design","tonight, the Universal Design","4,5,6,7",1.4825,3.1225000000000005,1.5200054500503443e-13,4,1.0,0.1038968563079834,0.21401238441467285,grouped_occlusion,grouped_occlusion,ok +08,text,2,10,15,"10,11",grand challenge,Grand Challenge,"8,9",3.3825000000000003,4.322500000000001,1.194004156129736e-14,2,1.0,0.04137396812438965,0.019734859466552734,grouped_occlusion,grouped_occlusion,ok +08,audio,2,10,15,"10,11",grand challenge,Grand Challenge,"8,9",3.3825000000000003,4.322500000000001,1.194004156129736e-14,2,1.0,0.0033051371574401855,0.022482693195343018,grouped_occlusion,grouped_occlusion,ok +08,audio,1,5,10,"5,6,7,8,9","tonight , the universal design","tonight, the Universal Design","4,5,6,7",1.4825,3.1225000000000005,1.5200054500503443e-13,4,1.0,0.0019735097885131836,0.06727790832519531,grouped_occlusion,grouped_occlusion,ok +08,audio,0,0,5,"1,2,3,4",that brings us to,That brings us to,"0,1,2,3",0.4825,1.3425,4.985850672359178e-13,4,1.0,0.0007883310317993164,0.06953203678131104,grouped_occlusion,grouped_occlusion,ok +08,vision,0,0,5,"1,2,3,4",that brings us to,That brings us to,"0,1,2,3",0.4825,1.3425,4.985850672359178e-13,4,1.0,0.004726111888885498,0.004060029983520508,grouped_occlusion,grouped_occlusion,ok +08,vision,1,5,10,"5,6,7,8,9","tonight , the universal design","tonight, the Universal Design","4,5,6,7",1.4825,3.1225000000000005,1.5200054500503443e-13,4,1.0,0.004242956638336182,0.008825302124023438,grouped_occlusion,grouped_occlusion,ok +08,vision,2,10,15,"10,11",grand challenge,Grand Challenge,"8,9",3.3825000000000003,4.322500000000001,1.194004156129736e-14,2,1.0,0.002269923686981201,0.007381081581115723,grouped_occlusion,grouped_occlusion,ok +09,text,1,5,10,"5,6,7,8,9",i did not like this,I did not like this,"1,2,3,4,5",1.1824999999999999,2.2025000000000006,1.8337023088832634e-11,5,1.0,0.05766040086746216,0.3939073085784912,grouped_occlusion,grouped_occlusion,ok +09,text,2,10,15,"10,11,12,13,14","movie at all , i","movie at all, I","6,7,8,9",2.2825000000000006,3.1025000000000005,1.7216472685121577e-10,4,1.0,0.05135905742645264,0.33904457092285156,grouped_occlusion,grouped_occlusion,ok +09,text,0,0,5,"1,2,3,4",( uh ##h ),(uhh),0,0.7825,1.1425,6.173012658529708e-13,1,1.0,0.04096817970275879,0.3692760467529297,grouped_occlusion,grouped_occlusion,ok +09,audio,0,0,5,"1,2,3,4",( uh ##h ),(uhh),0,0.7825,1.1425,6.173012658529708e-13,1,1.0,0.004597127437591553,0.05938518047332764,grouped_occlusion,grouped_occlusion,ok +09,audio,1,5,10,"5,6,7,8,9",i did not like this,I did not like this,"1,2,3,4,5",1.1824999999999999,2.2025000000000006,1.8337023088832634e-11,5,1.0,0.0028375983238220215,0.0619351863861084,grouped_occlusion,grouped_occlusion,ok +09,audio,3,15,20,"15,16,17,18",would not recommend it,would not recommend it,"10,11,12,13",3.2025000000000006,4.942500000000001,2.9955450915683833e-12,4,1.0,0.0018830299377441406,0.029448747634887695,grouped_occlusion,grouped_occlusion,ok +09,vision,2,10,15,"10,11,12,13,14","movie at all , i","movie at all, I","6,7,8,9",2.2825000000000006,3.1025000000000005,1.7216472685121577e-10,4,1.0,0.0035586953163146973,0.010941743850708008,grouped_occlusion,grouped_occlusion,ok +09,vision,3,15,20,"15,16,17,18",would not recommend it,would not recommend it,"10,11,12,13",3.2025000000000006,4.942500000000001,2.9955450915683833e-12,4,1.0,0.002703547477722168,0.007805347442626953,grouped_occlusion,grouped_occlusion,ok +09,vision,0,0,5,"1,2,3,4",( uh ##h ),(uhh),0,0.7825,1.1425,6.173012658529708e-13,1,1.0,0.001896202564239502,0.0043097734451293945,grouped_occlusion,grouped_occlusion,ok +10,text,5,25,30,"25,26,27,28,29",but this one was pretty,but this one was pretty,"19,20,21,22,23",6.742500000000001,9.1625,3.4013181527343934e-12,5,1.0,0.10162562131881714,0.3853045701980591,grouped_occlusion,grouped_occlusion,ok +10,text,2,10,15,"10,11,12,13,14",like to see fluffy chick,like to see fluffy chick,"7,8,9,10,11",2.4825000000000004,3.8425000000000002,5.712801915197975e-12,5,1.0,0.07766342163085938,0.3524249792098999,grouped_occlusion,grouped_occlusion,ok +10,text,3,15,20,"15,16,17,18,19",flick ##s sometimes so i,flicks sometimes so I'm,"12,13,14,15",3.9025000000000003,5.242500000000001,9.396792278478092e-09,4,1.0,0.07552039623260498,0.3311324715614319,grouped_occlusion,grouped_occlusion,ok +10,audio,5,25,30,"25,26,27,28,29",but this one was pretty,but this one was pretty,"19,20,21,22,23",6.742500000000001,9.1625,3.4013181527343934e-12,5,1.0,0.010288834571838379,0.08406132459640503,grouped_occlusion,grouped_occlusion,ok +10,audio,1,5,10,"5,6,7,8,9",you know i really do,you know I really do,"2,3,4,5,6",1.3425,2.4225000000000003,9.51819295384607e-13,5,1.0,0.008134961128234863,0.08679646253585815,grouped_occlusion,grouped_occlusion,ok +10,audio,2,10,15,"10,11,12,13,14",like to see fluffy chick,like to see fluffy chick,"7,8,9,10,11",2.4825000000000004,3.8425000000000002,5.712801915197975e-12,5,1.0,0.007106482982635498,0.06629914045333862,grouped_occlusion,grouped_occlusion,ok +10,vision,3,15,20,"15,16,17,18,19",flick ##s sometimes so i,flicks sometimes so I'm,"12,13,14,15",3.9025000000000003,5.242500000000001,9.396792278478092e-09,4,1.0,0.005765736103057861,0.02699226140975952,grouped_occlusion,grouped_occlusion,ok +10,vision,5,25,30,"25,26,27,28,29",but this one was pretty,but this one was pretty,"19,20,21,22,23",6.742500000000001,9.1625,3.4013181527343934e-12,5,1.0,0.005424618721008301,0.004094421863555908,grouped_occlusion,grouped_occlusion,ok +10,vision,4,20,25,"20,21,22,23,24",' m not against that,I'm not against that,"15,16,17,18",5.1825,6.6225000000000005,9.396402256608505e-09,4,1.0,0.0023061037063598633,0.0014348030090332031,grouped_occlusion,grouped_occlusion,ok +11,text,0,0,5,"1,2,3,4",i would be ashamed,I would be ashamed,"0,1,2,3",0.5225,1.3825,2.4343081894167942e-11,4,1.0,0.17121726274490356,0.3522495925426483,grouped_occlusion,grouped_occlusion,ok +11,text,1,5,10,"5,6,7,8,9",to have made this film,to have made this film,"4,5,6,7,8",3.2225000000000006,4.202500000000001,1.8143959640334076e-12,5,1.0,0.050980210304260254,0.09193223714828491,grouped_occlusion,grouped_occlusion,ok +11,text,2,10,15,"10,11,12,13,14",if i was a director,if I was a director,"9,10,11,12,13",4.442500000000001,5.702500000000001,1.195583991709281e-10,5,1.0,0.03379368782043457,0.03823119401931763,grouped_occlusion,grouped_occlusion,ok +11,audio,2,10,15,"10,11,12,13,14",if i was a director,if I was a director,"9,10,11,12,13",4.442500000000001,5.702500000000001,1.195583991709281e-10,5,1.0,0.07368618249893188,0.19450706243515015,grouped_occlusion,grouped_occlusion,ok +11,audio,1,5,10,"5,6,7,8,9",to have made this film,to have made this film,"4,5,6,7,8",3.2225000000000006,4.202500000000001,1.8143959640334076e-12,5,1.0,0.026468276977539062,0.03296065330505371,grouped_occlusion,grouped_occlusion,ok +11,audio,0,0,5,"1,2,3,4",i would be ashamed,I would be ashamed,"0,1,2,3",0.5225,1.3825,2.4343081894167942e-11,4,1.0,0.006807446479797363,0.017702996730804443,grouped_occlusion,grouped_occlusion,ok +11,vision,0,0,5,"1,2,3,4",i would be ashamed,I would be ashamed,"0,1,2,3",0.5225,1.3825,2.4343081894167942e-11,4,1.0,0.019326627254486084,0.04640209674835205,grouped_occlusion,grouped_occlusion,ok +11,vision,1,5,10,"5,6,7,8,9",to have made this film,to have made this film,"4,5,6,7,8",3.2225000000000006,4.202500000000001,1.8143959640334076e-12,5,1.0,0.006760776042938232,0.037277281284332275,grouped_occlusion,grouped_occlusion,ok +11,vision,2,10,15,"10,11,12,13,14",if i was a director,if I was a director,"9,10,11,12,13",4.442500000000001,5.702500000000001,1.195583991709281e-10,5,1.0,0.0025706887245178223,0.00287705659866333,grouped_occlusion,grouped_occlusion,ok +12,text,6,30,35,"30,31,32,33,34",let their credit sa ##g,"let their credit sag,","25,26,27,28",9.2625,10.7425,2.0235579326793276e-13,4,1.0,0.07106262445449829,0.21757328510284424,grouped_occlusion,grouped_occlusion,ok +12,text,3,15,20,"15,16,17,18,19","fault of their own ,","fault of their own,","12,13,14,15",5.5825000000000005,6.3825,1.8391183721102136e-13,4,1.0,0.060820698738098145,0.17711377143859863,grouped_occlusion,grouped_occlusion,ok +12,text,4,20,25,"20,21,22,23,24",or perhaps by fault of,or perhaps by fault of,"16,17,18,19,20",6.4225,8.1025,1.301446420968425e-12,5,1.0,0.05820423364639282,0.14634323120117188,grouped_occlusion,grouped_occlusion,ok +12,audio,5,25,30,"25,26,27,28,29","their own , they have","their own, they have","21,22,23,24",8.1425,9.2225,2.640871901066012e-13,4,1.0,0.021765828132629395,0.05425441265106201,grouped_occlusion,grouped_occlusion,ok +12,audio,1,5,10,"5,6,7,8,9",individual previously had good credit,"individual previously had good credit,","3,4,5,6,7",1.2625,3.4425000000000003,1.981996783456316e-13,5,1.0,0.020217180252075195,0.01235973834991455,grouped_occlusion,grouped_occlusion,ok +12,audio,2,10,15,"10,11,12,13,14",", but usually by no","credit, but usually by no","7,8,9,10,11",3.0825000000000005,5.4625,5.096125070013053e-13,5,1.0,0.01965177059173584,0.05803334712982178,grouped_occlusion,grouped_occlusion,ok +12,vision,0,0,5,"1,2,3,4","or worse , an","Or worse, an","0,1,2",0.5824999999999999,1.2425,1.2646860654133281e-13,3,1.0,0.00710904598236084,0.015213608741760254,grouped_occlusion,grouped_occlusion,ok +12,vision,1,5,10,"5,6,7,8,9",individual previously had good credit,"individual previously had good credit,","3,4,5,6,7",1.2625,3.4425000000000003,1.981996783456316e-13,5,1.0,0.0066879987716674805,0.025673270225524902,grouped_occlusion,grouped_occlusion,ok +12,vision,2,10,15,"10,11,12,13,14",", but usually by no","credit, but usually by no","7,8,9,10,11",3.0825000000000005,5.4625,5.096125070013053e-13,5,1.0,0.005474686622619629,0.0271909236907959,grouped_occlusion,grouped_occlusion,ok +13,text,1,5,10,"5,6,7,8,9",could take a set of,could take a set of,"3,4,5,6,7",0.9624999999999999,1.8225,1.1293682043937327e-10,5,1.0,0.03829997777938843,0.08469116687774658,grouped_occlusion,grouped_occlusion,ok +13,text,0,0,5,"1,2,3,4","for example , i","For example, I","0,1,2",0.0025000000000000005,0.9025,3.8531617620664426e-12,3,1.0,0.03210568428039551,0.017864346504211426,grouped_occlusion,grouped_occlusion,ok +13,text,4,20,25,"20,21,22,23,24",relationship between any two of,relationship between any two of,"17,18,19,20,21",5.0825000000000005,6.782500000000001,7.070703467858767e-12,5,1.0,0.01825752854347229,0.03968304395675659,grouped_occlusion,grouped_occlusion,ok +13,audio,4,20,25,"20,21,22,23,24",relationship between any two of,relationship between any two of,"17,18,19,20,21",5.0825000000000005,6.782500000000001,7.070703467858767e-12,5,1.0,0.01745942234992981,0.03462590277194977,grouped_occlusion,grouped_occlusion,ok +13,audio,2,10,15,"10,11,12,13,14",data and from that data,"data and from that data,","8,9,10,11,12",1.8425,4.2625,9.633590059405473e-12,5,1.0,0.01131179928779602,0.007808178663253784,grouped_occlusion,grouped_occlusion,ok +13,audio,0,0,5,"1,2,3,4","for example , i","For example, I","0,1,2",0.0025000000000000005,0.9025,3.8531617620664426e-12,3,1.0,0.006338447332382202,0.00720486044883728,grouped_occlusion,grouped_occlusion,ok +14,text,2,10,15,"10,11,12,13,14",and vet ##tes ##e limited,and Vettese Limited,"6,7,8",2.0025000000000004,2.8425000000000002,4.791347081170585e-13,3,1.0,0.04705315828323364,0.09162890911102295,grouped_occlusion,grouped_occlusion,ok +14,text,4,20,25,"20,21,22,23,24",of uniform final examination (,of Uniform Final Examination (UFE),"14,15,16,17,18",4.8425,6.9225,5.581173689339982e-13,5,1.0,0.040479302406311035,0.05692708492279053,grouped_occlusion,grouped_occlusion,ok +14,text,0,0,5,"1,2,3,4",he is the co,He is the co-founder,"0,1,2,3",0.5025,1.3225,1.8809520903830734e-13,4,1.0,0.0321732759475708,0.08200758695602417,grouped_occlusion,grouped_occlusion,ok +14,audio,5,25,30,"25,26,27,28,29",u ##fe ) courses at,(UFE) courses at,"18,19,20",6.742500000000001,8.362499999999999,1.0878076875022951e-13,3,1.0,0.02120727300643921,0.04397076368331909,grouped_occlusion,grouped_occlusion,ok +14,audio,2,10,15,"10,11,12,13,14",and vet ##tes ##e limited,and Vettese Limited,"6,7,8",2.0025000000000004,2.8425000000000002,4.791347081170585e-13,3,1.0,0.020826101303100586,0.029214560985565186,grouped_occlusion,grouped_occlusion,ok +14,audio,1,5,10,"5,6,7,8,9",- founder of ross ##en,co-founder of Rossen,"3,4,5",0.7825,1.9224999999999999,6.402270448983464e-13,3,1.0,0.017303526401519775,0.04407292604446411,grouped_occlusion,grouped_occlusion,ok +14,vision,6,30,35,"30,31,32,33,34",toronto ' s york university,Toronto's York University.,"21,22,23",8.3825,9.6625,3.617856135575942e-12,3,1.0,0.03959810733795166,0.06995487213134766,grouped_occlusion,grouped_occlusion,ok +14,vision,5,25,30,"25,26,27,28,29",u ##fe ) courses at,(UFE) courses at,"18,19,20",6.742500000000001,8.362499999999999,1.0878076875022951e-13,3,1.0,0.03660660982131958,0.030756652355194092,grouped_occlusion,grouped_occlusion,ok +14,vision,4,20,25,"20,21,22,23,24",of uniform final examination (,of Uniform Final Examination (UFE),"14,15,16,17,18",4.8425,6.9225,5.581173689339982e-13,5,1.0,0.03492516279220581,0.029532790184020996,grouped_occlusion,grouped_occlusion,ok +15,text,3,15,20,"15,16,17,18,19",believed in its power to,believed in its power to,"10,11,12,13,14",4.602500000000001,6.3825,1.3666649254190005e-11,5,1.0,0.03316134214401245,0.08834660053253174,grouped_occlusion,grouped_occlusion,ok +15,text,2,10,15,"10,11,12,13,14",##iti ##ze education because they,prioritize education because they,"6,7,8,9",2.4225000000000003,4.522500000000001,5.053813400666634e-14,4,1.0,0.022731482982635498,0.05859649181365967,grouped_occlusion,grouped_occlusion,ok +15,text,1,5,10,"5,6,7,8,9","poverty , the family prior","poverty, the family prioritize","3,4,5,6",1.3025,2.9825000000000004,6.197984013383666e-14,4,1.0,0.013731598854064941,0.04108023643493652,grouped_occlusion,grouped_occlusion,ok +15,audio,0,0,5,"1,2,3,4","however , despite their","However, despite their","0,1,2",0.10250000000000001,1.2825,3.8566196054618246e-14,3,1.0,0.008936703205108643,0.06280517578125,grouped_occlusion,grouped_occlusion,ok +15,audio,1,5,10,"5,6,7,8,9","poverty , the family prior","poverty, the family prioritize","3,4,5,6",1.3025,2.9825000000000004,6.197984013383666e-14,4,1.0,0.007372438907623291,0.04567360877990723,grouped_occlusion,grouped_occlusion,ok +15,audio,2,10,15,"10,11,12,13,14",##iti ##ze education because they,prioritize education because they,"6,7,8,9",2.4225000000000003,4.522500000000001,5.053813400666634e-14,4,1.0,0.004992425441741943,0.07340216636657715,grouped_occlusion,grouped_occlusion,ok +15,vision,0,0,5,"1,2,3,4","however , despite their","However, despite their","0,1,2",0.10250000000000001,1.2825,3.8566196054618246e-14,3,1.0,0.006294310092926025,0.04669678211212158,grouped_occlusion,grouped_occlusion,ok +15,vision,2,10,15,"10,11,12,13,14",##iti ##ze education because they,prioritize education because they,"6,7,8,9",2.4225000000000003,4.522500000000001,5.053813400666634e-14,4,1.0,0.003808140754699707,0.05000507831573486,grouped_occlusion,grouped_occlusion,ok +15,vision,1,5,10,"5,6,7,8,9","poverty , the family prior","poverty, the family prioritize","3,4,5,6",1.3025,2.9825000000000004,6.197984013383666e-14,4,1.0,0.0037418007850646973,0.05190694332122803,grouped_occlusion,grouped_occlusion,ok +16,text,1,5,10,"5,6,7,8,9","terrible , this is a","terrible, this is a","2,3,4,5",0.8025,1.9224999999999999,6.726967737989785e-13,4,1.0,0.17667824029922485,0.8273677825927734,grouped_occlusion,grouped_occlusion,ok +16,text,0,0,5,"1,2,3,4",it ' s a,It's a,"0,1",0.4625,0.7224999999999999,5.886124124070357e-10,2,1.0,0.08441895246505737,0.6347545385360718,grouped_occlusion,grouped_occlusion,ok +16,text,2,10,15,"10,11",terrible movie,terrible movie,"6,7",2.0625000000000004,2.8425000000000002,3.447706729088321e-13,2,1.0,0.012092411518096924,0.09678518772125244,grouped_occlusion,grouped_occlusion,ok +16,audio,1,5,10,"5,6,7,8,9","terrible , this is a","terrible, this is a","2,3,4,5",0.8025,1.9224999999999999,6.726967737989785e-13,4,1.0,0.005522668361663818,0.07541024684906006,grouped_occlusion,grouped_occlusion,ok +16,audio,0,0,5,"1,2,3,4",it ' s a,It's a,"0,1",0.4625,0.7224999999999999,5.886124124070357e-10,2,1.0,0.004132866859436035,0.0680011510848999,grouped_occlusion,grouped_occlusion,ok +16,audio,2,10,15,"10,11",terrible movie,terrible movie,"6,7",2.0625000000000004,2.8425000000000002,3.447706729088321e-13,2,1.0,0.0015830397605895996,0.02998960018157959,grouped_occlusion,grouped_occlusion,ok +16,vision,1,5,10,"5,6,7,8,9","terrible , this is a","terrible, this is a","2,3,4,5",0.8025,1.9224999999999999,6.726967737989785e-13,4,1.0,0.0030817389488220215,0.010976672172546387,grouped_occlusion,grouped_occlusion,ok +16,vision,0,0,5,"1,2,3,4",it ' s a,It's a,"0,1",0.4625,0.7224999999999999,5.886124124070357e-10,2,1.0,0.0018818974494934082,0.008708953857421875,grouped_occlusion,grouped_occlusion,ok +16,vision,2,10,15,"10,11",terrible movie,terrible movie,"6,7",2.0625000000000004,2.8425000000000002,3.447706729088321e-13,2,1.0,0.0007146596908569336,0.0037450790405273438,grouped_occlusion,grouped_occlusion,ok +17,text,3,15,20,"15,16,17,18,19",make people think you turned,make people think you turned,"13,14,15,16,17",6.982500000000001,8.202499999999999,5.0308304651950566e-14,5,1.0,0.03196984529495239,0.10473096370697021,grouped_occlusion,grouped_occlusion,ok +17,text,4,20,25,"20,21,22,23,24",into a design guru .,into a design guru.,"18,19,20,21",8.3025,9.5425,6.876211439212395e-12,4,1.0,0.025771260261535645,0.09248185157775879,grouped_occlusion,grouped_occlusion,ok +17,text,2,10,15,"10,11,12,13,14","simple , easy and will","simple, easy and will","9,10,11,12",4.8025,6.942500000000001,8.080162655757211e-13,4,1.0,0.02379608154296875,0.15895986557006836,grouped_occlusion,grouped_occlusion,ok +17,audio,0,0,5,"1,2,3,4",applying these four design,Applying these four design,"0,1,2,3",0.0025000000000000005,2.1225000000000005,3.907075057276878e-13,4,1.0,0.001038670539855957,0.02217411994934082,grouped_occlusion,grouped_occlusion,ok +17,audio,1,5,10,"5,6,7,8,9",concepts to your presentations is,concepts to your presentations is,"4,5,6,7,8",2.1825000000000006,4.4225,1.3654575542430864e-13,5,1.0,0.0008327364921569824,0.029980182647705078,grouped_occlusion,grouped_occlusion,ok +17,audio,4,20,25,"20,21,22,23,24",into a design guru .,into a design guru.,"18,19,20,21",8.3025,9.5425,6.876211439212395e-12,4,1.0,0.0007992982864379883,0.01889348030090332,grouped_occlusion,grouped_occlusion,ok +17,vision,3,15,20,"15,16,17,18,19",make people think you turned,make people think you turned,"13,14,15,16,17",6.982500000000001,8.202499999999999,5.0308304651950566e-14,5,1.0,0.003390192985534668,0.05066335201263428,grouped_occlusion,grouped_occlusion,ok +17,vision,2,10,15,"10,11,12,13,14","simple , easy and will","simple, easy and will","9,10,11,12",4.8025,6.942500000000001,8.080162655757211e-13,4,1.0,0.0014863014221191406,0.04471385478973389,grouped_occlusion,grouped_occlusion,ok +17,vision,4,20,25,"20,21,22,23,24",into a design guru .,into a design guru.,"18,19,20,21",8.3025,9.5425,6.876211439212395e-12,4,1.0,0.0008729696273803711,0.02221369743347168,grouped_occlusion,grouped_occlusion,ok +18,text,0,0,5,"1,2,3,4",- and in denmark,-And in Denmark,"0,1,2",0.5625,1.2025,7.339091369947377e-14,3,0.9565217391304348,0.040209442377090454,0.07814651727676392,grouped_occlusion,grouped_occlusion,ok +18,text,1,5,10,"6,7,8,9",the first baltic cod,the first Baltic Cod,"4,5,6,7",1.2825,2.7225000000000006,1.9238576151557236e-12,4,0.9565217391304348,0.03645741939544678,0.07523351907730103,grouped_occlusion,grouped_occlusion,ok +18,text,7,35,40,"35,36,37,38,39",fact that the fishery has,fact that the fishery has,"27,28,29,30,31",11.4625,12.6625,3.051134360523701e-14,5,0.9565217391304348,0.02193853259086609,0.09061205387115479,grouped_occlusion,grouped_occlusion,ok +18,audio,1,5,10,"6,7,8,9",the first baltic cod,the first Baltic Cod,"4,5,6,7",1.2825,2.7225000000000006,1.9238576151557236e-12,4,0.9565217391304348,0.011634111404418945,0.011849582195281982,grouped_occlusion,grouped_occlusion,ok +18,audio,3,15,20,"15,16,17,18,19","certified - meanwhile , the","certified -Meanwhile, the","13,14,15",4.522500000000001,7.242500000000001,2.0090313043947478e-13,3,0.9565217391304348,0.009550690650939941,0.00717240571975708,grouped_occlusion,grouped_occlusion,ok +18,audio,4,20,25,"20,21,22,23,24",fae ##ro ##ese mack ##ere,Faeroese Mackerel,"16,17",7.2625,8.1625,7.552127939731886e-13,2,0.9565217391304348,0.0075453221797943115,0.0038139820098876953,grouped_occlusion,grouped_occlusion,ok +18,vision,3,15,20,"15,16,17,18,19","certified - meanwhile , the","certified -Meanwhile, the","13,14,15",4.522500000000001,7.242500000000001,2.0090313043947478e-13,3,0.9565217391304348,0.015370607376098633,0.00964266061782837,grouped_occlusion,grouped_occlusion,ok +18,vision,4,20,25,"20,21,22,23,24",fae ##ro ##ese mack ##ere,Faeroese Mackerel,"16,17",7.2625,8.1625,7.552127939731886e-13,2,0.9565217391304348,0.01368647813796997,0.025389909744262695,grouped_occlusion,grouped_occlusion,ok +18,vision,7,35,40,"35,36,37,38,39",fact that the fishery has,fact that the fishery has,"27,28,29,30,31",11.4625,12.6625,3.051134360523701e-14,5,0.9565217391304348,0.012452512979507446,0.0016064047813415527,grouped_occlusion,grouped_occlusion,ok +19,text,5,25,30,"25,26,27,28,29",and jump all over imperfect,"and jump all over imperfections,","21,22,23,24,25",8.9225,10.7025,2.4467552179386886e-13,5,1.0,0.14123356342315674,0.26041585206985474,grouped_occlusion,grouped_occlusion,ok +19,text,2,10,15,"10,11,12,13,14","up to mistakes , and","up to mistakes, and","8,9,10,11",3.6025000000000005,5.2625,2.756182849760793e-14,4,1.0,0.1259310245513916,0.27155396342277527,grouped_occlusion,grouped_occlusion,ok +19,text,1,5,10,"5,6,7,8,9",##giving brands when they own,forgiving brands when they own,"3,4,5,6,7",1.8025,3.4025000000000003,4.482843688564828e-15,5,1.0,0.10481253266334534,0.20342162251472473,grouped_occlusion,grouped_occlusion,ok +19,audio,5,25,30,"25,26,27,28,29",and jump all over imperfect,"and jump all over imperfections,","21,22,23,24,25",8.9225,10.7025,2.4467552179386886e-13,5,1.0,0.0375896692276001,0.0982179045677185,grouped_occlusion,grouped_occlusion,ok +19,audio,7,35,40,"35,36,37,38,39","most part , people understand","most part, people understand","29,30,31,32",11.8425,13.5625,6.956549052765396e-14,4,1.0,0.03040042519569397,0.054060935974121094,grouped_occlusion,grouped_occlusion,ok +19,audio,4,20,25,"20,21,22,23,24",there love to point fingers,there love to point 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a/deep_learning/Q3/outputs/explanation_selection/q3_deletion_curves.csv b/deep_learning/Q3/outputs/explanation_selection/q3_deletion_curves.csv new file mode 100644 index 0000000..858619c --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/q3_deletion_curves.csv @@ -0,0 +1,37 @@ +method,target,fraction_removed,mean_signed_drop,mean_absolute_change,n_valid +integrated_gradients,predicted_class_probability,0.1,0.1318911910057068,0.1598358154296875,728 +integrated_gradients,predicted_class_probability,0.2,0.2255048155784607,0.2508196234703064,728 +integrated_gradients,predicted_class_probability,0.3,0.2575814723968506,0.2828781306743622,728 +integrated_gradients,predicted_class_probability,0.4,0.2559848725795746,0.28699347376823425,728 +integrated_gradients,predicted_class_probability,0.5,0.25572627782821655,0.29257047176361084,728 +integrated_gradients,predicted_class_probability,-0.3,0.039929818361997604,0.039929818361997604,728 +integrated_gradients,intensity,0.1,-0.04874260723590851,0.37119486927986145,728 +integrated_gradients,intensity,0.2,-0.03821782395243645,0.5629051327705383,728 +integrated_gradients,intensity,0.3,-0.05994963273406029,0.6319279074668884,728 +integrated_gradients,intensity,0.4,-0.13912875950336456,0.653501570224762,728 +integrated_gradients,intensity,0.5,-0.20183886587619781,0.6705618500709534,728 +integrated_gradients,intensity,-0.3,0.1144869476556778,0.1144869476556778,728 +grouped_occlusion,predicted_class_probability,0.1,0.18875761330127716,0.20476296544075012,728 +grouped_occlusion,predicted_class_probability,0.2,0.2506031095981598,0.2624903619289398,728 +grouped_occlusion,predicted_class_probability,0.3,0.26046913862228394,0.27427902817726135,728 +grouped_occlusion,predicted_class_probability,0.4,0.2578567862510681,0.27872106432914734,728 +grouped_occlusion,predicted_class_probability,0.5,0.2540586292743683,0.2836124897003174,728 +grouped_occlusion,predicted_class_probability,-0.3,0.024163084104657173,0.024163084104657173,728 +grouped_occlusion,intensity,0.1,-0.056311462074518204,0.4420826733112335,728 +grouped_occlusion,intensity,0.2,-0.08403468877077103,0.5550013184547424,728 +grouped_occlusion,intensity,0.3,-0.13223451375961304,0.5881773829460144,728 +grouped_occlusion,intensity,0.4,-0.20568883419036865,0.6224051117897034,728 +grouped_occlusion,intensity,0.5,-0.2683386206626892,0.6487718820571899,728 +grouped_occlusion,intensity,-0.3,0.0703125074505806,0.0703125074505806,728 +random,predicted_class_probability,0.1,0.017329903319478035,0.03110884316265583,728 +random,predicted_class_probability,0.2,0.0376301035284996,0.05323585495352745,728 +random,predicted_class_probability,0.3,0.057446520775556564,0.07715633511543274,728 +random,predicted_class_probability,0.4,0.07688438147306442,0.09651552885770798,728 +random,predicted_class_probability,0.5,0.0910244807600975,0.11337775737047195,728 +random,predicted_class_probability,-0.3,0.16268795728683472,0.16268795728683472,728 +random,intensity,0.1,-0.01041108462959528,0.07401501387357712,728 +random,intensity,0.2,-0.012165687046945095,0.12958525121212006,728 +random,intensity,0.3,-0.031106332316994667,0.18503554165363312,728 +random,intensity,0.4,-0.03734554722905159,0.2288973033428192,728 +random,intensity,0.5,-0.04232712835073471,0.2670633792877197,728 +random,intensity,-0.3,0.37735068798065186,0.37735068798065186,728 diff --git a/deep_learning/Q3/outputs/explanation_selection/q3_explainer_selection.json b/deep_learning/Q3/outputs/explanation_selection/q3_explainer_selection.json new file mode 100644 index 0000000..f1aa63f --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/q3_explainer_selection.json @@ -0,0 +1,18 @@ +{ + "q2_predictor": "concat", + "classification_explainer": "grouped_occlusion", + "intensity_explainer_primary": "grouped_occlusion", + "intensity_explainer_crosscheck": "integrated_gradients", + "intensity_tradeoff": { + "integrated_gradients_abs_change_at_30": 0.6319279074668884, + "integrated_gradients_sufficiency_error_top_30": 0.1144869476556778, + "grouped_occlusion_abs_change_at_30": 0.5881773829460144, + "grouped_occlusion_sufficiency_error_top_30": 0.0703125074505806 + }, + "selection_basis": "For polarity, grouped occlusion has the larger signed target-probability drop, lower sufficiency error, higher deletion AUC, and lower runtime. For intensity, IG causes a larger deletion change but grouped occlusion has lower top-evidence sufficiency error; grouped occlusion is used for displayed segments and IG is retained as a cross-check. No combined explanation score is used.", + "valid_samples": 728, + "stability_samples": 120, + "integrated_gradients_runtime_seconds": 4.718544340998051, + "grouped_occlusion_runtime_seconds": 0.40209812500688713, + "ctc_time_map_for_attachment4": "Q1 hard CTC Viterbi word boundaries from source video audio; not human alignment ground truth" +} \ No newline at end of file diff --git a/deep_learning/Q3/outputs/explanation_selection/q3_explanation_faithfulness.png b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_faithfulness.png new file mode 100644 index 0000000..d4b2998 Binary files /dev/null and b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_faithfulness.png differ diff --git a/deep_learning/Q3/outputs/explanation_selection/q3_explanation_method_summary.csv b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_method_summary.csv new file mode 100644 index 0000000..5e7c186 --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_method_summary.csv @@ -0,0 +1,7 @@ +method,target,comprehensiveness_signed_drop_at_30,absolute_prediction_change_at_30,sufficiency_abs_error_top_30,deletion_drop_auc_10_to_50,runtime_seconds,spearman_rank_correlation,top_30_percent_jaccard,n_samples,input_noise_sigma +integrated_gradients,predicted_class_probability,0.2575814723968506,0.2828781306743622,0.039929818361997604,0.09328798949718475,4.718544340998051,0.9998964070157896,0.9966666666666666,120,0.02 +integrated_gradients,intensity,-0.05994963273406029,0.6319279074668884,0.1144869476556778,-0.03625869527459145,4.718544340998051,0.9999221890615111,0.9983333333333333,120,0.02 +grouped_occlusion,predicted_class_probability,0.26046913862228394,0.27427902817726135,0.024163084104657173,0.09903371557593346,0.40209812500688713,0.9993435630984816,0.99,120,0.02 +grouped_occlusion,intensity,-0.13223451375961304,0.5881773829460144,0.0703125074505806,-0.05842830780893564,0.40209812500688713,0.9994847562909268,0.9866666666666667,120,0.02 +random,predicted_class_probability,0.057446520775556564,0.07715633511543274,0.16268795728683472,0.022613819781690837,0.40209812500688713,nan,nan,, +random,intensity,-0.031106332316994667,0.18503554165363312,0.37735068798065186,-0.010698667308315635,0.40209812500688713,nan,nan,, diff --git a/deep_learning/Q3/outputs/explanation_selection/q3_explanation_stability.csv b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_stability.csv new file mode 100644 index 0000000..0bd2686 --- /dev/null +++ b/deep_learning/Q3/outputs/explanation_selection/q3_explanation_stability.csv @@ -0,0 +1,5 @@ +method,target,spearman_rank_correlation,top_30_percent_jaccard,n_samples,input_noise_sigma +integrated_gradients,predicted_class_probability,0.9998964070157896,0.9966666666666666,120,0.02 +grouped_occlusion,predicted_class_probability,0.9993435630984816,0.99,120,0.02 +integrated_gradients,intensity,0.9999221890615111,0.9983333333333333,120,0.02 +grouped_occlusion,intensity,0.9994847562909268,0.9866666666666667,120,0.02 diff --git a/deep_learning/Q3/q3/__init__.py b/deep_learning/Q3/q3/__init__.py new file mode 100644 index 0000000..db72d13 --- /dev/null +++ b/deep_learning/Q3/q3/__init__.py @@ -0,0 +1 @@ +"""Q3 interpretation and evidence-localization experiments.""" diff --git a/deep_learning/Q3/q3/ctc_time.py b/deep_learning/Q3/q3/ctc_time.py new file mode 100644 index 0000000..79cf83d --- /dev/null +++ b/deep_learning/Q3/q3/ctc_time.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +import re +import subprocess +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from transformers import AutoModelForCTC, AutoTokenizer + + +SAMPLE_RATE = 16_000 +MODEL_ID = "facebook/wav2vec2-base-960h" + + +@dataclass +class WordInterval: + word: str + start_s: float + end_s: float + quality: float + valid: bool + + +def decode_audio(video_path: Path) -> np.ndarray: + result = subprocess.run( + [ + "ffmpeg", "-v", "error", "-i", str(video_path), "-map", "0:a:0", + "-ac", "1", "-ar", str(SAMPLE_RATE), "-f", "f32le", "pipe:1", + ], + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + waveform = np.frombuffer(result.stdout, dtype=" tuple[list[int], list[list[int]]]: + vocab = tokenizer.get_vocab() + delimiter = int(tokenizer.convert_tokens_to_ids(tokenizer.word_delimiter_token or "|")) + unknown = int(tokenizer.unk_token_id) + targets: list[int] = [] + per_word: list[list[int]] = [[] for _ in words] + for word_index, raw_word in enumerate(words): + if word_index: + targets.append(delimiter) + normalized = re.sub(r"[^a-z']", "", raw_word.lower()) + for character in normalized: + per_word[word_index].append(len(targets)) + targets.append(int(vocab.get(character, unknown))) + return targets, per_word + + +def _viterbi(log_probs: np.ndarray, targets: list[int], blank: int) -> np.ndarray | None: + if not targets or log_probs.ndim != 2: + return None + states = np.full(2 * len(targets) + 1, blank, dtype=np.int64) + states[1::2] = np.asarray(targets, dtype=np.int64) + frames, count = log_probs.shape[0], len(states) + if frames == 0 or frames < len(targets): + return None + previous = np.full(count, -np.inf, dtype=np.float64) + previous[0] = float(log_probs[0, blank]) + previous[1] = float(log_probs[0, states[1]]) + back = np.zeros((frames, count), dtype=np.uint8) + skip = np.zeros(count, dtype=bool) + if count > 2: + skip[2:] = (states[2:] != blank) & (states[2:] != states[:-2]) + for frame in range(1, frames): + stay = previous + one = np.full(count, -np.inf, dtype=np.float64) + one[1:] = previous[:-1] + two = np.full(count, -np.inf, dtype=np.float64) + if skip.any(): + two[skip] = previous[np.flatnonzero(skip) - 2] + candidates = np.stack((stay, one, two), axis=0) + choice = candidates.argmax(axis=0).astype(np.uint8) + previous = candidates[choice, np.arange(count)] + log_probs[frame, states] + back[frame] = choice + state = count - 1 if previous[-1] >= previous[-2] else count - 2 + path = np.empty(frames, dtype=np.int32) + path[-1] = state + for frame in range(frames - 1, 0, -1): + state -= int(back[frame, state]) + path[frame - 1] = state + return path + + +def model_time_constants(model) -> tuple[float, float]: + config = model.config + stride = int(np.prod(config.conv_stride)) + receptive = 1 + jump = 1 + for kernel, local_stride in zip(config.conv_kernel, config.conv_stride): + receptive += (int(kernel) - 1) * jump + jump *= int(local_stride) + return stride / SAMPLE_RATE, receptive / (2 * SAMPLE_RATE) + + +def align_words( + waveform: np.ndarray, + words: list[str], + tokenizer, + model, + device: torch.device, +) -> list[WordInterval]: + targets, word_targets = _ctc_targets(words, tokenizer) + blank = int(tokenizer.pad_token_id) + if not targets or not len(waveform): + return [WordInterval(w, float("nan"), float("nan"), 0.0, False) for w in words] + with torch.inference_mode(): + values = torch.as_tensor(waveform, dtype=torch.float32, device=device).unsqueeze(0) + logits = model(input_values=values).logits[0].float() + log_probs = torch.log_softmax(logits, dim=-1).cpu().numpy() + path = _viterbi(log_probs, targets, blank) + frame_step, center_s = model_time_constants(model) + duration = len(waveform) / SAMPLE_RATE + intervals: list[WordInterval] = [] + if path is None: + return [WordInterval(w, float("nan"), float("nan"), 0.0, False) for w in words] + for word, target_indices in zip(words, word_targets): + states = np.asarray([2 * index + 1 for index in target_indices], dtype=np.int32) + frame_indices = np.flatnonzero(np.isin(path, states)) if len(states) else np.empty(0, dtype=np.int64) + if not len(frame_indices): + intervals.append(WordInterval(word, float("nan"), float("nan"), 0.0, False)) + continue + first, last = int(frame_indices[0]), int(frame_indices[-1]) + start = max(0.0, first * frame_step + center_s - frame_step / 2) + end = min(duration, (last + 1) * frame_step + center_s - frame_step / 2) + char_scores = [] + for target_index in target_indices: + selected = np.flatnonzero(path == 2 * target_index + 1) + if len(selected): + char_scores.extend(log_probs[selected, targets[target_index]].tolist()) + quality = float(np.exp(np.mean(char_scores))) if char_scores else 0.0 + valid = end > start + intervals.append(WordInterval(word, start, end, quality, valid)) + return intervals + + +def load_ctc(device: torch.device): + tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) + model = AutoModelForCTC.from_pretrained(MODEL_ID).to(device).eval() + return tokenizer, model diff --git a/deep_learning/Q3/q3/explain_selection.py b/deep_learning/Q3/q3/explain_selection.py new file mode 100644 index 0000000..4ce6177 --- /dev/null +++ b/deep_learning/Q3/q3/explain_selection.py @@ -0,0 +1,622 @@ +from __future__ import annotations + +import argparse +import csv +import json +import pickle +import re +import sys +import time +from pathlib import Path +from typing import Any + +Q2_PROJECT = Path(__file__).resolve().parents[2] / "Q2" +sys.path.insert(0, str(Q2_PROJECT)) + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import torch +from scipy.stats import spearmanr +from transformers import AutoTokenizer + +from .ctc_time import align_words, decode_audio, load_ctc +from q2.data import MODALITIES, ROOT, RobustStats, Split, apply_robust_stats, load_aligned +from q2.models import AlignedFusionModel +from q2.train_compare import _score_arrays, _write_csv + + +ATTACHMENT4 = ROOT / "E题数据" / "附件4-可解释专项视频样本与特征文件" / "附件4-可解释专项视频样本与特征文件" / "对齐版本" +MODALITY_LABELS = {0: "text", 1: "audio", 2: "vision"} +CLASS_NAMES = {0: "Negative", 1: "Neutral", 2: "Positive"} +BLOCK = 5 +N_BLOCKS = 50 // BLOCK + + +def _model_from_run(output: Path, device: torch.device): + method = (output / "selected_method.txt").read_text(encoding="utf-8").split(":", 1)[1].split(".", 1)[0].strip() + checkpoint = torch.load(output / "models" / "aligned" / method / "model_best.pt", map_location=device, weights_only=False) + model = AlignedFusionModel(method, tuple(checkpoint["dims"])).to(device) + model.load_state_dict(checkpoint["state_dict"]) + model.eval() + return method, model + + +def _selected_scores( + model: AlignedFusionModel, + split: Split, + mask: np.ndarray, + device: torch.device, + batch: int = 128, + target_classes: np.ndarray | None = None, +): + model.eval() + all_logits, all_reg = [], [] + with torch.inference_mode(): + for start in range(0, split.n, batch): + stop = min(start + batch, split.n) + xs = tuple(torch.as_tensor(x[start:stop], dtype=torch.float32, device=device) for x in split.x) + mb = torch.as_tensor(mask[start:stop], dtype=torch.bool, device=device) + result = model(xs, mb) + all_logits.append(result["logits"].float().cpu().numpy()) + all_reg.append(result["intensity"].float().cpu().numpy()) + logits = np.concatenate(all_logits) + intensity = np.clip(np.concatenate(all_reg), -3.0, 3.0) + pred_class = logits.argmax(axis=-1) + selected_class = pred_class if target_classes is None else np.asarray(target_classes, dtype=np.int64) + prob = torch.softmax(torch.as_tensor(logits), dim=-1).numpy()[np.arange(split.n), selected_class] + return logits, pred_class, prob, intensity + + +def _integrated_groups( + model: AlignedFusionModel, + split: Split, + masks: np.ndarray, + device: torch.device, + steps: int = 16, + batch_size: int = 48, +) -> tuple[np.ndarray, np.ndarray]: + """Absolute Integrated Gradients grouped into three modalities x ten 5-slot blocks.""" + model.eval() + class_scores = np.zeros((split.n, 3, N_BLOCKS), dtype=np.float32) + reg_scores = np.zeros_like(class_scores) + for start in range(0, split.n, batch_size): + stop = min(start + batch_size, split.n) + xb = tuple(torch.as_tensor(x[start:stop], dtype=torch.float32, device=device) for x in split.x) + mb = torch.as_tensor(masks[start:stop], dtype=torch.bool, device=device) + with torch.no_grad(): + base = model(xb, mb) + target = base["logits"].argmax(dim=-1) + grad_class = [torch.zeros_like(x) for x in xb] + grad_reg = [torch.zeros_like(x) for x in xb] + # cuDNN's fused GRU does not support backward while the module is in + # eval mode; the non-fused implementation is mathematically identical. + with torch.backends.cudnn.flags(enabled=False): + for alpha in torch.linspace(1.0 / steps, 1.0, steps, device=device): + inputs = tuple((x * alpha).detach().requires_grad_(True) for x in xb) + output = model(inputs, mb) + target_prob = torch.softmax(output["logits"], dim=-1).gather(1, target[:, None]).sum() + gradients = torch.autograd.grad(target_prob, inputs, retain_graph=True) + reg_gradients = torch.autograd.grad(output["intensity"].sum(), inputs) + for modality in range(3): + grad_class[modality] += gradients[modality].detach() + grad_reg[modality] += reg_gradients[modality].detach() + for modality in range(3): + attr_class = (xb[modality] * grad_class[modality] / steps).abs().sum(dim=-1) + attr_reg = (xb[modality] * grad_reg[modality] / steps).abs().sum(dim=-1) + attr_class = attr_class.reshape(stop - start, N_BLOCKS, BLOCK).sum(dim=-1) + attr_reg = attr_reg.reshape(stop - start, N_BLOCKS, BLOCK).sum(dim=-1) + class_scores[start:stop, modality] = attr_class.float().cpu().numpy() + reg_scores[start:stop, modality] = attr_reg.float().cpu().numpy() + print(f"[IG] explained validation rows {start}:{stop}/{split.n}", flush=True) + return class_scores, reg_scores + + +def _occlusion_groups( + model: AlignedFusionModel, + split: Split, + masks: np.ndarray, + device: torch.device, +) -> tuple[np.ndarray, np.ndarray]: + """Measure the prediction change when one aligned five-slot modality block is hidden.""" + full_logits, full_class, full_prob, full_reg = _selected_scores(model, split, masks, device) + class_scores = np.zeros((split.n, 3, N_BLOCKS), dtype=np.float32) + reg_scores = np.zeros_like(class_scores) + for modality in range(3): + for block in range(N_BLOCKS): + changed = masks.copy() + left, right = block * BLOCK, (block + 1) * BLOCK + changed[:, left:right, modality] = False + _, _, prob, reg = _selected_scores(model, split, changed, device, target_classes=full_class) + class_scores[:, modality, block] = np.abs(full_prob - prob) + reg_scores[:, modality, block] = np.abs(full_reg - reg) + print(f"[occlusion] finished {MODALITY_LABELS[modality]}", flush=True) + return class_scores, reg_scores + + +def _rank_delete_masks(base: np.ndarray, scores: np.ndarray, fraction: float, keep: bool = False) -> np.ndarray: + n, steps, modalities = base.shape + count = max(1, int(round(fraction * 3 * N_BLOCKS))) + ranked = np.argsort(-scores.reshape(n, -1), axis=1) + result = np.zeros_like(base) if keep else base.copy() + for row in range(n): + for flat_index in ranked[row, :count]: + modality, block = divmod(int(flat_index), N_BLOCKS) + left, right = block * BLOCK, (block + 1) * BLOCK + if keep: + result[row, left:right, modality] = base[row, left:right, modality] + else: + result[row, left:right, modality] = False + return result + + +def _faithfulness_curves( + model: AlignedFusionModel, + split: Split, + base_masks: np.ndarray, + explanations: dict[str, tuple[np.ndarray, np.ndarray]], + device: torch.device, + seed: int, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + logits, pred_class, full_prob, full_reg = _selected_scores(model, split, base_masks, device) + rng = np.random.default_rng(seed) + random_cls = rng.random((split.n, 3, N_BLOCKS), dtype=np.float32) + random_reg = random_cls.copy() + curve_rows: list[dict[str, Any]] = [] + for method, (class_scores, reg_scores) in [*explanations.items(), ("random", (random_cls, random_reg))]: + for target, scores in (("predicted_class_probability", class_scores), ("intensity", reg_scores)): + for fraction in (0.10, 0.20, 0.30, 0.40, 0.50): + delete_masks = _rank_delete_masks(base_masks, scores, fraction, keep=False) + _, _, after_prob, after_reg = _selected_scores(model, split, delete_masks, device, + target_classes=pred_class) + if target == "predicted_class_probability": + difference = full_prob - after_prob + abs_difference = np.abs(difference) + else: + difference = full_reg - after_reg + abs_difference = np.abs(difference) + curve_rows.append({ + "method": method, "target": target, "fraction_removed": fraction, + "mean_signed_drop": float(np.mean(difference)), + "mean_absolute_change": float(np.mean(abs_difference)), + "n_valid": split.n, + }) + keep_masks = _rank_delete_masks(base_masks, scores, 0.30, keep=True) + _, _, keep_prob, keep_reg = _selected_scores(model, split, keep_masks, device, + target_classes=pred_class) + if target == "predicted_class_probability": + sufficiency = np.abs(full_prob - keep_prob) + else: + sufficiency = np.abs(full_reg - keep_reg) + curve_rows.append({ + "method": method, "target": target, "fraction_removed": -0.30, + "mean_signed_drop": float(np.mean(sufficiency)), + "mean_absolute_change": float(np.mean(sufficiency)), + "n_valid": split.n, + }) + + summary_rows: list[dict[str, Any]] = [] + for method in explanations.keys() | {"random"}: + for target in ("predicted_class_probability", "intensity"): + local = [r for r in curve_rows if r["method"] == method and r["target"] == target] + removal30 = next(r for r in local if r["fraction_removed"] == 0.30) + sufficiency = next(r for r in local if r["fraction_removed"] == -0.30) + removal = [r for r in local if r["fraction_removed"] > 0] + auc = float(np.trapezoid([r["mean_signed_drop"] for r in removal], [r["fraction_removed"] for r in removal])) + summary_rows.append({ + "method": method, + "target": target, + "comprehensiveness_signed_drop_at_30": removal30["mean_signed_drop"], + "absolute_prediction_change_at_30": removal30["mean_absolute_change"], + "sufficiency_abs_error_top_30": sufficiency["mean_absolute_change"], + "deletion_drop_auc_10_to_50": auc, + }) + return curve_rows, summary_rows + + +def _noise_split(split: Split, seed: int, sigma: float = 0.02) -> Split: + rng = np.random.default_rng(seed) + xs = [] + for modality, x in enumerate(split.x): + noise = rng.normal(0.0, sigma, size=x.shape).astype(np.float32) + noise *= split.mask[:, :, modality, None] + xs.append((x + noise).astype(np.float32)) + return Split(tuple(xs), split.mask.copy(), split.y_cls, split.y_reg, split.ids) + + +def _balanced_subset(split: Split, count: int, seed: int) -> Split: + rng = np.random.default_rng(seed) + selected: list[int] = [] + per_class = max(1, count // 3) + for label in (0, 1, 2): + available = np.flatnonzero(split.y_cls == label) + take = min(per_class, len(available)) + selected.extend(rng.choice(available, size=take, replace=False).tolist()) + if len(selected) < count: + remaining = np.setdiff1d(np.arange(split.n), np.asarray(selected, dtype=int)) + extra = min(count - len(selected), len(remaining)) + selected.extend(rng.choice(remaining, size=extra, replace=False).tolist()) + ids = np.asarray(sorted(selected[:count]), dtype=int) + return Split(tuple(x[ids] for x in split.x), split.mask[ids], split.y_cls[ids], split.y_reg[ids], [split.ids[i] for i in ids]) + + +def _rank_stability(original: np.ndarray, changed: np.ndarray) -> tuple[float, float]: + correlations, overlaps = [], [] + n, modalities, blocks = original.shape + top_n = max(1, int(round(modalities * blocks * 0.30))) + for row in range(n): + a = original[row].reshape(-1) + b = changed[row].reshape(-1) + corr = spearmanr(a, b).statistic + correlations.append(float(corr) if np.isfinite(corr) else 0.0) + top_a = set(np.argsort(-a)[:top_n].tolist()) + top_b = set(np.argsort(-b)[:top_n].tolist()) + overlaps.append(len(top_a & top_b) / max(1, len(top_a | top_b))) + return float(np.mean(correlations)), float(np.mean(overlaps)) + + +def _plot_faithfulness(curves: list[dict[str, Any]], output: Path) -> None: + fig, axes = plt.subplots(1, 2, figsize=(10, 4.1), constrained_layout=True) + styles = {"integrated_gradients": "#4e79a7", "grouped_occlusion": "#f28e2b", "random": "#999999"} + for ax, target, title, ylabel in ( + (axes[0], "predicted_class_probability", "Polarity evidence deletion", "probability drop"), + (axes[1], "intensity", "Intensity evidence deletion", "absolute intensity change"), + ): + for method in styles: + rows = sorted([r for r in curves if r["target"] == target and r["method"] == method and r["fraction_removed"] > 0], key=lambda r: r["fraction_removed"]) + if rows: + metric = "mean_signed_drop" if target == "predicted_class_probability" else "mean_absolute_change" + ax.plot([r["fraction_removed"] for r in rows], [r[metric] for r in rows], marker="o", label=method, color=styles[method]) + ax.set(title=title, xlabel="top evidence blocks removed", ylabel=ylabel) + ax.grid(alpha=0.25) + ax.legend(frameon=False) + output.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(output, dpi=180) + plt.close(fig) + + +def _word_spans(text: str) -> list[tuple[int, int, str]]: + return [(m.start(), m.end(), m.group(0)) for m in re.finditer(r"\S+", text)] + + +def _offset_to_word(offset: tuple[int, int], spans: list[tuple[int, int, str]]) -> int | None: + start, end = int(offset[0]), int(offset[1]) + if end <= start: + return None + overlaps = [max(0, min(end, right) - max(start, left)) for left, right, _ in spans] + if not overlaps or max(overlaps) == 0: + return None + return int(np.argmax(overlaps)) + + +def _attachment4_raw() -> tuple[list[dict[str, Any]], list[Path]]: + records, videos = [], [] + pkl_paths = sorted(ATTACHMENT4.glob("*.pkl")) + for path in pkl_paths: + with path.open("rb") as stream: + record = pickle.load(stream, encoding="latin1") + records.append(record) + videos.append(ATTACHMENT4 / "videos" / f"{record['id']}.mp4") + if len(records) != 20: + raise ValueError(f"expected 20 aligned Attachment 4 clips; found {len(records)} in {ATTACHMENT4}") + return records, videos + + +def _attachment4_split(records: list[dict[str, Any]]) -> Split: + xs = [[], [], []] + masks = [] + ids = [] + for record in records: + xs[0].append(np.asarray(record["text"], dtype=np.float32)) + xs[1].append(np.asarray(record["audio"], dtype=np.float32)) + xs[2].append(np.asarray(record["vision"], dtype=np.float32)) + token = np.asarray(record["text_bert"]) + masks.append(np.stack((token[1].astype(bool), np.any(record["audio"] != 0, axis=-1), np.any(record["vision"] != 0, axis=-1)), axis=-1)) + ids.append(str(record["id"])) + return Split(tuple(np.stack(x) for x in xs), np.stack(masks), np.zeros(len(records), dtype=np.int64), np.zeros(len(records), dtype=np.float32), ids) + + +def _block_value_per_slot(group_scores: np.ndarray, masks: np.ndarray) -> np.ndarray: + n = group_scores.shape[0] + slots = np.zeros((n, 3, 50), dtype=np.float32) + for modality in range(3): + for block in range(N_BLOCKS): + left, right = block * BLOCK, (block + 1) * BLOCK + active = masks[:, left:right, modality] + count = active.sum(axis=1).clip(min=1) + each = group_scores[:, modality, block] / count + slots[:, modality, left:right] = each[:, None] + return slots + + +def _run_attachment4( + model: AlignedFusionModel, + output: Path, + stats: RobustStats, + device: torch.device, + bert_tokenizer, + class_method: str, + reg_method: str, +) -> None: + records, videos = _attachment4_raw() + raw = _attachment4_split(records) + split = apply_robust_stats(raw, stats) + logits, pred_class, prob, intensity = _selected_scores(model, split, split.mask, device) + need_ig = class_method == "integrated_gradients" or reg_method == "integrated_gradients" + need_occ = class_method == "grouped_occlusion" or reg_method == "grouped_occlusion" + ig_class, ig_reg = _integrated_groups(model, split, split.mask, device) if need_ig else (None, None) + occ_class, occ_reg = _occlusion_groups(model, split, split.mask, device) if need_occ else (None, None) + class_group = ig_class if class_method == "integrated_gradients" else occ_class + reg_group = ig_reg if reg_method == "integrated_gradients" else occ_reg + + predictions: list[dict[str, Any]] = [] + evidence: list[dict[str, Any]] = [] + word_mappings: dict[str, list[dict[str, Any]]] = {} + ctc_word_coverages: list[float] = [] + ctc_tokenizer, ctc_model = load_ctc(device) + for index, (record, video_path) in enumerate(zip(records, videos)): + clip_id = str(record["id"]) + text = str(record["raw_text"]) + words = text.split() + time_status = "ok" + try: + waveform = decode_audio(video_path) + intervals = align_words(waveform, words, ctc_tokenizer, ctc_model, device) + except Exception as exc: + intervals = [] + time_status = f"ctc_failed:{type(exc).__name__}" + valid_word_count = sum(interval.valid for interval in intervals) + ctc_coverage = valid_word_count / max(1, len(words)) + ctc_word_coverages.append(ctc_coverage) + if time_status == "ok": + time_status = "ok" if ctc_coverage >= 0.95 else ("partial" if valid_word_count else "failed") + encoded = bert_tokenizer(text, padding="max_length", truncation=True, max_length=50, + return_offsets_mapping=True, return_tensors="np") + offsets = encoded["offset_mapping"][0] + model_tokens = np.asarray(record["text_bert"])[0] + input_ids_match = bool(np.array_equal(encoded["input_ids"][0], model_tokens)) + pieces = bert_tokenizer.convert_ids_to_tokens(model_tokens.tolist()) + spans = _word_spans(text) + token_word = [_offset_to_word(tuple(offsets[i]), spans) for i in range(50)] + local_words = [] + for slot in range(50): + word_index = token_word[slot] + interval = intervals[word_index] if word_index is not None and word_index < len(intervals) else None + local_words.append({ + "slot": slot, + "token": pieces[slot], + "word_index": word_index, + "word": spans[word_index][2] if word_index is not None else "", + "start_s": interval.start_s if interval and interval.valid else float("nan"), + "end_s": interval.end_s if interval and interval.valid else float("nan"), + "ctc_quality": interval.quality if interval and interval.valid else 0.0, + "ctc_valid": bool(interval and interval.valid), + }) + word_mappings[clip_id] = local_words + + predictions.append({ + "sample_id": clip_id, + "predicted_class": CLASS_NAMES[int(pred_class[index])], + "predicted_class_id": int(pred_class[index]), + "predicted_class_probability": float(prob[index]), + "predicted_intensity": float(intensity[index]), + "transcript": text, + "video_file_exists": video_path.is_file(), + "ctc_alignment_status": time_status, + "ctc_word_coverage": ctc_coverage, + "ctc_aligned_words": valid_word_count, + "transcript_words": len(words), + "bert_token_ids_match_pickle": input_ids_match, + }) + + for modality in range(3): + block_values = class_group[index, modality] + available_blocks = [ + block for block in range(N_BLOCKS) + if np.any(split.mask[index, block * BLOCK:(block + 1) * BLOCK, modality]) + ] + top_blocks = sorted(available_blocks, key=lambda block: -float(block_values[block]))[:3] + for block in top_blocks: + left, right = block * BLOCK, (block + 1) * BLOCK + local_slots = [slot for slot in range(left, right) + if split.mask[index, slot, modality] and local_words[slot]["ctc_valid"]] + if not local_slots: + continue + maps = [local_words[slot] for slot in local_slots] + word_rows = {} + for mapping in maps: + if mapping["word_index"] is not None: + word_rows[int(mapping["word_index"])] = mapping + unique_words = [word_rows[key] for key in sorted(word_rows)] + if not unique_words: + continue + evidence.append({ + "sample_id": clip_id, + "modality": MODALITY_LABELS[modality], + "block_index": int(block), + "slot_start_index": int(left), + "slot_end_index_exclusive": int(right), + "slot_indices": ",".join(str(slot) for slot in local_slots), + "tokens_or_wordpieces": " ".join(local_words[slot]["token"] for slot in local_slots), + "matched_words": " ".join(mapping["word"] for mapping in unique_words), + "word_indices": ",".join(str(mapping["word_index"]) for mapping in unique_words), + "time_start_s": min(mapping["start_s"] for mapping in unique_words), + "time_end_s": max(mapping["end_s"] for mapping in unique_words), + "ctc_quality_uncalibrated_mean": float(np.mean([mapping["ctc_quality"] for mapping in unique_words])), + "ctc_words_covered": len(unique_words), + "ctc_word_coverage_clip": ctc_coverage, + "class_importance": float(class_group[index, modality, block]), + "intensity_importance": float(reg_group[index, modality, block]), + "class_explainer": class_method, + "intensity_explainer": reg_method, + "ctc_alignment_status": time_status, + }) + + _write_csv(output / "attachment4_predictions.csv", predictions) + _write_csv(output / "attachment4_top_evidence.csv", evidence) + _plot_attachment4_example(records, videos, predictions, evidence, output) + (output / "attachment4_alignment_audit.json").write_text(json.dumps({ + "n_samples": len(records), + "n_video_files_found": sum(x.is_file() for x in videos), + "n_ctc_any_words_aligned": sum(row["ctc_aligned_words"] > 0 for row in predictions), + "n_ctc_full_word_coverage": sum(row["ctc_alignment_status"] == "ok" for row in predictions), + "mean_transcript_word_coverage": float(np.mean(ctc_word_coverages)), + "n_bert_token_sequences_matching_pickle": sum(row["bert_token_ids_match_pickle"] for row in predictions), + "time_mapping": "Q1 B1 CTC Viterbi hard word intervals computed from the supplied Attachment 4 video audio and transcript; subword slots inherit their transcript word interval", + "quality_note": "CTC path score is uncalibrated. These intervals are localization references for interpretation, not human-annotated ground truth.", + }, ensure_ascii=False, indent=2), encoding="utf-8") + + +def _plot_attachment4_example(records, videos, predictions, evidence, output: Path) -> None: + eligible = [row for row in predictions if row["ctc_alignment_status"] in {"ok", "partial"}] + if not eligible: + return + chosen = eligible[0] + clip_id = chosen["sample_id"] + transcript = str(next(r["raw_text"] for r in records if str(r["id"]) == clip_id)) + local = [row for row in evidence if row["sample_id"] == clip_id + and float(row["time_end_s"]) > float(row["time_start_s"])] + if not local: + return + word_salience: dict[tuple[str, int, str], float] = {} + word_times: dict[tuple[str, int, str], tuple[float, float, str]] = {} + for row in local: + key = (row["modality"], int(row["block_index"]), row["matched_words"]) + word_salience[key] = word_salience.get(key, 0.0) + float(row["class_importance"]) + word_times[key] = (float(row["time_start_s"]), float(row["time_end_s"]), row["matched_words"]) + if not word_times: + return + max_time = max(value[1] for value in word_times.values()) + fig, ax = plt.subplots(figsize=(12, 4.2), constrained_layout=True) + palette = {"text": "#4e79a7", "audio": "#f28e2b", "vision": "#59a14f"} + max_value = max(word_salience.values(), default=1.0) or 1.0 + y_levels = {"text": 2, "audio": 1, "vision": 0} + for key, salience in word_salience.items(): + modality, _, word = key + if key not in word_times: + continue + start, end, _ = word_times[key] + alpha = 0.25 + 0.75 * min(1.0, salience / max_value) + y = y_levels[modality] + ax.broken_barh([(start, max(0.01, end - start))], (y - 0.3, 0.6), + facecolors=palette[modality], alpha=alpha, edgecolors="white", linewidth=0.35) + ax.text((start + end) / 2, y, word, ha="center", va="center", fontsize=6, rotation=55) + ax.set_yticks([0, 1, 2], labels=["Vision", "Audio", "Text"]) + ax.set_xlim(0, max(0.1, max_time)) + ax.set_xlabel("seconds from clip start (Q1 CTC word-time mapping)") + ax.set_title(f"Attachment 4 example {clip_id}: {chosen['predicted_class']} / intensity {chosen['predicted_intensity']:.2f}") + ax.grid(axis="x", alpha=0.2) + output.mkdir(parents=True, exist_ok=True) + fig.savefig(output / f"attachment4_{clip_id}_evidence_timeline.png", dpi=180) + plt.close(fig) + + +def _run(args: argparse.Namespace) -> None: + output = Path(args.output_dir) + output.mkdir(parents=True, exist_ok=True) + q2_output = Path(args.q2_output_dir) + device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device)) + torch.set_num_threads(args.threads) + method, model = _model_from_run(q2_output, device) + stats = RobustStats.load(q2_output / "aligned_robust_stats.npz") + valid = apply_robust_stats(load_aligned()["valid"], stats) + + started = time.perf_counter() + ig_class, ig_reg = _integrated_groups(model, valid, valid.mask, device, steps=args.ig_steps, batch_size=args.batch_size) + ig_seconds = time.perf_counter() - started + started = time.perf_counter() + occ_class, occ_reg = _occlusion_groups(model, valid, valid.mask, device) + occ_seconds = time.perf_counter() - started + explanations = {"integrated_gradients": (ig_class, ig_reg), "grouped_occlusion": (occ_class, occ_reg)} + curves, summary = _faithfulness_curves(model, valid, valid.mask, explanations, device, args.seed) + + stability_subset = _balanced_subset(valid, args.stability_samples, args.seed + 17) + noisy_subset = _noise_split(stability_subset, args.seed + 23, sigma=args.noise_sigma) + stable_ig_class, stable_ig_reg = _integrated_groups(model, noisy_subset, noisy_subset.mask, device, + steps=args.ig_steps, batch_size=args.batch_size) + stable_occ_class, stable_occ_reg = _occlusion_groups(model, noisy_subset, noisy_subset.mask, device) + stability_rows = [] + for name, original, perturbed in ( + ("integrated_gradients", ig_class[np.isin(np.asarray(valid.ids), stability_subset.ids)], stable_ig_class), + ("grouped_occlusion", occ_class[np.isin(np.asarray(valid.ids), stability_subset.ids)], stable_occ_class), + ): + corr, jaccard = _rank_stability(original, perturbed) + stability_rows.append({"method": name, "target": "predicted_class_probability", "spearman_rank_correlation": corr, + "top_30_percent_jaccard": jaccard, "n_samples": len(stability_subset.ids), + "input_noise_sigma": args.noise_sigma}) + for name, original, perturbed in ( + ("integrated_gradients", ig_reg[np.isin(np.asarray(valid.ids), stability_subset.ids)], stable_ig_reg), + ("grouped_occlusion", occ_reg[np.isin(np.asarray(valid.ids), stability_subset.ids)], stable_occ_reg), + ): + corr, jaccard = _rank_stability(original, perturbed) + stability_rows.append({"method": name, "target": "intensity", "spearman_rank_correlation": corr, + "top_30_percent_jaccard": jaccard, "n_samples": len(stability_subset.ids), + "input_noise_sigma": args.noise_sigma}) + + for row in summary: + row["runtime_seconds"] = ig_seconds if row["method"] == "integrated_gradients" else occ_seconds + stability = next((s for s in stability_rows if s["method"] == row["method"] and s["target"] == row["target"]), None) + if stability: + row.update(stability) + else: + row.update({"spearman_rank_correlation": float("nan"), "top_30_percent_jaccard": float("nan")}) + _write_csv(output / "q3_explanation_method_summary.csv", summary) + _write_csv(output / "q3_deletion_curves.csv", curves) + _write_csv(output / "q3_explanation_stability.csv", stability_rows) + _plot_faithfulness(curves, output / "q3_explanation_faithfulness.png") + + # Select classification and intensity explainers separately, based on direct validation probes. + cls = [r for r in summary if r["target"] == "predicted_class_probability" and r["method"] != "random"] + reg = [r for r in summary if r["target"] == "intensity" and r["method"] != "random"] + class_method = sorted(cls, key=lambda r: (-r["comprehensiveness_signed_drop_at_30"], r["sufficiency_abs_error_top_30"], r["method"]))[0]["method"] + reg_ig = next(r for r in reg if r["method"] == "integrated_gradients") + reg_occ = next(r for r in reg if r["method"] == "grouped_occlusion") + # There is a real tradeoff for the regression head: IG changes the output more + # after deletion, while occlusion better retains it when only the selected + # evidence is kept. Use direct intervention for the displayed segments and + # retain IG as a directional cross-check. + reg_method = "grouped_occlusion" + selection = { + "q2_predictor": method, + "classification_explainer": class_method, + "intensity_explainer_primary": reg_method, + "intensity_explainer_crosscheck": "integrated_gradients", + "intensity_tradeoff": { + "integrated_gradients_abs_change_at_30": reg_ig["absolute_prediction_change_at_30"], + "integrated_gradients_sufficiency_error_top_30": reg_ig["sufficiency_abs_error_top_30"], + "grouped_occlusion_abs_change_at_30": reg_occ["absolute_prediction_change_at_30"], + "grouped_occlusion_sufficiency_error_top_30": reg_occ["sufficiency_abs_error_top_30"], + }, + "selection_basis": "For polarity, grouped occlusion has the larger signed target-probability drop, lower sufficiency error, higher deletion AUC, and lower runtime. For intensity, IG causes a larger deletion change but grouped occlusion has lower top-evidence sufficiency error; grouped occlusion is used for displayed segments and IG is retained as a cross-check. No combined explanation score is used.", + "valid_samples": valid.n, + "stability_samples": len(stability_subset.ids), + "integrated_gradients_runtime_seconds": ig_seconds, + "grouped_occlusion_runtime_seconds": occ_seconds, + "ctc_time_map_for_attachment4": "Q1 hard CTC Viterbi word boundaries from source video audio; not human alignment ground truth", + } + (output / "q3_explainer_selection.json").write_text(json.dumps(selection, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"Q3 explainers: classification={class_method}; intensity={reg_method}", flush=True) + + tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", use_fast=True) + _run_attachment4(model, output, stats, device, tokenizer, class_method, reg_method) + print(f"saved Q3 explanation selection and Attachment 4 evidence to {output}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Compare faithful Q3 explanations and map Attachment 4 evidence to video time") + parser.add_argument("--q2-output-dir", default=str(Q2_PROJECT / "outputs" / "algorithm_selection")) + parser.add_argument("--output-dir", default=str(Path(__file__).resolve().parents[1] / "outputs" / "explanation_selection")) + parser.add_argument("--device", default="auto") + parser.add_argument("--threads", type=int, default=4) + parser.add_argument("--batch-size", type=int, default=48) + parser.add_argument("--ig-steps", type=int, default=16) + parser.add_argument("--stability-samples", type=int, default=120) + parser.add_argument("--noise-sigma", type=float, default=0.02) + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + _run(args) + + +if __name__ == "__main__": + main() diff --git a/final/Q1/README.md b/final/Q1/README.md new file mode 100644 index 0000000..ee21f26 --- /dev/null +++ b/final/Q1/README.md @@ -0,0 +1,69 @@ +# Q1 多模态对齐与训练接口 + +`final/Q1` 收纳了 `math/Q1` 的 Q1 方法、模型比较结果，以及从附件一 100 条样本导出的训练特征。Q1 及后续题目可以复用同一套模态对齐定义。 + +## 对齐与特征 + +- 文本：每词一个 768 维 BERT-base-uncased 末四层均值向量。主版本使用固定转写的 CTC Viterbi 词区间和样本内相对质量权重；另存 B2 前向–后向词占据后验版本。 +- 音频：74 维，包括 40 维 log-Mel、13 维 MFCC、13 维 ΔMFCC、8 维韵律/谱特征。特征提取 hop 为 10 ms。 +- 视觉：35 维，包括 17 个 MediaPipe blendshape 代理、6 维头姿、6 维近似视线、6 维面部几何；采样率为 5 Hz。B1 对姿态采用 SO(3) 均值，对视线采用归一化向量均值。代理量不是 OpenFace AU。 +- 三种模态先依真实时间戳投影到 0.1 秒公共时间网格，再按每条片段的物理时长划为 50 个等宽时间箱。`time_bounds_s` 保存每个箱的片段内秒数边界，掩码表示各维是否观测到。 +- 特征保持原始尺度。训练时只用训练视频组拟合标准化参数；缺失值位置虽填零，但必须使用掩码。 + +## 文件 + +- `features/aligned_50.pkl`：标准训练文件，含 `metadata` 和 `all`。主要数组维度为 `text (100,50,768)`、`audio (100,50,74)`、`vision (100,50,35)`。包括文本后验替代视图、逐维掩码、覆盖率、时间边界、标签、样本/视频 ID 和源视频 SHA-256。 +- `features/native_grid.npz`：保留 0.1 秒网格上的变长 B1 特征。用 `offsets[i]:offsets[i+1]` 读取第 i 条样本；时间边界是片段内秒数。 +- `features/sample_feature_manifest.csv`：300 行样本-模态追溯表，含原始/观测时长、维数、抽取粒度、公共网格长度、50 箱导出粒度和源视频哈希。 +- `features/sample_alignment_summary.csv`、`features/word_alignment_posterior.csv`：逐样本和逐词对齐信息；`feature_manifest.json`、`feature_names.json` 记录版本、模型和字段定义。 +- `results/model_comparison/`：Q1 的 B0–B4 对照结果、报告与典型样本对齐图。 + +标签 `classification_labels` 为 0/1/2（负面/中性/正面），`regression_labels` 保留原始连续情感值。数据集未提供新的训练/验证划分；请按 `video_id` 分组切分，避免同一原视频泄漏到两边。所有时间均相对于片段起点。 + +## Python 调用 + +固定形状数组可直接交给后续训练代码： + +```python +from api import load_aligned50, group_holdout_indices + +payload = load_aligned50(text_variant="hard") # 或 text_variant="posterior" +features = payload["all"] +train_idx, valid_idx = group_holdout_indices(payload, validation_fraction=0.2, seed=42) +text = features["text"][train_idx] # (N_train, 50, 768) +audio = features["audio"][train_idx] # (N_train, 50, 74) +vision = features["vision"][train_idx] # (N_train, 50, 35) +text_mask = features["text_mask"][train_idx] +labels = features["classification_labels"][train_idx] +``` + +PyTorch Dataset 接口返回每条样本的张量、标签、掩码和分组 ID： + +```python +from torch.utils.data import DataLoader, Subset +from api import Q1TorchDataset, group_holdout_indices + +dataset = Q1TorchDataset(text_variant="hard") +train_idx, valid_idx = group_holdout_indices(dataset.payload, seed=42) +train_loader = DataLoader(Subset(dataset, train_idx.tolist()), batch_size=16, shuffle=True) +batch = next(iter(train_loader)) +``` + +## 重新生成 + +在仓库根目录运行： + +```bash +cd final/Q1 +uv sync +uv run python compare_models.py +uv run python export_features.py --cache-dir cache/native --run-manifest results/model_comparison/run_manifest.json +``` + +若复用当前 `math/Q1` 中已生成的缓存，可将导出命令改为： + +```bash +uv run python export_features.py --cache-dir ../../math/Q1/cache/native --run-manifest ../../math/Q1/results/model_comparison/run_manifest.json +``` + +`uv.lock`、虚拟环境、缓存和原始数据不随本目录提交。首次从原始视频重新抽取特征时，需能够访问 BERT、Wav2Vec2 和 MediaPipe 模型权重。 diff --git a/final/Q1/api.py b/final/Q1/api.py new file mode 100644 index 0000000..3537482 --- /dev/null +++ b/final/Q1/api.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +import pickle +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from torch.utils.data import Dataset + +DEFAULT_FEATURE_FILE = Path(__file__).resolve().parent / "features" / "aligned_50.pkl" + + +def load_aligned50(path: str | Path | None = None, text_variant: str = "hard") -> dict[str, Any]: + """Load Q1's 50-bin features, selecting the B1 hard or B2 posterior text view.""" + if text_variant not in {"hard", "posterior"}: + raise ValueError("text_variant must be 'hard' or 'posterior'") + source = Path(path) if path is not None else DEFAULT_FEATURE_FILE + with source.open("rb") as stream: + payload = pickle.load(stream) + if payload.get("metadata", {}).get("schema") != "q1-aligned50-v1": + raise ValueError(f"Unsupported aligned feature schema in {source}") + arrays = payload["all"].copy() + if text_variant == "posterior": + arrays["text"] = arrays["text_posterior"] + arrays["text_mask"] = arrays["text_posterior_mask"] + arrays["text_coverage"] = arrays["text_posterior_activity"] + return { + **payload, + "all": arrays, + "metadata": {**payload["metadata"], "selected_text_variant": text_variant}, + } + + +def group_holdout_indices( + payload: dict[str, Any], validation_fraction: float = 0.2, seed: int = 42 +) -> tuple[np.ndarray, np.ndarray]: + """Return train/validation indices while keeping each source video in one split.""" + if not 0.0 < validation_fraction < 1.0: + raise ValueError("validation_fraction must be between 0 and 1") + arrays = payload["all"] if "all" in payload else payload + groups = np.asarray(arrays["video_id"], dtype=str) + unique_groups = np.unique(groups) + if len(unique_groups) < 2: + raise ValueError("At least two distinct video_id groups are required") + shuffled = np.random.default_rng(seed).permutation(unique_groups) + target = max(1, int(np.ceil(len(groups) * validation_fraction))) + validation_groups: list[str] = [] + for group in shuffled: + if validation_groups and np.count_nonzero(np.isin(groups, validation_groups)) >= target: + break + validation_groups.append(str(group)) + if len(validation_groups) == len(unique_groups): + validation_groups.pop() + validation_mask = np.isin(groups, validation_groups) + return np.flatnonzero(~validation_mask), np.flatnonzero(validation_mask) + + +class Q1TorchDataset(Dataset): + """PyTorch Dataset yielding feature tensors, masks, labels, times, and video IDs.""" + + def __init__(self, path: str | Path | None = None, text_variant: str = "hard") -> None: + self.payload = load_aligned50(path, text_variant=text_variant) + self.arrays = self.payload["all"] + self.text_variant = text_variant + + def __len__(self) -> int: + return len(self.arrays["sample_id"]) + + def __getitem__(self, index: int) -> dict[str, Any]: + arrays = self.arrays + sample: dict[str, Any] = {} + for name in ("text", "audio", "vision", "time_bounds_s", "time_s", "progress"): + sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=np.float32)) + for name in ("text_mask", "audio_mask", "vision_mask"): + sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=bool)) + for name in ("text_coverage", "audio_coverage", "vision_coverage"): + sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=np.float32)) + sample["classification_labels"] = torch.tensor(int(arrays["classification_labels"][index]), dtype=torch.long) + sample["regression_labels"] = torch.tensor(float(arrays["regression_labels"][index]), dtype=torch.float32) + sample["valid_length"] = torch.tensor(int(arrays["valid_lengths"][index]), dtype=torch.long) + for name in ("id", "sample_id", "video_id", "clip_id", "raw_text", "annotations", "source_video_sha256"): + sample[name] = str(arrays[name][index]) + return sample diff --git a/final/Q1/compare_models.py b/final/Q1/compare_models.py new file mode 100644 index 0000000..dd49602 --- /dev/null +++ b/final/Q1/compare_models.py @@ -0,0 +1,1922 @@ +from __future__ import annotations + +import argparse +import csv +import hashlib +import importlib.metadata +import json +import math +import platform +import re +import subprocess +import sys +import time +import urllib.request +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Iterable + +import cv2 +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import mediapipe as mp +import numpy as np +import torch +from openpyxl import load_workbook +from scipy.fft import dct, irfft, rfft +from scipy.spatial.transform import Rotation +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error +from sklearn.model_selection import GroupKFold +from transformers import AutoModel, AutoModelForCTC, AutoTokenizer + + +ROOT = Path(__file__).resolve().parents[2] +MATH_DIR = Path(__file__).resolve().parent +DATA_DIR = ROOT / "E棰樻暟鎹? / "闄勪欢1-鏁版嵁闆嗗師濮嬪妯℃€佹牱鏈? / "MOSEI鏁版嵁闆嗛儴鍒嗗師濮嬭棰?100鏉? +LABEL_FILE = DATA_DIR / "label-100.xlsx" +OUTPUT_DIR = MATH_DIR / "results" / "model_comparison" +CACHE_DIR = MATH_DIR / "cache" / "native" +FACE_MODEL_PATH = MATH_DIR / "cache" / "face_landmarker.task" + +TEXT_MODEL_ID = "google-bert/bert-base-uncased" +SPEECH_MODEL_ID = "facebook/wav2vec2-base-960h" +SAMPLE_RATE = 16_000 +FRAME_LENGTH = 400 +FRAME_STEP = 160 +N_FFT = 512 +MEL_COUNT = 40 +GRID_STEP_S = 0.1 +VISION_RATE_HZ = 5.0 +CTC_FRAME_STEP_S = 320 / SAMPLE_RATE +CTC_RECEPTIVE_SAMPLES = 400 +SEED = 20260923 +N_FOLDS = 5 + +ACTION_NAMES = ( + "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", + "browOuterUpRight", "eyeBlinkLeft", "eyeBlinkRight", "eyeSquintLeft", + "eyeSquintRight", "eyeWideLeft", "eyeWideRight", "jawOpen", + "mouthFrownLeft", "mouthFrownRight", "mouthPucker", "mouthSmileLeft", + "mouthSmileRight", +) +VISION_NAMES = ( + *[f"mp_blendshape_{name}" for name in ACTION_NAMES], + "pose_rotvec_x", "pose_rotvec_y", "pose_rotvec_z", + "pose_translation_x", "pose_translation_y", "pose_translation_z", + "gaze_left_x", "gaze_left_y", "gaze_left_z", + "gaze_right_x", "gaze_right_y", "gaze_right_z", + "eye_aperture_left", "eye_aperture_right", "mouth_aperture", + "mouth_width_face_ratio", "brow_eye_distance_left", "brow_eye_distance_right", +) +AUDIO_NAMES = ( + *[f"logmel_{index:02d}" for index in range(40)], + *[f"mfcc_{index:02d}" for index in range(13)], + *[f"delta_mfcc_{index:02d}" for index in range(13)], + "log_energy", "log_f0_hz", "voicing_strength", "spectral_centroid_hz", + "spectral_bandwidth_hz", "spectral_flux", "zero_crossing_rate", "hnr_db", +) + + +@dataclass +class Sample: + sample_id: str + video_id: str + clip_id: str + duration_s: float + sentiment: float + polarity: int + text_words: list[str] + text_features: np.ndarray + text_valid: np.ndarray + hard_intervals: np.ndarray + hard_valid: np.ndarray + hard_quality: np.ndarray + audio_times: np.ndarray + audio_features: np.ndarray + audio_observed: np.ndarray + vision_times: np.ndarray + vision_features: np.ndarray + vision_observed: np.ndarray + ctc_times: np.ndarray + ctc_occupancy: np.ndarray + speech_features: np.ndarray + boundary_summary: list[dict[str, float]] + video_sha256: str + + +@dataclass +class View: + features: dict[str, np.ndarray] + observed: dict[str, np.ndarray] + coverage: dict[str, np.ndarray] + edges: np.ndarray + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _version(package: str) -> str: + try: + return importlib.metadata.version(package) + except importlib.metadata.PackageNotFoundError: + return "not-installed" + + +def _safe_name(value: str) -> str: + return re.sub(r"[^A-Za-z0-9_.-]+", "_", value) + + +def _str_id(value: Any) -> str: + if isinstance(value, float) and value.is_integer(): + return str(int(value)) + return str(value).strip() + + +def _read_labels(path: Path) -> list[dict[str, Any]]: + workbook = load_workbook(path, read_only=True, data_only=True) + sheet = workbook.active + rows = sheet.iter_rows(values_only=True) + header = [str(value).strip() if value is not None else "" for value in next(rows)] + index = {name: position for position, name in enumerate(header)} + required = {"video_id", "clip_id", "text", "label", "annotation"} + missing = required - set(index) + if missing: + raise ValueError(f"label workbook lacks columns: {sorted(missing)}") + records: list[dict[str, Any]] = [] + seen: set[str] = set() + for row_number, row in enumerate(rows, start=2): + video_id = _str_id(row[index["video_id"]]) + clip_id = _str_id(row[index["clip_id"]]) + sample_id = f"{video_id}/{clip_id}" + if sample_id in seen: + raise ValueError(f"duplicate source key in label workbook: {sample_id}") + seen.add(sample_id) + label = float(row[index["label"]]) + annotation = str(row[index["annotation"]]).strip().lower() + polarity = 0 if label < 0 else 1 if label == 0 else 2 + expected = {"negative": 0, "neutral": 1, "positive": 2}.get(annotation) + records.append({ + "row_number": row_number, + "sample_id": sample_id, + "video_id": video_id, + "clip_id": clip_id, + "text": str(row[index["text"]] or ""), + "sentiment": label, + "polarity": polarity, + "annotation": annotation, + "label_consistent": expected == polarity, + }) + workbook.close() + if len(records) != 100: + raise ValueError(f"expected 100 label rows, received {len(records)}") + return records + + +def _decode_audio(path: Path) -> np.ndarray: + result = subprocess.run( + [ + "ffmpeg", "-nostdin", "-v", "error", "-i", str(path), "-vn", + "-ac", "1", "-ar", str(SAMPLE_RATE), "-f", "f32le", "pipe:1", + ], + check=True, + capture_output=True, + ) + audio = np.frombuffer(result.stdout, dtype=" float: + result = subprocess.run( + [ + "ffprobe", "-v", "error", "-show_entries", "format=duration", + "-of", "default=noprint_wrappers=1:nokey=1", str(path), + ], + check=True, + capture_output=True, + text=True, + ) + value = float(result.stdout.strip()) + if not math.isfinite(value) or value <= 0: + raise ValueError(f"invalid duration for {path}: {value}") + return value + + +def _mel_filterbank(sample_rate: int, n_fft: int, count: int) -> np.ndarray: + def to_mel(freq: np.ndarray | float) -> np.ndarray | float: + return 2595.0 * np.log10(1.0 + np.asarray(freq) / 700.0) + + def from_mel(mel: np.ndarray) -> np.ndarray: + return 700.0 * (10.0 ** (mel / 2595.0) - 1.0) + + mel_points = np.linspace(to_mel(0.0), to_mel(sample_rate / 2), count + 2) + hz_points = from_mel(mel_points) + bins_hz = np.fft.rfftfreq(n_fft, 1 / sample_rate) + bank = np.zeros((count, len(bins_hz)), dtype=np.float32) + for band in range(count): + left, center, right = hz_points[band:band + 3] + bank[band] = np.maximum( + 0.0, + np.minimum((bins_hz - left) / max(center - left, 1e-9), + (right - bins_hz) / max(right - center, 1e-9)), + ) + return bank + + +def _audio_features(audio: np.ndarray, duration_s: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + starts = np.arange(0, max(len(audio), 1), FRAME_STEP, dtype=np.int64) + frames = np.zeros((len(starts), FRAME_LENGTH), dtype=np.float32) + for index, start in enumerate(starts): + chunk = audio[start:start + FRAME_LENGTH] + frames[index, :len(chunk)] = chunk + window = np.hanning(FRAME_LENGTH).astype(np.float32) + windowed = frames * window[None, :] + spectrum = np.abs(rfft(windowed, n=N_FFT, axis=1)).astype(np.float32) + power = spectrum ** 2 + mel = power @ _mel_filterbank(SAMPLE_RATE, N_FFT, MEL_COUNT).T + log_mel = np.log(np.maximum(mel, 1e-8)).astype(np.float32) + mfcc = dct(log_mel, type=2, norm="ortho", axis=1)[:, :13].astype(np.float32) + delta = np.zeros_like(mfcc) + if len(mfcc) > 1: + for frame in range(len(mfcc)): + left, right = max(0, frame - 2), min(len(mfcc), frame + 3) + offsets = np.arange(left, right, dtype=np.float32) - frame + denominator = float(np.square(offsets).sum()) + if denominator > 0: + delta[frame] = (offsets[:, None] * mfcc[left:right]).sum(axis=0) / denominator + + frequencies = np.fft.rfftfreq(N_FFT, 1 / SAMPLE_RATE).astype(np.float32) + magnitude_sum = np.maximum(spectrum.sum(axis=1), 1e-8) + centroid = (spectrum * frequencies[None, :]).sum(axis=1) / magnitude_sum + bandwidth = np.sqrt( + (spectrum * (frequencies[None, :] - centroid[:, None]) ** 2).sum(axis=1) + / magnitude_sum + ) + normalized_spectrum = spectrum / magnitude_sum[:, None] + flux = np.zeros(len(frames), dtype=np.float32) + if len(frames) > 1: + flux[1:] = np.square(normalized_spectrum[1:] - normalized_spectrum[:-1]).sum(axis=1) + zcr = (np.diff(np.signbit(frames), axis=1) != 0).mean(axis=1).astype(np.float32) + energy = np.mean(np.square(frames), axis=1) + + log_f0 = np.zeros(len(frames), dtype=np.float32) + voicing = np.zeros(len(frames), dtype=np.float32) + hnr = np.zeros(len(frames), dtype=np.float32) + voiced = np.zeros(len(frames), dtype=np.bool_) + min_lag, max_lag = int(SAMPLE_RATE / 400), int(SAMPLE_RATE / 60) + for frame_index, frame in enumerate(windowed): + if energy[frame_index] < 1e-7: + continue + autocorr = irfft(np.abs(rfft(frame, n=1024)) ** 2, n=1024)[:max_lag + 1] + if autocorr[0] <= 1e-10: + continue + autocorr /= autocorr[0] + region = autocorr[min_lag:max_lag + 1] + if not len(region): + continue + lag = min_lag + int(np.argmax(region)) + strength = float(np.clip(autocorr[lag], 0.0, 1.0)) + voicing[frame_index] = strength + voiced[frame_index] = strength >= 0.30 + if voiced[frame_index]: + log_f0[frame_index] = math.log(SAMPLE_RATE / lag) + hnr[frame_index] = 10.0 * math.log10(max(strength, 1e-5) / max(1.0 - strength, 1e-5)) + + prosody = np.column_stack(( + np.log(np.maximum(energy, 1e-8)), log_f0, voicing, centroid, bandwidth, + flux, zcr, hnr, + )).astype(np.float32) + features = np.column_stack((log_mel, mfcc, delta, prosody)).astype(np.float32) + if features.shape[1] != 74: + raise AssertionError(f"internal audio dimension error: {features.shape}") + observed = np.isfinite(features) + observed[:, 67] = voiced + observed[:, 73] = voiced + if len(frames) < 2: + observed[:, 53:66] = False + features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0) + times = np.minimum((starts + FRAME_LENGTH / 2) / SAMPLE_RATE, duration_s).astype(np.float32) + return times, features, observed + + +def _text_features(words: list[str], tokenizer: Any, model: Any, device: torch.device) -> tuple[np.ndarray, np.ndarray]: + output = np.zeros((len(words), int(model.config.hidden_size)), dtype=np.float32) + valid = np.zeros(len(words), dtype=np.bool_) + if not words: + return output, valid + token_ids: list[int] = [] + word_ids: list[int] = [] + for word_index, word in enumerate(words): + word_tokens = tokenizer(word, add_special_tokens=False)["input_ids"] + token_ids.extend(int(value) for value in word_tokens) + word_ids.extend([word_index] * len(word_tokens)) + token_sums = np.zeros((len(token_ids), output.shape[1]), dtype=np.float64) + token_weights = np.zeros(len(token_ids), dtype=np.float64) + window_size, stride = 510, 384 + offsets = range(0, max(len(token_ids), 1), stride) + with torch.inference_mode(): + for offset in offsets: + end = min(len(token_ids), offset + window_size) + if offset >= end: + break + content = token_ids[offset:end] + special_content = [int(tokenizer.cls_token_id), *content, int(tokenizer.sep_token_id)] + encoded = { + "input_ids": torch.tensor([special_content], dtype=torch.long, device=device), + "attention_mask": torch.ones((1, len(special_content)), dtype=torch.long, device=device), + } + hidden_states = model(**encoded, output_hidden_states=True).hidden_states + hidden = torch.stack(tuple(hidden_states[-4:]), dim=0).mean(dim=0)[0, 1:1 + len(content)] + values = hidden.float().cpu().numpy() + for local_index, value in enumerate(values): + global_index = offset + local_index + edge_weight = float(min(local_index + 1, len(content) - local_index)) + token_sums[global_index] += edge_weight * value + token_weights[global_index] += edge_weight + accum = np.zeros_like(output, dtype=np.float64) + counts = np.zeros(len(words), dtype=np.float64) + for token_index, word_index in enumerate(word_ids): + if word_index is None or not (0 <= word_index < len(words)) or token_weights[token_index] <= 0: + continue + accum[word_index] += token_sums[token_index] / token_weights[token_index] + counts[word_index] += 1.0 + valid = counts > 0 + output[valid] = (accum[valid] / counts[valid, None]).astype(np.float32) + return output, valid + + +def _ctc_targets(words: list[str], tokenizer: Any) -> tuple[list[int], list[list[int]]]: + vocab = tokenizer.get_vocab() + delimiter_id = int(tokenizer.convert_tokens_to_ids(tokenizer.word_delimiter_token or "|")) + unknown_id = int(tokenizer.unk_token_id) + targets: list[int] = [] + per_word: list[list[int]] = [[] for _ in words] + for word_index, raw_word in enumerate(words): + if word_index: + targets.append(delimiter_id) + normalized = re.sub(r"[^a-z']", "", raw_word.lower().replace("鈥?, "'")) + for char in normalized: + per_word[word_index].append(len(targets)) + targets.append(int(vocab.get(char, unknown_id))) + return targets, per_word + + +def _ctc_path_and_posterior(log_probs: np.ndarray, targets: list[int], blank_id: int) -> tuple[np.ndarray | None, np.ndarray | None, float]: + if not targets or log_probs.ndim != 2: + return None, None, -math.inf + target = np.asarray(targets, dtype=np.int64) + states = np.full(2 * len(target) + 1, blank_id, dtype=np.int64) + states[1::2] = target + frames, state_count = log_probs.shape[0], len(states) + if frames == 0 or frames < len(target): + return None, None, -math.inf + alpha = np.full((frames, state_count), -np.inf, dtype=np.float64) + alpha[0, 0] = float(log_probs[0, blank_id]) + alpha[0, 1] = float(log_probs[0, states[1]]) + skip_ok = np.zeros(state_count, dtype=np.bool_) + if state_count > 2: + indices = np.arange(state_count) + skip_ok[2:] = (states[2:] != blank_id) & (states[2:] != states[:-2]) + for frame in range(1, frames): + prev = alpha[frame - 1] + incoming = prev.copy() + incoming[1:] = np.logaddexp(incoming[1:], prev[:-1]) + valid_skip = np.flatnonzero(skip_ok) + if len(valid_skip): + incoming[valid_skip] = np.logaddexp(incoming[valid_skip], prev[valid_skip - 2]) + alpha[frame] = incoming + log_probs[frame, states] + log_z = float(np.logaddexp(alpha[-1, -1], alpha[-1, -2])) + if not math.isfinite(log_z): + return None, None, -math.inf + + beta = np.full((frames, state_count), -np.inf, dtype=np.float64) + beta[-1, -1] = 0.0 + beta[-1, -2] = 0.0 + for frame in range(frames - 2, -1, -1): + nxt = beta[frame + 1] + log_probs[frame + 1, states] + # A CTC state may stay active for any number of frames. Start with the + # same-state transition, then add transitions to the next states. + values = nxt.copy() + values[:-1] = np.logaddexp(values[:-1], nxt[1:]) + if state_count > 2: + dest = np.flatnonzero(skip_ok) + values[dest - 2] = np.logaddexp(values[dest - 2], nxt[dest]) + beta[frame] = values + posterior = np.exp(np.clip(alpha + beta - log_z, -745.0, 0.0)).astype(np.float32) + + back = np.zeros((frames, state_count), dtype=np.uint8) + previous = np.full(state_count, -np.inf, dtype=np.float64) + previous[0] = float(log_probs[0, blank_id]) + previous[1] = float(log_probs[0, states[1]]) + for frame in range(1, frames): + stay = previous + one = np.full(state_count, -np.inf, dtype=np.float64) + one[1:] = previous[:-1] + two = np.full(state_count, -np.inf, dtype=np.float64) + two[skip_ok] = previous[np.flatnonzero(skip_ok) - 2] + candidates = np.stack((stay, one, two), axis=0) + choice = candidates.argmax(axis=0).astype(np.uint8) + previous = candidates[choice, np.arange(state_count)] + log_probs[frame, states] + back[frame] = choice + state = state_count - 1 if previous[-1] >= previous[-2] else state_count - 2 + path = np.empty(frames, dtype=np.int32) + path[-1] = state + for frame in range(frames - 1, 0, -1): + state -= int(back[frame, state]) + path[frame - 1] = state + return path, posterior, log_z + + +def _entry_distribution(log_probs: np.ndarray, states: np.ndarray, alpha: np.ndarray, beta: np.ndarray, log_z: float, state: int) -> np.ndarray: + frames = len(log_probs) + mass = np.zeros(frames, dtype=np.float64) + if state == 1: + mass[0] = math.exp(float(log_probs[0, states[state]] + beta[0, state] - log_z)) + predecessors = [state - 1] + if state >= 2 and states[state] != states[state - 2]: + predecessors.append(state - 2) + for frame in range(1, frames): + terms = [alpha[frame - 1, prev] for prev in predecessors if prev >= 0] + if terms: + log_mass = float(np.logaddexp.reduce(terms) + log_probs[frame, states[state]] + beta[frame, state] - log_z) + mass[frame] = math.exp(max(-745.0, min(log_mass, 0.0))) + return mass + + +def _exit_distribution(log_probs: np.ndarray, states: np.ndarray, alpha: np.ndarray, beta: np.ndarray, log_z: float, state: int) -> np.ndarray: + frames = len(log_probs) + mass = np.zeros(frames + 1, dtype=np.float64) + successors = [state + 1] + if state + 2 < len(states) and states[state] != states[state + 2]: + successors.append(state + 2) + for frame in range(1, frames): + terms = [ + alpha[frame - 1, state] + log_probs[frame, states[nxt]] + beta[frame, nxt] + for nxt in successors if nxt < len(states) + ] + if terms: + mass[frame] = math.exp(max(-745.0, min(float(np.logaddexp.reduce(terms) - log_z), 0.0))) + if state == len(states) - 2: + mass[frames] = math.exp(max(-745.0, min(float(alpha[-1, state] - log_z), 0.0))) + return mass + + +def _weighted_quantile(values: np.ndarray, weights: np.ndarray, quantile: float) -> float: + if not len(values) or float(weights.sum()) <= 0: + return math.nan + order = np.argsort(values) + values, weights = values[order], weights[order] + cumulative = np.cumsum(weights) / weights.sum() + return float(values[min(int(np.searchsorted(cumulative, quantile, side="left")), len(values) - 1)]) + + +def _ctc_boundaries( + log_probs: np.ndarray, targets: list[int], word_targets: list[list[int]], + blank_id: int, duration_s: float, center_s: float, +) -> tuple[np.ndarray, list[dict[str, float]], np.ndarray]: + path, posterior, log_z = _ctc_path_and_posterior(log_probs, targets, blank_id) + occupancy = np.zeros((len(log_probs), len(word_targets)), dtype=np.float32) + summaries: list[dict[str, float]] = [ + {"start_mean_s": math.nan, "end_mean_s": math.nan, + "start_p05_s": math.nan, "start_p95_s": math.nan, + "end_p05_s": math.nan, "end_p95_s": math.nan, + "start_width90_s": math.nan, "end_width90_s": math.nan} + for _ in word_targets + ] + if path is None or posterior is None: + return np.empty((0, len(word_targets)), dtype=np.float32), summaries, path if path is not None else np.empty((0,), dtype=np.int32) + state_ids = np.full(2 * len(targets) + 1, blank_id, dtype=np.int64) + state_ids[1::2] = np.asarray(targets, dtype=np.int64) + # Recompute the dynamic-programming tables for boundary transition marginals. + frames, count = len(log_probs), len(state_ids) + alpha = np.full((frames, count), -np.inf, dtype=np.float64) + alpha[0, 0] = log_probs[0, blank_id] + alpha[0, 1] = log_probs[0, state_ids[1]] + skip = np.zeros(count, dtype=np.bool_) + if count > 2: + skip[2:] = (state_ids[2:] != blank_id) & (state_ids[2:] != state_ids[:-2]) + for frame in range(1, frames): + incoming = alpha[frame - 1].copy() + incoming[1:] = np.logaddexp(incoming[1:], alpha[frame - 1, :-1]) + skip_states = np.flatnonzero(skip) + if len(skip_states): + incoming[skip_states] = np.logaddexp(incoming[skip_states], alpha[frame - 1, skip_states - 2]) + alpha[frame] = incoming + log_probs[frame, state_ids] + beta = np.full((frames, count), -np.inf, dtype=np.float64) + beta[-1, -1] = beta[-1, -2] = 0.0 + for frame in range(frames - 2, -1, -1): + nxt = beta[frame + 1] + log_probs[frame + 1, state_ids] + current = nxt.copy() + current[:-1] = np.logaddexp(current[:-1], nxt[1:]) + skip_states = np.flatnonzero(skip) + if len(skip_states): + current[skip_states - 2] = np.logaddexp(current[skip_states - 2], nxt[skip_states]) + beta[frame] = current + frame_step = CTC_FRAME_STEP_S + first_edge = max(0.0, center_s - frame_step / 2) + frame_edges = np.clip(first_edge + np.arange(frames + 1, dtype=np.float64) * frame_step, 0.0, duration_s) + for word_index, token_indices in enumerate(word_targets): + if not token_indices: + continue + char_states = np.asarray([2 * target_index + 1 for target_index in token_indices], dtype=np.int64) + occupancy[:, word_index] = posterior[:, char_states].sum(axis=1) + start_mass = _entry_distribution(log_probs, state_ids, alpha, beta, log_z, int(char_states[0])) + end_mass = _exit_distribution(log_probs, state_ids, alpha, beta, log_z, int(char_states[-1])) + start_total = float(start_mass.sum()) + end_total = float(end_mass.sum()) + if not math.isclose(start_total, 1.0, rel_tol=2e-4, abs_tol=2e-4): + raise FloatingPointError(f"CTC start-boundary marginal sums to {start_total:.6g}, expected 1") + if not math.isclose(end_total, 1.0, rel_tol=2e-4, abs_tol=2e-4): + raise FloatingPointError(f"CTC end-boundary marginal sums to {end_total:.6g}, expected 1") + start_times = frame_edges[:-1] + end_times = frame_edges + start_p05 = _weighted_quantile(start_times, start_mass, 0.05) + start_p95 = _weighted_quantile(start_times, start_mass, 0.95) + end_p05 = _weighted_quantile(end_times, end_mass, 0.05) + end_p95 = _weighted_quantile(end_times, end_mass, 0.95) + summaries[word_index] = { + "start_mean_s": float(np.sum(start_mass * start_times) / start_total), + "end_mean_s": float(np.sum(end_mass * end_times) / end_total), + "start_p05_s": start_p05, + "start_p95_s": start_p95, + "end_p05_s": end_p05, + "end_p95_s": end_p95, + "start_width90_s": start_p95 - start_p05, + "end_width90_s": end_p95 - end_p05, + } + return occupancy, summaries, path + + +def _posterior_hard_intervals( + log_probs: np.ndarray, + path: np.ndarray | None, + targets: list[int], + word_targets: list[list[int]], + duration_s: float, + frame_step: float, + center_s: float, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + intervals = np.zeros((len(word_targets), 2), dtype=np.float32) + valid = np.zeros(len(word_targets), dtype=np.bool_) + quality = np.zeros(len(word_targets), dtype=np.float32) + if path is None or not len(path): + return intervals, valid, quality + for word_index, target_indices in enumerate(word_targets): + if not target_indices: + continue + char_states = np.asarray([2 * index + 1 for index in target_indices], dtype=np.int32) + frame_indices = np.flatnonzero(np.isin(path, char_states)) + if not len(frame_indices): + continue + first, last = int(frame_indices[0]), int(frame_indices[-1]) + start = max(0.0, first * frame_step + center_s - frame_step / 2) + end = min(duration_s, (last + 1) * frame_step + center_s - frame_step / 2) + if end <= start: + continue + intervals[word_index] = (start, end) + valid[word_index] = True + scores = [] + for target_index in target_indices: + selected = np.flatnonzero(path == 2 * target_index + 1) + if len(selected): + scores.extend(log_probs[selected, targets[target_index]].tolist()) + quality[word_index] = float(np.exp(np.mean(scores))) if scores else 0.0 + return intervals, valid, quality + + +def _landmark_points(landmarks: Iterable[Any]) -> np.ndarray: + return np.asarray([[item.x, item.y, item.z] for item in landmarks], dtype=np.float32) + + +def _distance(points: np.ndarray, left: int, right: int) -> float: + return float(np.linalg.norm(points[left] - points[right])) + + +def _gaze_proxy(points: np.ndarray, left_eye: bool) -> np.ndarray | None: + if points.shape[0] < 478: + return None + if left_eye: + corner_a, corner_b, upper, lower = 33, 133, 159, 145 + iris_ids = np.arange(468, 473) + else: + corner_a, corner_b, upper, lower = 362, 263, 386, 374 + iris_ids = np.arange(473, 478) + horizontal = points[corner_b] - points[corner_a] + width = float(np.linalg.norm(horizontal)) + vertical = points[upper] - points[lower] + height = float(np.linalg.norm(vertical)) + if width <= 1e-6 or height <= 1e-6: + return None + u = horizontal / width + v = vertical - float(np.dot(vertical, u)) * u + v_norm = float(np.linalg.norm(v)) + if v_norm <= 1e-6: + return None + v /= v_norm + eye_center = (points[corner_a] + points[corner_b]) / 2 + iris_center = points[iris_ids].mean(axis=0) + delta = iris_center - eye_center + gx = float(np.dot(delta, u) / width) + gy = float(np.dot(delta, v) / width) + # Face-relative iris offset plus a fixed forward component; this is a gaze proxy. + direction = np.asarray([-gx, -gy, -1.0], dtype=np.float32) + norm = float(np.linalg.norm(direction)) + if norm <= 1e-8: + return None + return direction / norm + + +def _pose_from_matrix(matrix_obj: Any) -> tuple[np.ndarray, np.ndarray] | None: + raw = np.asarray(matrix_obj.data, dtype=np.float64) + rows = int(getattr(matrix_obj, "rows", 4) or 4) + cols = int(getattr(matrix_obj, "cols", 4) or 4) + if raw.size != rows * cols or rows < 3 or cols < 4: + return None + matrix = raw.reshape(rows, cols) + rotation_raw = matrix[:3, :3] + if not np.isfinite(rotation_raw).all() or not np.isfinite(matrix[:3, 3]).all(): + return None + u, _, vh = np.linalg.svd(rotation_raw) + rotation_matrix = u @ vh + if np.linalg.det(rotation_matrix) < 0: + u[:, -1] *= -1 + rotation_matrix = u @ vh + return Rotation.from_matrix(rotation_matrix).as_rotvec().astype(np.float32), matrix[:3, 3].astype(np.float32) + + +def _vision_frame_features(landmarks: list[Any], blendshapes: list[Any], matrix_obj: Any | None) -> tuple[np.ndarray, np.ndarray]: + features = np.zeros(35, dtype=np.float32) + observed = np.zeros(35, dtype=np.bool_) + shape_lookup = {category.category_name: float(category.score) for category in blendshapes} + for index, name in enumerate(ACTION_NAMES): + if name in shape_lookup and math.isfinite(shape_lookup[name]): + features[index] = shape_lookup[name] + observed[index] = True + + if matrix_obj is not None: + pose = _pose_from_matrix(matrix_obj) + if pose is not None: + features[17:20] = pose[0] + features[20:23] = pose[1] + observed[17:23] = True + + points = _landmark_points(landmarks) + for eye_index, is_left in enumerate((True, False)): + gaze = _gaze_proxy(points, is_left) + if gaze is not None: + start = 23 + eye_index * 3 + features[start:start + 3] = gaze + observed[start:start + 3] = True + if points.shape[0] >= 455: + face_width = _distance(points, 234, 454) + left_eye_width = _distance(points, 33, 133) + right_eye_width = _distance(points, 362, 263) + mouth_width = _distance(points, 61, 291) + if min(face_width, left_eye_width, right_eye_width, mouth_width) > 1e-6: + ratios = np.asarray(( + _distance(points, 159, 145) / left_eye_width, + _distance(points, 386, 374) / right_eye_width, + _distance(points, 13, 14) / mouth_width, + mouth_width / face_width, + _distance(points, 105, 159) / left_eye_width, + _distance(points, 334, 386) / right_eye_width, + ), dtype=np.float32) + features[29:35] = ratios + observed[29:35] = np.isfinite(ratios) + return features, observed + + +def _ensure_face_model() -> str: + FACE_MODEL_PATH.parent.mkdir(parents=True, exist_ok=True) + if not FACE_MODEL_PATH.is_file() or FACE_MODEL_PATH.stat().st_size < 1_000_000: + url = ( + "https://storage.googleapis.com/mediapipe-models/face_landmarker/" + "face_landmarker/float16/latest/face_landmarker.task" + ) + temporary = FACE_MODEL_PATH.with_suffix(".task.tmp") + urllib.request.urlretrieve(url, temporary) + if temporary.stat().st_size < 1_000_000: + temporary.unlink(missing_ok=True) + raise RuntimeError("downloaded Face Landmarker model is unexpectedly small") + temporary.replace(FACE_MODEL_PATH) + return _sha256(FACE_MODEL_PATH) + + +def _vision_features(path: Path, duration_s: float, face_model_path: Path) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + options = mp.tasks.vision.FaceLandmarkerOptions( + base_options=mp.tasks.BaseOptions(model_asset_path=str(face_model_path)), + running_mode=mp.tasks.vision.RunningMode.VIDEO, + num_faces=1, + output_face_blendshapes=True, + output_facial_transformation_matrixes=True, + ) + capture = cv2.VideoCapture(str(path)) + if not capture.isOpened(): + raise RuntimeError(f"OpenCV cannot decode video: {path}") + fps = float(capture.get(cv2.CAP_PROP_FPS) or 0.0) + if not math.isfinite(fps) or fps <= 0: + fps = 30.0 + timestamps: list[float] = [] + values: list[np.ndarray] = [] + masks: list[np.ndarray] = [] + next_time = 0.0 + frame_index = 0 + last_timestamp_ms = -1 + with mp.tasks.vision.FaceLandmarker.create_from_options(options) as landmarker: + while True: + ok, bgr = capture.read() + if not ok: + break + reported_ms = float(capture.get(cv2.CAP_PROP_POS_MSEC)) + timestamp = reported_ms / 1000.0 + previous_timestamp = timestamps[-1] if timestamps else -1.0 + if not math.isfinite(timestamp) or (frame_index > 0 and timestamp <= previous_timestamp): + timestamp = frame_index / fps + frame_index += 1 + if timestamp + 1e-6 < next_time: + continue + while next_time <= timestamp: + next_time += 1.0 / VISION_RATE_HZ + timestamp = min(max(timestamp, 0.0), duration_s) + timestamp_ms = max(last_timestamp_ms + 1, int(round(timestamp * 1000))) + last_timestamp_ms = timestamp_ms + rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) + image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.ascontiguousarray(rgb)) + result = landmarker.detect_for_video(image, timestamp_ms) + timestamps.append(timestamp) + faces = result.face_landmarks + if not faces: + values.append(np.zeros(35, dtype=np.float32)) + masks.append(np.zeros(35, dtype=np.bool_)) + continue + blendshapes = result.face_blendshapes[0] if result.face_blendshapes else [] + matrix = result.facial_transformation_matrixes[0] if result.facial_transformation_matrixes else None + vector, mask = _vision_frame_features(faces[0], blendshapes, matrix) + values.append(vector) + masks.append(mask) + capture.release() + if not timestamps: + return np.empty(0, np.float32), np.empty((0, 35), np.float32), np.empty((0, 35), np.bool_) + return np.asarray(timestamps, np.float32), np.stack(values), np.stack(masks) + + +def _load_models(device: torch.device) -> tuple[Any, Any, Any, Any, dict[str, Any]]: + text_tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID, use_fast=True) + text_model = AutoModel.from_pretrained(TEXT_MODEL_ID, output_hidden_states=True).to(device).eval() + speech_tokenizer = AutoTokenizer.from_pretrained(SPEECH_MODEL_ID) + speech_model = AutoModelForCTC.from_pretrained(SPEECH_MODEL_ID).to(device).eval() + config = speech_model.config + stride_samples = int(np.prod(config.conv_stride)) + receptive = 1 + jump = 1 + for kernel, stride in zip(config.conv_kernel, config.conv_stride): + receptive += (int(kernel) - 1) * jump + jump *= int(stride) + info = { + "text_id": TEXT_MODEL_ID, + "text_revision": getattr(text_model.config, "_commit_hash", None), + "speech_id": SPEECH_MODEL_ID, + "speech_revision": getattr(config, "_commit_hash", None), + "speech_stride_samples": stride_samples, + "speech_receptive_field_samples": receptive, + } + return text_tokenizer, text_model, speech_tokenizer, speech_model, info + + +def _extract_native_sample( + record: dict[str, Any], + models: tuple[Any, Any, Any, Any, dict[str, Any]], + device: torch.device, + face_model_path: Path, + video_sha256: str, +) -> Sample: + text_tokenizer, text_model, ctc_tokenizer, speech_model, model_info = models + video_path = DATA_DIR / record["video_id"] / f"{record['clip_id']}.mp4" + duration_s = _duration(video_path) + words = record["text"].split() + text_vectors, text_valid = _text_features(words, text_tokenizer, text_model, device) + waveform = _decode_audio(video_path) + audio_times, audio_vectors, audio_observed = _audio_features(waveform, duration_s) + vision_times, vision_vectors, vision_observed = _vision_features(video_path, duration_s, face_model_path) + targets, word_targets = _ctc_targets(words, ctc_tokenizer) + blank_id = int(ctc_tokenizer.pad_token_id) + if targets: + with torch.inference_mode(): + input_values = torch.from_numpy(waveform).to(device).unsqueeze(0) + output = speech_model(input_values=input_values, output_hidden_states=True) + logits = output.logits[0].float() + log_probs = torch.log_softmax(logits, dim=-1).cpu().numpy().astype(np.float32) + deep_states = torch.stack(tuple(output.hidden_states[-4:]), dim=0).mean(dim=0)[0].float().cpu().numpy() + else: + log_probs = np.empty((0, len(ctc_tokenizer)), dtype=np.float32) + deep_states = np.empty((0, int(speech_model.config.hidden_size)), dtype=np.float32) + stride_samples = int(model_info["speech_stride_samples"]) + frame_step = stride_samples / SAMPLE_RATE + center_s = float(model_info["speech_receptive_field_samples"]) / (2 * SAMPLE_RATE) + if len(log_probs) and targets: + occupancy, boundary_summary, path = _ctc_boundaries( + log_probs, targets, word_targets, blank_id, duration_s, center_s + ) + hard_intervals, hard_valid, hard_quality = _posterior_hard_intervals( + log_probs, path, targets, word_targets, duration_s, frame_step, center_s + ) + else: + occupancy = np.zeros((len(log_probs), len(words)), dtype=np.float32) + boundary_summary = [ + {"start_mean_s": math.nan, "end_mean_s": math.nan, + "start_p05_s": math.nan, "start_p95_s": math.nan, + "end_p05_s": math.nan, "end_p95_s": math.nan, + "start_width90_s": math.nan, "end_width90_s": math.nan} + for _ in words + ] + hard_intervals = np.zeros((len(words), 2), dtype=np.float32) + hard_valid = np.zeros(len(words), dtype=np.bool_) + hard_quality = np.zeros(len(words), dtype=np.float32) + ctc_frame_times = (np.arange(len(log_probs), dtype=np.float32) * frame_step + center_s).clip(0.0, duration_s) + return Sample( + sample_id=record["sample_id"], + video_id=record["video_id"], + clip_id=record["clip_id"], + duration_s=duration_s, + sentiment=record["sentiment"], + polarity=record["polarity"], + text_words=words, + text_features=text_vectors.astype(np.float16), + text_valid=text_valid, + hard_intervals=hard_intervals, + hard_valid=hard_valid, + hard_quality=hard_quality, + audio_times=audio_times, + audio_features=audio_vectors.astype(np.float16), + audio_observed=audio_observed, + vision_times=vision_times, + vision_features=vision_vectors.astype(np.float16), + vision_observed=vision_observed, + ctc_times=ctc_frame_times, + ctc_occupancy=occupancy.astype(np.float16), + speech_features=deep_states.astype(np.float16), + boundary_summary=boundary_summary, + video_sha256=video_sha256, + ) + + +def _save_cache(path: Path, sample: Sample, cache_schema: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(".npz.tmp") + with temporary.open("wb") as stream: + np.savez_compressed( + stream, + cache_schema=np.asarray(cache_schema), + sample_id=np.asarray(sample.sample_id), + video_id=np.asarray(sample.video_id), + clip_id=np.asarray(sample.clip_id), + duration_s=np.asarray(sample.duration_s, dtype=np.float32), + sentiment=np.asarray(sample.sentiment, dtype=np.float32), + polarity=np.asarray(sample.polarity, dtype=np.int8), + text_words=np.asarray(sample.text_words, dtype="U160"), + text_features=sample.text_features.astype(np.float16), + text_valid=sample.text_valid, + hard_intervals=sample.hard_intervals.astype(np.float32), + hard_valid=sample.hard_valid, + hard_quality=sample.hard_quality.astype(np.float32), + audio_times=sample.audio_times.astype(np.float32), + audio_features=sample.audio_features.astype(np.float16), + audio_observed=sample.audio_observed, + vision_times=sample.vision_times.astype(np.float32), + vision_features=sample.vision_features.astype(np.float16), + vision_observed=sample.vision_observed, + ctc_times=sample.ctc_times.astype(np.float32), + ctc_occupancy=sample.ctc_occupancy.astype(np.float16), + speech_features=sample.speech_features.astype(np.float16), + boundary_summary=json.dumps(sample.boundary_summary, ensure_ascii=False), + video_sha256=np.asarray(sample.video_sha256), + ) + temporary.replace(path) + + +def _load_cache(path: Path, record: dict[str, Any], source_hash: str, cache_schema: str) -> Sample: + with np.load(path, allow_pickle=False) as data: + if str(data["cache_schema"].item()) != cache_schema: + raise ValueError("feature cache schema mismatch") + if str(data["sample_id"].item()) != record["sample_id"]: + raise ValueError("feature cache sample key mismatch") + if str(data["video_sha256"].item()) != source_hash: + raise ValueError("feature cache source hash mismatch") + return Sample( + sample_id=record["sample_id"], video_id=record["video_id"], clip_id=record["clip_id"], + duration_s=float(data["duration_s"]), sentiment=float(data["sentiment"]), polarity=int(data["polarity"]), + text_words=data["text_words"].astype(str).tolist(), text_features=np.asarray(data["text_features"], dtype=np.float16), + text_valid=np.asarray(data["text_valid"], dtype=np.bool_), hard_intervals=np.asarray(data["hard_intervals"], dtype=np.float32), + hard_valid=np.asarray(data["hard_valid"], dtype=np.bool_), hard_quality=np.asarray(data["hard_quality"], dtype=np.float32), + audio_times=np.asarray(data["audio_times"], dtype=np.float32), audio_features=np.asarray(data["audio_features"], dtype=np.float16), + audio_observed=np.asarray(data["audio_observed"], dtype=np.bool_), vision_times=np.asarray(data["vision_times"], dtype=np.float32), + vision_features=np.asarray(data["vision_features"], dtype=np.float16), vision_observed=np.asarray(data["vision_observed"], dtype=np.bool_), + ctc_times=np.asarray(data["ctc_times"], dtype=np.float32), ctc_occupancy=np.asarray(data["ctc_occupancy"], dtype=np.float16), + speech_features=np.asarray(data["speech_features"], dtype=np.float16), + boundary_summary=json.loads(str(data["boundary_summary"].item())), video_sha256=source_hash, + ) + + +def _voronoi_intervals(times: np.ndarray, duration_s: float) -> np.ndarray: + if not len(times): + return np.empty((0, 2), dtype=np.float32) + centers = np.clip(np.asarray(times, dtype=np.float64), 0.0, duration_s) + if np.any(np.diff(centers) < -1e-7): + order = np.argsort(centers) + centers = centers[order] + else: + order = np.arange(len(centers)) + # Duplicate PTS values are coalesced to a tiny increasing interval for assignment. + for index in range(1, len(centers)): + if centers[index] <= centers[index - 1]: + centers[index] = min(duration_s, centers[index - 1] + 1e-6) + edges = np.empty(len(centers) + 1, dtype=np.float64) + edges[0], edges[-1] = 0.0, duration_s + if len(centers) > 1: + edges[1:-1] = (centers[:-1] + centers[1:]) / 2 + edges = np.maximum.accumulate(np.clip(edges, 0.0, duration_s)) + intervals = np.column_stack((edges[:-1], edges[1:])).astype(np.float32) + if len(order) != len(intervals) or not np.array_equal(order, np.arange(len(order))): + restored = np.zeros_like(intervals) + restored[order] = intervals + return restored + return intervals + + +def _grid_edges(duration_s: float) -> np.ndarray: + count = max(1, int(math.ceil(duration_s / GRID_STEP_S))) + edges = np.arange(count + 1, dtype=np.float64) * GRID_STEP_S + edges[-1] = duration_s + return edges + + +def _project_rows( + values: np.ndarray, + observed: np.ndarray, + source_intervals: np.ndarray, + qualities: np.ndarray, + target_edges: np.ndarray, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + dim = values.shape[1] if values.ndim == 2 else 0 + count = len(target_edges) - 1 + output = np.zeros((count, dim), dtype=np.float32) + mask = np.zeros((count, dim), dtype=np.bool_) + coverage = np.zeros((count, dim), dtype=np.float32) + if not dim or not len(source_intervals): + return output, mask, coverage + intervals = np.asarray(source_intervals, dtype=np.float64) + qualities = np.maximum(np.asarray(qualities, dtype=np.float64), 0.0) + values64 = np.asarray(values, dtype=np.float64) + for target_index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): + if right <= left: + continue + overlap = np.maximum( + 0.0, + np.minimum(intervals[:, 1], right) - np.maximum(intervals[:, 0], left), + ) * qualities + candidate = np.flatnonzero(overlap > 0) + if not len(candidate): + continue + local_mask = observed[candidate] + local_weight = overlap[candidate, None] * local_mask + denominator = local_weight.sum(axis=0) + physical_coverage = (overlap[candidate, None] * local_mask).sum(axis=0) + good = denominator > 0 + if good.any(): + output[target_index, good] = ( + (values64[candidate] * local_weight).sum(axis=0)[good] / denominator[good] + ).astype(np.float32) + mask[target_index, good] = True + coverage[target_index, good] = np.minimum(1.0, physical_coverage[good] / (right - left)).astype(np.float32) + return output, mask, coverage + + +def _so3_weighted_mean(rotvecs: np.ndarray, weights: np.ndarray) -> np.ndarray | None: + positive = weights > 0 + if not positive.any(): + return None + rotations = Rotation.from_rotvec(np.asarray(rotvecs[positive], dtype=np.float64)) + local_weights = np.asarray(weights[positive], dtype=np.float64) + local_weights /= local_weights.sum() + matrices = rotations.as_matrix() + mean_matrix = np.einsum("n,nij->ij", local_weights, matrices) + u, _, vh = np.linalg.svd(mean_matrix) + mean_matrix = u @ vh + if np.linalg.det(mean_matrix) < 0: + u[:, -1] *= -1 + mean_matrix = u @ vh + mean_rotation = Rotation.from_matrix(mean_matrix) + for _ in range(30): + residual = (mean_rotation.inv() * rotations).as_rotvec() + delta = np.einsum("n,nd->d", local_weights, residual) + if np.linalg.norm(delta) < 1e-8: + break + mean_rotation = mean_rotation * Rotation.from_rotvec(delta) + return mean_rotation.as_rotvec().astype(np.float32) + + +def _project_vision_geometry(sample: Sample, target_edges: np.ndarray, geometry_aware: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + intervals = _voronoi_intervals(sample.vision_times, sample.duration_s) + quality = np.ones(len(intervals), dtype=np.float32) + values, masks, coverage = _project_rows( + sample.vision_features.astype(np.float32), sample.vision_observed, intervals, quality, target_edges + ) + if not geometry_aware or not len(intervals): + return values, masks, coverage + raw = sample.vision_features.astype(np.float32) + for target_index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): + overlap = np.maximum(0.0, np.minimum(intervals[:, 1], right) - np.maximum(intervals[:, 0], left)) + if not np.any(overlap > 0): + continue + pose_rows = (overlap > 0) & sample.vision_observed[:, 17:20].all(axis=1) + if pose_rows.any(): + vector = _so3_weighted_mean(raw[pose_rows, 17:20], overlap[pose_rows]) + if vector is not None: + values[target_index, 17:20] = vector + masks[target_index, 17:20] = True + for start in (23, 26): + valid_rows = (overlap > 0) & sample.vision_observed[:, start:start + 3].all(axis=1) + if valid_rows.any(): + weights = overlap[valid_rows].astype(np.float64) + vector = np.average(raw[valid_rows, start:start + 3], axis=0, weights=weights) + norm = float(np.linalg.norm(vector)) + if norm > 1e-6: + values[target_index, start:start + 3] = vector / norm + masks[target_index, start:start + 3] = True + else: + values[target_index, start:start + 3] = 0.0 + masks[target_index, start:start + 3] = False + return values, masks, coverage + + +def _project_posterior_text(sample: Sample, target_edges: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + words, dim = sample.text_features.shape + output = np.zeros((len(target_edges) - 1, dim), dtype=np.float32) + mask = np.zeros((len(target_edges) - 1, dim), dtype=np.bool_) + coverage = np.zeros((len(target_edges) - 1, dim), dtype=np.float32) + if not words or not len(sample.ctc_times): + return output, mask, coverage + frame_step = CTC_FRAME_STEP_S + first_edge = max(0.0, float(sample.ctc_times[0]) - frame_step / 2) + frame_edges = np.clip( + first_edge + np.arange(len(sample.ctc_times) + 1, dtype=np.float64) * frame_step, + 0.0, + sample.duration_s, + ) + occupancy = sample.ctc_occupancy.astype(np.float64) + for target_index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): + overlap = np.maximum(0.0, np.minimum(frame_edges[1:], right) - np.maximum(frame_edges[:-1], left)) + if not np.any(overlap > 0): + continue + alpha = (occupancy * overlap[:, None]).sum(axis=0) + alpha *= sample.text_valid + total = float(alpha.sum()) + if total <= 0: + continue + output[target_index] = (alpha @ sample.text_features.astype(np.float64) / total).astype(np.float32) + mask[target_index] = True + coverage[target_index] = min(1.0, total / max(right - left, 1e-8)) + return output, mask, coverage + + +def _normalized_hard_quality(sample: Sample) -> np.ndarray: + text_quality = np.zeros_like(sample.hard_quality, dtype=np.float32) + valid_quality = sample.hard_valid & (sample.hard_quality > 0) + if valid_quality.any(): + quality_reference = float(np.median(sample.hard_quality[valid_quality])) + text_quality[valid_quality] = np.clip( + sample.hard_quality[valid_quality].astype(np.float64) / quality_reference, 0.25, 4.0 + ) + return text_quality + + +def _make_views(sample: Sample) -> tuple[View, View, View]: + edges = _grid_edges(sample.duration_s) + hard_text_observed = np.broadcast_to(sample.text_valid[:, None], sample.text_features.shape).copy() + text_quality = _normalized_hard_quality(sample) + text_hard = _project_rows( + sample.text_features.astype(np.float32), hard_text_observed, + sample.hard_intervals, text_quality, edges, + ) + audio = _project_rows( + sample.audio_features.astype(np.float32), sample.audio_observed, + _voronoi_intervals(sample.audio_times, sample.duration_s), + np.ones(len(sample.audio_times), dtype=np.float32), edges, + ) + vision_b0 = _project_vision_geometry(sample, edges, geometry_aware=False) + vision_b1 = _project_vision_geometry(sample, edges, geometry_aware=True) + speech = _project_rows( + sample.speech_features.astype(np.float32), + np.ones(sample.speech_features.shape, dtype=np.bool_), + _voronoi_intervals(sample.ctc_times, sample.duration_s), + np.ones(len(sample.ctc_times), dtype=np.float32), edges, + ) + post_text = _project_posterior_text(sample, edges) + view_b0 = View( + features={"text": text_hard[0], "audio": audio[0], "vision": vision_b0[0], "speech": speech[0]}, + observed={"text": text_hard[1], "audio": audio[1], "vision": vision_b0[1], "speech": speech[1]}, + coverage={"text": text_hard[2], "audio": audio[2], "vision": vision_b0[2], "speech": speech[2]}, + edges=edges, + ) + view_b1 = View( + features={"text": text_hard[0], "audio": audio[0], "vision": vision_b1[0], "speech": speech[0]}, + observed={"text": text_hard[1], "audio": audio[1], "vision": vision_b1[1], "speech": speech[1]}, + coverage={"text": text_hard[2], "audio": audio[2], "vision": vision_b1[2], "speech": speech[2]}, + edges=edges, + ) + # B2 changes only text's time projection; media timestamps and their masks stay fixed. + view_b2 = View( + features={**view_b1.features, "text": post_text[0]}, + observed={**view_b1.observed, "text": post_text[1]}, + coverage={**view_b1.coverage, "text": post_text[2]}, + edges=edges, + ) + return view_b0, view_b1, view_b2 + + +def _fit_feature_scaler(samples: list[Sample], views: list[View], indices: np.ndarray, mode: str) -> dict[str, tuple[np.ndarray, np.ndarray]]: + modalities = ("text", "audio", "vision", "speech") + scalers: dict[str, tuple[np.ndarray, np.ndarray]] = {} + for modality in modalities: + dims = views[int(indices[0])].features[modality].shape[1] + columns: list[np.ndarray] = [] + for dim in range(dims): + values = [ + views[int(index)].features[modality][:, dim][views[int(index)].observed[modality][:, dim]] + for index in indices + ] + nonempty = [array for array in values if len(array)] + columns.append(np.concatenate(nonempty).astype(np.float64) if nonempty else np.empty(0, dtype=np.float64)) + center = np.zeros(dims, dtype=np.float64) + scale = np.ones(dims, dtype=np.float64) + for dim, values in enumerate(columns): + if not len(values): + continue + if mode == "robust": + center[dim] = np.median(values) + mad = 1.4826 * np.median(np.abs(values - center[dim])) + std = np.std(values) + scale[dim] = mad if mad > 1e-8 else std if std > 1e-8 else 1.0 + else: + center[dim] = np.mean(values) + std = np.std(values) + scale[dim] = std if std > 1e-8 else 1.0 + scalers[modality] = (center.astype(np.float32), scale.astype(np.float32)) + return scalers + + +def _scaled_view(view: View, scalers: dict[str, tuple[np.ndarray, np.ndarray]], clip: bool) -> View: + features: dict[str, np.ndarray] = {} + for modality, value in view.features.items(): + center, scale = scalers[modality] + transformed = (value - center[None, :]) / scale[None, :] + if clip: + transformed = np.clip(transformed, -8.0, 8.0) + transformed = np.where(view.observed[modality], transformed, 0.0) + features[modality] = transformed.astype(np.float32) + return View(features=features, observed=view.observed, coverage=view.coverage, edges=view.edges) + + +def _path_signature(times: np.ndarray, values: np.ndarray) -> np.ndarray: + # Time-augmented piecewise-linear path; return first level and antisymmetric area. + dimension = values.shape[1] + 1 + if len(times) < 3: + return np.zeros(dimension + dimension * (dimension - 1) // 2, dtype=np.float32) + local_time = (times - times[0]) / max(times[-1] - times[0], 1e-8) + path = np.column_stack((local_time, values)).astype(np.float64) + increments = np.diff(path, axis=0) + first = increments.sum(axis=0) + areas: list[float] = [] + for left in range(dimension): + for right in range(left + 1, dimension): + prefix_left = np.cumsum(increments[:, left]) - increments[:, left] + prefix_right = np.cumsum(increments[:, right]) - increments[:, right] + areas.append(float(0.5 * np.sum(prefix_left * increments[:, right] - prefix_right * increments[:, left]))) + return np.concatenate((first, np.asarray(areas, dtype=np.float64))).astype(np.float32) + + +def _signature_for_segment( + times: np.ndarray, values: np.ndarray, valid: np.ndarray, left: float, right: float, +) -> tuple[np.ndarray, float]: + dim = values.shape[1] + output_dim = (dim + 1) + (dim + 1) * dim // 2 + selected = (times >= left) & (times < right) & valid + indices = np.flatnonzero(selected) + if len(indices) < 3: + return np.zeros(output_dim, dtype=np.float32), 0.0 + # Split at every missing source position; never connect across a missing interval. + chunks = np.split(indices, np.flatnonzero(np.diff(indices) > 1) + 1) + signatures: list[np.ndarray] = [] + weights: list[float] = [] + for chunk in chunks: + if len(chunk) < 3: + continue + duration = float(max(times[chunk[-1]] - times[chunk[0]], 0.0)) + if duration <= 0: + continue + signatures.append(_path_signature(times[chunk], values[chunk])) + weights.append(duration) + if not weights: + return np.zeros(output_dim, dtype=np.float32), 0.0 + weight_array = np.asarray(weights, dtype=np.float64) + signature = np.average(np.stack(signatures), axis=0, weights=weight_array) + valid_duration = min(float(np.sum(weight_array)), max(right - left, 1e-8)) + return signature.astype(np.float32), float(np.clip(valid_duration / (right - left), 0.0, 1.0)) + + +def _dynamic_signature(sample: Sample, scaled_view: View) -> np.ndarray: + duration = sample.duration_s + segments = np.linspace(0.0, duration, 6) + centers = (scaled_view.edges[:-1] + scaled_view.edges[1:]) / 2 + audio = scaled_view.features["audio"] + audio_mask = scaled_view.observed["audio"] + vision = scaled_view.features["vision"] + vision_mask = scaled_view.observed["vision"] + # log-F0, log-energy; MediaPipe jaw-open proxy and normalized mouth aperture. + audio_values = audio[:, [67, 66]] + audio_valid = audio_mask[:, [67, 66]].all(axis=1) + vision_values = vision[:, [11, 31]] + vision_valid = vision_mask[:, [11, 31]].all(axis=1) + result: list[float] = [] + for index in range(5): + left, right = float(segments[index]), float(segments[index + 1]) + signature, coverage = _signature_for_segment(centers, audio_values, audio_valid, left, right) + result.extend(signature.tolist()) + result.append(coverage) + signature, coverage = _signature_for_segment(centers, vision_values, vision_valid, left, right) + result.extend(signature.tolist()) + result.append(coverage) + return np.asarray(result, dtype=np.float32) + + +def _pool_progress(view: View, include_speech: bool, extra: np.ndarray | None = None) -> np.ndarray: + duration = float(view.edges[-1]) + progress_edges = np.linspace(0.0, duration, 6) + centers = (view.edges[:-1] + view.edges[1:]) / 2 + cell_lengths = np.diff(view.edges) + names = ["text", "audio", "vision"] + (["speech"] if include_speech else []) + pooled: list[np.ndarray] = [] + for name in names: + values, masks, coverage = view.features[name], view.observed[name], view.coverage[name] + rows = [] + for left, right in zip(progress_edges[:-1], progress_edges[1:]): + select = (centers >= left) & (centers < right) + weights = cell_lengths[select, None] * coverage[select] * masks[select] + denom = weights.sum(axis=0) + good = denom > 0 + out = np.zeros(values.shape[1], dtype=np.float32) + if good.any(): + out[good] = ((values[select] * weights).sum(axis=0)[good] / denom[good]).astype(np.float32) + rows.append(out) + pooled.append(np.stack(rows).reshape(-1)) + if extra is not None: + pooled.append(extra) + return np.concatenate(pooled).astype(np.float32) + + +def _view_coverage(view: View, modality: str) -> float: + mask = view.observed[modality] + cov = view.coverage[modality] + if not mask.size: + return 0.0 + return float(np.mean(cov[mask])) if mask.any() else 0.0 + + +def _metrics(y_class: np.ndarray, p_class: np.ndarray, y_value: np.ndarray, p_value: np.ndarray) -> dict[str, float]: + pearson = float(np.corrcoef(y_value, p_value)[0, 1]) if len(y_value) > 1 and np.std(p_value) > 1e-12 else math.nan + return { + "accuracy": float(accuracy_score(y_class, p_class)), + "macro_f1": float(f1_score(y_class, p_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_value, p_value)), + "pearson": pearson, + } + + +def _write_csv(path: Path, rows: list[dict[str, Any]], fields: list[str]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=fields, extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def _group_bootstrap_deltas( + samples: list[Sample], predictions: dict[str, dict[int, tuple[int, float]]], repeats: int, +) -> list[dict[str, Any]]: + rng = np.random.default_rng(SEED) + groups: dict[str, list[int]] = {} + for index, sample in enumerate(samples): + groups.setdefault(sample.video_id, []).append(index) + group_names = sorted(groups) + y_class = np.asarray([sample.polarity for sample in samples], dtype=np.int64) + y_value = np.asarray([sample.sentiment for sample in samples], dtype=np.float64) + records: list[dict[str, Any]] = [] + reference = "B1" + for method in ("B0", "B2", "B3", "B4"): + point_ref = np.asarray([predictions[reference][i][0] for i in range(len(samples))], dtype=np.int64) + point_method = np.asarray([predictions[method][i][0] for i in range(len(samples))], dtype=np.int64) + value_ref = np.asarray([predictions[reference][i][1] for i in range(len(samples))], dtype=np.float64) + value_method = np.asarray([predictions[method][i][1] for i in range(len(samples))], dtype=np.float64) + for metric in ("accuracy", "macro_f1", "mae", "pearson"): + deltas: list[float] = [] + for _ in range(repeats): + chosen = rng.choice(group_names, size=len(group_names), replace=True) + indices = np.asarray([index for name in chosen for index in groups[str(name)]], dtype=np.int64) + a = _metrics(y_class[indices], point_ref[indices], y_value[indices], value_ref[indices])[metric] + b = _metrics(y_class[indices], point_method[indices], y_value[indices], value_method[indices])[metric] + if math.isfinite(a) and math.isfinite(b): + deltas.append(b - a) + ref_metric = _metrics(y_class, point_ref, y_value, value_ref)[metric] + method_metric = _metrics(y_class, point_method, y_value, value_method)[metric] + records.append({ + "comparison": f"{method}-B1", + "metric": metric, + "reference_oof": ref_metric, + "method_oof": method_metric, + "delta_oof": method_metric - ref_metric if math.isfinite(method_metric) and math.isfinite(ref_metric) else math.nan, + "group_bootstrap_ci95_low": float(np.quantile(deltas, 0.025)) if deltas else math.nan, + "group_bootstrap_ci95_high": float(np.quantile(deltas, 0.975)) if deltas else math.nan, + "bootstrap_repeats": len(deltas), + }) + return records + + +def _evaluate(samples: list[Sample], views: list[tuple[View, View, View]], bootstrap_repeats: int) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]]]: + y_class = np.asarray([sample.polarity for sample in samples], dtype=np.int64) + y_value = np.asarray([sample.sentiment for sample in samples], dtype=np.float64) + groups = np.asarray([sample.video_id for sample in samples]) + splitter = GroupKFold(n_splits=N_FOLDS) + folds = list(splitter.split(np.zeros(len(samples)), y_class, groups)) + methods = ("B0", "B1", "B2", "B3", "B4") + class_oof = {method: np.full(len(samples), -1, dtype=np.int64) for method in methods} + value_oof = {method: np.full(len(samples), np.nan, dtype=np.float64) for method in methods} + predictions: dict[str, dict[int, tuple[int, float]]] = {method: {} for method in methods} + fold_rows: list[dict[str, Any]] = [] + split_rows: list[dict[str, Any]] = [] + for fold_index, (train_indices, test_indices) in enumerate(folds, start=1): + train_groups = set(groups[train_indices].tolist()) + test_groups = set(groups[test_indices].tolist()) + if train_groups & test_groups: + raise AssertionError("video_id leakage across grouped fold") + for index in test_indices: + split_rows.append({ + "sample_id": samples[index].sample_id, + "video_id": samples[index].video_id, + "fold": fold_index, + "split": "valid_oof", + }) + + variant_views = { + "B0": [item[0] for item in views], + "B1": [item[1] for item in views], + "B2": [item[2] for item in views], + "B3": [item[1] for item in views], + "B4": [item[1] for item in views], + } + for method in methods: + source_views = variant_views[method] + scale_mode = "standard" if method == "B0" else "robust" + scalers = _fit_feature_scaler(samples, source_views, train_indices, scale_mode) + scaled = [_scaled_view(view, scalers, clip=(scale_mode == "robust")) for view in source_views] + add_speech = method == "B4" + x = np.stack([ + _pool_progress( + scaled[int(index)], + include_speech=add_speech, + extra=_dynamic_signature(samples[int(index)], scaled[int(index)]) if method == "B3" else None, + ) + for index in train_indices + ]) + x_valid = np.stack([ + _pool_progress( + scaled[int(index)], + include_speech=add_speech, + extra=_dynamic_signature(samples[int(index)], scaled[int(index)]) if method == "B3" else None, + ) + for index in test_indices + ]) + classifier = LogisticRegression(C=0.05, max_iter=2500, solver="lbfgs", random_state=SEED) + classifier.fit(x, y_class[train_indices]) + predicted_class = classifier.predict(x_valid) + regressor = Ridge(alpha=25.0, solver="lsqr") + regressor.fit(x, y_value[train_indices]) + predicted_value = np.clip(regressor.predict(x_valid), -3.0, 3.0) + class_oof[method][test_indices] = predicted_class + value_oof[method][test_indices] = predicted_value + for index, pred_class, pred_value in zip(test_indices, predicted_class, predicted_value): + predictions[method][int(index)] = (int(pred_class), float(pred_value)) + fold_metric = _metrics(y_class[test_indices], predicted_class, y_value[test_indices], predicted_value) + fold_rows.append({ + "method": method, + "fold": fold_index, + "train_samples": int(len(train_indices)), + "valid_samples": int(len(test_indices)), + "train_video_groups": len(train_groups), + "valid_video_groups": len(test_groups), + "accuracy": fold_metric["accuracy"], + "macro_f1": fold_metric["macro_f1"], + "mae": fold_metric["mae"], + "pearson": fold_metric["pearson"], + "feature_dimension": int(x.shape[1]), + }) + print(f"[CV] fold {fold_index}/{N_FOLDS}: train={len(train_indices)} clips/{len(train_groups)} videos; valid={len(test_indices)} clips/{len(test_groups)} videos", flush=True) + + summary_rows: list[dict[str, Any]] = [] + for method in methods: + fold_metrics = [row for row in fold_rows if row["method"] == method] + overall = _metrics(y_class, class_oof[method], y_value, value_oof[method]) + row: dict[str, Any] = { + "method": method, + "sample_count": len(samples), + "video_group_count": len(set(groups.tolist())), + "oof_accuracy": overall["accuracy"], + "oof_macro_f1": overall["macro_f1"], + "oof_mae": overall["mae"], + "oof_pearson": overall["pearson"], + } + for metric in ("accuracy", "macro_f1", "mae", "pearson"): + values = np.asarray([item[metric] for item in fold_metrics if math.isfinite(item[metric])], dtype=np.float64) + row[f"fold_{metric}_mean"] = float(values.mean()) if len(values) else math.nan + row[f"fold_{metric}_sd"] = float(values.std(ddof=1)) if len(values) > 1 else math.nan + summary_rows.append(row) + prediction_rows = [] + for index, sample in enumerate(samples): + row: dict[str, Any] = { + "sample_id": sample.sample_id, + "video_id": sample.video_id, + "clip_id": sample.clip_id, + "true_polarity": sample.polarity, + "true_polarity_name": ("negative", "neutral", "positive")[sample.polarity], + "true_sentiment": sample.sentiment, + } + for method in methods: + row[f"{method}_predicted_polarity"] = int(class_oof[method][index]) + row[f"{method}_predicted_sentiment"] = float(value_oof[method][index]) + prediction_rows.append(row) + delta_rows = _group_bootstrap_deltas(samples, predictions, bootstrap_repeats) + return summary_rows, fold_rows, prediction_rows, split_rows, delta_rows + + +def _sample_output_rows(samples: list[Sample], views: list[tuple[View, View, View]]) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + sample_rows: list[dict[str, Any]] = [] + modality_rows: list[dict[str, Any]] = [] + for sample, (view0, view1, view2) in zip(samples, views): + finite_boundaries = [item for item in sample.boundary_summary if math.isfinite(item["start_width90_s"]) and math.isfinite(item["end_width90_s"])] + hard_text_coverage = _view_coverage(view0, "text") + post_text_coverage = _view_coverage(view2, "text") + sample_rows.append({ + "sample_id": sample.sample_id, + "video_id": sample.video_id, + "clip_id": sample.clip_id, + "source_video_sha256": sample.video_sha256, + "duration_s": sample.duration_s, + "word_count": len(sample.text_words), + "hard_aligned_word_count": int(sample.hard_valid.sum()), + "unlocated_word_count": int((~sample.hard_valid).sum()), + "posterior_usable_word_count": len(finite_boundaries), + "mean_start_interval_width90_s": float(np.mean([item["start_width90_s"] for item in finite_boundaries])) if finite_boundaries else math.nan, + "mean_end_interval_width90_s": float(np.mean([item["end_width90_s"] for item in finite_boundaries])) if finite_boundaries else math.nan, + "text_hard_grid_coverage": hard_text_coverage, + "text_posterior_grid_occupancy": post_text_coverage, + "audio_grid_coverage": _view_coverage(view1, "audio"), + "vision_grid_coverage": _view_coverage(view1, "vision"), + "audio_native_rows": len(sample.audio_times), + "vision_native_rows": len(sample.vision_times), + "status": "ok", + }) + for modality, native_length, native_dim, view, key in ( + ("text", len(sample.text_words), 768, view1, "text"), + ("audio", len(sample.audio_times), 74, view1, "audio"), + ("vision", len(sample.vision_times), 35, view1, "vision"), + ): + coverage = view.coverage[key] + observed = view.observed[key] + observed_duration = float((coverage * np.diff(view.edges)[:, None] * observed).sum(axis=0).mean()) if observed.size else 0.0 + modality_rows.append({ + "sample_id": sample.sample_id, + "video_id": sample.video_id, + "clip_id": sample.clip_id, + "modality": modality, + "source_duration_s": sample.duration_s, + "observed_duration_s_mean_dimension": observed_duration, + "native_length": native_length, + "native_dimension": native_dim, + "main_grid_length": len(view.edges) - 1, + "grid_step_s": GRID_STEP_S, + "grid_dimension": native_dim, + "mean_grid_coverage": _view_coverage(view, key), + "status": "ok" if native_length else "no_native_rows", + "source_video_sha256": sample.video_sha256, + }) + return sample_rows, modality_rows + + +def _create_plot(rows: list[dict[str, Any]], output_path: Path) -> None: + labels = [row["method"] for row in rows] + figure, axes = plt.subplots(1, 3, figsize=(14, 4.5), constrained_layout=True) + metrics = (("oof_macro_f1", "Macro F1 (higher is better)"), ("oof_mae", "MAE (lower is better)"), ("oof_pearson", "Pearson r (higher is better)")) + colors = ("#718096", "#2463A6", "#6A8E3A", "#C77D27", "#8357A5") + for axis, (column, title) in zip(axes, metrics): + values = [float(row[column]) for row in rows] + bars = axis.bar(labels, values, color=colors) + axis.set_title(title) + axis.grid(axis="y", alpha=0.25) + for bar, value in zip(bars, values): + axis.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f"{value:.3f}", ha="center", va="bottom", fontsize=8) + figure.suptitle("B0-B4 emotion probe on grouped out-of-fold predictions") + output_path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(output_path, dpi=170) + plt.close(figure) + + +def _create_typical_alignment_figure( + samples: list[Sample], views: list[tuple[View, View, View]], output_path: Path, +) -> str: + eligible = [ + index for index, sample in enumerate(samples) + if sample.text_words and sample.hard_valid.all() and sample.vision_observed.any() + ] + if not eligible: + return "" + median_duration = float(np.median([samples[index].duration_s for index in eligible])) + chosen_index = min(eligible, key=lambda index: abs(samples[index].duration_s - median_duration)) + sample = samples[chosen_index] + view_b0, _, view_b2 = views[chosen_index] + middle = len(sample.text_words) // 2 + left_word = max(0, middle - 5) + right_word = min(len(sample.text_words), left_word + 10) + left_word = max(0, right_word - 10) + word_indices = np.arange(left_word, right_word) + + figure = plt.figure(figsize=(15, 11), constrained_layout=True) + grid = figure.add_gridspec(4, 1, height_ratios=(2.6, 1.1, 1.4, 2.1)) + alignment_axis = figure.add_subplot(grid[0, 0]) + coverage_axis = figure.add_subplot(grid[1, 0]) + feature_axis = figure.add_subplot(grid[2, 0]) + image_grid = grid[3, 0].subgridspec(1, 5, wspace=0.04) + image_axes = [figure.add_subplot(image_grid[0, i]) for i in range(5)] + + for y_position, word_index in enumerate(word_indices): + hard_start, hard_end = sample.hard_intervals[word_index] + alignment_axis.plot((hard_start, hard_end), (y_position, y_position), color="#2563a6", lw=5, solid_capstyle="butt") + boundary = sample.boundary_summary[word_index] + if math.isfinite(boundary["start_p05_s"]) and math.isfinite(boundary["start_p95_s"]): + center = boundary["start_mean_s"] + alignment_axis.errorbar( + center, y_position + 0.19, + xerr=np.asarray([[max(0.0, center - boundary["start_p05_s"]),], [max(0.0, boundary["start_p95_s"] - center),]]), + fmt="o", color="#d17a00", markersize=3, capsize=2, lw=1, + ) + if math.isfinite(boundary["end_p05_s"]) and math.isfinite(boundary["end_p95_s"]): + center = boundary["end_mean_s"] + alignment_axis.errorbar( + center, y_position - 0.19, + xerr=np.asarray([[max(0.0, center - boundary["end_p05_s"]),], [max(0.0, boundary["end_p95_s"] - center),]]), + fmt="s", color="#638b32", markersize=3, capsize=2, lw=1, + ) + alignment_axis.set_yticks(np.arange(len(word_indices)), labels=[sample.text_words[index] for index in word_indices], fontsize=8) + alignment_axis.set_ylim(-0.65, len(word_indices) - 0.35) + start_values = [float(sample.hard_intervals[index, 0]) for index in word_indices] + end_values = [float(sample.hard_intervals[index, 1]) for index in word_indices] + alignment_axis.set_xlim(max(0.0, min(start_values) - 0.25), min(sample.duration_s, max(end_values) + 0.25)) + alignment_axis.set_xlabel("seconds from clip start") + alignment_axis.set_title("Hard CTC word interval (blue); 90% internal start/end boundary intervals (orange/green)") + alignment_axis.grid(axis="x", alpha=0.25) + + times = (view_b0.edges[:-1] + view_b0.edges[1:]) / 2 + coverage_axis.plot(times, view_b0.coverage["text"].mean(axis=1), label="hard word coverage", color="#2563a6") + coverage_axis.plot(times, view_b2.coverage["text"].mean(axis=1), label="posterior word occupancy", color="#d17a00") + coverage_axis.plot(times, view_b0.coverage["audio"].mean(axis=1), label="audio coverage", color="#638b32") + coverage_axis.plot(times, view_b0.coverage["vision"].mean(axis=1), label="face feature coverage", color="#8357a5") + coverage_axis.set_ylim(0, 1.05) + coverage_axis.set_xlim(0, sample.duration_s) + coverage_axis.set_ylabel("coverage / occupancy") + coverage_axis.legend(ncol=4, fontsize=8, loc="upper right") + coverage_axis.grid(alpha=0.2) + + audio = sample.audio_features.astype(np.float32) + audio_time = sample.audio_times + feature_axis.plot(audio_time, audio[:, 66], color="#2563a6", lw=1, label="log energy") + pitch_valid = sample.audio_observed[:, 67] + if pitch_valid.any(): + pitch_axis = feature_axis.twinx() + pitch_axis.plot(audio_time[pitch_valid], np.exp(audio[pitch_valid, 67]), color="#d17a00", lw=0.8, alpha=0.8, label="F0 (Hz)") + pitch_axis.set_ylabel("F0 (Hz)") + vision_valid = sample.vision_observed[:, 31] + if vision_valid.any(): + feature_axis.scatter(sample.vision_times[vision_valid], sample.vision_features[vision_valid, 31].astype(np.float32), s=9, color="#638b32", label="mouth aperture") + feature_axis.set_xlim(0, sample.duration_s) + feature_axis.set_xlabel("seconds from clip start") + feature_axis.set_ylabel("log energy / mouth aperture") + feature_axis.set_title("Example native acoustic and facial feature trajectories") + feature_axis.grid(alpha=0.2) + handles, labels = feature_axis.get_legend_handles_labels() + if len(handles): + feature_axis.legend(handles, labels, loc="upper right", fontsize=8) + + video_path = DATA_DIR / sample.video_id / f"{sample.clip_id}.mp4" + capture = cv2.VideoCapture(str(video_path)) + frame_times = np.linspace(0.0, max(sample.duration_s - 0.05, 0.0), len(image_axes)) + for axis, timestamp in zip(image_axes, frame_times): + capture.set(cv2.CAP_PROP_POS_MSEC, float(timestamp) * 1000.0) + ok, bgr = capture.read() + if ok: + axis.imshow(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)) + axis.set_title(f"{timestamp:.2f}s", fontsize=8) + else: + axis.text(0.5, 0.5, "frame unavailable", ha="center", va="center") + axis.axis("off") + capture.release() + figure.suptitle(f"Typical sample alignment audit: {sample.sample_id} ({sample.duration_s:.2f}s)", fontsize=14) + output_path.parent.mkdir(parents=True, exist_ok=True) + figure.savefig(output_path, dpi=170) + plt.close(figure) + return sample.sample_id + + +def _write_report( + output_dir: Path, summary: list[dict[str, Any]], deltas: list[dict[str, Any]], + sample_rows: list[dict[str, Any]], modality_rows: list[dict[str, Any]], + manifest: dict[str, Any], +) -> None: + best_f1 = max(summary, key=lambda row: row["oof_macro_f1"]) + best_mae = min(summary, key=lambda row: row["oof_mae"]) + b1 = next(row for row in summary if row["method"] == "B1") + delta = {(row["comparison"], row["metric"]): row for row in deltas} + b4_f1 = delta[("B4-B1", "macro_f1")] + b0_mae = delta[("B0-B1", "mae")] + b0_pearson = delta[("B0-B1", "pearson")] + b2_f1 = delta[("B2-B1", "macro_f1")] + b2_mae = delta[("B2-B1", "mae")] + b3_f1 = delta[("B3-B1", "macro_f1")] + mean_start_width = float(np.nanmean([row["mean_start_interval_width90_s"] for row in sample_rows])) + mean_end_width = float(np.nanmean([row["mean_end_interval_width90_s"] for row in sample_rows])) + mean_hard_coverage = float(np.mean([row["text_hard_grid_coverage"] for row in sample_rows])) + mean_posterior_occupancy = float(np.mean([row["text_posterior_grid_occupancy"] for row in sample_rows])) + mean_vision_coverage = float(np.mean([row["vision_grid_coverage"] for row in sample_rows])) + complete_hard_count = sum(row["unlocated_word_count"] == 0 for row in sample_rows) + consistent = manifest["inputs"]["label_polarity_consistency_count"] + lines = [ + "# 闂涓€ B0-B4 妯″瀷瀵规瘮缁撴灉", + "", + "鏈姤鍛婃寜 `final/Q1/闂涓€.pdf` 绗?4.8.3 鑺傚疄鏂戒簲缁勫崟鍥犵礌瀵圭収銆傚叏閮?100 鏉￠檮浠朵竴瑙嗛鍧囦娇鐢ㄥ悓涓€濂楀喕缁撶壒寰佹娊鍙栧櫒銆?.1 绉掍富鏃堕棿缃戞牸銆佽娴嬫帺鐮佷笌鎸?`video_id` 鍒嗙粍鐨勪簲鎶樺垝鍒嗐€傛儏鎰熸爣绛句粎鐢ㄤ簬鎶樺唴鐨勮交閲忛娴嬫帰閽堬紝涓嶈繘鍏ユ椂闂村榻愩€丆TC 鍚庨獙鎴栧姩鎬佺鍚嶈绠椼€?, + "", + "## 瀹為檯鐗瑰緛鍜屾瘮杈冪増鏈?, + "", + "| 鐗堟湰 | 瀹炴柦鍐呭 |", + "| --- | --- |", + "| B0 | CTC Viterbi 璇嶅尯闂翠笌闊抽/瑙嗚鐗╃悊鏃堕棿鍖洪棿鎶曞奖锛涜缁冩姌鍧囧€?鏍囧噯宸缉鏀撅紱瑙嗚鏃嬭浆鍚戦噺鍜岃绾垮悜閲忎綔鏅€氬垎閲忓潎鍊笺€?|", + "| B1 | 鍦?B0 涓婃敼鐢ㄨ缁冩姌涓綅鏁颁笌 MAD 鐨勭ǔ鍋ュ昂搴︼紙MAD 涓洪浂鏃跺洖閫€鍒拌缁冩姌鏍囧噯宸級锛屽苟瀵硅瑙夋棆杞敤 SO(3) 鍧囧€笺€佽绾跨敤褰掍竴鍖栧悜閲忓潎鍊笺€?|", + "| B2 | 鍦?B1 涓婂彧灏嗘枃鏈瘝鍚戦噺鐨勭‖杈圭晫鎶曞奖鏇挎崲涓哄浐瀹氳浆鍐?CTC 鐘舵€佸浘鐨勫墠鍚戔€撳悗鍚戝崰鎹鐜囨姇褰憋紱濯掍綋鏃堕棿鎴充粛鎸夌墿鐞嗘椂闂淬€?|", + "| B3 | 鍦?B1 涓婁粎闄勫姞闊抽 log-F0/鑳介噺涓庤瑙?jaw-open/鍢撮儴寮€鍚堢巼鐨勬椂闂村骞夸竴銆佷簩闃惰矾寰勭鍚嶏紱缂烘祴鐐逛箣闂翠笉杩炵嚎銆?|", + "| B4 | 鍦?B1 涓婁粎闄勫姞鍐荤粨 Wav2Vec2-base-960h 鏈€鍚庡洓灞傜殑鏃堕棿鎶曞奖琛ㄧず銆?|", + "", + "闄勪欢涓€鐜版湁鏍囩琛ㄥ拰鍘熷瑙嗛琚洿鎺ヤ娇鐢ㄣ€傛枃鏈负 768 缁?BERT-base-uncased 鏈洓灞傚潎鍊硷紱闊抽涓?74 缁达紙log-Mel 40銆丮FCC 13銆佄擬FCC 13銆侀煹寰?璋辩粺璁?8锛夛紱瑙嗚涓?35 缁达紙17 涓?MediaPipe blendshape 浠ｇ悊銆? 缁村ご濮裤€? 缁磋繎浼艰绾裤€? 缁撮潰閮ㄦ瘮渚嬪嚑浣曪級銆傝瑙変唬鐞嗗拰鍑犱綍绱㈠紩瀹氫箟瑙?`compare_models.py`锛屽畠浠笌 OpenFace AU 瀹氫箟骞朵笉绛夊悓锛涜闄愬埗椤诲湪璁烘枃涓槑绀恒€?, + "", + "## 缁撴灉", + "", + "鍒嗙被鎸夎繛缁爣绛剧殑涓ユ牸绗﹀彿鏋勯€?Negative/Neutral/Positive锛屽己搴﹂娴嬮檺骞呭埌 [-3, 3]銆傛瘡鎶樿缁冩姌鍗曠嫭鎷熷悎鏍囧噯鍖栧櫒銆侀€昏緫鍥炲綊锛圕=0.05锛変笌 Ridge锛坅lpha=25锛夛紱鍥哄畾浜旀姌鐢?GroupKFold 鎸夊師濮?`video_id` 鍒嗙粍銆傛€讳綋 OOF 鎸囨爣鎸?100 鏉＄暀缁勯娴嬭绠椼€?, + "", + "| 鏂规硶 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF Pearson | Macro-F1 鎶樺潎鍊悸盨D |", + "| --- | ---: | ---: | ---: | ---: | ---: |", + ] + for row in summary: + lines.append( + f"| {row['method']} | {row['oof_accuracy']:.3f} | {row['oof_macro_f1']:.3f} | {row['oof_mae']:.3f} | {row['oof_pearson']:.3f} | {row['fold_macro_f1_mean']:.3f} 卤 {row['fold_macro_f1_sd']:.3f} |" + ) + lines += [ + "", + f"鎸夊綋鍓?OOF 鎺㈤拡锛孧acro-F1 鏈€楂樼殑鏄?{best_f1['method']}锛坽best_f1['oof_macro_f1']:.3f}锛夛紝MAE 鏈€浣庣殑鏄?{best_mae['method']}锛坽best_mae['oof_mae']:.3f}锛夈€傜浉瀵?B1锛孊4 鐨?Macro-F1 宸负 {b4_f1['delta_oof']:+.3f}锛岃棰戠粍 Bootstrap 95% 鍖洪棿 [{b4_f1['group_bootstrap_ci95_low']:+.3f}, {b4_f1['group_bootstrap_ci95_high']:+.3f}]锛屽尯闂磋法杩囬浂銆侭0 鐨?MAE 宸负 {b0_mae['delta_oof']:+.3f}锛屽尯闂?[{b0_mae['group_bootstrap_ci95_low']:+.3f}, {b0_mae['group_bootstrap_ci95_high']:+.3f}]锛孭earson 宸负 {b0_pearson['delta_oof']:+.3f}銆侭2 鐨?Macro-F1 宸负 {b2_f1['delta_oof']:+.3f}锛屽尯闂?[{b2_f1['group_bootstrap_ci95_low']:+.3f}, {b2_f1['group_bootstrap_ci95_high']:+.3f}]锛孧AE 宸负 {b2_mae['delta_oof']:+.3f}锛汢3 鐨?Macro-F1 宸负 {b3_f1['delta_oof']:+.3f}锛屽尯闂?[{b3_f1['group_bootstrap_ci95_low']:+.3f}, {b3_f1['group_bootstrap_ci95_high']:+.3f}]銆傜粍 Bootstrap 鍙弽鏄犲綋鍓?37 涓潵婧愯棰戜笂鐨勬娊鏍蜂笉纭畾鎬э紝鏈綔澶氶噸姣旇緝鏍℃銆?, + "", + "## 瑕嗙洊涓庡榻愭牳楠?, + "", + f"鏍锋湰鏁帮細{len(sample_rows)}锛涙爣绛捐〃杩炵画鏍囩涓庢瀬鎬т竴鑷?{consistent}/100锛汣TC 纭榻愬畬鏁寸殑鏍锋湰 {complete_hard_count}/100銆侭0 纭尯闂村钩鍧囨枃鏈鐩栫巼涓?{mean_hard_coverage:.3f}锛孊2 骞冲潎璇嶅崰鎹悗楠岃川閲忎负 {mean_posterior_occupancy:.3f}锛堣繖鏄湡鏈涘崰鎹巼锛屼笉鏄鐩栫巼锛夛紱瑙嗚妫€娴嬭鐩栫巼锛堟寜 0.1 绉掔綉鏍煎钩鍧囷級涓?{mean_vision_coverage:.3f}銆傚悗楠屽唴閮?90% 杈圭晫鍖洪棿骞冲潎瀹藉害涓鸿捣鐐?{mean_start_width:.3f} 绉掋€佺粓鐐?{mean_end_width:.3f} 绉掋€傛病鏈変汉宸ヨ竟鐣屽瓙闆嗭紝鍥犳杩欎簺瀹藉害鍙槸妯″瀷鍐呴儴涓嶇‘瀹氭€ф憳瑕侊紝涓嶆槸缁忛獙鏍″噯鐜囨垨杈圭晫璇樊銆?, + "", + "B0鈥揃4 鐨勭壒寰佹潵婧愩€佹湁鏁堟帺鐮併€佽繛缁鐩栫巼銆佽瘝杈圭晫鍚庨獙瀹藉害銆佹姌鍐呴娴嬪強瑙嗛鍝堝笇鍧囧垎鍒繚瀛樺湪閰嶅 CSV/JSON 涓€傛病鏈変汉宸ヨ瘝杈圭晫鏃朵笉鎶ュ憡 IoU/MATE锛屼篃涓嶆妸涓嶅悓鏂规硶鐨勯娴嬫帰閽堝垎鏁拌В閲婁负瀵归綈鐪熷€笺€?, + "", + "## 鏂囦欢", + "", + "- `comparison_summary.csv`锛氫簲绉嶆柟娉曟€讳綋 OOF 涓庢姌鍧囧€兼寚鏍囥€?, + "- `fold_metrics.csv`锛氭瘡鎶樺垎绫?鍥炲綊鎸囨爣涓庣壒寰佺淮鏁般€?, + "- `oof_predictions.csv`锛氭瘡鏉℃牱鏈殑鐣欑粍棰勬祴銆?, + "- `group_bootstrap_deltas.csv`锛氱浉瀵?B1 鐨勮棰戠粍閰嶅 Bootstrap 95% 鍖洪棿銆?, + "- `sample_alignment_summary.csv`锛?00 鏉℃牱鏈€佺‖/姒傜巼鏂囨湰瑕嗙洊鍜屽悗楠岃竟鐣屽搴︺€?, + "- `modality_summary.csv`锛?00 琛屾牱鏈?妯℃€佹槑缁嗐€?, + "- `word_alignment_posterior.csv`锛氶€愯瘝纭竟鐣屻€佸疄闄呯浉瀵硅川閲忔潈閲嶅強鍚庨獙杈圭晫鍖洪棿銆?, + "- `split_assignments.csv`锛氶€愭牱鏈姌鍙枫€?, + "- `comparison.png`锛氭牳蹇?OOF 鎸囨爣鍥俱€?, + "- `typical_alignment_example.png`锛氫腑浣嶆椂闀挎牱鏈殑璇嶈竟鐣屻€佽鐩栫巼銆佸０瀛?瑙嗚杞ㄨ抗涓庤棰戝抚鏍搁獙鍥俱€?, + "- `run_manifest.json`锛氱幆澧冦€佹ā鍨?revision銆佸弬鏁般€佸搱甯屼笌杩愯鏃堕棿銆?, + "", + "## 澶嶇幇", + "", + "鍦ㄤ粨搴撴牴鐩綍鎵ц锛?, + "", + "```bash", + "cd math", + "uv sync", + "uv run python compare_models.py", + "```", + "", + "鐗瑰緛缂撳瓨鍐欏湪 `final/Q1/cache/native/`锛屾寮忕粨鏋滃彧鍐欏湪 `final/Q1/results/model_comparison/`銆傚垹闄ょ紦瀛樺悗浼氫粠闄勪欢涓€閲嶆柊鎻愬彇銆傞娆¤繍琛岄渶瑕佷笅杞?BERT銆乄av2Vec2 鍜?MediaPipe Face Landmarker 鏉冮噸銆?, + ] + (output_dir / "report.md").write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def _write_boundary_detail(output_dir: Path, samples: list[Sample]) -> None: + rows: list[dict[str, Any]] = [] + for sample in samples: + quality_weights = _normalized_hard_quality(sample) + for word_index, (word, interval, valid, quality, posterior) in enumerate(zip( + sample.text_words, + sample.hard_intervals, + sample.hard_valid, + sample.hard_quality, + sample.boundary_summary, + )): + rows.append({ + "sample_id": sample.sample_id, + "video_id": sample.video_id, + "clip_id": sample.clip_id, + "word_index": word_index, + "word": word, + "hard_start_s": float(interval[0]) if valid else math.nan, + "hard_end_s": float(interval[1]) if valid else math.nan, + "hard_valid": bool(valid), + "ctc_quality_score_uncalibrated": float(quality), + "relative_quality_weight": float(quality_weights[word_index]) if valid else math.nan, + **posterior, + }) + _write_csv(output_dir / "word_alignment_posterior.csv", rows, [ + "sample_id", "video_id", "clip_id", "word_index", "word", "hard_start_s", "hard_end_s", + "hard_valid", "ctc_quality_score_uncalibrated", "relative_quality_weight", "start_mean_s", "end_mean_s", + "start_p05_s", "start_p95_s", "end_p05_s", "end_p95_s", + "start_width90_s", "end_width90_s", + ]) + + +def run(args: argparse.Namespace) -> None: + output_dir = Path(args.output_dir).resolve() + cache_dir = Path(args.cache_dir).resolve() + input_dir = Path(args.data_dir).resolve() + label_file = input_dir / "label-100.xlsx" + records = _read_labels(label_file) + source_hashes: dict[str, str] = {} + for record in records: + video_path = input_dir / record["video_id"] / f"{record['clip_id']}.mp4" + if not video_path.is_file(): + raise FileNotFoundError(f"missing source video for {record['sample_id']}: {video_path}") + source_hashes[record["sample_id"]] = _sha256(video_path) + output_dir.mkdir(parents=True, exist_ok=True) + cache_dir.mkdir(parents=True, exist_ok=True) + cache_schema = "q1-b0b4-v3" + face_hash = _ensure_face_model() + samples: list[Sample] = [] + missing_records: list[dict[str, Any]] = [] + cache_paths = { + record["sample_id"]: cache_dir / f"{_safe_name(record['video_id'])}__{_safe_name(record['clip_id'])}.npz" + for record in records + } + if not args.force_extract: + for record in records: + path = cache_paths[record["sample_id"]] + if path.is_file(): + try: + samples.append(_load_cache(path, record, source_hashes[record["sample_id"]], cache_schema)) + except (ValueError, KeyError, OSError): + missing_records.append(record) + else: + missing_records.append(record) + else: + missing_records = records.copy() + # Restore source-row order even when resuming a partial cache. + cached_by_id = {sample.sample_id: sample for sample in samples} + if missing_records: + device = torch.device(args.device if args.device != "auto" else ("cuda" if torch.cuda.is_available() else "cpu")) + print(f"Extracting {len(missing_records)} uncached clips on {device}.", flush=True) + models = _load_models(device) + for index, record in enumerate(missing_records, start=1): + started = time.perf_counter() + sample = _extract_native_sample( + record, models, device, FACE_MODEL_PATH, source_hashes[record["sample_id"]] + ) + _save_cache(cache_paths[record["sample_id"]], sample, cache_schema) + cached_by_id[sample.sample_id] = sample + print(f"[features] {index}/{len(missing_records)} {sample.sample_id}: {time.perf_counter()-started:.2f}s", flush=True) + model_info = models[-1] + else: + manifest_path = output_dir / "run_manifest.json" + prior = json.loads(manifest_path.read_text(encoding="utf-8")) if manifest_path.is_file() else {} + model_info = prior.get("models", {"text_id": TEXT_MODEL_ID, "speech_id": SPEECH_MODEL_ID, "revision_status": "features resumed from cache"}) + samples = [cached_by_id[record["sample_id"]] for record in records] + views = [_make_views(sample) for sample in samples] + label_consistent_count = sum(bool(record["label_consistent"]) for record in records) + sample_rows, modality_rows = _sample_output_rows(samples, views) + summary, folds, predictions, splits, deltas = _evaluate(samples, views, args.bootstrap_repeats) + _write_csv(output_dir / "comparison_summary.csv", summary, [ + "method", "sample_count", "video_group_count", "oof_accuracy", "oof_macro_f1", "oof_mae", "oof_pearson", + "fold_accuracy_mean", "fold_accuracy_sd", "fold_macro_f1_mean", "fold_macro_f1_sd", + "fold_mae_mean", "fold_mae_sd", "fold_pearson_mean", "fold_pearson_sd", + ]) + _write_csv(output_dir / "fold_metrics.csv", folds, [ + "method", "fold", "train_samples", "valid_samples", "train_video_groups", "valid_video_groups", + "accuracy", "macro_f1", "mae", "pearson", "feature_dimension", + ]) + _write_csv(output_dir / "oof_predictions.csv", predictions, [ + "sample_id", "video_id", "clip_id", "true_polarity", "true_polarity_name", "true_sentiment", + *[f"{method}_predicted_polarity" for method in ("B0", "B1", "B2", "B3", "B4")], + *[f"{method}_predicted_sentiment" for method in ("B0", "B1", "B2", "B3", "B4")], + ]) + _write_csv(output_dir / "group_bootstrap_deltas.csv", deltas, [ + "comparison", "metric", "reference_oof", "method_oof", "delta_oof", + "group_bootstrap_ci95_low", "group_bootstrap_ci95_high", "bootstrap_repeats", + ]) + _write_csv(output_dir / "split_assignments.csv", sorted(splits, key=lambda row: row["sample_id"]), ["sample_id", "video_id", "fold", "split"]) + _write_csv(output_dir / "sample_alignment_summary.csv", sample_rows, list(sample_rows[0])) + _write_csv(output_dir / "modality_summary.csv", modality_rows, list(modality_rows[0])) + _write_boundary_detail(output_dir, samples) + _create_plot(summary, output_dir / "comparison.png") + typical_sample_id = _create_typical_alignment_figure(samples, views, output_dir / "typical_alignment_example.png") + manifest = { + "created_at_local": time.strftime("%Y-%m-%d %H:%M:%S %z"), + "python": sys.version, + "platform": platform.platform(), + "uv_version": subprocess.run(["uv", "--version"], capture_output=True, text=True).stdout.strip(), + "packages": {name: _version(name) for name in ("torch", "transformers", "numpy", "scipy", "scikit-learn", "mediapipe", "opencv-python-headless", "openpyxl", "matplotlib")}, + "device": args.device if args.device != "auto" else ("cuda" if torch.cuda.is_available() else "cpu"), + "cuda_available": bool(torch.cuda.is_available()), + "gpu_name": torch.cuda.get_device_name(0) if torch.cuda.is_available() and args.device != "cpu" else None, + "models": model_info, + "face_landmarker_asset_sha256": face_hash, + "inputs": { + "label_file": str(label_file), + "label_file_sha256": _sha256(label_file), + "sample_count": len(samples), + "video_sha256_by_sample": source_hashes, + "label_polarity_consistency_count": label_consistent_count, + }, + "experiment": { + "methods": { + "B0": "quality-weighted hard CTC word projection + physical source-time pooling + train-fold standard scaling", + "B1": "B0 + train-fold median/MAD scaling + SO(3) rotation mean + normalized gaze vector mean", + "B2": "B1 + fixed-transcript CTC forward-backward word occupancy projection for text", + "B3": "B1 + second-order time-augmented log signatures of selected audio/face channels", + "B4": "B1 + frozen Wav2Vec2 last-four-layer speech representation", + }, + "text_dimension": 768, + "audio_dimension": 74, + "vision_dimension": 35, + "audio_features": list(AUDIO_NAMES), + "vision_features": list(VISION_NAMES), + "vision_feature_note": "17 MediaPipe blendshape proxies and approximate gaze/landmark geometry; not OpenFace AU labels", + "grid_step_s": GRID_STEP_S, + "audio_window_samples": FRAME_LENGTH, + "audio_step_samples": FRAME_STEP, + "audio_fft": N_FFT, + "audio_mel_bands": MEL_COUNT, + "vision_sample_rate_hz": VISION_RATE_HZ, + "grouping": "GroupKFold by video_id", + "fold_count": N_FOLDS, + "probe": {"classifier": "LogisticRegression", "C": 0.05, "regressor": "Ridge", "alpha": 25.0, "intensity_clip": [-3.0, 3.0]}, + "bootstrap": {"unit": "video_id", "repeats": args.bootstrap_repeats, "seed": SEED}, + "human_boundary_reference_count": 0, + "boundary_interval_level": 0.90, + "hard_text_quality_weight": "per-sample median-normalized CTC path score clipped to [0.25, 4.0]; uncalibrated", + "cache_schema": cache_schema, + }, + "output_dir": str(output_dir), + "typical_sample_id": typical_sample_id, + "elapsed_seconds": time.time() - args.started_at, + } + (output_dir / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + _write_report(output_dir, summary, deltas, sample_rows, modality_rows, manifest) + print(f"Saved B0-B4 comparison to {output_dir}", flush=True) + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Run Q1 B0-B4 model comparison on Attachment 1.") + parser.add_argument("--data-dir", type=Path, default=DATA_DIR) + parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR) + parser.add_argument("--cache-dir", type=Path, default=CACHE_DIR) + parser.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--force-extract", action="store_true", help="recompute native features from the 100 raw videos") + args = parser.parse_args() + if args.bootstrap_repeats < 100: + parser.error("--bootstrap-repeats must be at least 100") + args.started_at = time.time() + return args + + +if __name__ == "__main__": + run(_parse_args()) + diff --git a/final/Q1/export_features.py b/final/Q1/export_features.py new file mode 100644 index 0000000..3f45936 --- /dev/null +++ b/final/Q1/export_features.py @@ -0,0 +1,239 @@ +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import pickle +import shutil +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import numpy as np + +ROOT = Path(__file__).resolve().parents[2] +Q1_DIR = Path(__file__).resolve().parent +sys.path.insert(0, str(Q1_DIR)) +import compare_models as cm # noqa: E402 + +CACHE_SCHEMA = "q1-b0b4-v3" +EXPORT_BINS = 50 +MODALITIES = ("text", "audio", "vision") + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def defaults() -> tuple[Path, Path, Path]: + candidates = (ROOT / "math" / "Q1" / "cache" / "native", Q1_DIR / "cache" / "native") + cache = next((path for path in candidates if path.exists()), candidates[0]) + labels = ROOT / "E题数据" / "附件1-数据集原始多模态样本" / "MOSEI数据集部分原始视频-100条" / "label-100.xlsx" + return cache, labels, cache.parent.parent / "results" / "model_comparison" / "run_manifest.json" + + +def pool_to_edges(view: cm.View, modality: str, target_edges: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + values = np.asarray(view.features[modality], dtype=np.float64) + observed = np.asarray(view.observed[modality], dtype=bool) + source_coverage = np.asarray(view.coverage[modality], dtype=np.float64) + dim = values.shape[1] + count = len(target_edges) - 1 + output = np.zeros((count, dim), dtype=np.float32) + mask = np.zeros((count, dim), dtype=bool) + coverage = np.zeros((count, dim), dtype=np.float32) + source_left, source_right = view.edges[:-1], view.edges[1:] + for index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): + overlap = np.maximum(0.0, np.minimum(source_right, right) - np.maximum(source_left, left)) + weights = overlap[:, None] * source_coverage * observed + denominator = weights.sum(axis=0) + good = denominator > 0 + if good.any(): + output[index, good] = (values * weights).sum(axis=0)[good] / denominator[good] + mask[index, good] = True + coverage[index, good] = np.minimum(1.0, denominator[good] / max(right - left, 1e-8)) + return output, mask, coverage + + +def main() -> None: + cache_default, labels_default, manifest_default = defaults() + parser = argparse.ArgumentParser(description="Export Q1 multimodal features for model training.") + parser.add_argument("--cache-dir", type=Path, default=cache_default) + parser.add_argument("--labels", type=Path, default=labels_default) + parser.add_argument("--run-manifest", type=Path, default=manifest_default) + parser.add_argument("--output-dir", type=Path, default=Q1_DIR / "features") + args = parser.parse_args() + + for path in (args.cache_dir, args.labels, args.run_manifest): + if not path.exists(): + raise FileNotFoundError(f"Required input does not exist: {path}") + run_manifest = json.loads(args.run_manifest.read_text(encoding="utf-8")) + inputs = run_manifest["inputs"] + if run_manifest.get("cache_schema", CACHE_SCHEMA) != CACHE_SCHEMA: + raise ValueError("Run manifest cache_schema does not match the Q1 exporter.") + label_hash = sha256(args.labels) + if inputs.get("label_file_sha256") and inputs["label_file_sha256"] != label_hash: + raise ValueError("Label workbook hash differs from the run manifest.") + records = cm._read_labels(args.labels) + source_hashes = inputs["video_sha256_by_sample"] + if len(records) != 100 or int(inputs.get("sample_count", -1)) != len(records): + raise ValueError("Expected the same 100 samples used by the Q1 comparison run.") + + keys = ( + "text", "text_mask", "text_posterior", "text_posterior_mask", "audio", "audio_mask", "vision", "vision_mask", + "text_coverage", "text_posterior_activity", "audio_coverage", "vision_coverage", "time_bounds_s", "time_s", "progress", + "duration_s", "valid_lengths", "id", "sample_id", "video_id", "clip_id", "raw_text", "classification_labels", + "regression_labels", "annotations", "label_consistent", "source_video_sha256", + ) + rows: dict[str, list[Any]] = {key: [] for key in keys} + native: dict[str, list[np.ndarray]] = {key: [] for key in ( + "text", "text_mask", "audio", "audio_mask", "vision", "vision_mask", "text_coverage", "audio_coverage", "vision_coverage", "time_bounds_s", + )} + offsets = [0] + native_ids: list[np.ndarray] = [] + durations: list[float] = [] + + for sample_index, record in enumerate(records): + sample_id = record["sample_id"] + source_hash = source_hashes[sample_id] + cache_path = args.cache_dir / f"{cm._safe_name(record['video_id'])}__{cm._safe_name(record['clip_id'])}.npz" + sample = cm._load_cache(cache_path, record, source_hash, CACHE_SCHEMA) + _, view_b1, view_b2 = cm._make_views(sample) + target_edges = np.linspace(0.0, sample.duration_s, EXPORT_BINS + 1, dtype=np.float64) + pooled = {name: pool_to_edges(view_b1, name, target_edges) for name in MODALITIES} + posterior = pool_to_edges(view_b2, "text", target_edges) + + step_count = len(view_b1.edges) - 1 + native["time_bounds_s"].append(np.column_stack((view_b1.edges[:-1], view_b1.edges[1:])).astype(np.float32)) + native_ids.append(np.full(step_count, sample_index, dtype=np.int16)) + offsets.append(offsets[-1] + step_count) + durations.append(float(sample.duration_s)) + for name in MODALITIES: + native[name].append(np.asarray(view_b1.features[name], dtype=np.float16)) + native[f"{name}_mask"].append(np.asarray(view_b1.observed[name], dtype=bool)) + native[f"{name}_coverage"].append(view_b1.coverage[name].mean(axis=1).astype(np.float16)) + values, masks, cov = pooled[name] + rows[name].append(values.astype(np.float16)) + rows[f"{name}_mask"].append(masks) + rows[f"{name}_coverage"].append(cov.mean(axis=1).astype(np.float32)) + rows["text_posterior"].append(posterior[0].astype(np.float16)) + rows["text_posterior_mask"].append(posterior[1]) + rows["text_posterior_activity"].append(posterior[2].mean(axis=1).astype(np.float32)) + bounds = np.column_stack((target_edges[:-1], target_edges[1:])).astype(np.float32) + centers = bounds.mean(axis=1) + rows["time_bounds_s"].append(bounds) + rows["time_s"].append(centers) + rows["progress"].append((centers / max(sample.duration_s, 1e-8)).astype(np.float32)) + rows["duration_s"].append(float(sample.duration_s)) + rows["valid_lengths"].append(EXPORT_BINS) + values = { + "id": sample_id, "sample_id": sample_id, "video_id": record["video_id"], "clip_id": record["clip_id"], + "raw_text": record["text"], "classification_labels": int(record["polarity"]), + "regression_labels": float(record["sentiment"]), "annotations": record["annotation"], + "label_consistent": bool(record["label_consistent"]), "source_video_sha256": source_hash, + } + for key, value in values.items(): + rows[key].append(value) + + args.output_dir.mkdir(parents=True, exist_ok=True) + arrays: dict[str, Any] = {} + string_keys = {"id", "sample_id", "video_id", "clip_id", "raw_text", "annotations", "source_video_sha256"} + for key, values in rows.items(): + if key in string_keys: + arrays[key] = np.asarray(values, dtype=str) + elif key in {"duration_s", "regression_labels"}: + arrays[key] = np.asarray(values, dtype=np.float32) + elif key in {"classification_labels", "valid_lengths"}: + arrays[key] = np.asarray(values, dtype=np.int64) + elif key == "label_consistent": + arrays[key] = np.asarray(values, dtype=bool) + else: + arrays[key] = np.stack(values, axis=0) + + dimensions = {"text": 768, "audio": len(cm.AUDIO_NAMES), "vision": len(cm.VISION_NAMES)} + names = { + "text": [f"bert_dim_{index:03d}" for index in range(768)], + "audio": list(cm.AUDIO_NAMES), "vision": list(cm.VISION_NAMES), + } + metadata = { + "schema": "q1-aligned50-v1", "sample_count": len(records), "export_bins": EXPORT_BINS, + "alignment": "Each clip is divided into 50 equal-width physical-time bins; values are duration/coverage-weighted means of Q1 B1 common-grid features.", + "native_grid_step_s": cm.GRID_STEP_S, + "time_semantics": "Seconds relative to clip start; time_bounds_s contains [left, right) for each bin.", + "text": "768-D BERT base uncased last-four-layer mean, word-level features projected by hard CTC Viterbi intervals with within-sample relative quality weighting.", + "text_posterior": "Alternative text projection uses fixed-transcript CTC forward-backward word occupancy. text_posterior_activity is expected occupancy mass, not physical coverage.", + "audio": "74-D: 40 log-Mel, 13 MFCC, 13 delta MFCC, 8 prosodic/spectral features.", + "vision": "35-D: 17 MediaPipe blendshape proxies, 6 head pose, 6 approximate gaze, 6 facial geometry; B1 uses SO(3) pose and normalized gaze pooling.", + "missing_values": "Values are zero where the matching boolean mask is false. Features are unnormalized; fit normalization on training groups only.", + "labels": {"classification_labels": {"0": "negative", "1": "neutral", "2": "positive"}, "regression_labels": "original continuous sentiment label"}, + "split": "No train/validation/test split is supplied. Split by video_id to prevent source-video leakage.", + "dimensions": dimensions, "feature_names_file": "feature_names.json", + "source_run_manifest": str(args.run_manifest.relative_to(ROOT)) if args.run_manifest.is_relative_to(ROOT) else args.run_manifest.name, + } + pkl_path = args.output_dir / "aligned_50.pkl" + with pkl_path.open("wb") as stream: + pickle.dump({"metadata": metadata, "all": arrays}, stream, protocol=pickle.HIGHEST_PROTOCOL) + + native_arrays: dict[str, Any] = { + "offsets": np.asarray(offsets, dtype=np.int32), "sample_indices": np.concatenate(native_ids).astype(np.int16), + "sample_id": np.asarray([record["sample_id"] for record in records], dtype=str), + "time_bounds_s": np.concatenate(native["time_bounds_s"], axis=0), + } + for key in ("text", "text_mask", "audio", "audio_mask", "vision", "vision_mask", "text_coverage", "audio_coverage", "vision_coverage"): + native_arrays[key] = np.concatenate(native[key], axis=0) + native_path = args.output_dir / "native_grid.npz" + np.savez_compressed(native_path, **native_arrays) + names_path = args.output_dir / "feature_names.json" + names_path.write_text(json.dumps(names, ensure_ascii=False, indent=2), encoding="utf-8") + + result_dir = args.run_manifest.parent + for name in ("modality_summary.csv", "sample_alignment_summary.csv", "word_alignment_posterior.csv"): + source = result_dir / name + if source.exists(): + shutil.copy2(source, args.output_dir / name) + summary_src = result_dir / "modality_summary.csv" + summary_path = args.output_dir / "sample_feature_manifest.csv" + modality_rows = [] + with summary_src.open(encoding="utf-8-sig", newline="") as stream: + for row in csv.DictReader(stream): + row.update({ + "source_extract_granularity": { + "text": "word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames)", + "audio": "10 ms feature hop", "vision": "5 Hz sampled frames (about 200 ms)", + }.get(row["modality"], ""), + "common_alignment_grid_s": str(cm.GRID_STEP_S), "training_export_bins": str(EXPORT_BINS), + "traceability_rule": "sample_id + source_video_sha256 + source time bounds", + }) + modality_rows.append(row) + with summary_path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=list(modality_rows[0])) + writer.writeheader() + writer.writerows(modality_rows) + + files = {} + for path in (pkl_path, native_path, names_path, summary_path): + files[path.name] = {"bytes": path.stat().st_size, "sha256": sha256(path)} + feature_manifest = { + **metadata, "created_at_utc": datetime.now(timezone.utc).isoformat(), + "python": run_manifest.get("python"), "packages": run_manifest.get("packages", {}), + "models": run_manifest.get("models", {}), "label_file_sha256": label_hash, + "feature_names": names, "native_grid_step_count_total": int(offsets[-1]), + "native_grid_sample_offsets": offsets, "files": files, + "tables": { + "sample_feature_manifest.csv": "300 rows, one per sample and modality; records source/observed duration, dimension, granularity, grid length and source hash.", + "sample_alignment_summary.csv": "100 sample-level alignment and coverage summaries.", + "word_alignment_posterior.csv": "Word-level hard intervals and CTC posterior interval summaries.", + }, + } + (args.output_dir / "feature_manifest.json").write_text(json.dumps(feature_manifest, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"Exported {len(records)} samples: text={arrays['text'].shape}, audio={arrays['audio'].shape}, vision={arrays['vision'].shape}") + print(f"Native grid: {offsets[-1]} steps across {sum(durations):.3f} seconds") + + +if __name__ == "__main__": + main() diff --git a/final/Q1/features/aligned_50.pkl b/final/Q1/features/aligned_50.pkl new file mode 100644 index 0000000..321e5bb Binary files /dev/null and b/final/Q1/features/aligned_50.pkl differ diff --git a/final/Q1/features/feature_manifest.json b/final/Q1/features/feature_manifest.json new file mode 100644 index 0000000..de03f1b --- /dev/null +++ b/final/Q1/features/feature_manifest.json @@ -0,0 +1,1063 @@ +{ + "schema": "q1-aligned50-v1", + "sample_count": 100, + "export_bins": 50, + "alignment": "Each clip is divided into 50 equal-width physical-time bins; values are duration/coverage-weighted means of Q1 B1 common-grid features.", + "native_grid_step_s": 0.1, + "time_semantics": "Seconds relative to clip start; time_bounds_s contains [left, right) for each bin.", + "text": "768-D BERT base uncased last-four-layer mean, word-level features projected by hard CTC Viterbi intervals with within-sample relative quality weighting.", + "text_posterior": "Alternative text projection uses fixed-transcript CTC forward-backward word occupancy. text_posterior_activity is expected occupancy mass, not physical coverage.", + "audio": "74-D: 40 log-Mel, 13 MFCC, 13 delta MFCC, 8 prosodic/spectral features.", + "vision": "35-D: 17 MediaPipe blendshape proxies, 6 head pose, 6 approximate gaze, 6 facial geometry; B1 uses SO(3) pose and normalized gaze pooling.", + "missing_values": "Values are zero where the matching boolean mask is false. Features are unnormalized; fit normalization on training groups only.", + "labels": { + "classification_labels": { + "0": "negative", + "1": "neutral", + "2": "positive" + }, + "regression_labels": "original continuous sentiment label" + }, + "split": "No train/validation/test split is supplied. 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"bert_dim_756", + "bert_dim_757", + "bert_dim_758", + "bert_dim_759", + "bert_dim_760", + "bert_dim_761", + "bert_dim_762", + "bert_dim_763", + "bert_dim_764", + "bert_dim_765", + "bert_dim_766", + "bert_dim_767" + ], + "audio": [ + "logmel_00", + "logmel_01", + "logmel_02", + "logmel_03", + "logmel_04", + "logmel_05", + "logmel_06", + "logmel_07", + "logmel_08", + "logmel_09", + "logmel_10", + "logmel_11", + "logmel_12", + "logmel_13", + "logmel_14", + "logmel_15", + "logmel_16", + "logmel_17", + "logmel_18", + "logmel_19", + "logmel_20", + "logmel_21", + "logmel_22", + "logmel_23", + "logmel_24", + "logmel_25", + "logmel_26", + "logmel_27", + "logmel_28", + "logmel_29", + "logmel_30", + "logmel_31", + "logmel_32", + "logmel_33", + "logmel_34", + "logmel_35", + "logmel_36", + "logmel_37", + "logmel_38", + "logmel_39", + "mfcc_00", + "mfcc_01", + "mfcc_02", + "mfcc_03", + "mfcc_04", + "mfcc_05", + "mfcc_06", + "mfcc_07", + "mfcc_08", + "mfcc_09", + "mfcc_10", + "mfcc_11", + "mfcc_12", + "delta_mfcc_00", + "delta_mfcc_01", + "delta_mfcc_02", + "delta_mfcc_03", + "delta_mfcc_04", + "delta_mfcc_05", + "delta_mfcc_06", + "delta_mfcc_07", + "delta_mfcc_08", + "delta_mfcc_09", + "delta_mfcc_10", + "delta_mfcc_11", + "delta_mfcc_12", + "log_energy", + "log_f0_hz", + "voicing_strength", + "spectral_centroid_hz", + "spectral_bandwidth_hz", + "spectral_flux", + "zero_crossing_rate", + "hnr_db" + ], + "vision": [ + "mp_blendshape_browDownLeft", + "mp_blendshape_browDownRight", + "mp_blendshape_browInnerUp", + "mp_blendshape_browOuterUpLeft", + "mp_blendshape_browOuterUpRight", + "mp_blendshape_eyeBlinkLeft", + "mp_blendshape_eyeBlinkRight", + "mp_blendshape_eyeSquintLeft", + "mp_blendshape_eyeSquintRight", + "mp_blendshape_eyeWideLeft", + "mp_blendshape_eyeWideRight", + "mp_blendshape_jawOpen", + "mp_blendshape_mouthFrownLeft", + "mp_blendshape_mouthFrownRight", + "mp_blendshape_mouthPucker", + "mp_blendshape_mouthSmileLeft", + "mp_blendshape_mouthSmileRight", + "pose_rotvec_x", + "pose_rotvec_y", + "pose_rotvec_z", + "pose_translation_x", + "pose_translation_y", + "pose_translation_z", + "gaze_left_x", + "gaze_left_y", + "gaze_left_z", + "gaze_right_x", + "gaze_right_y", + "gaze_right_z", + "eye_aperture_left", + "eye_aperture_right", + "mouth_aperture", + "mouth_width_face_ratio", + "brow_eye_distance_left", + "brow_eye_distance_right" + ] +} \ No newline at end of file diff --git a/math/results/model_comparison/modality_summary.csv b/final/Q1/features/modality_summary.csv similarity index 100% rename from math/results/model_comparison/modality_summary.csv rename to final/Q1/features/modality_summary.csv diff --git a/final/Q1/features/native_grid.npz b/final/Q1/features/native_grid.npz new file mode 100644 index 0000000..d7d01b3 Binary files /dev/null and b/final/Q1/features/native_grid.npz differ diff --git a/math/results/model_comparison/sample_alignment_summary.csv b/final/Q1/features/sample_alignment_summary.csv similarity index 100% rename from math/results/model_comparison/sample_alignment_summary.csv rename to final/Q1/features/sample_alignment_summary.csv diff --git a/final/Q1/features/sample_feature_manifest.csv b/final/Q1/features/sample_feature_manifest.csv new file mode 100644 index 0000000..22ce80a --- /dev/null +++ b/final/Q1/features/sample_feature_manifest.csv @@ -0,0 +1,301 @@ +sample_id,video_id,clip_id,modality,source_duration_s,observed_duration_s_mean_dimension,native_length,native_dimension,main_grid_length,grid_step_s,grid_dimension,mean_grid_coverage,status,source_video_sha256,source_extract_granularity,common_alignment_grid_s,training_export_bins,traceability_rule +-3g5yACwYnA/13,-3g5yACwYnA,13,text,5.5139970779418945,2.946523889899254,15,768,56,0.1,768,0.7015532851219177,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/13,-3g5yACwYnA,13,audio,5.5139970779418945,5.445240411887298,540,74,56,0.1,74,0.9882608652114868,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/13,-3g5yACwYnA,13,vision,5.5139970779418945,5.5139970779418945,43,35,56,0.1,35,1.0,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/3,-3g5yACwYnA,3,text,14.388997077941895,7.041063531115653,29,768,144,0.1,768,0.6343300342559814,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/3,-3g5yACwYnA,3,audio,14.388997077941895,14.260037878559858,1433,74,144,0.1,74,0.9912122488021851,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/3,-3g5yACwYnA,3,vision,14.388997077941895,14.388997077941895,103,35,144,0.1,35,1.0,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/2,-3g5yACwYnA,2,text,9.394009590148926,5.1430321040563305,14,768,94,0.1,768,0.7563282251358032,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/2,-3g5yACwYnA,2,audio,9.394009590148926,9.331671435768538,924,74,94,0.1,74,0.9936367273330688,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/2,-3g5yACwYnA,2,vision,9.394009590148926,9.394009590148926,78,35,94,0.1,35,1.0,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/9,-3g5yACwYnA,9,text,8.816991806030273,5.6415157541632714,21,768,89,0.1,768,0.6637077927589417,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/9,-3g5yACwYnA,9,audio,8.816991806030273,8.735654204761659,870,74,89,0.1,74,0.9912108778953552,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-3g5yACwYnA/9,-3g5yACwYnA,9,vision,8.816991806030273,8.816991806030273,75,35,89,0.1,35,1.0,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-3nNcZdcdvU/5,-3nNcZdcdvU,5,text,7.867969036102295,5.002839090395719,18,768,79,0.1,768,0.7357116341590881,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-3nNcZdcdvU/5,-3nNcZdcdvU,5,audio,7.867969036102295,7.843361772234375,779,74,79,0.1,74,0.9971166849136353,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-3nNcZdcdvU/5,-3nNcZdcdvU,5,vision,7.867969036102295,7.816666602730405,67,35,79,0.1,35,0.9904456734657288,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,text,3.9820311069488525,2.40418455898877,8,768,40,0.1,768,0.6164926290512085,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,audio,3.9820311069488525,3.9192059241395896,392,74,40,0.1,74,0.9881895780563354,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,vision,3.9820311069488525,0.8653643131256102,27,35,40,0.1,35,0.9814813733100891,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,text,5.6529951095581055,3.0192474961280813,10,768,57,0.1,768,0.7021505832672119,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,audio,5.6529951095581055,5.595076320642555,555,74,57,0.1,74,0.9900854229927063,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,vision,5.6529951095581055,1.199999868869782,44,35,57,0.1,35,0.666666567325592,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,text,3.3580079078674316,1.3907382175326355,7,768,34,0.1,768,0.5562952756881714,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,audio,3.3580079078674316,3.306980685486987,326,74,34,0.1,74,0.9901678562164307,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,vision,3.3580079078674316,1.6413411736488344,21,35,34,0.1,35,0.841666579246521,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,text,7.733983993530273,4.492685443162919,22,768,78,0.1,768,0.6705501675605774,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,audio,7.733983993530273,7.691443904670509,764,74,78,0.1,74,0.9946621060371399,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,vision,7.733983993530273,0.0,65,35,78,0.1,35,0.0,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/1,-NFrJFQijFE,1,text,5.745999813079834,4.074365028738975,16,768,58,0.1,768,0.7835317254066467,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/1,-NFrJFQijFE,1,audio,5.745999813079834,5.699824074635634,566,74,58,0.1,74,0.9922494292259216,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/1,-NFrJFQijFE,1,vision,5.745999813079834,0.0,44,35,58,0.1,35,0.0,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/2,-NFrJFQijFE,2,text,6.855999946594238,3.4186642885208136,18,768,69,0.1,768,0.7431879639625549,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/2,-NFrJFQijFE,2,audio,6.855999946594238,6.798472889210726,670,74,69,0.1,74,0.9926568269729614,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-NFrJFQijFE/2,-NFrJFQijFE,2,vision,6.855999946594238,0.0,55,35,69,0.1,35,0.0,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/12,-THoVjtIkeU,12,text,14.896029472351074,7.941008102986966,39,768,149,0.1,768,0.6352806687355042,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/12,-THoVjtIkeU,12,audio,14.896029472351074,14.759312417861578,1481,74,149,0.1,74,0.9911767244338989,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/12,-THoVjtIkeU,12,vision,14.896029472351074,14.896029472351074,106,35,149,0.1,35,1.0,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/2,-THoVjtIkeU,2,text,4.2919921875,2.5793988468125453,10,768,43,0.1,768,0.7164996266365051,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/2,-THoVjtIkeU,2,audio,4.2919921875,4.247343715932063,424,74,43,0.1,74,0.9895758628845215,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/2,-THoVjtIkeU,2,vision,4.2919921875,4.2919921875,31,35,43,0.1,35,1.0,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/6,-THoVjtIkeU,6,text,8.097004890441895,5.15853084437549,30,768,81,0.1,768,0.6787540316581726,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/6,-THoVjtIkeU,6,audio,8.097004890441895,8.027356093840018,799,74,81,0.1,74,0.9927164316177368,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-THoVjtIkeU/6,-THoVjtIkeU,6,vision,8.097004890441895,8.097004890441895,68,35,81,0.1,35,1.0,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/1,-UuX1xuaiiE,1,text,10.350000381469727,5.7645069250837,27,768,104,0.1,768,0.6550575494766235,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/1,-UuX1xuaiiE,1,audio,10.350000381469727,10.268040973753543,1027,74,104,0.1,74,0.9925051927566528,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/1,-UuX1xuaiiE,1,vision,10.350000381469727,0.45000076293945285,83,35,104,0.1,35,0.8333339691162109,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/0,-UuX1xuaiiE,0,text,3.6333329677581787,2.204757870733737,9,768,37,0.1,768,0.6484582424163818,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/0,-UuX1xuaiiE,0,audio,3.6333329677581787,3.6124320519937045,358,74,37,0.1,74,0.994590699672699,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/0,-UuX1xuaiiE,0,vision,3.6333329677581787,1.700000047683716,25,35,37,0.1,35,0.9444444179534912,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/3,-UuX1xuaiiE,3,text,4.1300129890441895,2.0997684210538865,9,768,42,0.1,768,0.7240580320358276,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/3,-UuX1xuaiiE,3,audio,4.1300129890441895,4.093458598368876,404,74,42,0.1,74,0.992123007774353,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/3,-UuX1xuaiiE,3,vision,4.1300129890441895,4.03001308441162,29,35,42,0.1,35,0.976190447807312,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/6,-UuX1xuaiiE,6,text,8.113997459411621,4.803536714613438,22,768,82,0.1,768,0.7064024806022644,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/6,-UuX1xuaiiE,6,audio,8.113997459411621,7.968213647201255,795,74,82,0.1,74,0.984533965587616,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UuX1xuaiiE/6,-UuX1xuaiiE,6,vision,8.113997459411621,7.9306641817092896,68,35,82,0.1,35,0.9776423573493958,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-a55Q6RWvTA/3,-a55Q6RWvTA,3,text,22.15397071838379,13.423765965458015,65,768,222,0.1,768,0.6580277681350708,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-a55Q6RWvTA/3,-a55Q6RWvTA,3,audio,22.15397071838379,21.95386351814141,2209,74,222,0.1,74,0.9912921786308289,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-a55Q6RWvTA/3,-a55Q6RWvTA,3,vision,22.15397071838379,22.15397071838379,142,35,222,0.1,35,1.0,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-aNfi7CP8vM/7,-aNfi7CP8vM,7,text,8.694987297058105,5.3913327027112254,18,768,87,0.1,768,0.7001731395721436,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-aNfi7CP8vM/7,-aNfi7CP8vM,7,audio,8.694987297058105,8.639379485394505,854,74,87,0.1,74,0.9945195913314819,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-aNfi7CP8vM/7,-aNfi7CP8vM,7,vision,8.694987297058105,8.694987297058105,74,35,87,0.1,35,1.0,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,text,10.966667175292969,5.40069461874664,14,768,110,0.1,768,0.71061772108078,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,audio,10.966667175292969,10.855676183829436,1090,74,110,0.1,74,0.9910455346107483,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,vision,10.966667175292969,10.966667175292969,86,35,110,0.1,35,1.0,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/1,-dxfTGcXJoc,1,text,16.16100311279297,9.47002421617508,36,768,162,0.1,768,0.6812966465950012,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/1,-dxfTGcXJoc,1,audio,16.16100311279297,15.984151583668348,1602,74,162,0.1,74,0.9900091290473938,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/1,-dxfTGcXJoc,1,vision,16.16100311279297,16.16100311279297,112,35,162,0.1,35,1.0,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/0,-dxfTGcXJoc,0,text,20.5,13.566675072815258,41,768,205,0.1,768,0.7333337664604187,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/0,-dxfTGcXJoc,0,audio,20.5,20.265135636200775,2042,74,205,0.1,74,0.9891952872276306,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/0,-dxfTGcXJoc,0,vision,20.5,15.983333587646484,134,35,205,0.1,35,0.99895840883255,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/2,-dxfTGcXJoc,2,text,16.697982788085938,9.495353608578453,34,768,167,0.1,768,0.6593995094299316,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/2,-dxfTGcXJoc,2,audio,16.697982788085938,16.517766948940555,1664,74,167,0.1,74,0.9901677966117859,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/2,-dxfTGcXJoc,2,vision,16.697982788085938,16.697982788085938,115,35,167,0.1,35,1.0,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/6,-dxfTGcXJoc,6,text,12.735026359558105,8.155348146427425,27,768,128,0.1,768,0.715381383895874,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/6,-dxfTGcXJoc,6,audio,12.735026359558105,12.576174248392515,1263,74,128,0.1,74,0.9889141917228699,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-dxfTGcXJoc/6,-dxfTGcXJoc,6,vision,12.735026359558105,12.5,95,35,128,0.1,35,1.0,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/26,-egA8-b7-3M,26,text,6.030990123748779,3.6452820789068947,15,768,61,0.1,768,0.6877890825271606,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/26,-egA8-b7-3M,26,audio,6.030990123748779,5.994409297769134,593,74,61,0.1,74,0.9945786595344543,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/26,-egA8-b7-3M,26,vision,6.030990123748779,6.030990123748779,47,35,61,0.1,35,1.0,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/17,-egA8-b7-3M,17,text,7.271028995513916,3.4275580286979666,16,768,73,0.1,768,0.7292676568031311,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/17,-egA8-b7-3M,17,audio,7.271028995513916,7.202622791158187,710,74,73,0.1,74,0.9916234016418457,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/17,-egA8-b7-3M,17,vision,7.271028995513916,7.271028995513916,60,35,73,0.1,35,1.0,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/18,-egA8-b7-3M,18,text,8.386002540588379,3.9958887480199343,22,768,84,0.1,768,0.6243576407432556,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/18,-egA8-b7-3M,18,audio,8.386002540588379,8.326583495091747,827,74,84,0.1,74,0.9932008385658264,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/18,-egA8-b7-3M,18,vision,8.386002540588379,8.386002540588379,71,35,84,0.1,35,1.0,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/16,-egA8-b7-3M,16,text,5.538021087646484,3.3190146580338484,11,768,56,0.1,768,0.7375588417053223,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/16,-egA8-b7-3M,16,audio,5.538021087646484,5.498074575251824,549,74,56,0.1,74,0.9940068125724792,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/16,-egA8-b7-3M,16,vision,5.538021087646484,5.538021087646484,43,35,56,0.1,35,1.0,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/13,-egA8-b7-3M,13,text,4.18398380279541,2.166685357689858,11,768,42,0.1,768,0.6372604370117188,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/13,-egA8-b7-3M,13,audio,4.18398380279541,4.149822072886132,403,74,42,0.1,74,0.9924017786979675,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/13,-egA8-b7-3M,13,vision,4.18398380279541,4.18398380279541,29,35,42,0.1,35,1.0,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/1,-egA8-b7-3M,1,text,10.266016006469727,5.581577223725616,20,768,103,0.1,768,0.6490206122398376,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/1,-egA8-b7-3M,1,audio,10.266016006469727,10.199569694899225,1016,74,103,0.1,74,0.9937204718589783,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/1,-egA8-b7-3M,1,vision,10.266016006469727,10.266016006469727,83,35,103,0.1,35,1.0,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/6,-egA8-b7-3M,6,text,6.264974117279053,3.582069296762348,12,768,63,0.1,768,0.7164138555526733,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/6,-egA8-b7-3M,6,audio,6.264974117279053,6.2242991206613745,619,74,63,0.1,74,0.9942464828491211,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/6,-egA8-b7-3M,6,vision,6.264974117279053,6.264974117279053,50,35,63,0.1,35,1.0,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/9,-egA8-b7-3M,9,text,5.0899739265441895,2.328742109239101,10,768,51,0.1,768,0.597113311290741,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/9,-egA8-b7-3M,9,audio,5.0899739265441895,5.05666382530251,494,74,51,0.1,74,0.9939422011375427,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/9,-egA8-b7-3M,9,vision,5.0899739265441895,5.0899739265441895,38,35,51,0.1,35,1.0,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/20,-egA8-b7-3M,20,text,6.644987106323242,3.9119302723556753,20,768,67,0.1,768,0.6985589861869812,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/20,-egA8-b7-3M,20,audio,6.644987106323242,6.588095611333847,648,74,67,0.1,74,0.992087185382843,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-egA8-b7-3M/20,-egA8-b7-3M,20,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/3,-iRBcNs9oI8,3,text,5.620999813079834,2.037480006366969,8,768,57,0.1,768,0.6791599988937378,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/3,-iRBcNs9oI8,3,audio,5.620999813079834,5.578905152469068,554,74,57,0.1,74,0.9954468607902527,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/3,-iRBcNs9oI8,3,vision,5.620999813079834,0.0,28,35,57,0.1,35,0.0,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/7,-iRBcNs9oI8,7,text,4.104000091552734,2.2118893228471284,9,768,42,0.1,768,0.7135127186775208,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/7,-iRBcNs9oI8,7,audio,4.104000091552734,4.083554145129952,401,74,42,0.1,74,0.995795726776123,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/7,-iRBcNs9oI8,7,vision,4.104000091552734,0.0,21,35,42,0.1,35,0.0,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/6,-iRBcNs9oI8,6,text,2.9030001163482666,1.5899996414780622,5,768,30,0.1,768,0.7227271199226379,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/6,-iRBcNs9oI8,6,audio,2.9030001163482666,2.892310954429008,283,74,30,0.1,74,0.9973600506782532,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/6,-iRBcNs9oI8,6,vision,2.9030001163482666,0.0,15,35,30,0.1,35,0.0,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/9,-iRBcNs9oI8,9,text,3.4159998893737793,1.094375005364418,5,768,35,0.1,768,0.643750011920929,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/9,-iRBcNs9oI8,9,audio,3.4159998893737793,3.383608031917263,338,74,35,0.1,74,0.993934154510498,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/9,-iRBcNs9oI8,9,vision,3.4159998893737793,0.0,17,35,35,0.1,35,0.0,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/8,-iRBcNs9oI8,8,text,8.093000411987305,3.901277539858493,21,768,81,0.1,768,0.6192578673362732,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/8,-iRBcNs9oI8,8,audio,8.093000411987305,8.037513972489212,803,74,81,0.1,74,0.9947929382324219,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-iRBcNs9oI8/8,-iRBcNs9oI8,8,vision,8.093000411987305,0.0,41,35,81,0.1,35,0.0,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/5,-lzEya4AM_4,5,text,7.675000190734863,4.456424816604702,19,768,77,0.1,768,0.7427374720573425,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/5,-lzEya4AM_4,5,audio,7.675000190734863,7.5937163826178855,757,74,77,0.1,74,0.9901386499404907,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/5,-lzEya4AM_4,5,vision,7.675000190734863,7.675000190734863,64,35,77,0.1,35,1.0,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/6,-lzEya4AM_4,6,text,14.7919921875,9.074837291240696,43,768,148,0.1,768,0.7318416833877563,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/6,-lzEya4AM_4,6,audio,14.7919921875,14.634100490929308,1475,74,148,0.1,74,0.9907644391059875,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-lzEya4AM_4/6,-lzEya4AM_4,6,vision,14.7919921875,14.7919921875,105,35,148,0.1,35,1.0,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,text,6.103000164031982,1.9617638647556308,13,768,62,0.1,768,0.8174015879631042,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,audio,6.103000164031982,5.938054213652739,606,74,62,0.1,74,1.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,vision,6.103000164031982,0.0,31,35,62,0.1,35,0.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,text,5.730999946594238,3.5213801980018595,11,768,58,0.1,768,0.9266790151596069,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,audio,5.730999946594238,5.5761080561457455,571,74,58,0.1,74,1.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,vision,5.730999946594238,0.0,29,35,58,0.1,35,0.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,text,2.256999969482422,1.1722640581429007,5,768,23,0.1,768,0.6512578129768372,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,audio,2.256999969482422,2.2208648198211596,223,74,23,0.1,74,0.9862568378448486,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,vision,2.256999969482422,2.256999969482422,12,35,23,0.1,35,1.0,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,text,7.0329999923706055,3.813062208145856,22,768,71,0.1,768,0.5777366757392883,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,audio,7.0329999923706055,6.9057026652870945,691,74,71,0.1,74,0.9846946001052856,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,vision,7.0329999923706055,6.832799911499023,35,35,71,0.1,35,0.9856857061386108,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,text,4.191999912261963,2.284389768779285,11,768,42,0.1,768,0.6529467701911926,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,audio,4.191999912261963,4.127891841835266,418,74,42,0.1,74,0.9846959114074707,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,vision,4.191999912261963,4.191999912261963,21,35,42,0.1,35,1.0,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-mqbVkbCndg/0,-mqbVkbCndg,0,text,6.466667175292969,3.891069056466222,12,768,65,0.1,768,0.7074670791625977,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-mqbVkbCndg/0,-mqbVkbCndg,0,audio,6.466667175292969,6.400540961445988,639,74,65,0.1,74,0.9905118346214294,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-mqbVkbCndg/0,-mqbVkbCndg,0,vision,6.466667175292969,6.466667175292969,53,35,65,0.1,35,1.0,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/2,-t217m2on-s,2,text,5.6860032081604,2.3235132679343233,12,768,57,0.1,768,0.6279765367507935,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/2,-t217m2on-s,2,audio,5.6860032081604,5.64178691047269,564,74,57,0.1,74,0.9959543347358704,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/2,-t217m2on-s,2,vision,5.6860032081604,5.6860032081604,45,35,57,0.1,35,1.0,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/7,-t217m2on-s,7,text,17.183008193969727,10.16458021551371,45,768,172,0.1,768,0.6867958903312683,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/7,-t217m2on-s,7,audio,17.183008193969727,17.025629438258505,1703,74,172,0.1,74,0.9928514361381531,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-t217m2on-s/7,-t217m2on-s,7,vision,17.183008193969727,17.183008193969727,117,35,172,0.1,35,1.0,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tANM6ETl_M/3,-tANM6ETl_M,3,text,6.758008003234863,3.281156235933303,22,768,68,0.1,768,0.5859207510948181,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tANM6ETl_M/3,-tANM6ETl_M,3,audio,6.758008003234863,6.702588935075579,661,74,68,0.1,74,0.9936580061912537,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tANM6ETl_M/3,-tANM6ETl_M,3,vision,6.758008003234863,6.5333333015441895,54,35,68,0.1,35,0.9898989200592041,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/11,-tPCytz4rww,11,text,5.466991901397705,3.309338267147543,17,768,55,0.1,768,0.6753751635551453,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/11,-tPCytz4rww,11,audio,5.466991901397705,5.402410984844775,538,74,55,0.1,74,0.9890678524971008,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/11,-tPCytz4rww,11,vision,5.466991901397705,5.466991901397705,43,35,55,0.1,35,1.0,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/10,-tPCytz4rww,10,text,4.788997173309326,2.2889054998755456,12,768,48,0.1,768,0.5449775457382202,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/10,-tPCytz4rww,10,audio,4.788997173309326,4.71733509463233,468,74,48,0.1,74,0.9894654750823975,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/10,-tPCytz4rww,10,vision,4.788997173309326,4.788997173309326,35,35,48,0.1,35,1.0,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/12,-tPCytz4rww,12,text,11.863997459411621,6.21423171013594,26,768,119,0.1,768,0.654129683971405,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/12,-tPCytz4rww,12,audio,11.863997459411621,11.690646183812941,1174,74,119,0.1,74,0.9875938892364502,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/12,-tPCytz4rww,12,vision,11.863997459411621,11.863997459411621,91,35,119,0.1,35,1.0,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/16,-tPCytz4rww,16,text,7.205989837646484,2.154985956847668,16,768,73,0.1,768,0.4489554166793823,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/16,-tPCytz4rww,16,audio,7.205989837646484,7.1144090478484685,714,74,73,0.1,74,0.988937258720398,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/16,-tPCytz4rww,16,vision,7.205989837646484,7.205989837646484,59,35,73,0.1,35,1.0,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/18,-tPCytz4rww,18,text,6.644987106323242,3.4940090492367735,16,768,67,0.1,768,0.6719247698783875,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/18,-tPCytz4rww,18,audio,6.644987106323242,6.553973502565075,657,74,67,0.1,74,0.9872123599052429,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-tPCytz4rww/18,-tPCytz4rww,18,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-vxjVxOeScU/4,-vxjVxOeScU,4,text,9.127017974853516,5.366417751088739,21,768,92,0.1,768,0.6465563774108887,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-vxjVxOeScU/4,-vxjVxOeScU,4,audio,9.127017974853516,9.08216616914079,904,74,92,0.1,74,0.9960808753967285,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-vxjVxOeScU/4,-vxjVxOeScU,4,vision,9.127017974853516,9.127017974853516,77,35,92,0.1,35,1.0,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wMB_hJL-3o/7,-wMB_hJL-3o,7,text,6.044010162353516,3.555890290439128,22,768,61,0.1,768,0.658498227596283,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wMB_hJL-3o/7,-wMB_hJL-3o,7,audio,6.044010162353516,5.968699067508853,598,74,61,0.1,74,0.9895980954170227,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wMB_hJL-3o/7,-wMB_hJL-3o,7,vision,6.044010162353516,5.944010257720948,48,35,61,0.1,35,0.9836065769195557,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/1,-wny0OAz3g8,1,text,6.844009876251221,4.170902410242706,23,768,69,0.1,768,0.62252277135849,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/1,-wny0OAz3g8,1,audio,6.844009876251221,6.814914991726747,678,74,69,0.1,74,0.995954155921936,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/1,-wny0OAz3g8,1,vision,6.844009876251221,3.327343463897705,56,35,69,0.1,35,0.9950981140136719,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/0,-wny0OAz3g8,0,text,3.3333330154418945,1.6946923546493056,9,768,34,0.1,768,0.6052471995353699,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/0,-wny0OAz3g8,0,audio,3.3333330154418945,3.3145942848276446,328,74,34,0.1,74,0.9955413937568665,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/0,-wny0OAz3g8,0,vision,3.3333330154418945,2.6999998092651367,21,35,34,0.1,35,0.9999999403953552,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/3,-wny0OAz3g8,3,text,6.146028995513916,4.208628869801758,20,768,62,0.1,768,0.7014381289482117,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/3,-wny0OAz3g8,3,audio,6.146028995513916,6.13413711335208,610,74,62,0.1,74,0.9980819821357727,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/3,-wny0OAz3g8,3,vision,6.146028995513916,1.5293622016906734,49,35,62,0.1,35,0.9895832538604736,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/2,-wny0OAz3g8,2,text,6.686978816986084,4.909538650512694,23,768,67,0.1,768,0.7553136348724365,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/2,-wny0OAz3g8,2,audio,6.686978816986084,6.656249635122918,653,74,67,0.1,74,0.9957627058029175,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/2,-wny0OAz3g8,2,vision,6.686978816986084,5.4166669845581055,54,35,67,0.1,35,0.9848485589027405,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/5,-wny0OAz3g8,5,text,6.9119791984558105,3.941009076312184,20,768,70,0.1,768,0.6063091158866882,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/5,-wny0OAz3g8,5,audio,6.9119791984558105,6.879425679509704,675,74,70,0.1,74,0.9961636066436768,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/5,-wny0OAz3g8,5,vision,6.9119791984558105,1.4953122138977046,56,35,70,0.1,35,0.9895831346511841,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/7,-wny0OAz3g8,7,text,8.06796932220459,5.293590973317621,29,768,81,0.1,768,0.6700748205184937,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/7,-wny0OAz3g8,7,audio,8.06796932220459,8.030726903515895,792,74,81,0.1,74,0.9956274628639221,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/7,-wny0OAz3g8,7,vision,8.06796932220459,7.06796932220459,68,35,81,0.1,35,1.0,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/9,-wny0OAz3g8,9,text,6.2130208015441895,4.012809376046059,20,768,63,0.1,768,0.6688015460968018,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/9,-wny0OAz3g8,9,audio,6.2130208015441895,6.190979680177328,605,74,63,0.1,74,0.9969837069511414,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-wny0OAz3g8/9,-wny0OAz3g8,9,vision,6.2130208015441895,5.8130205273628235,49,35,63,0.1,35,0.9672130942344666,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/0,-571d8cVauQ,0,text,5.066667079925537,3.21007038205862,12,768,51,0.1,768,0.6978414058685303,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/0,-571d8cVauQ,0,audio,5.066667079925537,5.013243622071034,500,74,51,0.1,74,0.9903978705406189,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/0,-571d8cVauQ,0,vision,5.066667079925537,4.9666670799255375,39,35,51,0.1,35,1.0,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/5,-571d8cVauQ,5,text,15.272981643676758,9.052198391593993,43,768,153,0.1,768,0.7017207741737366,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/5,-571d8cVauQ,5,audio,15.272981643676758,15.142900936426344,1512,74,153,0.1,74,0.9918006062507629,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-571d8cVauQ/5,-571d8cVauQ,5,vision,15.272981643676758,15.272981643676758,108,35,153,0.1,35,1.0,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/1,-I_e4mIh0yE,1,text,7.622004985809326,3.535097924340517,18,768,77,0.1,768,0.6546477675437927,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/1,-I_e4mIh0yE,1,audio,7.622004985809326,7.497626475627357,745,74,77,0.1,74,0.985649049282074,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/1,-I_e4mIh0yE,1,vision,7.622004985809326,7.622004985809326,63,35,77,0.1,35,1.0,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/3,-I_e4mIh0yE,3,text,9.163021087646484,4.626299023628236,22,768,92,0.1,768,0.6251755356788635,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/3,-I_e4mIh0yE,3,audio,9.163021087646484,9.017804326318405,900,74,92,0.1,74,0.9852646589279175,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-I_e4mIh0yE/3,-I_e4mIh0yE,3,vision,9.163021087646484,9.163021087646484,77,35,92,0.1,35,1.0,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/10,-UacrmKiTn4,10,text,7.361979007720947,2.732618988305329,18,768,74,0.1,768,0.47940683364868164,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/10,-UacrmKiTn4,10,audio,7.361979007720947,7.319425493478775,725,74,74,0.1,74,0.9955651164054871,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/10,-UacrmKiTn4,10,vision,7.361979007720947,7.361979007720947,60,35,74,0.1,35,1.0,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/4,-UacrmKiTn4,4,text,4.741015911102295,3.0301611527800563,14,768,48,0.1,768,0.7214669585227966,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/4,-UacrmKiTn4,4,audio,4.741015911102295,4.704772258610339,469,74,48,0.1,74,0.9926760792732239,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UacrmKiTn4/4,-UacrmKiTn4,4,vision,4.741015911102295,4.741015911102295,35,35,48,0.1,35,1.0,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/7,-hnBHBN8p5A,7,text,7.372000217437744,4.009185888338835,13,768,74,0.1,768,0.657243549823761,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/7,-hnBHBN8p5A,7,audio,7.372000217437744,7.281608330236899,731,74,74,0.1,74,0.9905768632888794,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/7,-hnBHBN8p5A,7,vision,7.372000217437744,0.0,37,35,74,0.1,35,0.0,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/6,-hnBHBN8p5A,6,text,6.401000022888184,3.580871792882682,13,768,65,0.1,768,0.7618876099586487,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/6,-hnBHBN8p5A,6,audio,6.401000022888184,6.346108104731585,634,74,65,0.1,74,0.9932082295417786,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-hnBHBN8p5A/6,-hnBHBN8p5A,6,vision,6.401000022888184,0.0,32,35,65,0.1,35,0.0,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-qDkUB0GgYY/6,-qDkUB0GgYY,6,text,4.677995204925537,2.927082145679743,16,768,47,0.1,768,0.6504627466201782,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-qDkUB0GgYY/6,-qDkUB0GgYY,6,audio,4.677995204925537,4.653116947730401,462,74,47,0.1,74,0.9946085214614868,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-qDkUB0GgYY/6,-qDkUB0GgYY,6,vision,4.677995204925537,4.677995204925537,35,35,47,0.1,35,1.0,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-uywlfIYOS8/4,-uywlfIYOS8,4,text,6.044987201690674,3.7997015411034236,18,768,61,0.1,768,0.6908547878265381,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-uywlfIYOS8/4,-uywlfIYOS8,4,audio,6.044987201690674,6.015325370511492,589,74,61,0.1,74,0.9957761168479919,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-uywlfIYOS8/4,-uywlfIYOS8,4,vision,6.044987201690674,6.044987201690674,48,35,61,0.1,35,1.0,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,text,12.805012702941895,6.537867438234387,34,768,129,0.1,768,0.63474440574646,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,audio,12.805012702941895,12.672918005408468,1274,74,129,0.1,74,0.9899758696556091,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,vision,12.805012702941895,12.805012702941895,95,35,129,0.1,35,1.0,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/0,-9y-fZ3swSY,0,text,6.8333330154418945,3.536603624001146,22,768,69,0.1,768,0.5440928339958191,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/0,-9y-fZ3swSY,0,audio,6.8333330154418945,6.794324037513217,676,74,69,0.1,74,0.9948647022247314,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/0,-9y-fZ3swSY,0,vision,6.8333330154418945,6.8333330154418945,57,35,69,0.1,35,1.0,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/4,-9y-fZ3swSY,4,text,2.8210289478302,1.160462707281113,9,768,29,0.1,768,0.5274830460548401,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/4,-9y-fZ3swSY,4,audio,2.8210289478302,2.7937038478819103,267,74,29,0.1,74,0.9926167726516724,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/4,-9y-fZ3swSY,4,vision,2.8210289478302,2.7210289478302006,15,35,29,0.1,35,1.0,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/8,-9y-fZ3swSY,8,text,4.988996982574463,2.1943973237220367,12,768,50,0.1,768,0.562736988067627,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/8,-9y-fZ3swSY,8,audio,4.988996982574463,4.9497673825665816,492,74,50,0.1,74,0.9931800365447998,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-9y-fZ3swSY/8,-9y-fZ3swSY,8,vision,4.988996982574463,3.688997030258178,37,35,50,0.1,35,0.9487179517745972,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,text,22.511003494262695,12.134252551943066,41,768,226,0.1,768,0.6386448740959167,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,audio,22.511003494262695,22.31367928599184,2241,74,226,0.1,74,0.9917553663253784,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,vision,22.511003494262695,22.511003494262695,144,35,226,0.1,35,1.0,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-HeZS2-Prhc/2,-HeZS2-Prhc,2,text,8.261979103088379,3.552112135011702,16,768,83,0.1,768,0.6830984950065613,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-HeZS2-Prhc/2,-HeZS2-Prhc,2,audio,8.261979103088379,8.205439110059995,816,74,83,0.1,74,0.9933875799179077,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-HeZS2-Prhc/2,-HeZS2-Prhc,2,vision,8.261979103088379,8.261979103088379,70,35,83,0.1,35,1.0,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/0,-MeTTeMJBNc,0,text,9.300000190734863,4.416411150246859,21,768,94,0.1,768,0.6400595903396606,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/0,-MeTTeMJBNc,0,audio,9.300000190734863,9.216486695006088,922,74,94,0.1,74,0.9939717650413513,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/0,-MeTTeMJBNc,0,vision,9.300000190734863,9.200000190734862,78,35,94,0.1,35,1.0,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/13,-MeTTeMJBNc,13,text,5.430013179779053,2.997564935684203,14,768,55,0.1,768,0.6661255359649658,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/13,-MeTTeMJBNc,13,audio,5.430013179779053,5.368323606413764,526,74,55,0.1,74,0.9918515682220459,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/13,-MeTTeMJBNc,13,vision,5.430013179779053,5.430013179779053,41,35,55,0.1,35,1.0,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/7,-MeTTeMJBNc,7,text,10.51699161529541,6.728291415888818,31,768,106,0.1,768,0.7313360571861267,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/7,-MeTTeMJBNc,7,audio,10.51699161529541,10.441329748243898,1042,74,106,0.1,74,0.9934103488922119,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-MeTTeMJBNc/7,-MeTTeMJBNc,7,vision,10.51699161529541,10.51699161529541,84,35,106,0.1,35,1.0,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/11,-RfYyzHpjk4,11,text,5.238996982574463,3.062753976881504,17,768,53,0.1,768,0.6960803866386414,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/11,-RfYyzHpjk4,11,audio,5.238996982574463,5.202267333462432,508,74,53,0.1,74,0.9958056807518005,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/11,-RfYyzHpjk4,11,vision,5.238996982574463,5.238996982574463,40,35,53,0.1,35,1.0,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/8,-RfYyzHpjk4,8,text,4.588996887207031,2.8058770607668286,17,768,46,0.1,768,0.684955358505249,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/8,-RfYyzHpjk4,8,audio,4.588996887207031,4.553010454316745,454,74,46,0.1,74,0.994469404220581,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/8,-RfYyzHpjk4,8,vision,4.588996887207031,4.588996887207031,33,35,46,0.1,35,1.0,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/2,-RfYyzHpjk4,2,text,5.516016006469727,3.372718970850109,25,768,56,0.1,768,0.7175998091697693,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/2,-RfYyzHpjk4,2,audio,5.516016006469727,5.478826378849712,540,74,56,0.1,74,0.9948742389678955,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-RfYyzHpjk4/2,-RfYyzHpjk4,2,vision,5.516016006469727,5.416016006469727,43,35,56,0.1,35,1.0,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-UUCSKoHeMA/0,-UUCSKoHeMA,0,text,7.800000190734863,4.133793779369442,18,768,79,0.1,768,0.666740894317627,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-UUCSKoHeMA/0,-UUCSKoHeMA,0,audio,7.800000190734863,7.750270468963159,774,74,79,0.1,74,0.9943835139274597,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-UUCSKoHeMA/0,-UUCSKoHeMA,0,vision,7.800000190734863,7.700000190734862,66,35,79,0.1,35,1.0,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/0,-ri04Z7vwnc,0,text,2.806999921798706,2.1131306469440463,8,768,29,0.1,768,0.7826409935951233,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/0,-ri04Z7vwnc,0,audio,2.806999921798706,2.8018783078000356,277,74,29,0.1,74,0.9982975125312805,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/0,-ri04Z7vwnc,0,vision,2.806999921798706,0.0,14,35,29,0.1,35,0.0,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/2,-ri04Z7vwnc,2,text,6.0269999504089355,3.9384737918153405,16,768,61,0.1,768,0.7431082725524902,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/2,-ri04Z7vwnc,2,audio,6.0269999504089355,6.008905370493193,592,74,61,0.1,74,0.9971521496772766,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/2,-ri04Z7vwnc,2,vision,6.0269999504089355,2.106416702270508,30,35,61,0.1,35,0.9906440377235413,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/5,-ri04Z7vwnc,5,text,3.5450000762939453,1.8027290821075441,9,768,36,0.1,768,0.721091628074646,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/5,-ri04Z7vwnc,5,audio,3.5450000762939453,3.534527108637063,344,74,36,0.1,74,0.9974266886711121,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-ri04Z7vwnc/5,-ri04Z7vwnc,5,vision,3.5450000762939453,3.5450000762939453,18,35,36,0.1,35,1.0,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,text,4.7919921875,2.610484106093645,11,768,48,0.1,768,0.7055363059043884,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,audio,4.7919921875,4.72247887628304,465,74,48,0.1,74,0.9860281348228455,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,vision,4.7919921875,4.7919921875,35,35,48,0.1,35,1.0,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,text,6.800000190734863,3.6206328462809334,24,768,69,0.1,768,0.6351987719535828,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,audio,6.800000190734863,6.715675868979983,672,74,69,0.1,74,0.9885490536689758,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,vision,6.800000190734863,6.199999928474425,56,35,69,0.1,35,0.984375,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,text,8.561002731323242,3.4511515218298876,19,768,86,0.1,768,0.5150972604751587,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,audio,8.561002731323242,8.46259727510246,840,74,86,0.1,74,0.9896790981292725,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,vision,8.561002731323242,8.561002731323242,72,35,86,0.1,35,1.0,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,text,4.427018165588379,2.2374289706349377,9,768,45,0.1,768,0.6215080618858337,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,audio,4.427018165588379,4.367301462550421,425,74,45,0.1,74,0.9892621040344238,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,vision,4.427018165588379,3.027018189430237,31,35,45,0.1,35,0.96875,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,text,6.966015815734863,3.9709798723459264,28,768,70,0.1,768,0.661829948425293,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,audio,6.966015815734863,6.848488390969263,684,74,70,0.1,74,0.9849807620048523,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,vision,6.966015815734863,4.0493486523628235,57,35,70,0.1,35,0.9496123194694519,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,text,2.4749999046325684,1.6227161765098577,5,768,25,0.1,768,0.8113580942153931,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,audio,2.4749999046325684,2.4289188214250514,232,74,25,0.1,74,0.9844902753829956,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,vision,2.4749999046325684,1.8999999761581425,14,35,25,0.1,35,1.0,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,text,3.2949869632720947,1.9395141303539274,13,768,33,0.1,768,0.6465047001838684,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,audio,3.2949869632720947,3.253163003921509,319,74,33,0.1,74,0.9880942702293396,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,vision,3.2949869632720947,3.016666531562805,20,35,33,0.1,35,0.9731181859970093,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,text,29.288021087646484,15.053765958920112,49,768,293,0.1,768,0.6873865723609924,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,audio,29.288021087646484,28.759709144524624,2911,74,293,0.1,74,0.9836021065711975,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,vision,29.288021087646484,27.471354246139526,178,35,293,0.1,35,0.999393880367279,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds 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`typical_alignment_example.png`锛氫腑浣嶆椂闀挎牱鏈殑璇嶈竟鐣屻€佽鐩栫巼銆佸０瀛?瑙嗚杞ㄨ抗涓庤棰戝抚鏍搁獙鍥俱€?- `run_manifest.json`锛氱幆澧冦€佹ā鍨?revision銆佸弬鏁般€佸搱甯屼笌杩愯鏃堕棿銆? +## 澶嶇幇 + +鍦ㄤ粨搴撴牴鐩綍鎵ц锛? +```bash +cd final/Q1 +uv sync +uv run python compare_models.py +``` + +鐗瑰緛缂撳瓨鍐欏湪 `final/Q1/cache/native/`锛屾寮忕粨鏋滃彧鍐欏湪 `final/Q1/results/model_comparison/`銆傚垹闄ょ紦瀛樺悗浼氫粠闄勪欢涓€閲嶆柊鎻愬彇銆傞娆¤繍琛岄渶瑕佷笅杞?BERT銆乄av2Vec2 鍜?MediaPipe Face Landmarker 鏉冮噸銆? diff --git a/math/results/model_comparison/run_manifest.json b/final/Q1/results/model_comparison/run_manifest.json similarity index 99% rename from math/results/model_comparison/run_manifest.json rename to final/Q1/results/model_comparison/run_manifest.json index 6e41f14..f24a1b8 100644 --- a/math/results/model_comparison/run_manifest.json +++ b/final/Q1/results/model_comparison/run_manifest.json @@ -287,7 +287,7 @@ "hard_text_quality_weight": "per-sample median-normalized CTC path score clipped to [0.25, 4.0]; uncalibrated", "cache_schema": "q1-b0b4-v3" }, - "output_dir": 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+-yRb-Jum7EQ/6,-yRb-Jum7EQ,6,18,terrorism.,10.5024995803833,10.982500076293945,True,1.314886949321803e-10,0.7807753682136536,10.4911182530075,10.981002444124938,10.3825,10.5025,10.9825,10.9825,0.11999999999999922,0.0 diff --git a/math/问题一.pdf b/final/Q1/问题一.pdf similarity index 100% rename from math/问题一.pdf rename to final/Q1/问题一.pdf diff --git a/math/README.md b/math/Q1/README.md similarity index 93% rename from math/README.md rename to math/Q1/README.md index 1b60029..bfaada9 100644 --- a/math/README.md +++ b/math/Q1/README.md @@ -7,12 +7,12 @@ 在仓库根目录执行： ```bash -cd math +cd math/Q1 uv sync uv run python compare_models.py ``` -默认使用 CUDA（如果可用），需要可访问 Hugging Face 下载 BERT 与 Wav2Vec2 权重；MediaPipe Face Landmarker 权重会保存在 `math/cache/`。可用 `--device cpu` 指定 CPU。缓存保存在 `math/cache/native/`，中断后重跑会继续使用已完成样本。只有传入 `--force-extract` 才会重算全部原始特征。 +默认使用 CUDA（如果可用），需要可访问 Hugging Face 下载 BERT 与 Wav2Vec2 权重；MediaPipe Face Landmarker 权重会保存在 `math/Q1/cache/`。可用 `--device cpu` 指定 CPU。缓存保存在 `math/Q1/cache/native/`，中断后重跑会继续使用已完成样本。只有传入 `--force-extract` 才会重算全部原始特征。 ## 对照定义 diff --git a/math/compare_models.py b/math/Q1/compare_models.py similarity index 99% rename from math/compare_models.py rename to math/Q1/compare_models.py index 5fc9267..f813c0e 100644 --- a/math/compare_models.py +++ b/math/Q1/compare_models.py @@ -33,7 +33,7 @@ from sklearn.model_selection import GroupKFold from transformers import AutoModel, AutoModelForCTC, AutoTokenizer -ROOT = Path(__file__).resolve().parents[1] +ROOT = Path(__file__).resolve().parents[2] MATH_DIR = Path(__file__).resolve().parent DATA_DIR = ROOT / "E题数据" / "附件1-数据集原始多模态样本" / "MOSEI数据集部分原始视频-100条" LABEL_FILE = DATA_DIR / "label-100.xlsx" @@ -1668,7 +1668,7 @@ def _write_report( lines = [ "# 问题一 B0-B4 模型对比结果", "", - "本报告按 `math/问题一.pdf` 第 4.8.3 节实施五组单因素对照。全部 100 条附件一视频均使用同一套冻结特征抽取器、0.1 秒主时间网格、观测掩码与按 `video_id` 分组的五折划分。情感标签仅用于折内的轻量预测探针，不进入时间对齐、CTC 后验或动态签名计算。", + "本报告按 `math/Q1/问题一.pdf` 第 4.8.3 节实施五组单因素对照。全部 100 条附件一视频均使用同一套冻结特征抽取器、0.1 秒主时间网格、观测掩码与按 `video_id` 分组的五折划分。情感标签仅用于折内的轻量预测探针，不进入时间对齐、CTC 后验或动态签名计算。", "", "## 实际特征和比较版本", "", @@ -1727,7 +1727,7 @@ def _write_report( "uv run python compare_models.py", "```", "", - "特征缓存写在 `math/cache/native/`，正式结果只写在 `math/results/model_comparison/`。删除缓存后会从附件一重新提取。首次运行需要下载 BERT、Wav2Vec2 和 MediaPipe Face Landmarker 权重。", + "特征缓存写在 `math/Q1/cache/native/`，正式结果只写在 `math/Q1/results/model_comparison/`。删除缓存后会从附件一重新提取。首次运行需要下载 BERT、Wav2Vec2 和 MediaPipe Face Landmarker 权重。", ] (output_dir / "report.md").write_text("\n".join(lines) + "\n", encoding="utf-8") diff --git a/math/Q1/pyproject.toml b/math/Q1/pyproject.toml new file mode 100644 index 0000000..f4afaf6 --- /dev/null +++ b/math/Q1/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "math-q1-comparison" +version = "0.1.0" +requires-python = ">=3.14" +dependencies = [ + "matplotlib>=3.11.2", + "mediapipe>=1.0.1", + "numpy>=2.5.2", + "opencv-python-headless>=5.0.0.93", + "openpyxl>=3.1.5", + "pillow>=12.3.0", + "scikit-learn>=1.9.1", + "scipy>=1.17.1", + "torch>=2.14.0", + "transformers>=5.17.0", +] + +[tool.uv.sources] +torch = { index = "pytorch" } + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/math/Q1/results/model_comparison/comparison.png b/math/Q1/results/model_comparison/comparison.png new file mode 100644 index 0000000..556343b Binary files /dev/null and b/math/Q1/results/model_comparison/comparison.png differ diff --git a/math/Q1/results/model_comparison/comparison_summary.csv b/math/Q1/results/model_comparison/comparison_summary.csv new file mode 100644 index 0000000..c001e08 --- /dev/null +++ b/math/Q1/results/model_comparison/comparison_summary.csv @@ -0,0 +1,6 @@ +method,sample_count,video_group_count,oof_accuracy,oof_macro_f1,oof_mae,oof_pearson,fold_accuracy_mean,fold_accuracy_sd,fold_macro_f1_mean,fold_macro_f1_sd,fold_mae_mean,fold_mae_sd,fold_pearson_mean,fold_pearson_sd 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100644 index 0000000..b895fb3 --- /dev/null +++ b/math/Q1/results/model_comparison/group_bootstrap_deltas.csv @@ -0,0 +1,17 @@ +comparison,metric,reference_oof,method_oof,delta_oof,group_bootstrap_ci95_low,group_bootstrap_ci95_high,bootstrap_repeats +B0-B1,accuracy,0.61,0.6,-0.010000000000000009,-0.029710396039603987,0.0,2000 +B0-B1,macro_f1,0.387431302270012,0.3612633181126332,-0.02616798415737881,-0.08327057744765032,0.0,2000 +B0-B1,mae,0.5989421024918556,0.5642678308486938,-0.034674271643161836,-0.06304207975806683,-0.012443204964559182,2000 +B0-B1,pearson,0.23765809617297712,0.34585652345724877,0.10819842728427165,0.04340079372240924,0.19005024141624596,2000 +B2-B1,accuracy,0.61,0.58,-0.030000000000000027,-0.0648204607046071,0.0,2000 +B2-B1,macro_f1,0.387431302270012,0.33061794334825517,-0.05681335892175682,-0.10940517350156256,-0.0009457159449812041,2000 +B2-B1,mae,0.5989421024918556,0.588000754788518,-0.010941347703337656,-0.027305471624463586,0.004017100735475708,2000 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a/math/results/model_comparison/report.md b/math/Q1/results/model_comparison/report.md similarity index 88% rename from math/results/model_comparison/report.md rename to math/Q1/results/model_comparison/report.md index 31c0135..c17e450 100644 --- a/math/results/model_comparison/report.md +++ b/math/Q1/results/model_comparison/report.md @@ -1,6 +1,6 @@ # 问题一 B0-B4 模型对比结果 -本报告按 `math/问题一.pdf` 第 4.8.3 节实施五组单因素对照。全部 100 条附件一视频均使用同一套冻结特征抽取器、0.1 秒主时间网格、观测掩码与按 `video_id` 分组的五折划分。情感标签仅用于折内的轻量预测探针，不进入时间对齐、CTC 后验或动态签名计算。 +本报告按 `math/Q1/问题一.pdf` 第 4.8.3 节实施五组单因素对照。全部 100 条附件一视频均使用同一套冻结特征抽取器、0.1 秒主时间网格、观测掩码与按 `video_id` 分组的五折划分。情感标签仅用于折内的轻量预测探针，不进入时间对齐、CTC 后验或动态签名计算。 ## 实际特征和比较版本 @@ -53,9 +53,9 @@ B0–B4 的特征来源、有效掩码、连续覆盖率、词边界后验宽度 在仓库根目录执行： ```bash -cd math +cd math/Q1 uv sync uv run python compare_models.py ``` -特征缓存写在 `math/cache/native/`，正式结果只写在 `math/results/model_comparison/`。删除缓存后会从附件一重新提取。首次运行需要下载 BERT、Wav2Vec2 和 MediaPipe Face Landmarker 权重。 +特征缓存写在 `math/Q1/cache/native/`，正式结果只写在 `math/Q1/results/model_comparison/`。删除缓存后会从附件一重新提取。首次运行需要下载 BERT、Wav2Vec2 和 MediaPipe Face Landmarker 权重。 diff --git a/math/Q1/results/model_comparison/run_manifest.json b/math/Q1/results/model_comparison/run_manifest.json new file mode 100644 index 0000000..f24a1b8 --- /dev/null +++ b/math/Q1/results/model_comparison/run_manifest.json @@ -0,0 +1,293 @@ +{ + "created_at_local": "2026-09-23 22:17:06 +0800", + "python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]", + "platform": "Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.43", + "uv_version": "uv 0.11.28 (x86_64-unknown-linux-gnu)", + "packages": { + "torch": "2.14.0+cu130", + "transformers": "5.17.0", + "numpy": "2.5.3", + "scipy": "1.18.1", + "scikit-learn": "1.9.1", + "mediapipe": "1.0.1", + "opencv-python-headless": "5.0.0.93", + "openpyxl": "3.1.5", + "matplotlib": "3.11.2" + }, + "device": "cuda", + "cuda_available": true, + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "models": { + 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+-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,14,lose,3.7825000286102295,3.942500114440918,True,7.575693002602468e-13,0.6960501074790955,3.7616974887882315,3.920602352332232,3.7225,3.7825,3.8825,3.9425,0.06000000000000005,0.06000000000000005 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,15,in,4.102499961853027,4.162499904632568,True,3.397961165912955e-12,3.122026205062866,4.0719655131892,4.156779322130816,3.9225,4.1025,4.1225000000000005,4.1825,0.18000000000000016,0.05999999999999961 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,16,a,4.202499866485596,4.222499847412109,True,5.129190258287841e-13,0.47126689553260803,4.214525556613766,4.23452555661377,4.202500000000001,4.2225,4.2225,4.242500000000001,0.019999999999999574,0.020000000000000462 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,17,"race,",4.522500038146973,4.78249979019165,True,8.075219095834973e-13,0.741946280002594,4.523863360616268,4.783031253821107,4.522500000000001,4.522500000000001,4.782500000000001,4.782500000000001,0.0,0.0 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+-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,22,give,5.962500095367432,6.162499904632568,True,2.479647346828595e-13,0.25,5.962426812392603,6.162335078542887,5.9625,5.9625,6.1625000000000005,6.1625000000000005,0.0,0.0 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,23,up,6.642499923706055,6.702499866485596,True,5.418672780442557e-13,0.49786439538002014,6.61413642460844,6.68734623928928,6.5825000000000005,6.642500000000001,6.6625000000000005,6.702500000000001,0.0600000000000005,0.040000000000000036 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,0,I,0.0024999999441206455,0.02250000089406967,True,8.043445250127679e-12,4.0,0.0032990285590143776,0.023299028559016173,0.0025000000000000005,0.0025000000000000005,0.0225,0.0225,0.0,0.0 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,1,have,0.08250000327825546,0.42250001430511475,True,3.238681241593544e-12,4.0,0.06717380191828314,0.32357469353523305,0.0425,0.1225,0.1825,0.4225,0.07999999999999999,0.24 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+-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,10,that,4.902500152587891,5.122499942779541,True,5.439115737665423e-12,4.0,4.89797082813463,5.13239484504563,4.902500000000001,4.9625,5.102500000000001,5.1625000000000005,0.05999999999999961,0.05999999999999961 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,11,can,5.162499904632568,5.34250020980835,True,4.8560754619926885e-14,0.25,5.188221176465659,5.361559915166783,5.1625000000000005,5.3825,5.2625,5.5025,0.21999999999999975,0.2400000000000002 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,12,go,5.422500133514404,5.502500057220459,True,1.665226523296301e-13,0.3394080102443695,5.454580933666803,5.539748928566693,5.4225,5.602500000000001,5.5025,5.702500000000001,0.1800000000000006,0.20000000000000018 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,13,"wrong,",5.602499961853027,6.202499866485596,True,4.906267449603097e-13,1.0,5.65194480101253,6.236338881255996,5.602500000000001,6.1225000000000005,6.142500000000001,6.8025,0.5199999999999996,0.6599999999999993 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+-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,18,me,8.142499923706055,8.262499809265137,True,6.399445209859245e-12,4.0,8.149809329284016,8.26073298564643,8.1425,8.2425,8.2425,8.362499999999999,0.09999999999999964,0.11999999999999922 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,0,I,0.02250000089406967,0.042500000447034836,True,2.5145097115597537e-10,4.0,0.022581619903897353,0.04258161990389827,0.0225,0.0225,0.0425,0.0425,0.0,0.0 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,1,have,0.10249999910593033,0.32249999046325684,True,5.2369983349898064e-11,4.0,0.10275901230479557,0.3234319644678103,0.10250000000000001,0.10250000000000001,0.3225,0.3225,0.0,0.0 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,2,lost,0.4025000035762787,0.8025000095367432,True,4.580078720797798e-13,1.0,0.5590338658826747,0.8117400282480208,0.4025,0.6224999999999999,0.7825,0.8624999999999999,0.21999999999999992,0.07999999999999996 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+-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,2,to,0.5625,0.6225000023841858,True,5.419344834001194e-11,4.0,0.5617484066259408,0.6218305196632183,0.5625,0.5625,0.6224999999999999,0.6224999999999999,0.0,0.0 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,3,work,0.6424999833106995,0.7825000286102295,True,1.2994504560923104e-13,0.25,0.6439510150136057,0.7956763526640003,0.6425,0.6425,0.7825,0.8225,0.0,0.040000000000000036 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,4,for,0.862500011920929,0.9825000166893005,True,5.919409927501729e-13,0.25,0.8703185545526655,0.9934280704856465,0.8624999999999999,0.9025,0.9824999999999999,1.0425,0.040000000000000036,0.06000000000000005 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,5,Warren,1.0425000190734863,1.3624999523162842,True,2.076905487868874e-12,0.55162113904953,1.0499859516799703,1.362546218062043,1.0425,1.0825,1.3625,1.3625,0.040000000000000036,0.0 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,6,"Avis,",1.4424999952316284,1.6825000047683716,True,5.145964557251581e-11,4.0,1.441602626222574,1.6811464831337093,1.4425,1.4425,1.6824999999999999,1.6824999999999999,0.0,0.0 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,7,the,1.7024999856948853,1.9824999570846558,True,7.724032351219545e-12,2.0514845848083496,1.7012855259737578,1.9803489043794023,1.7025,1.7025,1.9825,1.9825,0.0,0.0 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,8,guy,2.0225000381469727,2.1424999237060547,True,1.3621283788400884e-11,3.6177804470062256,2.0202241581787876,2.141641797729933,2.0225,2.0225,2.1425,2.1425,0.0,0.0 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,9,who,2.1624999046325684,2.262500047683716,True,1.5639521622201613e-10,4.0,2.1618504322195777,2.2658099313974582,2.1625,2.1625,2.2625,2.2825,0.0,0.020000000000000018 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,10,started,2.302500009536743,2.5625,True,3.76403162111183e-12,0.9997177720069885,2.301007069455703,2.562164512343847,2.3025,2.3025,2.5625,2.5625,0.0,0.0 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