整理 Q1-Q3 实验代码与结果
This commit is contained in:
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Seven frozen-alignment comparisons on 100 held-out clips from 37 video IDs. 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.
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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.
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`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.
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`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.
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method,view,true_class,predicted_class,count
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M3_noSourceTime,text,Negative,Negative,4
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M3_noSourceTime,text,Negative,Neutral,6
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M3_noSourceTime,text,Negative,Positive,8
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M3_noSourceTime,text,Neutral,Negative,2
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M3_noSourceTime,text,Neutral,Neutral,5
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M3_noSourceTime,text,Neutral,Positive,18
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M3_noSourceTime,text,Positive,Negative,2
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M3_noSourceTime,text,Positive,Neutral,8
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M3_noSourceTime,text,Positive,Positive,47
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M3_noSourceTime,audio,Negative,Negative,6
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M3_noSourceTime,audio,Negative,Neutral,4
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M3_noSourceTime,audio,Negative,Positive,8
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M3_noSourceTime,audio,Neutral,Negative,4
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M3_noSourceTime,audio,Neutral,Neutral,2
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M3_noSourceTime,audio,Neutral,Positive,19
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M3_noSourceTime,audio,Positive,Negative,5
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M3_noSourceTime,audio,Positive,Neutral,8
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M3_noSourceTime,audio,Positive,Positive,44
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M3_noSourceTime,vision,Negative,Negative,2
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M3_noSourceTime,vision,Negative,Neutral,5
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M3_noSourceTime,vision,Negative,Positive,11
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M3_noSourceTime,vision,Neutral,Negative,3
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M3_noSourceTime,vision,Neutral,Neutral,6
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M3_noSourceTime,vision,Neutral,Positive,16
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M3_noSourceTime,vision,Positive,Negative,6
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M3_noSourceTime,vision,Positive,Neutral,13
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M3_noSourceTime,vision,Positive,Positive,38
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M3_noSourceTime,all_modalities,Negative,Negative,3
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M3_noSourceTime,all_modalities,Negative,Neutral,3
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M3_noSourceTime,all_modalities,Negative,Positive,12
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M3_noSourceTime,all_modalities,Neutral,Negative,4
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M3_noSourceTime,all_modalities,Neutral,Neutral,4
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M3_noSourceTime,all_modalities,Neutral,Positive,17
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M3_noSourceTime,all_modalities,Positive,Negative,3
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M3_noSourceTime,all_modalities,Positive,Neutral,5
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M3_noSourceTime,all_modalities,Positive,Positive,49
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M3_sourceTime,text,Negative,Negative,2
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M3_sourceTime,text,Negative,Neutral,5
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M3_sourceTime,text,Negative,Positive,11
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M3_sourceTime,text,Neutral,Negative,2
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M3_sourceTime,text,Neutral,Neutral,5
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M3_sourceTime,text,Neutral,Positive,18
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M3_sourceTime,text,Positive,Negative,3
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M3_sourceTime,text,Positive,Neutral,11
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M3_sourceTime,text,Positive,Positive,43
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M3_sourceTime,audio,Negative,Negative,4
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M3_sourceTime,audio,Negative,Neutral,4
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M3_sourceTime,audio,Negative,Positive,10
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M3_sourceTime,audio,Neutral,Negative,4
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M3_sourceTime,audio,Neutral,Neutral,3
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M3_sourceTime,audio,Neutral,Positive,18
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M3_sourceTime,audio,Positive,Negative,5
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M3_sourceTime,audio,Positive,Neutral,7
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M3_sourceTime,audio,Positive,Positive,45
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M3_sourceTime,vision,Negative,Negative,4
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M3_sourceTime,vision,Negative,Neutral,2
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M3_sourceTime,vision,Negative,Positive,12
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M3_sourceTime,vision,Neutral,Negative,6
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M3_sourceTime,vision,Neutral,Neutral,5
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M3_sourceTime,vision,Neutral,Positive,14
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M3_sourceTime,vision,Positive,Negative,16
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M3_sourceTime,vision,Positive,Neutral,10
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M3_sourceTime,vision,Positive,Positive,31
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M3_sourceTime,all_modalities,Negative,Negative,3
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M3_sourceTime,all_modalities,Negative,Neutral,5
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M3_sourceTime,all_modalities,Negative,Positive,10
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M3_sourceTime,all_modalities,Neutral,Negative,2
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M3_sourceTime,all_modalities,Neutral,Neutral,4
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M3_sourceTime,all_modalities,Neutral,Positive,19
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M3_sourceTime,all_modalities,Positive,Negative,0
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M3_sourceTime,all_modalities,Positive,Neutral,4
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M3_sourceTime,all_modalities,Positive,Positive,53
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M4_sourceTime,text,Negative,Negative,5
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M4_sourceTime,text,Negative,Neutral,1
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M4_sourceTime,text,Negative,Positive,12
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M4_sourceTime,text,Neutral,Negative,4
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M4_sourceTime,text,Neutral,Neutral,5
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M4_sourceTime,text,Neutral,Positive,16
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M4_sourceTime,text,Positive,Negative,0
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M4_sourceTime,text,Positive,Neutral,10
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M4_sourceTime,text,Positive,Positive,47
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M4_sourceTime,audio,Negative,Negative,6
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M4_sourceTime,audio,Negative,Neutral,2
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M4_sourceTime,audio,Negative,Positive,10
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M4_sourceTime,audio,Neutral,Negative,4
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M4_sourceTime,audio,Neutral,Neutral,4
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M4_sourceTime,audio,Neutral,Positive,17
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M4_sourceTime,audio,Positive,Negative,4
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M4_sourceTime,audio,Positive,Neutral,6
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M4_sourceTime,audio,Positive,Positive,47
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M4_sourceTime,vision,Negative,Negative,5
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M4_sourceTime,vision,Negative,Neutral,4
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M4_sourceTime,vision,Negative,Positive,9
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M4_sourceTime,vision,Neutral,Negative,6
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M4_sourceTime,vision,Neutral,Neutral,5
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M4_sourceTime,vision,Neutral,Positive,14
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M4_sourceTime,vision,Positive,Negative,21
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M4_sourceTime,vision,Positive,Neutral,10
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M4_sourceTime,vision,Positive,Positive,26
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M4_sourceTime,all_modalities,Negative,Negative,3
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M4_sourceTime,all_modalities,Negative,Neutral,3
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M4_sourceTime,all_modalities,Negative,Positive,12
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M4_sourceTime,all_modalities,Neutral,Negative,6
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M4_sourceTime,all_modalities,Neutral,Neutral,3
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M4_sourceTime,all_modalities,Neutral,Positive,16
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M4_sourceTime,all_modalities,Positive,Negative,4
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M4_sourceTime,all_modalities,Positive,Neutral,7
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M4_sourceTime,all_modalities,Positive,Positive,46
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TSFA-main,text,Negative,Negative,5
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TSFA-main,text,Negative,Neutral,1
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TSFA-main,text,Negative,Positive,12
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TSFA-main,text,Neutral,Negative,4
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TSFA-main,text,Neutral,Neutral,5
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TSFA-main,text,Neutral,Positive,16
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TSFA-main,text,Positive,Negative,0
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TSFA-main,text,Positive,Neutral,10
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TSFA-main,text,Positive,Positive,47
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TSFA-main,audio,Negative,Negative,6
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TSFA-main,audio,Negative,Neutral,7
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TSFA-main,audio,Negative,Positive,5
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TSFA-main,audio,Neutral,Negative,6
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TSFA-main,audio,Neutral,Neutral,7
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TSFA-main,audio,Neutral,Positive,12
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TSFA-main,audio,Positive,Negative,5
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TSFA-main,audio,Positive,Neutral,12
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TSFA-main,audio,Positive,Positive,40
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TSFA-main,vision,Negative,Negative,5
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TSFA-main,vision,Negative,Neutral,3
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TSFA-main,vision,Negative,Positive,10
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TSFA-main,vision,Neutral,Negative,9
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TSFA-main,vision,Neutral,Neutral,7
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TSFA-main,vision,Neutral,Positive,9
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TSFA-main,vision,Positive,Negative,24
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TSFA-main,vision,Positive,Neutral,11
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TSFA-main,vision,Positive,Positive,22
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TSFA-main,all_modalities,Negative,Negative,5
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TSFA-main,all_modalities,Negative,Neutral,5
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TSFA-main,all_modalities,Negative,Positive,8
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TSFA-main,all_modalities,Neutral,Negative,7
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TSFA-main,all_modalities,Neutral,Neutral,7
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TSFA-main,all_modalities,Neutral,Positive,11
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TSFA-main,all_modalities,Positive,Negative,7
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TSFA-main,all_modalities,Positive,Neutral,8
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TSFA-main,all_modalities,Positive,Positive,42
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{
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"created_utc": "2026-09-23T15:57:28.473166+00:00",
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"experiment": "Grouped five-fold emotion probes on frozen TSFA and M3/M4 representations",
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"sample_count": 100,
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"video_id_count": 37,
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"fold_count": 5,
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"heldout_prediction_count_per_method_view": 100,
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"split_rule": "Fixed five-fold GroupKFold by group_id/video_id, loaded from the Q1 method-comparison split file.",
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"seed": 42,
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"batch_size_for_feature_inference": 8,
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"input_paths": {
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"feature_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/q1_features/features",
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"feature_manifest": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/audit/manifest.csv",
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"grouped_splits": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/method_comparison/splits.json",
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"alignment_checkpoint_root": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/alignment_debug/heldout",
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"tsfa_output_dir": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa",
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"tsfa_probe_checkpoint": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa/probe_checkpoints.pt"
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},
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"class_mapping": {
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"label_lt_0": "Negative",
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"label_eq_0": "Neutral",
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"label_gt_0": "Positive"
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},
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"class_counts": {
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"Negative": 18,
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"Neutral": 25,
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"Positive": 57
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},
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"classification": {
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"estimator": "LogisticRegression",
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"C": 0.05,
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"max_iter": 5000,
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"features": "StandardScaler fitted on the training fold, then 5-segment pooled aligned representations",
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"macro_f1": "fixed labels [Negative, Neutral, Positive]; zero_division=0"
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},
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"temporal_pooling": "Five contiguous equal-width bins over the 50 shared slots; mean each bin and concatenate.",
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"regression": {
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"estimator": "Ridge",
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"alpha": 25.0,
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"features": "StandardScaler fitted on the training fold, then the same 5-segment pooled representations",
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"prediction_clipping": [
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-3.0,
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3.0
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],
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"reported_mae_pearson": "computed on clipped predictions; unclipped values are also retained for diagnosis"
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},
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"methods": [
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"M3_noSourceTime",
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"M3_sourceTime",
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"M4_sourceTime",
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"TSFA-main"
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],
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"views": {
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"text": [
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"text"
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],
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"audio": [
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"audio"
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],
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"vision": [
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"vision"
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],
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"all_modalities": [
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"text",
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"audio",
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"vision"
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]
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},
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"folds": [
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{
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"fold": 1,
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"train_count": 80,
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"heldout_count": 20,
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"train_video_id_count": 31,
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"heldout_video_id_count": 6,
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"video_id_overlap": []
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},
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{
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"fold": 2,
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"train_count": 80,
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"heldout_count": 20,
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"train_video_id_count": 30,
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"heldout_video_id_count": 7,
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"video_id_overlap": []
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},
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{
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"fold": 3,
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"train_count": 80,
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"heldout_count": 20,
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"train_video_id_count": 29,
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"heldout_video_id_count": 8,
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"video_id_overlap": []
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},
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{
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"fold": 4,
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"train_count": 80,
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"heldout_count": 20,
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"train_video_id_count": 29,
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"heldout_video_id_count": 8,
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"video_id_overlap": []
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},
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{
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"fold": 5,
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"train_count": 80,
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"heldout_count": 20,
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"train_video_id_count": 29,
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"heldout_video_id_count": 8,
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"video_id_overlap": []
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}
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],
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"emotion_labels_used_to_train_alignment": false,
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"alignment_models_retrained": false,
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"feature_extractors_changed": false,
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"label_agreement": "Sign-derived classes were checked against the annotation class for all samples.",
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"device_for_feature_inference": "cuda",
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"python": "3.14.7",
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"scikit_learn": "1.9.1",
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"elapsed_seconds": 9.420637607574463
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}
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@@ -0,0 +1,17 @@
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method,view,sample_count,negative_support,neutral_support,positive_support,majority_class_accuracy_baseline,majority_class_macro_f1_baseline,accuracy,macro_f1_fixed_three_classes,macro_f1_fold_mean,macro_f1_fold_sd,mae,pearson,mae_unclipped,pearson_unclipped
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M3_noSourceTime,text,100,18,25,57,0.57,0.24203821656050953,0.56,0.4193473193473194,0.3935929502596169,0.074362900297241,0.5759748092293739,0.43980908481228476,0.5759748092293739,0.43980908481228476
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M3_noSourceTime,audio,100,18,25,57,0.57,0.24203821656050953,0.52,0.3845668220668221,0.37067460317460316,0.1416055457051453,0.9140998442471028,-0.030093566044876657,0.9822070105373859,-0.047495382613340686
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M3_noSourceTime,vision,100,18,25,57,0.57,0.24203821656050953,0.46,0.33525993777952107,0.3172046472046472,0.07054558342109876,1.0294348095357417,-0.00795168066899003,1.0294348095357417,-0.00795168066899003
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M3_noSourceTime,all_modalities,100,18,25,57,0.57,0.24203821656050953,0.56,0.3854759521426188,0.34746029601102063,0.09795635492150204,0.6980327500402927,0.22519812567675992,0.6980327500402927,0.22519812567675992
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M3_sourceTime,text,100,18,25,57,0.57,0.24203821656050953,0.5,0.3480193236714976,0.32777528984425536,0.029808103523138257,0.6237337172031403,0.35461376502034914,0.6237337172031403,0.35461376502034914
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M3_sourceTime,audio,100,18,25,57,0.57,0.24203821656050953,0.52,0.3680727874276261,0.3449955168288502,0.13105230803186657,0.8000495155155659,0.10887772347308033,0.930488339215517,0.01876887732270692
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M3_sourceTime,vision,100,18,25,57,0.57,0.24203821656050953,0.4,0.3212576896787423,0.28414664867296446,0.058625111747612896,0.8534768795967103,-0.04002702290165609,0.8534768795967103,-0.04002702290165609
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M3_sourceTime,all_modalities,100,18,25,57,0.57,0.24203821656050953,0.6,0.41132860302147295,0.3666158617882756,0.09426404732091778,0.7609647013247013,0.11655298812556718,0.7671955634653568,0.10931654686932527
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M4_sourceTime,text,100,18,25,57,0.57,0.24203821656050953,0.57,0.4421313405053242,0.4162430902430902,0.0907006237958674,0.7823496595025062,0.11584620062706881,0.7823496595025062,0.11584620062706881
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M4_sourceTime,audio,100,18,25,57,0.57,0.24203821656050953,0.57,0.4362578227082044,0.37886010140912096,0.16443238707930552,0.7489147992432117,0.149285091888255,0.7511225195229053,0.15025193089440161
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M4_sourceTime,vision,100,18,25,57,0.57,0.24203821656050953,0.36,0.30594625500285877,0.28843484184660656,0.0378793113343027,1.0398883034288884,-0.029152853648826693,1.0398883034288884,-0.029152853648826693
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M4_sourceTime,all_modalities,100,18,25,57,0.57,0.24203821656050953,0.52,0.35124440009158575,0.32653813175552304,0.03466118333244695,0.7358243429660797,0.23899348634942238,0.7358243429660797,0.23899348634942238
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TSFA-main,text,100,18,25,57,0.57,0.24203821656050953,0.57,0.4421313405053242,0.4162430902430902,0.0907006237958674,0.7823496595025062,0.11584620062706881,0.7823496595025062,0.11584620062706881
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TSFA-main,audio,100,18,25,57,0.57,0.24203821656050953,0.53,0.43970711091454123,0.38823268564466384,0.09681573486792315,0.91133147880435,0.2644341902369279,1.070846926420927,0.21808956563727705
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TSFA-main,vision,100,18,25,57,0.57,0.24203821656050953,0.34,0.3106329488317066,0.2808153931838142,0.08305859786958904,1.2014864695072174,-0.23188469972889592,1.2014864695072174,-0.23188469972889592
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TSFA-main,all_modalities,100,18,25,57,0.57,0.24203821656050953,0.54,0.4310819293870141,0.36259462759462757,0.09089510359034791,0.8202796086668969,0.10443752941446603,0.8202796086668969,0.10443752941446603
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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
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TSFA-main-B0,accuracy,0.54,0.6,-0.05999999999999994,-0.180974025974026,0.06315789473684208,2000,37
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TSFA-main-B0,macro_f1,0.4310819293870141,0.3612633181126332,0.06981861127438094,-0.09934840226417828,0.2109305481889764,2000,37
|
||||
TSFA-main-B0,mae,0.8202796086668969,0.5642678308486938,0.25601177781820306,0.15376872038723483,0.36909862418913025,2000,37
|
||||
TSFA-main-B0,pearson,0.10443752941446602,0.34585652345724877,-0.24141899404278275,-0.44706364005897437,-0.04767360711715707,2000,37
|
||||
TSFA-main-B1,accuracy,0.54,0.61,-0.06999999999999995,-0.18753472222222223,0.05263157894736836,2000,37
|
||||
TSFA-main-B1,macro_f1,0.4310819293870141,0.387431302270012,0.04365062711700213,-0.12939326679939706,0.19139547894337253,2000,37
|
||||
TSFA-main-B1,mae,0.8202796086668969,0.5989421024918556,0.22133750617504122,0.11161344594318404,0.3367952022053393,2000,37
|
||||
TSFA-main-B1,pearson,0.10443752941446602,0.23765809617297715,-0.13322056675851113,-0.3559809117591378,0.0973743902537723,2000,37
|
||||
TSFA-main-B2,accuracy,0.54,0.58,-0.039999999999999925,-0.16456068503350713,0.08069088792816739,2000,37
|
||||
TSFA-main-B2,macro_f1,0.4310819293870141,0.33061794334825517,0.10046398603875895,-0.04786265172473772,0.2201939321817236,2000,37
|
||||
TSFA-main-B2,mae,0.8202796086668969,0.588000754788518,0.23227885387837888,0.1234589938257658,0.34566582162118786,2000,37
|
||||
TSFA-main-B2,pearson,0.10443752941446602,0.27904934700064177,-0.17461181758617575,-0.38207451511934154,0.03535714039400071,2000,37
|
||||
TSFA-main-B3,accuracy,0.54,0.59,-0.04999999999999993,-0.1724244359301831,0.07552935010482148,2000,37
|
||||
TSFA-main-B3,macro_f1,0.4310819293870141,0.3587962962962963,0.07228563309071784,-0.08688940161710516,0.2054570678172808,2000,37
|
||||
TSFA-main-B3,mae,0.8202796086668969,0.5997931832820177,0.22048642538487917,0.11104341519789566,0.3342257911866213,2000,37
|
||||
TSFA-main-B3,pearson,0.10443752941446602,0.23011191320162022,-0.1256743837871542,-0.34911837302613763,0.10267699365087976,2000,37
|
||||
TSFA-main-B4,accuracy,0.54,0.6,-0.05999999999999994,-0.18629054425508526,0.06455416920267787,2000,37
|
||||
TSFA-main-B4,macro_f1,0.4310819293870141,0.4222603610475464,0.008821568339467734,-0.15492832109369248,0.16531044831081673,2000,37
|
||||
TSFA-main-B4,mae,0.8202796086668969,0.59276591360569,0.2275136950612069,0.10797742395141324,0.35732217997788107,2000,37
|
||||
TSFA-main-B4,pearson,0.10443752941446602,0.19864510121331097,-0.09420757179884495,-0.3378284536622349,0.1439965978613739,2000,37
|
||||
|
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"experiment": "Paired OOF comparison of TSFA-main all-modalities probe against math B0-B4",
|
||||
"sample_count": 100,
|
||||
"video_group_count": 37,
|
||||
"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": [
|
||||
"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"
|
||||
}
|
||||
Reference in New Issue
Block a user