整理 Q1-Q3 实验代码与结果

2026-09-24 16:25:15 +08:00
parent 0261ecdfba
commit 8f5c2c3be6
247 changed files with 69828 additions and 19 deletions
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+5,39,547,1.367179775238037
+5,40,547,1.3640939593315125
@@ -0,0 +1,37 @@
+method,true_class,predicted_class,count
+TSFA-T,Negative,Negative,5
+TSFA-T,Negative,Neutral,5
+TSFA-T,Negative,Positive,8
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+TSFA-T,Neutral,Neutral,7
+TSFA-T,Neutral,Positive,11
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+TSFA-T,Positive,Neutral,8
+TSFA-T,Positive,Positive,42
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+TSFA-AV,Neutral,Positive,13
+TSFA-AV,Positive,Negative,5
+TSFA-AV,Positive,Neutral,11
+TSFA-AV,Positive,Positive,41
+TSFA-T+Private,Negative,Negative,3
+TSFA-T+Private,Negative,Neutral,5
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+TSFA-T+Private,Neutral,Negative,4
+TSFA-T+Private,Neutral,Neutral,3
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+TSFA-T+Private,Positive,Negative,0
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+TSFA-AV+Private,Neutral,Negative,5
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+TSFA-AV+Private,Neutral,Positive,19
+TSFA-AV+Private,Positive,Negative,2
+TSFA-AV+Private,Positive,Neutral,3
+TSFA-AV+Private,Positive,Positive,52
@@ -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
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@@ -0,0 +1,25 @@
+contrast,metric,point_estimate,ci95_low,ci95_high,video_group_count,bootstrap_repeats
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+Private_without_AV,accuracy,0.04999999999999993,-0.05130208333333326,0.1566265060240964,37,2000
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+Private_without_AV,pearson,0.26336535244475046,0.11138266753794468,0.43364646506810123,37,2000
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+Private_with_AV,accuracy,0.030000000000000027,-0.08573489010989008,0.16488095238095216,37,2000
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+Private_with_AV,mae,-0.22422014296054837,-0.3420639706047667,-0.12405316124714572,37,2000
+Private_with_AV,pearson,0.2644677563866406,0.03837506341172564,0.4638561621697696,37,2000
+Both_vs_original,accuracy,0.010000000000000009,-0.10232007575757578,0.12768590688856113,37,2000
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+Both_vs_original,pearson,0.2613093028724872,0.09767720793199632,0.43703956098502666,37,2000
+AV_x_Private_interaction,accuracy,-0.019999999999999907,-0.10317460317460314,0.07142857142857145,37,2000
+AV_x_Private_interaction,macro_f1,-0.05458050334276726,-0.12802264024059792,0.03072023310213152,37,2000
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+AV_x_Private_interaction,pearson,0.0011024039418901926,-0.14538877254600854,0.12016769572852865,37,2000
@@ -0,0 +1,401 @@
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@@ -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
+TSFA-T+Private-B1,macro_f1,0.3900927013970492,0.387431302270012,0.002661399127037223,-0.11933542874650578,0.12194784681760674,2000,37
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+TSFA-T+Private-B2,accuracy,0.59,0.58,0.010000000000000009,-0.04001010101010094,0.061855670103092786,2000,37
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+TSFA-T+Private-B4,macro_f1,0.3900927013970492,0.4222603610475464,-0.03216765965049717,-0.20756556506823165,0.11207293133188895,2000,37
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+TSFA-T+Private-B4,pearson,0.36780288185921645,0.19864510121331097,0.16915778064590548,0.01151986302354387,0.3181525030927952,2000,37
@@ -0,0 +1,38 @@
+{
+  "experiment": "Paired OOF comparison of TSFA-T+Private against math B0-B4",
+  "candidate_method": "TSFA-T+Private",
+  "candidate_view": "all_modalities",
+  "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_av_private_ablation/predictions.csv",
+  "output": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv"
+}
@@ -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. Full AV branch checkpoints remain one level up.
@@ -0,0 +1,201 @@
+sample_id,video_id,direction,source_modality,candidate_count_mean,attention_center_error_normalized_time,attention_row_sum_max_error,fold
+-3g5yACwYnA/13,-3g5yACwYnA,audio_to_vision,vision,5.460000038146973,0.009770148433744907,1.1920928955078125e-07,1
+-3g5yACwYnA/13,-3g5yACwYnA,vision_to_audio,audio,109.18000030517578,0.0053259143605828285,1.1920928955078125e-07,1
+-3g5yACwYnA/3,-3g5yACwYnA,audio_to_vision,vision,14.420000076293945,0.009445015341043472,1.1920928955078125e-07,1
+-3g5yACwYnA/3,-3g5yACwYnA,vision_to_audio,audio,286.0799865722656,0.0032956562936306,1.1920928955078125e-07,1
+-3g5yACwYnA/2,-3g5yACwYnA,audio_to_vision,vision,9.300000190734863,0.017511611804366112,1.1920928955078125e-07,1
+-3g5yACwYnA/2,-3g5yACwYnA,vision_to_audio,audio,186.17999267578125,0.003908350598067045,1.1920928955078125e-07,1
+-3g5yACwYnA/9,-3g5yACwYnA,audio_to_vision,vision,8.779999732971191,0.0118460888043046,1.1920928955078125e-07,1
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+TSFA-AV+Private,Positive,Negative,2
+TSFA-AV+Private,Positive,Neutral,3
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@@ -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
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@@ -0,0 +1,25 @@
+contrast,metric,point_estimate,ci95_low,ci95_high,video_group_count,bootstrap_repeats
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+AV_x_Private_interaction,accuracy,-0.019999999999999907,-0.10317460317460314,0.07142857142857145,37,2000
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@@ -0,0 +1,401 @@
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@@ -0,0 +1,38 @@
+{
+  "experiment": "Paired OOF comparison of TSFA-T+Private against math B0-B4",
+  "candidate_method": "TSFA-T+Private",
+  "candidate_view": "all_modalities",
+  "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_av_private_ablation/predictions.csv",
+  "output": "/home/gloamxun/modeling_zhaocui/deep_learning/Q1/outputs/tsfa_av_private_ablation/private_vs_math.csv"
+}
@@ -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."
+  ]
+}
@@ -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."
+  ]
+}