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fix calibration bootstrap and correct factual errors in data pipeline
- ECE/Brier bootstrap now resamples at patient level instead of row level, consistent with how AUROC/AUPRC CIs are computed - recomputed calibration metrics for all 5 models, added to both output dirs - fixed wrong pretraining dataset size in Stage 0 description - corrected temperature unit (Fahrenheit, not Celsius) and updated clip bounds - clarified split size table to distinguish u_ignore from full uncertain table - documented actual early stopping behaviour and training loss progression - updated Makefile calibration target to cover all models
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‎Makefile‎

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@@ -45,7 +45,16 @@ calibration:
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--output-dir artifacts/evaluation/calibration_stronger_lr_v3 \
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--n-bins 10 --bootstrap --n-bootstrap 2000 \
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--model "Image" artifacts/models/image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3/test_predictions.csv \
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--model "Multimodal" artifacts/models/multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3/test_predictions.csv
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--model "Multimodal" artifacts/models/multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3/test_predictions.csv \
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--model "Attention Fusion" artifacts/models/multimodal_pneumonia_attn_fusion_u_ignore_temporal_v1/test_predictions.csv \
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--model "Clinical Logistic" artifacts/models/clinical_baseline_u_ignore_temporal_strong_v2/test_predictions.csv \
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--model "Clinical XGBoost" artifacts/models/clinical_xgb_u_ignore_temporal_strong_v2/test_predictions.csv
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$(PYTHON) -m src.evaluation.calibration_analysis \
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--output-dir artifacts/evaluation/calibration_final \
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--n-bins 10 --bootstrap --n-bootstrap 2000 \
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--model "Image" artifacts/models/image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3/test_predictions.csv \
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--model "Multimodal" artifacts/models/multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3/test_predictions.csv \
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--model "Attention Fusion" artifacts/models/multimodal_pneumonia_attn_fusion_u_ignore_temporal_v1/test_predictions.csv
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dca:
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$(PYTHON) -m src.evaluation.decision_curve_analysis \

‎artifacts/evaluation/calibration_final/calibration_metrics.json‎

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"Image": {
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"df": 8,
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"p_value": 3.844138716593881e-05,
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"n_bins": 10,
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"interpretation": "miscalibrated (p<0.05)"
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},
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"bootstrap_level": "patient",
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‎artifacts/evaluation/calibration_final/calibration_summary.csv‎

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model_name,predictions_file,n,positive_rate,mean_pred_prob,brier_score,ece,mce,brier_ci_low,brier_ci_high,ece_ci_low,ece_ci_high
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Image,artifacts\models\image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.5155365507062092,0.2063056283325257,0.06735261101362316,0.1651922317884736,0.19484493342059997,0.21752672175509358,0.050746011882847136,0.0962283274843249
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Multimodal,artifacts\models\multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.4867857715071634,0.20686361636847,0.04034054008334182,0.13100612232851427,0.1959735764158624,0.21757103591602542,0.03188740681744245,0.07387384917266493
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Attention Fusion,artifacts\models\multimodal_pneumonia_attn_fusion_u_ignore_temporal_v1\test_predictions.csv,1075,0.4530232558139535,0.3347818323519341,0.2237598511044562,0.13184638911554977,0.18999010324478144,0.2084217977602914,0.2392248918116715,0.10867873433474887,0.16144290632460004
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Image,artifacts\models\image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.5155365507062092,0.2063056283325257,0.06735261101362316,0.1651922317884736,0.19487731347983986,0.21767078669813353,0.04888447340778319,0.098045721579259
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Multimodal,artifacts\models\multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.4867857715071634,0.20686361636847,0.04034054008334182,0.13100612232851427,0.1956323213861885,0.21792121422298644,0.03155512478833137,0.07341358602576348
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Attention Fusion,artifacts\models\multimodal_pneumonia_attn_fusion_u_ignore_temporal_v1\test_predictions.csv,1075,0.4530232558139535,0.3347818323519341,0.2237598511044562,0.13184638911554977,0.18999010324478144,0.2089938156446144,0.2392397932129165,0.10806000197672912,0.16133756148580614

‎artifacts/evaluation/calibration_stronger_lr_v3/attention_fusion_bins.csv‎

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bin,bin_left,bin_right,bin_center,count,fraction,avg_confidence,avg_accuracy,abs_gap
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‎artifacts/evaluation/calibration_stronger_lr_v3/attention_fusion_reliability.png‎

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‎artifacts/evaluation/calibration_stronger_lr_v3/calibration_metrics.json‎

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‎artifacts/evaluation/calibration_stronger_lr_v3/calibration_summary.csv‎

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model_name,predictions_file,n,positive_rate,mean_pred_prob,brier_score,ece,mce,brier_ci_low,brier_ci_high,ece_ci_low,ece_ci_high
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Clinical Logistic strong_v2,artifacts\models\clinical_baseline_u_ignore_temporal_strong_v2\test_predictions.csv,1075,0.4530232558139535,0.49049101122161404,0.2422399144852579,0.03746775540766057,0.4888774142933826,0.2356938230698796,0.24912445502239938,0.022935921341670655,0.06865093539911815
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Clinical XGB strong_v2,artifacts\models\clinical_xgb_u_ignore_temporal_strong_v2\test_predictions.csv,1075,0.4530232558139535,0.4924128443162791,0.2405105491123096,0.04602061266976742,0.28860111499999996,0.23622433943144514,0.2449158713052029,0.02083843655697669,0.07495407097209297
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Image stronger_lr_v3,artifacts\models\image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.5155365507062092,0.2063056283325257,0.06735261101362316,0.1651922317884736,0.19484493342059997,0.21752672175509358,0.050746011882847136,0.0962283274843249
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Multimodal stronger_lr_v3,artifacts\models\multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.4867857715071634,0.20686361636847,0.04034054008334182,0.13100612232851427,0.1959735764158624,0.21757103591602542,0.03188740681744245,0.07387384917266493
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Image,artifacts\models\image_pneumonia_finetune_densenet121_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.5155365507062092,0.2063056283325257,0.06735261101362316,0.1651922317884736,0.19487731347983986,0.21767078669813353,0.04888447340778319,0.098045721579259
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Multimodal,artifacts\models\multimodal_pneumonia_densenet121_triage_u_ignore_temporal_stronger_lr_v3\test_predictions.csv,1075,0.4530232558139535,0.4867857715071634,0.20686361636847,0.04034054008334182,0.13100612232851427,0.1956323213861885,0.21792121422298644,0.03155512478833137,0.07341358602576348
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Attention Fusion,artifacts\models\multimodal_pneumonia_attn_fusion_u_ignore_temporal_v1\test_predictions.csv,1075,0.4530232558139535,0.3347818323519341,0.2237598511044562,0.13184638911554977,0.18999010324478144,0.2089938156446144,0.2392397932129165,0.10806000197672912,0.16133756148580614
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Clinical Logistic,artifacts\models\clinical_baseline_u_ignore_temporal_strong_v2\test_predictions.csv,1075,0.4530232558139535,0.49049101122161404,0.2422399144852579,0.03746775540766057,0.4888774142933826,0.23595316757042104,0.249020027042658,0.022497452480586877,0.071451646847089
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Clinical XGBoost,artifacts\models\clinical_xgb_u_ignore_temporal_strong_v2\test_predictions.csv,1075,0.4530232558139535,0.4924128443162791,0.2405105491123096,0.04602061266976742,0.28860111499999996,0.23629184270765427,0.24478614521223518,0.020621634208176698,0.07718190618141428

‎artifacts/evaluation/calibration_stronger_lr_v3/clinical_logistic_bins.csv‎

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‎artifacts/evaluation/calibration_stronger_lr_v3/clinical_logistic_reliability.png‎

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‎artifacts/evaluation/calibration_stronger_lr_v3/clinical_xgboost_bins.csv‎

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