metrics/set_03_eval_large_evaluation.json

{
  "schema_version": "1.0.0",
  "exported_at": "2026-03-05T22:01:19.725796+00:00",
  "data": [
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "accuracy",
      "value": 0.9682721252257676
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "precision",
      "value": 0.53422756706753
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "recall",
      "value": 0.9608985024958403
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "f1",
      "value": 0.6866825208085613
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "specificity",
      "value": 0.9685489412205635
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "balanced_accuracy",
      "value": 0.9647237218582019
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "mcc",
      "value": 0.7036546851218309
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "fpr",
      "value": 0.03145105877943657
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "fnr",
      "value": 0.039101497504159734
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "n_samples",
      "value": 33220
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "auc",
      "value": 0.99392952736964
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "kappa",
      "value": 0.6713997379297411
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "tpr",
      "value": 0.9608985024958403
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "tnr",
      "value": 0.9685489412205635
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "detection_rate",
      "value": 0.9608985024958403
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "miss_rate",
      "value": 0.039101497504159734
    },
    {
      "model": "gat",
      "scenario": "val",
      "metric_name": "pr_auc",
      "value": 0.9618977299047055
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "accuracy",
      "value": 0.621944611679711
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "precision",
      "value": 0.06596346403730032
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "recall",
      "value": 0.7179700499168054
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "f1",
      "value": 0.1208260413020651
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "specificity",
      "value": 0.6183396839277906
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "balanced_accuracy",
      "value": 0.668154866922298
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "mcc",
      "value": 0.12853982833352529
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "fpr",
      "value": 0.3816603160722094
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "fnr",
      "value": 0.2820299500831947
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "n_samples",
      "value": 33220
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "auc",
      "value": 0.7306358143594145
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "optimal_threshold",
      "value": 0.04131942242383957
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "youden_j",
      "value": 0.3371416806000036
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "kappa",
      "value": 0.05842115068799236
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "tpr",
      "value": 0.7179700499168054
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "tnr",
      "value": 0.6183396839277906
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "detection_rate",
      "value": 0.7179700499168054
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "miss_rate",
      "value": 0.2820299500831947
    },
    {
      "model": "vgae",
      "scenario": "val",
      "metric_name": "pr_auc",
      "value": 0.13663669863930417
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "accuracy",
      "value": 0.9967
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "precision",
      "value": 0.9841584158415841
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "recall",
      "value": 0.9229340761374187
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "f1",
      "value": 0.9525634882606613
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "specificity",
      "value": 0.9994468070393804
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "balanced_accuracy",
      "value": 0.9611904415883996
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "mcc",
      "value": 0.9513821947352465
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "fpr",
      "value": 0.0005531929606195762
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "fnr",
      "value": 0.07706592386258125
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "n_samples",
      "value": 30000
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "auc",
      "value": 0.957340915852166
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "kappa",
      "value": 0.9508558527700918
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "tpr",
      "value": 0.9229340761374187
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "tnr",
      "value": 0.9994468070393804
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "detection_rate",
      "value": 0.9229340761374187
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "miss_rate",
      "value": 0.07706592386258125
    },
    {
      "model": "fusion",
      "scenario": "val",
      "metric_name": "pr_auc",
      "value": 0.942007805825993
    }
  ]
}