final_metrics.json

{
  "architecture": "gin",
  "seed": 42,
  "best_val_mcc": 0.5609368123898547,
  "val_metrics": {
    "mcc": 0.5609368123898547,
    "accuracy": 0.7771431385100201,
    "balanced_accuracy": 0.7748098221414874,
    "precision": 0.7410443478973686,
    "recall": 0.8686461035841261,
    "f1": 0.7997876669910643,
    "roc_auc": 0.8413672351687064,
    "pr_auc": 0.8093612949314712
  },
  "test_metrics": {
    "mcc": 0.5641958688616364,
    "accuracy": 0.7782756413412772,
    "balanced_accuracy": 0.775832009081207,
    "precision": 0.7400178905424087,
    "recall": 0.874495483374976,
    "f1": 0.8016561687882658,
    "roc_auc": 0.8417462134967497,
    "pr_auc": 0.8087144857201518
  },
  "confusion_matrix": {
    "counts": [
      [
        6706,
        3197
      ],
      [
        1306,
        9100
      ]
    ],
    "normalised": [
      [
        0.6771685347874381,
        0.32283146521256184
      ],
      [
        0.12550451662502402,
        0.874495483374976
      ]
    ],
    "counts_path": "runs/gin_seed42/test_confusion_counts.csv",
    "normalised_path": "runs/gin_seed42/test_confusion_normalised.csv",
    "figure_path": "runs/gin_seed42/test_confusion_matrix.png"
  },
  "classification_report": "              precision    recall  f1-score   support\n\n           0     0.8370    0.6772    0.7486      9903\n           1     0.7400    0.8745    0.8017     10406\n\n    accuracy                         0.7783     20309\n   macro avg     0.7885    0.7758    0.7752     20309\nweighted avg     0.7873    0.7783    0.7758     20309\n"
}