iforest training

model_training/tools/evaluation_tools.py

@@ -1 +1,23 @@
-import pandas as pd
+import pandas as pd
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc
+import matplotlib.pyplot as plt
+
+def plot_confusion_matrix(true_labels, predictions, label_names):
+    for normalize in [None, 'true']:
+      cm = confusion_matrix(true_labels, predictions, normalize=normalize)
+      cm_disp = ConfusionMatrixDisplay(cm,  display_labels=label_names)
+      cm_disp.plot(cmap="Blues")
+
+
+def plot_roc_curve_IF(true_labels, scores):
+    fpr, tpr, thr = roc_curve(true_labels, -scores, pos_label=-1)
+    auc_score = auc(fpr, tpr)
+
+    plt.figure()
+    plt.plot(fpr, tpr, '-')
+    plt.text(0.5, 0.5, f'AUC: {auc_score:.4f}')
+    plt.xlabel('False positive rate')
+    plt.ylabel('True positive rate')
+    plt.show()
+
+