comapre two models

utils.py

@@ -99,3 +99,48 @@ def scaling_handler(data_frame, method="robust_scaling"):
     data_frame_scaled["label"] = labels.values
 
     return data_frame_scaled
+
+
+from sklearn.metrics import (
+    accuracy_score,
+    f1_score,
+    fbeta_score,
+    precision_score,
+    recall_score,
+)
+
+
+def get_metrics(y_true, y_pred, prefix=""):
+    metrics = {}
+    metrics[f"{prefix}accuracy"] = accuracy_score(y_true, y_pred)
+    metrics[f"{prefix}f1_macro"] = f1_score(y_true, y_pred, average="macro")
+    metrics[f"{prefix}f2_macro"] = fbeta_score(y_true, y_pred, beta=2, average="macro")
+    metrics[f"{prefix}recall_macro"] = recall_score(y_true, y_pred, average="macro")
+    metrics[f"{prefix}precision_macro"] = precision_score(
+        y_true, y_pred, average="macro"
+    )
+
+    # Per-class scores
+    f1_scores = f1_score(y_true, y_pred, average=None, zero_division=0)
+    f2_scores = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=0)
+    recall_scores = recall_score(y_true, y_pred, average=None, zero_division=0)
+    precision_scores = precision_score(y_true, y_pred, average=None, zero_division=0)
+
+    for i in range(len(f1_scores)):
+        metrics[f"{prefix}f1_class{i}"] = f1_scores[i]
+        metrics[f"{prefix}f2_class{i}"] = f2_scores[i]
+        metrics[f"{prefix}recall_class{i}"] = recall_scores[i]
+        metrics[f"{prefix}precision_class{i}"] = precision_scores[i]
+
+    # Confusion-matrix components
+    TP = sum((y_true == 1) & (y_pred == 1))
+    TN = sum((y_true == 0) & (y_pred == 0))
+    FP = sum((y_true == 0) & (y_pred == 1))
+    FN = sum((y_true == 1) & (y_pred == 0))
+
+    metrics[f"{prefix}TP"] = TP
+    metrics[f"{prefix}TN"] = TN
+    metrics[f"{prefix}FP"] = FP
+    metrics[f"{prefix}FN"] = FN
+
+    return metrics