Update app.py

app.py

@@ -6,6 +6,7 @@ import time
  
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
+from sklearn.metrics import f1_score
  
 # Load and prepare data
 data = pd.read_csv("parkinsons.data")
@@ -30,9 +31,11 @@ def run_dare_demo(delete_index=25):
  
     y_pred = model.predict(X_test)
     acc_before = accuracy_score(y_test, y_pred)
+    f1_before = f1_score(y_test, y_pred, average='macro')
  
     logs += f"✅ Initial training completed in {train_time:.4f} seconds\n"
-    logs += f"🎯 Accuracy before unlearning: {acc_before:.4f}\n"
+    #logs += f"🎯 Accuracy before unlearning: {acc_before:.4f}\n"
+    logs += f"🎯 F1-score before unlearning: {f1_before:.4f}\n"
  
     # Delete a data point
     try:
@@ -41,9 +44,11 @@ def run_dare_demo(delete_index=25):
         delete_time = time.perf_counter() - start_del
         y_pred_after = model.predict(X_test.astype(np.float32))
         acc_after = accuracy_score(y_test, y_pred_after)
+        f1_after = f1_score(y_test, y_pred_after, average='macro')
  
         logs += f"\n🧽 Deleted index {delete_index} in {delete_time:.5f} seconds\n"
-        logs += f"🎯 Accuracy after unlearning: {acc_after:.4f}\n"
+        #logs += f"🎯 Accuracy after unlearning: {acc_after:.4f}\n"
+        logs += f"🎯 F1-score after unlearning: {acc_after:.4f}\n"
     except Exception as e:
         logs += f"\n⚠️ Error during unlearning: {str(e)}\n"
  
@@ -59,10 +64,12 @@ def run_dare_demo(delete_index=25):
 
         y_pred_retrain = retrain_model.predict(X_test)
         acc_retrain = accuracy_score(y_test, y_pred_retrain)
+        f1_unlearn = f1_score(y_test, y_pred_retrain, average='macro')
  
  
         logs += f"\n🔁 Retraining completed in {retrain_time:.5f} seconds (without index {delete_index})\n"
-        logs += f"🎯 Accuracy after retraining: {acc_retrain:.4f}\n"
+        #logs += f"🎯 Accuracy after retraining: {acc_retrain:.4f}\n"
+        logs += f"🎯 F1-score after retraining: {f1_unlearn:.4f}\n"
     except Exception as e:
         logs += f"\n⚠️ Error during retraining: {str(e)}\n"