Update app.py

app.py

@@ -28,6 +28,12 @@ def homework01_solution1(K, X1, X2):
   pred = neigh.predict(X)
   squared_error = (pred-y)**2
 
+  #(Q2.3) evaluate mean squared error 
+  import sklearn
+  y_pred = neigh.predict(X)
+  mse = sklearn.metrics.mean_squared_error(y,y_pred)
+
+
   train_data['Predicted Label ('+str(K)+'-NN)'] = pred
   train_data['Squaredd Error'] = squared_error
   train_data['Predicted Label ('+str(K)+'-NN)'] = train_data['Predicted Label ('+str(K)+'-NN)'].round(3)
@@ -62,7 +68,7 @@ def homework01_solution1(K, X1, X2):
   results = results.sort_values(by='Rank of closest neighbor')
   results['Distance to query data'] = results['Distance to query data'].round(3)
   
-  return train_data, results, predicted_label
+  return train_data, results, predicted_label, mse
 
 
 
@@ -76,11 +82,12 @@ set_X2 = gr.Number(value=2)
 set_output_traindata = gr.Dataframe(type='pandas', label ='Train Dataset')
 set_output_q1a = gr.Dataframe(type='pandas', label ='Question 1: KNN-Regressor Search')
 set_output_q1b = gr.Textbox(label ='Question 1: KNN-Regressor Prediction')
+set_output_q3 = gr.Textbox(label ='Question 3: KNN-Regressor MSE (Training data)')
 
 ### configure Gradio
 interface = gr.Interface(fn=homework01_solution1, 
                          inputs=[set_K, set_X1, set_X2], 
-                         outputs=[set_output_traindata, set_output_q1a, set_output_q1b],
+                         outputs=[set_output_traindata, set_output_q1a, set_output_q1b, set_output_q3],
                          
                          title="CSCI4750/5750(hw01-PartI): Mathematics for KNN (Question 1: KNN-Regressor Search)", 
                          description= "Click examples below for a quick demo",