Update app.py

app.py

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+def homework04_solution(theta0, theta1, theta2, learning_rate):
+  import numpy as np
+  import pandas as pd
+
+  def linear_predict(b0, b1, b2, x1, x2):
+    y_hat = b0 + b1*x1 + b2*x2
+    return y_hat
+
+  def get_linear_results(data, theta0, theta1, theta2):
+    ## (2) make linear prediction
+    y_hat_list = []
+
+    theta0_grad = 0
+    theta1_grad = 0
+    theta2_grad = 0
+    for i in range(len(data)):
+      x1 = data.iloc[i,0]
+      x2 = data.iloc[i,1]
+      y = data.iloc[i,2]
+      y_hat = linear_predict(theta0, theta1, theta2, x1, x2)
+      y_hat_list.append(y_hat)
+
+      ## (3) calculate gradients
+      theta0_grad = theta0_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*1.0
+
+      theta1_grad = theta1_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*x1
+
+      theta2_grad = theta2_grad - 2/len(data)*(y - theta0 - theta1*x1 - theta2*x2)*x2
+
+    data['y_hat'] = y_hat_list
+
+    data['y-y_hat'] = data['y'] - data['y_hat']
+    data['(y-y_hat)^2'] = data['y-y_hat']*data['y-y_hat']
+    return data, theta0_grad, theta1_grad, theta2_grad
+
+  ## (1) load data
+  X = np.array([[15,20], [30,16], [12,6.5], [13,20], [18,18]])
+  y = [4.9, 5.8,6.5,7.3,7.2]
+  data = pd.DataFrame(X, columns=['X1','X2'])
+  data['y'] = y 
+
+  ## (2) get regression table, gradients
+  data, theta0_grad, theta1_grad, theta2_grad = get_linear_results(data, theta0, theta1, theta2)
+
+
+  ### (3) summarize gradient results for question 3a
+
+  data_t = data.T
+
+  data_t = data_t.round(2)
+  
+  data_t.insert(loc=0, column='Name', value=['X1', 'X2', 'y', 'y_hat', 'y-y_hat', '(y-y_hat)^2'])
+  
+
+  ### (4) summarize gradient results for question 3b
+
+  MSE = data['(y-y_hat)^2'].mean()
+
+  q3_mse  = MSE
+  
+  ### summarize gradient results for question 4 (2)
+
+  ### update parameter using gradient descent  4 (3)
+  
+  theta0_new = theta0 - learning_rate*theta0_grad
+  theta1_new = theta1 - learning_rate*theta1_grad
+  theta2_new = theta2 - learning_rate*theta2_grad
+
+
+  ### (5) recalculate linear regression table using new gradients
+  data4,_,_,_ = get_linear_results(data, theta0_new, theta1_new, theta2_new)
+
+
+  ### (6) summarize gradient results for question 4 (4)
+
+  MSE = data4['(y-y_hat)^2'].mean()
+
+  q4_mse  = MSE
+  
+  ### (7) return all results for Gradio visualization
+  return data_t, q3_mse, theta0_grad, theta1_grad , theta2_grad, theta0_new, theta1_new, theta2_new, q4_mse
+
+import numpy as np
+
+import gradio as gr
+
+
+### configure inputs
+set_theta0 = gr.inputs.Number()
+set_theta1 = gr.inputs.Number()
+set_theta2 = gr.inputs.Number()
+set_ita = gr.inputs.Number()
+
+
+### configure outputs
+set_output_q3a = gr.outputs.Dataframe(type='pandas', label ='Question 3a')
+set_output_q3b = gr.outputs.Textbox(label ='Question: What\'s Initial MSE loss')
+set_output_q4a0 = gr.outputs.Textbox(label ='Question: What\'s theta0_grad')
+set_output_q4a1 = gr.outputs.Textbox(label ='Question: What\'s theta1_grad')
+set_output_q4a2 = gr.outputs.Textbox(label ='Question: What\'s theta2_grad')
+
+set_output_q4b0 = gr.outputs.Textbox(label ='Question: What\'s theta0_new: updated by gradient descent')
+set_output_q4b1 = gr.outputs.Textbox(label ='Question: What\'s theta1_new: updated by gradient descent')
+set_output_q4b2 = gr.outputs.Textbox(label ='Question: What\'s theta2_new: updated by gradient descent')
+
+set_output_q4b4 = gr.outputs.Textbox(label ='Question: What\'s New MSE after update the parameters using gradient descent')
+
+### configure Gradio
+interface = gr.Interface(fn=homework04_solution, 
+                         inputs=[set_theta0, set_theta1, set_theta2, set_ita], 
+                         outputs=[set_output_q3a, set_output_q3b, 
+                                  set_output_q4a0, set_output_q4a1, set_output_q4a2, 
+                                  set_output_q4b0, set_output_q4b1, set_output_q4b2,
+                                  set_output_q4b4],
+                         examples_per_page = 2,
+                         examples=[
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                              np.round(np.random.uniform(0, 1, (3,)),2).tolist()+[0.001],
+                          ],
+                         title="CSCI4750/5750(gradient descent): Linear Regression/Optimization", 
+                         description= "Click examples below for a quick demo",
+                         theme = 'huggingface',
+                         layout = 'horizontal',
+                         live=True
+                         )
+
+
+interface.launch()