Create linear_regression_GradientDescent.py

2-Regression/3-Linear/linear_regression_GradientDescent.py

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+import pandas as pd
+import numpy as np
+dataframe = pd.read_csv('Cricket_chirps.csv')
+
+a = 0.6
+dataset1 = dataframe.copy()
+train_dataset = dataset1.sample(frac = a)
+dataset1 = dataset1.drop(train_dataset.index)
+a = 0.5
+validation_dataset = dataset1.sample(frac = a)
+test_dataset = dataset1.drop(validation_dataset.index)
+
+print("train_dataset.....")
+print(train_dataset)
+
+print("validation_dataset.....")
+print(validation_dataset)
+
+print("test_dataset.....")
+print(test_dataset)
+
+def MSE_ofGivenDataset(dataset):
+
+  max1 = dataset['X'].max()
+  max2 = dataset['Y'].max()
+
+
+  learning_rates = [0.01, 0.001, 0.3, 0.5, 1]
+  w0, w1 = 3, 3;
+  #h = w0  + w1*x
+  pre_j = 0
+  epoch = 100
+  p = 0.0000001
+  dataset = dataset.to_numpy()
+
+  #normalize the datset
+  for i in range(len(dataset)):
+    dataset[i][0] /= max1
+    dataset[i][1] /= max2
+
+  for lr in learning_rates:
+    for itr  in range(epoch):
+      j = 0
+      m = len(dataset)
+      temp1 = 0
+      temp2 = 0
+      #print("total datset..", m)
+      for ind in range(len(dataset)):
+        j = j + ((w0 + w1*dataset[ind][0]) - dataset[ind][1])**2
+        temp1 = temp1 +  (((w0 + w1*dataset[ind][0]) - dataset[ind][1])*1)
+        temp2 = temp2 + (((w0 + w1*dataset[ind][0]) - dataset[ind][1])*dataset[ind][0])
+      
+      j = j/(2*m)
+      j = round(j, 8)
+      w0 = w0 - (lr * temp1)/m
+      w0 = round(w0, 8)
+      w1 = w1 - (lr * temp2)/m
+      w1 = round(w1, 8)
+      #print("cofficient", w0, w1)
+
+      if abs(pre_j - j) <= p or itr == epoch - 1:
+        print("For learning rate", lr, "......")
+        print("MSE =", j)
+        break
+      pre_j = j   
+
+
+print("For Training dataset............\n")
+MSE_ofGivenDataset(train_dataset)
+
+print("\n\n\n\n\nFor validation dataset............\n")
+MSE_ofGivenDataset(validation_dataset)
+
+print("\n\n\n\n\nFor test dataset............\n")
+MSE_ofGivenDataset(test_dataset)
+
+
+
+def MSE_givenLearningRate(dataset):
+
+  max1 = dataset['X'].max()
+  max2 = dataset['Y'].max()
+  MSE_ofEachEpoch = []
+  lr = 0.1
+  w0, w1 = 3, 3;
+  #h = w0  + w1*x
+  epoch = 100
+  dataset = dataset.to_numpy()
+
+  #normalize the datset
+  for i in range(len(dataset)):
+    dataset[i][0] /= max1
+    dataset[i][1] /= max2
+
+  for itr  in range(epoch):
+    j = 0
+    m = len(dataset)
+    temp1 = 0
+    temp2 = 0
+    #print("total datset..", m)
+    for ind in range(len(dataset)):
+      j = j + ((w0 + w1*dataset[ind][0]) - dataset[ind][1])**2
+      temp1 = temp1 +  (((w0 + w1*dataset[ind][0]) - dataset[ind][1])*1)
+      temp2 = temp2 + (((w0 + w1*dataset[ind][0]) - dataset[ind][1])*dataset[ind][0])
+    
+    j = j/(2*m)
+    j = round(j, 8)
+    w0 = w0 - (lr * temp1)/m
+    w0 = round(w0, 8)
+    w1 = w1 - (lr * temp2)/m
+    w1 = round(w1, 8)
+    MSE_ofEachEpoch.append(j)
+    
+
+  return MSE_ofEachEpoch
+
+print(MSE_givenLearningRate(train_dataset)) # This will print MSE of your model trained
+
+x_axis = [i for i in range(1, 101)]
+print(x_axis)
+y_axis = MSE_givenLearningRate(train_dataset)
+print(y_axis)
+
+
+import matplotlib.pyplot as plt
+
+plt.plot(x_axis, y_axis)
+
+plt.xlabel('epoch')
+plt.ylabel('MSE')
+
+plt.show() # this will plot graph epoch by epoch
+
+