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Polynomial Regression ( From Scratch using Python )

  • Difficulty Level : Medium
  • Last Updated : 22 Jun, 2021
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Prerequisites 

  1. Linear Regression
  2. Gradient Descent

Introduction

Linear Regression finds the correlation between the dependent variable ( or target variable ) and independent variables ( or features ). In short, it is a linear model to fit the data linearly. But it fails to fit and catch the pattern in non-linear data. 

Let’s first apply Linear Regression on non-linear data to understand the need for Polynomial Regression. The Linear Regression model used in this article is imported from sklearn. You can refer to the separate article for the implementation of the Linear Regression model from scratch.

Python3




# Importing libraries
 
import numpy as np
 
import pandas as pd
 
from sklearn.model_selection import train_test_split
 
import matplotlib.pyplot as plt
 
from sklearn.linear_model import LinearRegression
 
# driver code
 
def main() :
     
    # Create dataset
     
    X = np.array( [ [1], [2], [3], [4], [5], [6], [7] ] )
     
    Y = np.array( [ 45000, 50000, 60000, 80000, 110000, 150000, 200000 ] )
     
    # Model training
     
    model = LinearRegression()
 
    model.fit( X, Y )
     
    # Prediction
 
    Y_pred = model.predict( X )
     
    # Visualization
     
    plt.scatter( X, Y, color = 'blue' )
     
    plt.plot( X, Y_pred, color = 'orange' )
     
    plt.title( 'X vs Y' )
     
    plt.xlabel( 'X' )
     
    plt.ylabel( 'Y' )
     
    plt.show()
     
     
if __name__ == "__main__" :
     
    main()

 
 

 

 



Output : 

Visualization

As shown in the output visualization, Linear Regression even failed to fit the training data well ( or failed to decode the pattern in the Y with respect to X ). Because its hypothetical function is linear in nature and Y is a non-linear function of X in the data. 

 

For univariate linear regression : 

h( x ) = w * x

here,  x is the feature vector.
and w is the weight vector.

 This problem is also called as underfitting. To overcome the underfitting, we introduce new features vectors just by adding power to the original feature vector.

 

For univariate polynomial regression : 
 
h( x ) = w1x + w2x2  + .... + wnxn 
 
here, w is the weight vector. 
where x2  is the derived feature from x. 

After transforming the original X into their higher degree terms, it will make our hypothetical function able to fit the non-linear data. Here is the implementation of the Polynomial Regression model from scratch and validation of the model on a dummy dataset.

 

Python




# Importing libraries
 
import numpy as np
 
import math
 
import matplotlib.pyplot as plt
 
# Univariate Polynomial Regression
 
class PolynomailRegression() :
     
    def __init__( self, degree, learning_rate, iterations ) :
         
        self.degree = degree
         
        self.learning_rate = learning_rate
         
        self.iterations = iterations
         
    # function to transform X
     
    def transform( self, X ) :
         
        # initialize X_transform
         
        X_transform = np.ones( ( self.m, 1 ) )
         
        j = 0
     
        for j in range( self.degree + 1 ) :
             
            if j != 0 :
                 
                x_pow = np.power( X, j )
                 
                # append x_pow to X_transform
                 
                X_transform = np.append( X_transform, x_pow.reshape( -1, 1 ), axis = 1 )
 
        return X_transform  
     
    # function to normalize X_transform
     
    def normalize( self, X ) :
         
        X[:, 1:] = ( X[:, 1:] - np.mean( X[:, 1:], axis = 0 ) ) / np.std( X[:, 1:], axis = 0 )
         
        return X
         
    # model training
     
    def fit( self, X, Y ) :
         
        self.X = X
     
        self.Y = Y
     
        self.m, self.n = self.X.shape
     
        # weight initialization
     
        self.W = np.zeros( self.degree + 1 )
         
        # transform X for polynomial  h( x ) = w0 * x^0 + w1 * x^1 + w2 * x^2 + ........+ wn * x^n
         
        X_transform = self.transform( self.X )
         
        # normalize X_transform
         
        X_normalize = self.normalize( X_transform )
                 
        # gradient descent learning
     
        for i in range( self.iterations ) :
             
            h = self.predict( self.X )
         
            error = h - self.Y
             
            # update weights
         
            self.W = self.W - self.learning_rate * ( 1 / self.m ) * np.dot( X_normalize.T, error )
         
        return self
     
    # predict
     
    def predict( self, X ) :
      
        # transform X for polynomial  h( x ) = w0 * x^0 + w1 * x^1 + w2 * x^2 + ........+ wn * x^n
         
        X_transform = self.transform( X )
         
        X_normalize = self.normalize( X_transform )
         
        return np.dot( X_transform, self.W )
       
       
# Driver code    
 
def main() :   
     
    # Create dataset
     
    X = np.array( [ [1], [2], [3], [4], [5], [6], [7] ] )
     
    Y = np.array( [ 45000, 50000, 60000, 80000, 110000, 150000, 200000 ] )
  
    # model training
     
    model = PolynomailRegression( degree = 2, learning_rate = 0.01, iterations = 500 )
 
    model.fit( X, Y )
     
    # Prediction on training set
 
    Y_pred = model.predict( X )
     
    # Visualization
     
    plt.scatter( X, Y, color = 'blue' )
     
    plt.plot( X, Y_pred, color = 'orange' )
     
    plt.title( 'X vs Y' )
     
    plt.xlabel( 'X' )
     
    plt.ylabel( 'Y' )
     
    plt.show()
 
 
if __name__ == "__main__" :
     
    main()

 
 

Output : 

Visualization

We also normalized the X before feeding into the model just to avoid gradient vanishing and exploding problems.

 

Output visualization showed Polynomial Regression fit the non-linear data by generating a curve.

 

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