Skip to content
Related Articles

Related Articles

Polynomial Regression using Turicreate

View Discussion
Improve Article
Save Article
Like Article
  • Difficulty Level : Medium
  • Last Updated : 24 Jan, 2021

In this article, we will discuss the implementation of Polynomial Regression using Turicreate. Polynomial Regression: Polynomial regression is a form of regression analysis that models the relationship between a dependent say y and an independent variable say x as a nth degree polynomial. It is expressed as : 

y= b0+b1x1+ b2x12+ b2x13+…… bnx1n

[where b0, b1, b2, …… bn are regression coefficients]

So let’s learn this concept through practicals.

Step 1: Import the important libraries and generate a very small data set using SArray and SFrame in turicreate that we are going to use to perform Polynomial Regression.


# importing required python libraries
import turicreate
import matplotlib.pyplot as plt
import random
# Generating datapoints
X = [data for data in range(1, 21)]
Y = [random.randrange(100, 1000, 1) for data in range(20)]
# Creating Sarrays from the generated data points
Xs = turicreate.SArray(X, dtype=float)
Ys = turicreate.SArray(Y, dtype=float)
print(f"""Xs : {Xs}
Ys : {Ys}""")


Step 2: Plotting the generated data


# plotting the generated data
plt.scatter(Xs, Ys)

Step 3: Create an SFrame containing the input, its polynomial_degrees, and the output in order to fit our regression model.


# Creating an Sframe where all the inputs and the polynomial degree and output
def createSframe(inputs, pol_degree):
    datapoints = turicreate.SFrame({'x1': inputs})
    for degree in range(2, pol_degree+1):
        datapoints[f'x{degree}'] = datapoints[f'x{degree-1}']*datapoints['x1']
    return datapoints
# Creating a SFrame with polynomial degree 20
data_points = createSframe(Xs, 20)
data_points['y'] = Ys
# showing the first 10 entries in the SFrame

Step 4: Fitting Polynomial Regression to the generated Data set.


# Polynomial Regression
features = [f'x{i}' for i in range(1, 21)]
poly_model = turicreate.linear_regression.create(
    data_points, features=features, target='y')

Step 5: Predicting the result using the fitted model and storing the result in the SFrame.


# predicting the some data
# Generating test datapoints
test_X = [random.randrange(1, 60, 1) for data in range(20)]
test_Xs = turicreate.SArray(X, dtype=float)
test_data = createSframe(test_Xs, 5)
data_points['predicted_y'] = poly_model.predict(test_data)

Step 6: Measuring the accuracy of our predicted result


# Measuring the accuracy
# Generating test datapoints
test_X = [random.randrange(1, 60, 1) for data in range(20)]
test_Xs = turicreate.SArray(X, dtype=float)
test_data = createSframe(test_Xs, 20)

Step 7: Visualizing the Polynomial Regression results using scatter plot and line plot of the input data and the predicted result.


plt.scatter(data_points['x1'], data_points['y'])
plt.plot(data_points['x1'], data_points['predicted_y'])

My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!