Model evaluation using MSE

After generating the predicted ratings from the test data using ALS model, in this final part of the exercise, you'll prepare the data for calculating Mean Square Error (MSE) of the model. The MSE is the average value of (original rating – predicted rating)**2 for all users and indicates the absolute fit of the model to the data.

To do this, first, you'll organize both the ratings_final and predictions RDDs to make a tuple of ((user, product), rating)). In both RDDs the mapping is:

0: user
1: product
2: rating

Then you'll join transformed RDDs and finally apply a squared difference function along with mean() to get the MSE.

Remember, you have a SparkContext sc available in your workspace. Also, ratings_final and predictions RDD are already available in your workspace.

This exercise is part of the course

Big Data Fundamentals with PySpark

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Exercise instructions

Organize ratings RDD to make ((user, product), rating).
Organize predictions RDD to make ((user, product), rating).
Join the prediction RDD with the ratings RDD.
Evaluate the model using MSE between original rating and predicted rating and print it.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Prepare ratings data
rates = ratings_final.____(lambda r: ((r[0], r[1]), ____))

# Prepare predictions data
preds = predictions.____(lambda r: ((____, ____), ____))

# Join the ratings data with predictions data
rates_and_preds = rates.____(preds)

# Calculate and print MSE
MSE = rates_and_preds.____(lambda r: (r[1][0] - r[1]____)**2).mean()
print("Mean Squared Error of the model for the test data = {:.2f}".format(____))

Edit and Run Code