Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell

Computing performance statistics

In the previous recipes, we have already seen some values predicted by our classification and regression models and how far or how close they were from/to the original values. In this recipe, we will learn how to fully calculate the performance statistics for these models.

Getting ready

In order to execute this recipe, you need to have a working Spark environment and you should have gone through the Predicting hours of work for census respondents and Forecasting income levels of census respondents recipes presented earlier in this chapter.

No other prerequisites are required.

How to do it...

Getting the performance metrics for regression and classification in Spark is extremely simple:

import pyspark.mllib.evaluation as ev


metrics_lm = ev.RegressionMetrics(true_pred_reg)


metrics_lr = ev.BinaryClassificationMetrics(true_pred_class_lr)

How it works...

First, we load the evaluation module; doing this exposes the .RegressionMetrics(...) and the .BinaryClassificationMetrics...