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

Tuning hyperparameters

Many models already mentioned in this chapter have multiple parameters that determine how the model will perform. Selecting some is relatively straightforward, but there are many that we simply cannot set intuitively. That's where hyperparameters-tuning comes to play. The hyperparameters-tuning methods help us select the best (or close to) set of parameters that maximizes some metric we defined.

In this recipe, we will show you two approaches for hyperparameter-tuning.

Getting ready

To execute this recipe, you will need a working Spark environment and you would have already loaded the data into the forest DataFrame. You would also have gone through all the previous recipes as we assume you have a working knowledge of Transformers, Estimators, Pipelines, and some of the regression models.

No other prerequisites are required.

How to do it...

We start with grid search. It is a brute-force method that simply loops through specific values of parameters, building new models and...