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

Introducing Estimators

The Estimator class, just like the Transformer class, was introduced in Spark 1.3. The Estimators, as the name suggests, estimate the parameters of a model or, in other words, fit the models to data.

In this recipe, we will introduce two models: the linear SVM acting as a classification model, and a linear regression model predicting the forest elevation.

Here is a list of all of the Estimators, or machine learning models, available in the ML module:

  • Classification:
    • LinearSVC is an SVM model for linearly separable problems. The SVM's kernel has the 

       form (a hyperplane), where 

       is the coefficients (or a normal vector to the hyperplane), 

       is the records, and b is the offset.

    • LogisticRegressionis a default, go-to classification model for linearly separable problems. It uses a logit function to calculate the probability of a record being a member of a particular class.

    • DecisionTreeClassifier is a decision tree-based model used for classification purposes. It builds a binary...