You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Learning on massive click logs with Spark

Normally, in order to take advantage of Spark, data is stored using Hadoop Distributed File System (HDFS), which is a distributed file system designed to store large volumes of data, and computation occurs over multiple nodes on clusters. For demonstration purposes, we will keep the data on a local machine and run Spark locally. This is no different from running it on a distributed computing cluster.

Loading click logs

To train a model on massive click logs, we first need to load the data in Spark. We do so by taking the following steps:

We spin up the PySpark shell by using the following command:
```
./bin/pyspark --master local[*]  --driver-memory 20G
```
Here, we specify a large driver memory as we are dealing with a dataset of more than 6 GB.

A driver program is responsible for collecting and storing processed results from executors. So, a large driver memory helps complete jobs where...