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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Custom functions on DataFrames

So far, we have only used built-in functions to operate on DataFrame columns. While these are often sufficient, we sometimes need greater flexibility. Spark lets us apply custom transformations to every row through user-defined functions (UDFs). Let's assume that we want to use the equation that we derived in Chapter 2, Manipulating Data with Breeze, for the probability of a person being male, given their height and weight. We calculated that the decision boundary was given by:

Custom functions on DataFrames

Any person with f > 0 is more likely to be male than female, given their height and weight and the training set used for Chapter 2, Manipulating Data with Breeze (which was based on students, so is unlikely to be representative of the population as a whole). To convert from a height in centimeters to the normalized height, rescaledHeight, we can use this formula:

Custom functions on DataFrames

Similarly, to convert a weight (in kilograms) to the normalized weight, rescaledWeight, we can use:

Custom functions on DataFrames

The average and...

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