Introduction to Machine Learning on Apache Spark MLlib by Juliet Hougland, Senior Data Scientist, Cloudera

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Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. It will cover the use of Spark's DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier.

Software

‹#›© Cloudera, Inc. All rights reserved.
Introduction to
Apache Spark
& Spark MlLib
Juliet Hougland

‹#›© Cloudera, Inc. All rights reserved.
What are we doing here?

‹#›© Cloudera, Inc. All rights reserved.
Spark Overview

‹#›© Cloudera, Inc. All rights reserved.
Spark: Easy and Fast Big Data
• Easy to Develop
• Rich APIs in Java,
Scala, Python
• Interactive shell
• Fast to Run
• General execution
graphs
• In-memory storage

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
Apache Spark: Ecosystem
•Dataframes
•ML Lib
•Streaming

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
Spark in CDH
YARN
Spark
Streaming
GraphX MLlib
HDFS, HBase
HivePig
MapReduce2
SQL
SearchImpala
Spark
Pyspark

‹#›© Cloudera, Inc. All rights reserved.
Spark Execution Model
sc.textFile(“hdfs://…”, 4)
.map(to_series)
.filter(has_outlier)
.count()

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
What do you mean, lazy evaluation?

‹#›© Cloudera, Inc. All rights reserved.
• map
• flatmap
• filter
• distinct
• sample
• union
• intersection
• subtract
• cartesian
Transformations

‹#›© Cloudera, Inc. All rights reserved.
• collect()
• count()
• take(num)
• takeOrdered(num)(ordering)
• reduce(function)
• aggregate(zeroValue)(seqOp,
combOp)
• foreach(function)
Actions

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
RDDs
sc.textFile(“hdfs://…”, 4)
.map(to_series)
.filter(has_outlier)
.count()
HDFS
Partition 1
Partition 2
Partition 3
Partition 4
Thanks: Kostas Sakellis

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
RDDs
…RDD
HDFS
Partition 1
Partition 2
Partition 3
Partition 4
Thanks: Kostas Sakellis
sc.textFile(“hdfs://…”, 4)
.map(to_series)
.filter(has_outlier)
.count()

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
RDDs
…RDD …RDD
HDFS
Partition 1
Partition 2
Partition 3
Partition 4
Partition 1
Partition 2
Partition 3
Partition 4
Thanks: Kostas Sakellis
sc.textFile(“hdfs://…”, 4)
.map(to_series)
.filter(has_outlier)
.count()

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
…RDD …RDD
RDDs
HDFS
Partition 1
Partition 2
Partition 3
Partition 4
Partition 1
Partition 2
Partition 3
Partition 4
…RDD
Partition 1
Partition 2
Partition 3
Partition 4
Count
Thanks: Kostas Sakellis
sc.textFile(“hdfs://…”, 4)
.map(to_series)
.filter(has_outlier)
.count()

‹#›© Cloudera, Inc. All rights reserved.
Complex, In-Memory Processing
join
filter
groupBy
B: B:
C: D: E:
F:
Ç√Ω
map
A:
map
take

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
Predicting Churn:
Machine Learning with
Spark MlLib

‹#›© Cloudera, Inc. All rights reserved.
Modeling Churn

‹#›© Cloudera, Inc. All rights reserved.

‹#›© Cloudera, Inc. All rights reserved.
KS, 128, 415, 382-4657, no, yes, 25, 265.1, 110, 45.07, 197.4, 99, 16.78, 244.7, 91, 11.01, 10, 3, 2.7, 1, False.
OH, 107, 415, 371-7191, no, yes, 26, 161.6, 123, 27.47, 195.5, 103, 16.62, 254.4, 103, 11.45, 13.7, 3, 3.7, 1, False.
NJ, 137, 415, 358-1921, no, no, 0, 243.4, 114, 41.38, 121.2, 110, 10.3, 162.6, 104, 7.32, 12.2, 5, 3.29, 0, False.
OH, 84, 408, 375-9999, yes, no, 0, 299.4, 71, 50.9, 61.9, 88, 5.26, 196.9, 89, 8.86, 6.6, 7, 1.78, 2, False.
OK, 75, 415, 330-6626, yes, no, 0, 166.7, 113, 28.34, 148.3, 122, 12.61, 186.9, 121, 8.41, 10.1, 3, 2.73, 3, False
The Dataset

‹#›© Cloudera, Inc. All rights reserved.
Create Dataframe

‹#›© Cloudera, Inc. All rights reserved.
Why DataFrames?

‹#›© Cloudera, Inc. All rights reserved.
Modeling Lifecycle

‹#›© Cloudera, Inc. All rights reserved.
Specify Feature Extraction

‹#›© Cloudera, Inc. All rights reserved.
Evaluating Classifiers: ROC

‹#›© Cloudera, Inc. All rights reserved.
Model Evaluation

‹#›© Cloudera, Inc. All rights reserved.
Evaluating Classifiers

‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved.
Find the entire example at
github.com/jhlch/ds-for-telco

‹#›© Cloudera, Inc. All rights reserved.
Thank You
Juliet Hougland
@j_houg
github.com/jhlch/ds-for-telco

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Introduction to Machine Learning on Apache Spark MLlib by Juliet Hougland, Senior Data Scientist, Cloudera

4. ‹#›© Cloudera, Inc. All rights reserved. Spark: Easy and Fast Big Data • Easy to Develop • Rich APIs in Java, Scala, Python • Interactive shell • Fast to Run • General execution graphs • In-memory storage

11. ‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved. RDDs sc.textFile(“hdfs://…”, 4) .map(to_series) .filter(has_outlier) .count() HDFS Partition 1 Partition 2 Partition 3 Partition 4 Thanks: Kostas Sakellis

12. ‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved. RDDs …RDD HDFS Partition 1 Partition 2 Partition 3 Partition 4 Thanks: Kostas Sakellis sc.textFile(“hdfs://…”, 4) .map(to_series) .filter(has_outlier) .count()

13. ‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved. RDDs …RDD …RDD HDFS Partition 1 Partition 2 Partition 3 Partition 4 Partition 1 Partition 2 Partition 3 Partition 4 Thanks: Kostas Sakellis sc.textFile(“hdfs://…”, 4) .map(to_series) .filter(has_outlier) .count()

14. ‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved. RDDs …RDD …RDD HDFS Partition 1 Partition 2 Partition 3 Partition 4 Partition 1 Partition 2 Partition 3 Partition 4 …RDD Partition 1 Partition 2 Partition 3 Partition 4 Thanks: Kostas Sakellis sc.textFile(“hdfs://…”, 4) .map(to_series) .filter(has_outlier) .count()

15. ‹#›© Cloudera, Inc. All rights reserved. ‹#›© Cloudera, Inc. All rights reserved. …RDD …RDD RDDs HDFS Partition 1 Partition 2 Partition 3 Partition 4 Partition 1 Partition 2 Partition 3 Partition 4 …RDD Partition 1 Partition 2 Partition 3 Partition 4 Count Thanks: Kostas Sakellis sc.textFile(“hdfs://…”, 4) .map(to_series) .filter(has_outlier) .count()

20. ‹#›© Cloudera, Inc. All rights reserved. KS, 128, 415, 382-4657, no, yes, 25, 265.1, 110, 45.07, 197.4, 99, 16.78, 244.7, 91, 11.01, 10, 3, 2.7, 1, False. OH, 107, 415, 371-7191, no, yes, 26, 161.6, 123, 27.47, 195.5, 103, 16.62, 254.4, 103, 11.45, 13.7, 3, 3.7, 1, False. NJ, 137, 415, 358-1921, no, no, 0, 243.4, 114, 41.38, 121.2, 110, 10.3, 162.6, 104, 7.32, 12.2, 5, 3.29, 0, False. OH, 84, 408, 375-9999, yes, no, 0, 299.4, 71, 50.9, 61.9, 88, 5.26, 196.9, 89, 8.86, 6.6, 7, 1.78, 2, False. OK, 75, 415, 330-6626, yes, no, 0, 166.7, 113, 28.34, 148.3, 122, 12.61, 186.9, 121, 8.41, 10.1, 3, 2.73, 3, False The Dataset

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Introduction to Machine Learning on Apache Spark MLlib by Juliet Hougland, Senior Data Scientist, Cloudera

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